Fly Page

This sheet is left blank and not counted. Please delete these words when ready to print

STRUCTURAL AND DYNAMIC ANALYSIS OF BIOLOGICAL NETWORKS

APPROVED BY SUPERVISING COMMITTEE:

______Mo Jamshidi, Ph.D., Chair

______Yu-Fang Jin, Ph.D., Co-chair

______Keying Ye, Ph.D.

______Daniel Pack, Ph.D.

Accepted: ______Dean, Graduate School

DEDICATION

This page is optional and is counted but the number is not typed on the page. The dedication should be single spaced, italicized, and printed in 12 pt. font. The dedication should be no longer than 7-10 lines. OMID, MAMAN AND HELYA

STRUCTURAL AND DYNAMIC ANALYSIS OF BIOLOGICAL NETWORKS

by

ELMIRA MOHYEDIN BONAB, BSE

DISSERTATION Presented to the Graduate Faculty of The University of Texas at San Antonio In Partial Fulfillment Of the Requirements For the Degree of

BACHELORE OF SCIENCE IN ELECTRICAL ENGINEERING

IMAM KHOMEINIY INTERNATIONAL UNIVERSITY Department of Electrical and Computer Engineering August 2008 ACKNOWLEDGEMENTS

Indent paragraph. This is a brief paragraph expressing recognition of and appreciation for special professional assistance extended to you by academic personnel, agencies, and institutions. Acknowledgements may not exceed one page.

MARK ROSVALL

(month & year you will graduate centered above page number)

December 2014

iv

STRUCTURAL AND DYNAMIC ANALYSIS OF BIOLOGICAL NETWORKS

ELMIRA MOHYEDIN BONAB, PHD. The University of Texas at San Antonio, 2014

Supervising Professor: Mo Jamshidi, Ph.D.

Indent paragraph. This should be a concise summary of the entire research project; it states the purpose of the study, delineates the basic method of research, and summarizes the conclusions. It should not exceed 250 words (approximately one and a half pages double spaced).

v

TABLE OF CONTENTS

Acknowledgements ...... iv

Abstract ...... v ......

List of Tables ...... ix

List of Figures ...... x

Chapter One: INTRODUCTION ...... 1

Why network analysis of biological systems? ...... 1

Specific aims ...... 2

Outline...... 4

Chapter Two: STRUCTURAL ANALYSISOF BIOLOGICAL NETWORKS ...... 5

Vertices, edges and degree ...... 5

Path and distance calculations ...... 7

Random walk for distance calculation ...... 8

Small-world property ...... 8

Centrality analysis ...... 9

Closeness centrality ...... 9

Betweenness centrality...... 9

Betweenness calculation techniques for large biological networks ...... 11

Degree distribution...... 13

Scale-free property ...... 13

Mixing property: assortative and disassortative networks ...... 14

Network organization...... 15

vi

Clustering ...... 16

Complex community detection algorithms ...... 18

Conclusion ...... 20

Chapter Three: TEMPORAL MODELING OF BIOLOGICAL NETWORKS ...... 22

General assumption in building process of biological networks ...... 22

Connection patterns in networks: from random networks to regular networks ...... 23

Metabolic Networks ...... 25

Network modeling using ordinary differential equations ...... 25

Gene regulatory networks ...... 26

Incorporating temporal gene expression with DNA-binding knowledge ...... 27

Time delay estimation in gene regulatory networks ...... 28

Method ...... 28

Multiple-delayed regression model...... 30

Parameter estimation ...... 31

Statistical analysis ...... 31

Materials/Data ...... 32

Results ...... 33

Conclusion ...... 36

Chapter Four: MODELING DYNAMIC PROCESSES IN SIGNALING NETWORKS ...... 38

Flow modeling in signaling and regulatory networks...... 39

Memory in networks ...... 40

How to model empirical pathways of signaling ...... 41

vii

Fundamental principles about memory mathematical representation ...... 42

Performance evaluation parameters ...... 44

Significant analysis with Bootstrap re-sampling ...... 44

Community detection algorithm ...... 45

Entropy rate ...... 45

Return rates, Module size and module assignment ...... 46

Flow-generation model for human signaling pathways ...... 46

Bootstrap re-sampling of human pathway trigrams ...... 47

Analyzing memory effect on high flow-volume modules ...... 48

Pathway enrichment method ...... 50

Overlapping genes and top genes in high-volume modules ...... 51

Biological interpretation of identified pathways in overlapping regions ...... 56

Angiogenesis ...... 56

Wnt signaling pathways ...... 57

MicroRNAs in cardiomyocyte hypertrophy ...... 58

Flow-generation model for MAPK signaling pathway ...... 59

Boolean dynamic modeling of signaling pathways ...... 60

Data acquisition model: Trigram construction using state-transitions ...... 61

Chapter Five: FUTURE WORK ...... 63

Appendices...... 66

References ...... 261

Vita

viii

LIST OF TABLES

Table 1 Comparison of Erdos-Renyi and Watts-Strogatz models ...... 24

Table 2 Statistical comparison results for target genes with 2 or 3 regulators...... 36

Table 3 Statistic results for first- and second-order Markov dynamics for human

signaling network ...... 48

Table 4 Selected 16 modules containing %75 of the flow volume of the network with

first-order Markov dynamics ...... 66

Table 5 Selected 17 modules containing %75 of the flow volume of the network with

second-order Markov dynamics ...... 80

Table 6 Enriched pathways in 16 modules of the network with first-order Markov

dynamics ...... 97

Table 7 Enriched pathways in 17 modules of the network with second-order Markov

dynamics ...... 145

Table 8 Common Enriched pathway between modules 3, 8 and 17 ...... 257

Table 9 Common Enriched pathway between modules 3 and 8 ...... 257

Table 10 Common enriched pathways between modules 8 and 17 ...... 258

ix

Table 11 Common enriched pathways between modules 3 and 17 ...... 258

Table 12 Common pathways between modules 8 and 16 ...... 258

LIST OF FIGURES

Figure 1 Graph representation ...... 6

Figure 2 Calculation of shortest-path betweenness for one BFS structure...... 12

Figure 3 Global clustering coefficients for three example graphs ...... 17

Figure 4 Local clustering coefficients for each vertex ...... 17

Figure 5 Organization chart of community detection algorithms ...... 20

Figure 6 Random rewiring procedure in creating Watts-Strogatz model ...... 24

x

Figure 7 Steps for designing a multiple-delayed linear regression model ...... 29

Figure 8 Regulatory network structure at each time delay ...... 35

Figure 9 Overlap and top genes in four modules of second-order Markov dynamics ...... 50

Figure 10 The procedure for identifying overlapping genes that participate in enrichment pathways of top genes of each module ...... 52

Figure 11 Van diagram of number of enriched pathways containing top genes of modules 3 and 8 ...... 53

Figure 12 Van diagram of number of enriched pathways containing top genes of modules 3 and 8 and 17 ...... 54

Figure 13 Van diagram of number of enriched pathways containing top genes of modules 8 and 16 ...... 55

xi

CHAPTER ONE: INTRODUCTION

(Remember: EVERY CHAPTER MUST BEGIN ON A NEW PAGE.

Chapter titles will be bold, all caps, centered.)

Studying individual molecular interactions cannot provide a complete picture about dynamic organization of biological processes. A network representation of such processes can reveal underlying design principles. This thesis is about the application of network analysis techniques, which capture structural and dynamic properties of biological processes.

Why network analysis of biological systems?

The complexity of biological processes highlights the need for a systematic approach in order to infer their fundamental properties. These properties that emerge from the collective behavior in such systems are not explainable by summing the behaviors of individual interactions(Auyang 1998). Networks can significantly simplify the modeling process by focusing solely on the existence of interactions. Detailed biological specifications of molecular activities are ignored in the process of network modeling.

Large scale biological datasets are becoming available at an unprecedented rate due to advances in high-throughput technologies. Network modeling can help with discovering organizational patterns in the massive datasets, which may lead to identification of governing rules of biology systems.

Network-based analysis makes it possible to deploy multidisciplinary techniques in engineering, graph, and information theories(Alon 2003). A prior knowledge about findings in other types of networks, such as social network, would help in understanding unknown features of biological networks. The resemblance of such networks can lead to an acceptable prediction of biological system properties.

1

Specific aims It is interesting to study how structural properties of a biological network not only, affect primary molecular activities but also, affect cell function in a bigger picture. Different measures are developed that can discover the relationship between network’s connectivity and the function of biological processes. Each metric has its own application and addresses particular types of research questions. The evolution of networks stimulates the idea that there are some organizational rules that govern general attachment patterns in networks. Statistical measures of structural properties provide the opportunity to summarize key features of large biological networks. In this thesis, I aimed to provide a broad review of structural and statistical properties of different types of biological network. When the network model is fixed, structural properties of a network may highlight basic knowledge about biological network structure; but, it cannot capture the transient changes of biological phenomena over time. Therefore, the need for a dynamic model is more felt to capture these changes.

The goal of dynamic studies is to understand the relationship between complex dynamics of biological networks with temporal interactions on a molecular level. When prior knowledge about the sequence of biological processes over all time points is available, the function of activated interactions at given time interval can be predicted. Another approach is designing models that can track the information/signaling/material flow in different types of networks within the cell. Different activities in a cell are governed by signaling, metabolic, and regulatory networks. Signaling transduction pathways are responsible for transmitting information within the cell and metabolic pathways, as chains of chemical reactions generate critical chemical components for a cell. Generation of proteins within cell occurs in gene regulatory networks. The flow in a metabolic network is actual materials transferring among components, whereas the

2 flow in a signaling network is defined as an information flow. In chapter four, I investigate modeling techniques for modeling information flow in signaling networks.

The main goal of this thesis is to establish models for information flow in signaling transduction pathways and to study dynamics and structural properties of these networks.

Generally, random walk is used in modeling the dynamic flow in networks as a stochastic process, where the walker moves among entities of the network, and its next destination is determined given the full history of all previous entities that it visited(Vespignani 2012). This leads to next important discussion, namely as memory in networks. Rosvall and Esquivel et al studied the presence of memory in different types of networks including flight itineraries between US airports, journal citations, movement of patient between hospital wards, GPS- tracked taxis and email forward and reply networks(Rosvall, Esquivel et al. 2013). In their investigations, they verified that where the flow originates determines its course in their studied networks, in other words these networks are memory networks. They illustrated in memory networks the second-order Markov model of the flow can better extract fundamental network dynamics compared to the first-order Markov model of the flow, which no memory is taken into account (Rosvall, Esquivel et al. 2013). In their study, the impact of memory on community detection problem is studied. In chapter four, I am interested to investigate memory in different biological networks. To achieve this, flow information should be embedded in modeling of biological networks such that second-order Markov model can describe empirical flow in the network. I proposed two modeling techniques with different applications for modeling empirical flow in biological pathways. Modules identified by first- and second-order Markov models are compared and biologically studied to capture the essential dynamical processes in all modules and overlap of modules.

3

Outline In modeling biological networks, three topics are of interest: model generation strategies, relationship between structural properties and biological functions, and dynamics in a network.

The thesis is organized as follows: First chapter covers a broad review of structural properties of biological networks. In this chapter, graph theory techniques are applied to reveal structural properties of the network that affects its dynamic properties such as communication patterns.

Some of the concepts, definitions and methods discussed in this section may be used in the remainder of the thesis. Chapter three discusses about various conventional modeling techniques for biological networks and also a dynamic model of regulatory networks is designed as a first step into temporal modeling of biological networks. In chapter 4, the essential dynamic properties of biological networks in the concept of memory networks will be investigated. Two techniques for modeling information flow in signaling pathway will be introduced and results are demonstrated.

4

CHAPTER TWO: STRUCTURAL ANALYSIS OF BIOLOGICAL NETWORKS

Biological processes support fundamental activities of living organs and include hundreds of molecular interactions. Advances in high throughput technologies address the need for systematic procedure to interpret generated large datasets. Due to the complexity of biological processes, many groups use graphs to simplify their visual mining process and identify important patterns. In a derived graph, each molecule is denoted as a vertex and each interaction is denoted as an edge.

Graph theory has been applied widely to biological networks to reveal their key structural properties, including construction(Mohyedinbonab, Ghasemi et al. 2013), patterns recognition(Adamcsek, Palla et al. 2006), community detection(Yoon, Blumer et al. 2006, Liu,

Wong et al. 2009, Nepusz, Yu et al. 2012), robustness(Vogelstein, Lane et al. 2000), evolution(Babu, Luscombe et al. 2004), functional organization(Voy, Scharff et al. 2006), and several other statistical properties of biological networks(Jeong, Oltvai et al. 2002, del Sol and

O'Meara 2005, Schmith, Lemke et al. 2005).

Vertices,edges and degree

A biological network can be represented as a graph, noted by G=(V,E), with a nonempty set of vertices (nodes) V as molecules and edges (links) E as interactions. In figure 1, Edge e linking two adjacent vertices 퐴 and C is denoted as AC.

5

(a) (b)

Figure 1. Graph representation (a) Undirected graph and (b) directed graph, edge e connects adjacent vertices A and C.

A weight or a direction or both can be assigned to an edge. Subsequently, networks can be directed/undirected and weighted/unweighted. Two examples of graphs, one undirected and one directed, are shown in Figure 1. The direction of edges can be determined by the direction of the regulatory interaction in a biological network(Wagner 2001, Babu, Luscombe et al. 2004).

Weights of edges connecting molecules in a biological network can be calculated based on correlation between expressions of molecules, confidence level of connection, or other specifications of biological experiments. These specifications are affected by the accuracy of experiments and interaction predication methods(Nabieva, Jim et al. 2005, Zhang and Horvath

2005). The assigned weights can also be calculated based on the structural properties of a graph.

Detailed information about physical properties of interactions provides more insight into principle regulation mechanisms. Inferring directions and weights of interactions is not an easy task and requires multiple sources of data, and targeted experiments.

The number of connections a vertex has to other vertices is called the degree (number of edges incident to a vertex). In Figure 1 (part a), the degree of vertices A, B, C, D, E and F are 2, 3,

6

4, 3 and 2 respectively. Vertices, which have high degree, are called hubs. In several biological studies, it is found that highly connected vertices or hubs are associated with molecules with essential biological functions, such as somatic cancer genes in(Goh, Cusick et al. 2007), photosynthesis genes in(Aluru, Zola et al. 2013) and cancer genes in(Jiang, Li et al. 2008). A detection threshold for statistically important hubs is obtained for networks with Poisson degree distribution in(Jiang, Li et al. 2008).

Path and distance calculations

Interrelationship between interactions can be investigated by studying chains of interactions in a network. The concepts of path, shortest path, and average shortest path are introduced for this purpose. A path in the graph is defined as a sequence of connected vertices.

Two vertices can be connected by more than one path. The path length is measured by the number of edges that the path traverses. In Figure 1 (part a), there are 4 paths between A and C with lengths of 1(A, C), 2 (A, B, C) , 3 (A, B, D, C) and 4 (A, B, D, E, C). The shortest path between a pair of vertices is the one with the least number of traversed edges. In Figure 1 (part a), there is only one shortest path between vertices A and C with length of 1 (A, C). It is also possible to have more than one shortest path between two vertices which must be the same length. The distance between a pair of vertices is measured by the shortest path between them.

The average shortest path is defined as the expected distance among randomly selected vertices, and is obtained by averaging all shortest paths across the graph.

The shortest path between vertices is used to identify clusters of similar biological functions and is applied in constructing biological networks such as aging networks(Witten and

Bonchev 2007, Managbanag, Witten et al. 2008). The average shortest path of biological networks is typically small, which results in rapid information flow among distant vertices in the

7 network(Wagner 2001, Tong, Lesage et al. 2004, Yook, Oltvai et al. 2004, Yu, Greenbaum et al.

2004).

Random walk for distance calculations

Random walks can be used to determine distances between vertices(Fortunato 2010), This is accomplished by averaging the number of in-between edges which a random walker crosses(Zhou 2003). Close vertices are likely to belong to the same community. A bias random walk is defined where walkers preferentially move toward vertices which share a large number of vertices with the starting vertex. Based on that, Zhou and Lipowsky defined proximity index, which indicates how close a pair of vertices are to all other vertices(Zhou and Lipowsky 2004).

Another distance measure between vertices was introduced by Latapy and Pons in(Pons and

Latapy 2005). They measured the distance based on the probability that the random walker moves form one vertex to another in a certain number of steps.

Small-world property

Based upon the average shortest path, Milgram(Milgram 1967) and Kochen(Kochen

1989) introduced an important network property, termed as a small-world property, and also known as six degrees of separation by Guare(Guare 1992), to study the speed of information spreading in the graph. In a small-world network, starting from any random vertex, most other vertices are accessible by passing through a few vertices. This implies that each pair of vertices can be connected by a short path in a small-world network. Watts and Strogatz reported that the expected distance between two random vertices in a small-world network is proportional to the logarithm of the number of vertices in the graph (Watts and Strogatz 1998).

Considering that biological networks are huge, the small average shortest path can be due to the existence of a few edges that connect distant vertices together. In other words, biological 8 networks are small-world networks(Watts and Strogatz 1998, Albert, Jeong et al. 2000, Jeong,

Tombor et al. 2000).

Centrality analysis

Centrality measures assess the relationship between connectivity and communication properties of a graph. There are different types of centrality measures. Closeness and betweenness centralities, as the two main centrality measures, explain the availability of paths across a graph(Newman 2008). In the next sections, these measures are defined for the undirected graphs.

Closeness centrality

Newman defined the closeness centrality for each vertex as the average shortest distance from that vertex to all accessible vertices (excluding those vertices that are not in any of the paths)(Newman 2008). Vertices with low closeness centrality are more central on average to other vertices across the network. For example in Figure 1 (part a), the closeness centrality of

1+1+1+1 vertex C is = 1 which has the smallest value among all vertices. This measure can 4 explain the fast communication property of central vertices in the network, and can be used for ranking molecules in biological networks. For example, Özgür et al. constructed the disease- specific gene-interaction network and identified a number of unknown gene-disease associations using closeness centrality metric(Özgür, Vu et al. 2008).

Betweenness centrality

Betweenness centrality is a measure that estimates the extent that a vertex acts as an intermediate vertex in paths among other vertices in the graph. Freeman proposed the first measure of betweenness centrality of a vertex in the network. Betweenness measure is defined for a vertex 퐶, as the fraction of shortest paths between other vertices that pass through 퐶

9

(Freeman 1977, Newman 2005, Newman 2008). For example, the betweenness value of vertex C in Figure 1 (part a) is 3/8, this means 3 paths {(A, C, E), (B, C, E), (A, C, D)} out of 8 paths in the graph {(A, C, E), (B, C, E), (B, D, E), (B, D), (D, E), (A, B), (A, C, D), (A, B, D)} cross over vertex C

(all 8 paths should not be started or ended at vertex C). In the same graph, the betweenness

1 1 values of other vertices A, D, E, B, F are 0, , 0, , 0 respectively. These values quantify the 7 7 amount of regulation that each vertex has on the flow of information in the graph. Shortest paths are typically considered in calculation of betweenness measures like in Freeman’s betweenness(Freeman 1977); however, the flow of information will not always pass through the shortest paths in the network. Consequently, other types of betweenness measures are introduced to take into account this issue, such as flow betweenness by Freeman et al.(Freeman, Borgatti et al. 1991) and random-walk betweenness by Newman(Newman 2005). A similar metric can be defined for edges to detect most between edges in the network. Edges, which link vertices with high betweenness, are called bridges. The more bridges in a network, the less the average path length, and the more fragile to form disconnected modules in their absence. Newman and Girvan presented the concept of betweenness for edges to detect central edges like bridges in a graph(Girvan and Newman 2002). They proposed that edge betweenness centrality for an edge e is defined as the degree to which information between all vertices across the network flows along the edge e.

Vertex betweenness is used in several studies to investigate the regulation effect of individual molecules on different types of flows across all molecules in biological networks, assuming the flow spreads along the shortest paths(Potapov, Voss et al. 2005, Yu and Gerstein

2006). Koschützki and Schreibe confirmed the strong correlation between the degree property and Newman random-walk betweenness measure in protein-protein-interaction network and

10 transcriptional regulation network(Koschützki and Schreiber 2004). Newman and Girvan defined edge betweenness to identify most between edges in the hierarchical clustering of food webs(Girvan and Newman 2002). They introduced an intuitive method to partition a network into densely connected communities by gradually removing the so-called edges. Pinney and

Westhead developed a modified version of Girvan’s and Newman’s algorithm such that overlapping communities can be detected as well(Pinney and Westhead 2006). In their algorithm, an edge with the highest betweenness is removed if its adjacent vertices have similar vertex betweenness.

Betweenness calculation techniques for large biological networks

In this section, different solutions to speed up the betweenness calculation for large biological networks are introduced. Newman and Girvan minimized the repetitive betweenness calculation in their method by parallelizing the computation load over multiple processors. For further improvement, they only recalculated edge betweenness of those edges that were affected by the edge removal process (Girvan and Newman 2002, Newman and Girvan 2004). Newman also proposed a novel computation method based on a modified version of the Sedgewick’s

Breadth-first search (BFS) (Sedgewick 1988) to accelerate calculation of vertex betweenness

(Newman 2001). Newman and Girvan adopted Newman’s method in another paper, in which in a hierarchical structure of a graph (BFS structure), vertices’ reachability by their top vertices are measured (Newman and Girvan 2004). Each BFS structure has only one source vertex. In each

BFS structure, vertices, which are not in the middle of any shortest paths across all vertices in the graph, are denoted as leaves and the score of 1 is assigned to their connected edges. Moving upward from leaves to the source vertex, the procedure of assigning scores to edges is as following: the score of an upper-level edge is equal to 1 plus the sum of the scores of

11 immediately below neighboring edges. At the end, the number of reachable vertices from a vertex is equal to the sum of the betweenness of edges below it. This process will be repeated for the rest of BFS structures and other source vertices, and finally all obtained scores on one edge are summed up to get the total betweenness. The calculation procedure of the shortest-path betweenness for a BFS structure in two different graphs is shown in Figure 2. Similar to

Newman’s method, Brandes introduced a recursive betweenness computation method which exploits the sparsity nature of large networks and has the capability to parallelize the process at multiple levels (Brandes 2001). In Brandes’ algorithm, the shortest path counts are calculated using a BFS method. The Brandes’ algorithm is based on the assumption that the dependency of a specific vertex on another vertex can be calculated from dependencies on vertices one edge farther away. The vertex betweenness of a given vertex can be computed by the sum of all dependencies.

(a) (b) Figure 2. Calculation of shortest-path betweenness for one BFS structure. based on the

Newman and Girvan method adopted from ref (Newman 2001) (a) The simplest case when there is only one shortest path from source vertex s to all other vertices (b) There is more than one shortest path between source vertex s and some vertices in this graph. For each figure, it is clear

12 that the number of reachable vertices from a vertex is equal to the sum of the betweenness of edges below it. For the source vertex in (a) and (b), the total number of reachable vertices is six and four, respectively.

Degree distribution

Important properties of network organization can be inferred by studying their degree distributions. Degree distribution (vertex connectivities) P(k) is defined as the fraction of vertices in the graph with degree k. In networks with Poisson degree distribution, the probability that a given vertex has degree k follows P(k) ≈ e-k. In these networks, most of vertices have similar degree about the average degree; thus, the graph’s degree distribution has a huge peak at its average degree.

In several biological studies, it is found that highly connected vertices or hubs are associated with molecules with essential biological functions, such as somatic cancer genes in(Goh, Cusick et al. 2007), photosynthesis genes in(Aluru, Zola et al. 2013) and cancer genes in(Jiang, Li et al. 2008). They proposed a detection threshold for statistically important hubs is obtained for networks with Poisson degree distribution.

Scale-free property

In many networks, the majority of vertices have small degree, while a few vertices with relatively high degree (hubs) are connected to them (inhomogeneous degree distribution). The degree distribution of these networks follows a Power-law distribution, where the probability of having a vertex with degree k is given as P(k) ≈ k-a, where a is the degree exponent, between 2 and 3 (Barabási and Oltvai 2004). Barabasi and Albert introduced a new concept, namely scale- free networks, after studying the Power-law degree distribution of these networks(Barabasi and

Albert 1999). They described the emergence of a Scale-free state in networks as the consequence

13 of preferential attachment during a network expansion, such that new vertices tend to connect to the existing well-connected vertices(Barabasi and Albert 1999).Therefore, the degree of a vertex with higher initial degree increases at faster speed compared to other vertices; hence, an initial difference between the degrees of two vertices with dissimilar degree will increase rapidly as the network expands(Barabasi and Albert 1999). Scale-free networks are self-similar, which means any section of the network has a similar structure to the whole network structure. These networks also have the small-world property(Albert, Jeong et al. 2000, Jeong, Tombor et al. 2000). Albert,

Jeong and Barabasi reported that scale-free networks are robust to random vertex removals. This is due to their inhomogeneous degree distribution. Alternatively, these networks are significantly vulnerable to targeted attacks to their hubs(Albert, Jeong et al. 2000).

The scale-free property has emerged as a key concept in the study of various biological networks, such as Metabolic networks(Jeong, Tombor et al. 2000), protein domain networks(Wuchty 2001) or cellular networks(Barabási and Oltvai 2004). Jeong et. al. , who studied metabolic networks of 43 organisms, discovered that despite their dissimilarities, all these networks inherit the self-similarity property, the robustness property to random vertex removal and the evolution property of Scale-free networks(Jeong, Tombor et al. 2000).

Mixing property: assortative and disassortative networks

Newman studied vertices’ tendency to connect to other vertices by looking at the network structure form a statistical point of view (Newman 2002). As a result, two attachment patterns are characterized for graphs: assortative and disassortative mixing patterns. In an assortative graph, high degree vertices tend to share edges with vertices with similar degree. Newman defined the scalar measure for assortative mixing property based on the degree correlations between each pair of vertices at either ends of an edge (Newman 2002). In a disassortative graph,

14 a considerable portion of the neighbors of high degree vertices are low degree vertices. This suggests a new strategy for robustness analysis of biological networks (Jeong, Tombor et al.

2000, Holme, Kim et al. 2002, Newman 2002). Newman discovered that hubs removal does not significantly affect the connectivity of assortative networks, since hubs are strongly interconnected; while, disassortative networks are more vulnerable to lose their connectivity in case of removing their hubs(Newman 2002).

The degree distribution of biological networks reveals their mixing properties. Most biological networks are disassortative(Newman 2002, Barabási and Oltvai 2004). Newman discovered that using his defined scalar measure for the assortative property of a network(Newman 2002). Maslov and Sneppen, who studied yeast transcription regulatory network, discovered that the network is disassortative (Maslov and Sneppen 2002). Their statistical measure compares two values to calculate the mixing property of the network: One value is the connectivity likelihood between two molecules with degree k1 and k2 in the network; and the other is the similar quantity but for the randomized version of the network.

Their findings suggest the decrease in the likelihood of crosstalk between modules in the network; but, increase in the robustness of the network at random removal of edges

Network Organization

Hintze and Adami predicted that biological networks contain multiple modules with different functions, because these networks need to be robust to various environmental variables as they evolve to perform numerous functional goals(Hintze and Adami 2008). Vertices are well- connected in each of these communities; however, there is relatively less connectivity between communities(Lancichinetti and Fortunato 2009). Community detection analysis provides a modular view of a network’s dynamics where each community performs certain function to

15 some extent independent of one another. Although, there is no assurance that community detection techniques identify the natural division of a network; each method provides promising results which may help to describe a network organization(Newman 2006). In these types of analysis, first we need to investigate clustering properties of the graph to have a general understanding about a network organization; after that we can continue with more complex and advanced methods.

Clustering

Clustering properties explain the tendency of vertices to form cliques. A clique is a sub- graph where its vertices are all interconnected. Different researches have studied the potential relation between vertices in cliques with functional or biological annotations of correlated molecules(Adamcsek, Palla et al. 2006, Zhang, Song et al. 2010). For example in(Zhang, Song et al. 2010), a set of differentially co-expressed disease-related genes are identified in cliques of the graph, that is built based on mutual information between each pair of genes in normal and disease samples. It is assumed that gene pairs in cliques have high mutual information values in healthy state but low values in disease state.

To quantify clustering properties of a graph, two metrics are introduced. Global clustering coefficient, as a general measure of a graph clustering, is the fraction of network’s sub-graphs which contains cliques with three vertices. Figure 3 illustrates the global clustering coefficient of three example graphs. First, Luce and Perry used this metric to determine the structure of friendship groups(Luce and Perry 1949). The second metric, namely local clustering coefficient, is defined for a vertex 푠, as the ratio of available number of edges between 푠’s neighbors to a maximum possible number of such edges. Local clustering coefficients for every vertex of an example graph are shown in Figure 4. The value of local clustering coefficient of

16 vertex “1” is the ratio of 1 (among vertices “2”, “3” and “4”, only one edge connects vertices “2” to “4”) to 3 (maximum possible number of edges between vertices “2”, “3” and “4”). In the absence of any organizing principle in the network structure, high clustering coefficients are not achievable. Distribution of clustering coefficients with respect to vertices’ degree may reveal underlying structure of the network. The clustering distribution is obtained by averaging clustering coefficients of vertices with similar degree(Watts and Strogatz 1998). The distribution of average clustering coefficients of metabolic networks follows a scaling law C(k)~k-1, where

푘 denotes a degree value(Ravasz, Somera et al. 2002). This implies a hierarchical structure in a network, such that vertices with small degree (high clustering coefficients) belong to small highly interconnected modules; while, vertices with relatively higher degree (Hubs) connect different modules together(Barabási and Oltvai 2004).

2 1 퐶 = 퐶 = 퐶 = 1 ∆ 4 ∆ 3 ∆

Figure 3. Global clustering coefficients for three example graphs.

1 퐶(1) = ⁄3 퐶(2) = 1 퐶(3) = 0 퐶(4) = 1

Figure 4. Local clustering coefficients for each vertex. Complex community detection algorithms 17

In this section, we will give a review about complicated and automated community detection algorithms. One of the first proposed community detection algorithms was Newman-

Girvan method (Newman and Girvan 2004). This is a divisive algorithm, in which edges are removed iteratively based on their betweenness values, since high-betweenness edges act as connectors between communities. The process is repeated until their modularity measure reaches its maximum. Their modularity function measures the quality of selected communities in a sense of how densely connected they are, and quantifies the value between 0 and 1. This quantity is obtained by subtracting the fraction of edges within communities of the actual graph from the same quantity in random model of the graph. For a graph with n vertices, the algorithm runs in worst-case time O(n3) on sparse graphs. Later, faster algorithm with a complexity of O(n2) for sparse graphs was proposed by Newman(Newman 2004). This method performs hierarchical clustering; thus, the process of selecting modules is different than the previous Newman-Girvan algorithm. It starts with considering vertices as initial modules where iteratively joined together with a condition of increasing the modularity value. Another modification of this algorithm, called fast greedy modularity, was proposed by Clauset, Newman and Moore(Clauset, Newman et al. 2004), wherein the update law of the modularity function in(Newman 2004) is optimized

,and as a result it runs in worst-case time 푂(푛 푙표푔2 푛) on sparse graphs. Since calculating betweenness is computationally expensive for large networks, researchers attempted to come up with new ways of quick calculating betweenness measure for divisive algorithms or even replace betweenness metric with other similar metrics. Radicchi et al. proposed an algorithm which uses edge clustering coefficient as an alternative metric for betweenness measure (Radicchi,

Castellano et al. 2004).Their algorithm achieved a complexity of 푂(푛2) on sparse graphs with their algorithm. Communities can be identified using random walk flow in a network, since a

18 random walker spends a long time inside densely connected communities. One of the most applied methods in biological networks is the Markov clustering algorithm(Dongen 2000), which simulates a random walk flow in the network by applying two different transformational techniques on a probability matrix of the network: expansion and inflation. The probability matrix is a column-normalized form of the adjacency matrix of the network. In the expansion step, the probability matrix is powered to an integer to reflect the effect of distribution of the flow in different regions of the network, such that edges within communities have higher flow value compared to those edges between communities. In inflation stage, the effect of the flow is more enhanced by taking each element of the matrix to an integer power, and is followed by a normalization step. This will reinforce strong flows and weaken weak flows. The expansion and inflation steps are repeated until the matrix reaches the steady state, whereas communities appear in the matrix structure. This method is widely used in bioinformatics(Enright, Van Dongen et al.

2002, Vlasblom and Wodak 2009, Dongen and Abreu-Goodger 2012) and has a complexity of

O(n k2), with this condition that after every inflation step, only k leading elements of the resulting matrix are preserved, and the others are set to zero(Lancichinetti and Fortunato 2009).

Another application of a random walk flow is its capability in generating a map of empirical flow in a network. This map of flow can be decoded to identify communities. Rosvall and Bergstrom proposed an algorithm called infomap(Rosvall and Bergstrom 2008) which is an information theoretic approach that detect communities by compressing the description of a trajectory of random walks in a network. The trajectory is defined as a map of vertices that visited by the random walker and can describe the network structure. They optimized their algorithm using greedy search and simulated annealing methods. Interestingly, in the analysis by (Lancichinetti and Fortunato 2009) all above algorithms were evaluated on the LFR benchmark(Lancichinetti,

19

Fortunato et al. 2008, Lancichinetti and Fortunato 2009), and infomap illustrated the best

performance in detecting communities in a networks. A summary of all mentioned community

detection algorithms is shown in Figure 5.

Community detection in biological network

Traditional Modularity- Divisive Dynamic methods based methods algorithms algorithms

-Girvan-Newman algorithm and other -Markov Clustering -Clustering properties -Fast greedy derived algorithms such Algorithm of a graph modularity by Clauset, as algorithm by -Infomap by Rosvall -Identifying cliques Newman and Moore Radicchi et al. and Bergstrom

Figure 5. Organization chart of community detection algorithms.

Conclusion

This chapter provides a review about the application of graph theory in studying

structural properties of biological networks. With advancement in high throughput technologies

and availability of large datasets, the need for systematic procedure for interpreting complex

biological processes is more felt. Network analysis provides a systematic approach for studying

the underlying organization of biological processes such as communication between components

and modularity properties. Statistical analysis of the network properties provides a tool for

quantitative assessment of biological phenomena, for example scale-free, small-world and many

other properties are define for biological processes after observing similar properties in their

networks. In addition, the modularity of biological networks and their capability to perform 20 multiple functions are studied through applying community detection algorithms on these networks. The quality of partitioning and speed of these algorithms, as important factors for modularity analysis, are addresses in this chapter. All these investigations are useful and provide a general understanding about the network dynamics, but the evolution of the network in time can be studied by examining temporal gene profiles. Information about gene activations and their interactions at each time point is needed for this type of analysis. In the next chapter, we are interested in investigating the dynamics of biological networks, particularly gene regulatory networks, when temporal data is available. In a deeper analysis, we estimated the time delay in transferring regulation effect of one gene to another gene.

CHAPTER THREE: TEMPORAL MODELING OF BIOLOGICAL NETWORKS

21

General assumption in building process of biological networks

The building process of biological networks depends on several assumptions which control the results of further analysis on the network. These assumptions are relevant to applied experimental technologies, data acquisition methods, and research design. Genomic technologies allow researchers to analyze the effect of perturbation of one molecule on other molecules. For example, microarray technologies help researchers to measure expression levels of numerous molecules under various environmental conditions over a number of time points. The underlying assumption in many biological studies is that molecules with similar expression profiles will have similar biological functions or participate in similar biological processes(Wolfe, Kohane et al. 2005). Sequencing technologies can also help in prediction of potential physical bindings between molecules. Interactions between molecules can be characterized as direct and indirect interactions. Direct interaction happens a third molecule does not interact with a pair of molecules. Also, interactions can be distinguished into association relationship and physical interactions. Researchers built their network models based on association relationships between expression profiles of molecules(Manfield, Jen et al. 2006, Borate, Chesler et al. 2009, Perkins and Langston 2009), or based on known physical molecular interactions. These expression profiles are the measurements of the activity of thousands of molecules at different time points and generally are used to detect molecules responses to a particular treatment. Some combined both types of interactions in their modeling process to get a better prediction about temporal interactions in a network. We will discuss this type of modeling in the section titled time delay estimation in regulatory networks. Some studies aimed to detect functional associations by analyzing multiple data sources(Dandekar, Snel et al. 1998, Wagner 2001). Also, connections in

22 networks are often defined according to a set of desired metrics related to the research problem(Zhang, Song et al. 2010).

Connection patterns in networks: from random networks to regular networks

There are two general connection patterns in modeling biological networks including completely random and completely regular patterns. This type of modeling is not applicable to the networks where their connection patterns are neither of these patterns and exist between these two extremes(Watts and Strogatz 1998). Some algorithms are developed to merge the properties of random networks and properties of regular networks through changing the pattern of random wiring of vertices in the network. The newly constructed network will have some of the properties of pure random networks (e.g., Erdos-Renyi model)(Erdos and Rényi 1960) and of regular network (e.g., Watts-Strogatz model)(Watts and Strogatz 1998). These properties consist of high clustering coefficients of regular networks and small-world property of random networks. The Erdos-Renyi random model has small Clustering Coefficients and small characteristic path lengths. However, the Watts-Strogatz model has high Clustering

Coefficientsand small characteristic path lengths. Table 1 shows the differences between theErdos-Renyi and Watts-Strogatz models from these two perspectives. In the process of constructing the Watts-Strogatz model(Watts and Strogatz 1998), the wiring probability p is assigned to each edge, where p varies between 0 (regular) and 1(random). To start from a regular network, all n vertices are placed in a ring, and each vertex is connected to its k nearest neighbors. A simple example of this procedure is shown on part (a) in Figure 6 (regular network) where k = 4 and n = 8 . To restructure regular networks and bring randomness into play, each edge which connects its end vertex to the end vertex’s first-nearest vertex, is reconnected to a random vertex with probability p . This procedure is repeated from one vertex to another,

23 clockwise, until one lap is completed. To remove redundancy, duplicate edges in the wiring process are avoided. Next, the similar process will be continued but for edges that connect vertices to their second-nearest neighbors, until every edge in the original regular network has been considered once. For example, if there are nk ⁄ 2 edges in the original network, the rewiring process stops after k ⁄ 2 laps. For values of p between 0 and 1, the resulted graph has the short path length property of random graphs while keeping the high Clustering Coefficients of regular networks (Watts and Strogatz 1998)(see part (b) in Figure 6). The rewiring procedure for different values of p is shown in Figure 6.

Table 1. Comparison of Erdos-Renyi and Watts-Strogatz models.

Network models Clustering Coefficients Shortest path length Erdos-Renyi model low low Watts-Strogatz model high low

(a) (b) (c)

Figure 6. Random rewiring procedure in creating Watts-Strogatz model. Adopted from ref(Watts and Strogatz 1998). From part (a) to part(c), the rewiring probability increases

(randomness increases). Graph in part (a) is a regular network with high clustering coefficient.

Graph in part (c) is a random network which has a very small shortest-path and small-world

24 property. Graph in part (b) inherits both property of large clustering coefficient and small shortest-path.

Metabolic networks

Metabolic networks are composed of series of biochemical reactions occurring in a metabolic pathway, where enzymes catalyze chemical compounds (substrates), to produce substances (products) for succeeding interactions or end products of a pathway such as proteins.

These activities are essential to cell functionality and sustainability. The simplest form of these enzymatic reactions is depicted below, where enzyme 퐸 catalyzes substrate 퐴 to produce product

푃. The association, redissociation and dissociation rates are shown with 푘1, 푘−1 and 퐾푐푎푡, respectively.

퐾1 퐾푐푎푡 퐴 + 퐸 ⇄ 퐸퐴 → 퐸 + 푃 (1) 퐾−1

Enzyme 퐸 reduces the activation energy of the above reaction by binding to the substrate

퐴 and forming enzyme-substrate compound 퐸퐴.

Network modeling using ordinary differential equations

Dynamic changes of the concentrations of all entities in a kinetic reaction can be described using ordinary differential equations according to the law of mass action, such that the rate of an irreversible reaction is proportional to multiplication of the concentrations of its reactants. Considering all forward and backward reactions in above equation individually as irreversible reactions, the following equations are derived:

푑푝 = 퐾 ∗ (퐸퐴) 푑푡 푐푎푡 (2) 푑(퐸퐴) = 퐾 ∗ 퐴 ∗ 퐸 − (퐾 + 퐾 ) ∗ (퐸퐴) 푑푡 1 −1 푐푎푡

25

푑(퐸) = −퐾 ∗ 퐴 ∗ 퐸 + (퐾 + 퐾 ) ∗ (퐸퐴) 푑푡 1 −1 푐푎푡 푑(퐴) = −퐾 ∗ 퐴 ∗ 퐸 + 퐾 ∗ (퐸퐴) 푑푡 1 −1 A short while after initiating the interaction, the consumption and production of compound 퐸퐴 are balanced; therefore, its concentration remains constant. In this steady-state condition, the nonlinear 푂퐷퐸푠 can be simplified to scalar 푂퐷퐸푠, and the equation for reaction rate can be obtained by the Michaelis-Menten equation as follows(Michaelis and Menten 1913):

푣 ∗ 퐴 (3) 푉 = 푚푎푥 퐾푚 + 퐴

Where 푣푚푎푥 is the product of 퐾푐푎푡 and total concentration of enzyme in the system, and

퐾푚 is the Michaelis-Menten constant as follows:

퐾−1 + 퐾푐푎푡 (4) 퐾푚 = 퐾1 The enzyme kinetic parameters for biochemical interactions can be derived from enzyme databases for example BRENDA (http://www.brenda-enzymes.info/ ). One problem with ODE approach in modeling metabolic networks is the large number of unknown parameters in reactions. Unrevealed structure of these equations can make the mathematical modeling even harder.

Gene regulatory networks

Inside the nucleus, the messenger Ribonucleic acid (mRNA) is continuously transcribed from a part of the Deoxyribonucleic acid (DNA) in response to intracellular signals and is carried out outside of the nucleus to be decoded by ribosomes in cytoplasm to produce the proteins, responsible for vital functions in a cell. DNA is a double-stranded helix molecule that encodes genetic instructions for the cell functioning and development. mRNA, a single-stranded molecule, is a small copy of part of a DNA. Particular type of proteins, called transcription

26 factors (TFs), regulates the concentration of mRNA by repressing or initiating the transcription process in the nucleus. Availability of large microarray data sets gives a detailed insight about gene expression changes over time. Descriptive network models, that are constructed using different computational techniques such as correlation analysis, reveal the underlying knowledge of the regulatory mechanisms.

Incorporating temporal gene expression with DNA-binding knowledge

To model gene regulatory networks, we need to differentiate physical interactions from association relationships among genes. Thanks to advances in sequencing technology, a priori knowledge about the potential physical binding of many transcription factors to target genes is available; however, this possibility is not sufficient to determine the occurrence of the reaction at the time of transcription initiation. Temporal gene expression profiles, which disclose substantial information about biological functions at each time interval, provide more insight about the possibility of regulatory interactions along the time. Therefore, incorporating insights from gene expression profiles with physical binding information of reactions reveal the accurate structure of gene regulatory networks at each time interval. Such that, some of the suggestive interactions obtained by profile analysis may not be accurate in case the transcription factor in the reaction is incompetent for DNA-binding to the target gene, and similar analysis for reverse condition when the binding information is available but no specific information can be found in gene expression profiles. Identifying potential binding sites of transcription factors to DNA and combining it with microarray profiles were investigated in multiple papers as a valid approach for constructing gene regulatory networks(Ren, Robert et al. 2000, Lee, Rinaldi et al. 2002, Li and Chan 2004,

Boulesteix and Strimmer 2005).

27

Time delay estimation in regulatory networks

Some delay may occur in transferring the impact of transcription factor regulation to target genes. This Time-delayed phenomenon underlying gene regulatory networks is extracted using various modeling approaches such as correlation analysis (Li, Rao et al. 2006), decision- tree-related classifiers (Soinov, Krestyaninova et al. 2003), and Boolean models (Silvescu and

Honavar 2001). These methods are capable to estimate delay as one unit time lag; however, in many regulation processes, there are multiple units of time lag during which the change of the regulator expression is transmitted to the associated target gene. The model, that poses the ability to estimate multiple-time delays of gene regulations, will expand our knowledge about evolution of the biological processes.

Method

Assuming that gene expression profiles are continuous and linear trend of changes within each time interval, we adopted linear regression models to uncover the network structure, and estimated the time-delayed responses for each pair of target genes and transcription factors. It is essential to know that regulators of a same target gene may have different time delays in our model. The steps taken to model gene regulatory networks are depicted in figure 7.

We used a multiple-delayed linear regression model to estimate the time-delay effect of transcription factors on their target genes. First, we need to estimate the time delay, and then regression coefficients using the estimated delay. The data sets used for modeling is the temporal gene expressions of mice post myocardial infarction (MI). To construct regression model, the transcription factors and target genes in the data sets should be identified using TRANSFAC database. Genes which have high degree of connectivity in the network may be potential

28 biomarkers for myocardial infarction. For evaluation purpose, we compared the constructed multiple-delayed regression model with the cross correlation-based delay model, and the no- delay regression model.

STAR T

Find transcription factors 푥푖,푗s

interacting with the target gene 푦푗

Delay τ=0

푚

푦 푗 = 훼0,푗 − 훼푖,푗푥푖,푗 푖=1

Update τ and emin

Find 훼푖s using LS

τfinal = Current τ 2 푒 = (푦푗 − 푦 푗) τ = Previous τ final Yes

e No < e τ min = τmax No Yes

Delay = τfinal

End

29

Figure 7. Steps for designing a multiple-delayed linear regression model.

The statistical analysis is performed using Adjusted R2 and ANOVA F-test. The statistical measures confirmed that our multiple-delayed regression model performs better than the cross-correlation-based delay model, and the no-delay model.

Multiple-delayed regression model

According to Granger causality analysis, a target gene expression will not be impacted by the regulators, before the change in regulator expression is transmitted. The delay is the time takes to transfer a transcription factor regulation to a target gene (repression or activation). The impact can be appeared instantly (zero delay) or within a reasonable amount of delay.

The linear regression model is described as follows:

푚

푦 푗[푘] = 훼0,푗 + 훼푖,푗,휏푥푖,푗[푘 − 푛푖,푗] (5) 푖=1 휏

where 푦 푗[푘] is the estimated target gene expression which interacts with transcription factors 푥푖,푗s, 푚 is the number of transcription factors affecting target gene 푦푗, 푛푖,푗shows the delay between the transcription factor 푥푖,푗 and target gene, 푦푗, and 훼푖,푗s are the regression model coefficients.

In the regulation of a target gene, regression coefficient of a regulator may vary at different time points. This is due to the shared distributed effect of transcription factors on a target gene in a regulatory module of transcription factors. Therefore, the regression coefficients may change when a new transcription factor is introduces to the target gene’s regulatory model.

In equation (5), this concept is represented by the variable 휏 in 훼푖,푗,휏 which indicates the variability of the regression coefficients at different time points for transcription factor 푖 affecting target gene 푗. 30

Parameter estimation

A traditional technique to estimate delays between two discrete signals is average square difference function(ASDF). It is a fast algorithm offered by Jacovitti et al. that identifies the global minimum of the cost function defined in equation (6)(Jacovitti and Scarano 1993).

(푁−1)푇 1 퐽 = 푎푟푔푚푖푛( (푦 [k] − 푦 [푘])2 ) (6) 푁 푗 푗 푘=0

where 푁 is the total number of time samples, 푇 is sampling time period, 푦푗[k] and 푦 푗[푘] represent the experimental values of the target genes푗 , and its estimation, respectively.

In our regression model, Least Square Estimation (LSE) was adopted to determine the regression coefficients. The objective of LSE is to minimize the sum of squared errors (SSE), between the estimated target gene expressions and their available experimental values, shown in equation (7):

(푁−1)푇 푚 1 퐽 = 푎푟푔푚푖푛( (푦 [k] − 훼 − 훼 푥 [푘 − 푛 ] )2 ) (7) 푁 푗 0,푗 푖,푗,휏 푖,푗 푖,푗 푘=0 푖=1 휏

Statistical analysis

The derived regression model was evaluated with two statistical tests: adjusted 푅2 and

ANOVA F-test. The 푅2 statistic measures the percentage of total difference of estimated and

2 2 actual value of 푦푗. The adjusted 푅 is a modified version of 푅 which includes the numbers of time points and transcription factors in its formula, and is defined as:

푁 − 1 푎푑푗푢푠푡푒푑 푅2 = 1 − (1 − 푅2) (8) 푁 − 푛tf − 1

31

where 푛tf is the number of transcription factors that interacts with an specific target gene and 푁 is the total number of time points.

The second statistical measure is an ANOVA F-test. It is used to test the significance of overall regression results (goodness of fit). This measure is derived by dividing the sum of square residuals (SSR) to SSE as shown in equation (9):

푆푆푅 퐹 = ~퐹(푣 , 푣 ) 푆푆퐸 1 2 (9) ⁄(푛 − 2)

This test follows an F distribution with two degrees of freedom 푣1 and 푣2, with the values of 푁 − 푛tf and 푛tf − 1, respectively. The p-value is measured based on the calculated F-statistic.

The overall regression is statistically significant at the level of significance of 0.05 (p-value ≤

0.05).

Materials/Data

Datasets used for this model are gene expression profiles of the liver of young C57BL/6J mice available at 7 time points for post-myocardial infarction (MI). The sampling time points are as follows: control at time 0, 12 hours, 24 hours (1 day), 72 hours (3 days), 120 hours (5 days),

240 hours (10 days), and 720 hours (30 days). There are 30774 biomarkers, where 7435 of them has p-values less than 0.05 at all their sampling time points. The TRANSFAC database was used to identify possible bindings between transcription factors and target genes. 110 pairs of transcription factors and target genes were discovered in our datasets for the regression model

(number of target genes = 66, number of transcription factors = 39).

Gene expressions measurements were converted to the log scale for the remainder of analysis. The fold changes of the log-scaled gene expression at each time point with respect to

32 the control measurements were derived (day 0). The fold change of each gene was standardized with respect to its mean and variance.

The original gene expression data sets have few time points that are not equally spaced.

Also, there is a long time gap between last sampling time points. To obtain a meaningful correlation for the delay calculation, additional internally data points are interpolated consistent with the original data samples as a computational aid. The new generated dataset has more samples, which are equally spaced. The minimum time interval in the new dataset is 12 hours.

Results

Different amounts of delay were estimated for the temporal gene regulatory network.

Figure 8 illustrates the network models at different time delays. In Figure 8, genes are connected when the regression coefficient associated with the regulator is not zero. At each time delay, the network structure is re-drawn by adding new connections, while the previous connections are kept in the network. The underlying assumption in our model is that the expression level of target genes at each time point is affected by the expression level of their regulators at previous time points. Consequently, the final network contains all target genes and regulators of previous networks. It is essential to know that except the time lag of zero in the first network, we did not identify any concurrent interactions in other networks.

The red connections are old interactions that remain from previously drawn networks, that the regulatory effect of their transcription factors has been transferred to their target gene, and the blue connections are current interactions, which the effect of transcription factors are transferred to the target genes at the given time delay. The amounts of estimated delays are: 0, 1,

2, 3, 4, 5, 6, 7, 8, 9, 10, 11 and more. In figure 8, the size of node in the network increases as its number of connections to other genes increases. High-degree nodes (called hubs), such as trance-

33 acting transcription factor 1 (SP1) and v-rel reticuloendotheliosis viral oncogene homolog A

(Rela), may play an important biological role in the network, since they participated in many interactions and impacted the connectivity of the network.

n=0 n=1 n=2 n=3

n=4 n=5

n=6 n=7

n=8 n=9

n=10

n=11

34

Final graph Figure 8. Regulatory network structure at each time delay. Delay: n = 0, 1,…,11. The red connections are old interactions that remain from previously drawn networks, that the regulatory effect of their transcription factors has been transferred to their target gene, and the blue connections are current interactions, which the effect of transcription factors are transferred to the target genes at the given time delay. The size of node increase as their degree increases.

To evaluate our results, we compared them with the results realized by two other models: no-delay regression and cross correlation-based delay models. Based on F-score statistics, Our model was able to successfully identified 29 significant target genes regulated by transcription factors at different time points (p-value ≤ 0.05) while this number decreased to 27 in cross correlation-based delay model and to 15 in no delay model. More importantly, all target genes regulated by two transcription factors are identified in our model, while the other two models failed to detect all these target genes. For example, 22 target genes were regulated by 2 transcription factors in our model, however; only 20 and 8 target genes were selected in cross correlation-based delay model and no delay model, respectively. Target genes regulated by 3, 4, and 6 transcription factors were identified in all three models.

The numbers of target genes with two regulators with adjusted 푅2 value of over 0.60 for are 10, 9 and 8 in our model, cross correlation-based model and no-delay model, respectively.

The adjusted 푅2 value varies between [0.13, 0.96] in our model, [0.037, 0.98] in cross

35 correlation-based model, and [0.006, 0.98] in no delay model. Also the maximum p-values in our model, cross correlation-based model, and no delay model are 0.033, 0.037, and 0.34, respectively.

Target genes with three regulators in our model have the adjusted 푅2 value over 0.73, while the other two models have at least one target gene that has an adjusted 푅2less than 0.7. For the same target genes, the maximum p-value is 6.55E-6 in our model, versus 2.91E-5 in cross correlation model and finally 3.56E-3 in no-delay model.

The adjusted 푅2 for target genes with 4 regulators is 0.657 for both delay-based models while decreased to 0.497 for no-delay model. The estimated p-value is 5.23E-10 for delay-based models versus 4.54E-4 in no-delay model.

Table 2. Statistical comparison results for target genes with 2 or 3 regulators.

Number of Number of TFs Possible # of Average adjusted Average p- regulators of Parameters 퐑ퟐ value target genes Two Our Model 3,4 0.60 0.0086 regulators Cross 3,4 0.54 0.0189 correlation- based delay model No-delay model 3 0.53 0.0312 Three Our Model 4,5,6,7 0.82 3.39E-6 regulators Cross 4,5,6,7 0.80 1.026E-5 correlation- based delay model No-delay model 4 0.75 3.75E-3

Conclusion

In this chapter, ordinary differential equations and linear regression models are studied for modeling dynamics of metabolic and gene regulatory networks, respectively. Each of these

36 computational techniques address different features of network dynamics, for example in metabolic networks, the kinetic reactions can be modeled as ODEs, while in gene regulatory we are interested in modeling activation of target genes at different time points. Temporal expressions/concentrations of network’s biological entities can be combined with other biological information about the underlying processes to configure network dynamics model. For example, unknown parameters of regression model of gene regulatory networks are determined by combining gene expression profiles with DNA-binding information of their transcription factors. Also, other features of a dynamic process such as delay can be estimated during the modeling process. For evaluating models, statistical measures such as adjusted 푅2 and ANOVA

F-test are applied. In the second chapter, we discussed about structural properties of biological networks from the graph theory point of view, while in the third chapter, more biological information are introduced into the network which helps to examine dynamic properties of the network statistically and even estimate other unknown parameters such as delay in the network.

In the next chapter, new methods for modeling information flow in signaling network, particular type of biological networks, are introduced with the goal of extracting dynamic properties of a network from its structural properties in an innovative approach.

37

CHAPTER FOUR: MODELING DYNAMIC PROCESSES IN SIGNALING NETWORKS

Genetic information from Deoxyribonucleic acid (DNA) in nucleus is carried on by messenger RNAs to cytoplasm to be used for generating proteins in a cell (Crick 1958, Crick

1970). This theory, called central dogma of molecular biology, is a backbone of molecular biology. Transferring information from the DNA to a protein is an explicit example of an application of flow analysis in understating biological mechanisms in a cell. Multifunctional

Cellular activities in a cell can be modeled as an integrated network of signaling, metabolic, and regulatory pathways. The dynamics in such networks can be modeled through analyzing flow of information/materials among cellular entities. How the flow distributed in a network, determined network design principles. For example, central molecules or modules are those that receive significant amount of flow in the network. In a metabolic network, the flow is described by the way metabolites (products of metabolic reaction) transport in a cell. Metabolic pathways provide a general picture about the flow distribution in these networks. In a signaling network, the flow is described by the propagation of information genes, proteins and other cell components. These networks are composed of one or more signaling pathways that may interact with each other.

In general, flow is modeled by a random-walk in a network, since a random walk uses all information in a network structure and nothing more (Rosvall and Bergstrom 2008). as a stochastic process, the walker moves among entities of the network, and its next destination is determined given the full history of all previous positions that it visited(Vespignani 2012). The goal is to reveal dynamics of a network from its structural properties. The process of random visiting of nodes generate the dynamics from the network structure alone by emphasizing on the dependency between dynamics and structural properties of the networks.(Ziv, Middendorf et al.

38

2005). For example, a random walker statistically spends more time within densely connected modules in networks.

Flow modeling in signaling and regulatory networks

The information starts propagating in signaling transduction pathways when a signaling molecule binds to a cell surface receptor to change the cell’s behavior. Following that, cascades of intracellular molecules in cytoplasm are activated sequentially in response to the initial trigger, and transfer the signal to the nucleus for initiating transcription process. Appropriate transcription factors in the nucleus initiate the transcription process, where specific sections of the DNA are copied. These copies carry genetic information for making required proteins, and are called messenger Ribonucleic acid (mRNAs). Then, mRNAs, travels to the ribosome in the cytoplasm. Ribosomes translate mRNAs coding regions into proteins that are vital for the cell function. The signaling transduction process should be terminated to prevent the cell from losing its responsiveness to new signals, otherwise uncontrolled functions occur in a cell, for example cancer(Berg, Tymoczko et al. 2002).

In signaling transduction processes, a same cellular response may be provoked by several signaling pathways(Lodish, Berk et al. 2000). Also, each signaling pathway may have multiple cellular responses. For example, vascular endothelial growth factor (VEGF) signaling pathway advances angiogenesis through diverse cellular activities including endothelial cell proliferation, survival and migration, and vascular permeability(Spyridopoulos, Luedemann et al. 2002).

Pathways interact with each other to fine tune cellular activities and achieve complex biological goals(Lodish, Berk et al. 2000). All of these notions illustrate the complexity of a network of signaling pathways. Dynamic analysis of networks containing these pathways reveals interdependency of underlying functions. Particularly, these analyses get more attention when

39 some alterations of components in signaling pathways transform cell’s behavior. The alterations have been observed in many diseases such as tumor and cancers. For example, the relationship between the alterations and human tumors has been discovered for the transforming growth factor-beta (TGFβ) pathway(Levy and Hill 2006).

The propagation of information within signaling and regulatory networks exposes important properties of network’s modularity. Modules are a set of cellular reactions that are assumed to cooperate together to accomplish certain biological functions(Conzelmann, Saez-

Rodriguez et al. 2006). Studying modules provides a systematic approach for describing the dynamics of the signaling transduction networks while reducing their complexity.

Memory in networks

First-order Markov models have been used for modeling dynamics in networks (Dongen

2000). In the first-order Markov model, given the current state of a random walk, future and past states are independent, thus, the dynamics is characterized as memoryless and it is assumed that the first-order Markov dynamics is able to describe fundamental properties of the organization of various networks. It has been found that first-order Markov modeling cannot capture essential features of empirical flow of the network, because the direction of flow depends on where the flow comes from (Chierichetti, Kumar et al. 2012, Pfitzner, Scholtes et al. 2013, Rosvall,

Esquivel et al. 2013). Later, Shannon introduced higher-order Markov modeling (Shannon 2001) and brought the concept of memory into network dynamic analysis. In higher order models, the direction of the flow not only depends on the current state of a random walker but also to the sequence of events that preceded it. Higher-order models impose more complexity into flow analysis. The concept of memory in networks and its application on network organization (e.g, community detection problem) is reintroduced by Rosvall and Esquivel et al (2013) with a new

40 approach at the implementation procedure. They applied their method on different social and biological systems including the flight itineraries between US airports, journal citations, movement of patient between hospital wards, GPS-tracked taxis and email forward and reply networks(Rosvall, Esquivel et al. 2013). They investigate the presence of memory in a network by comparing the results of modeling the random walker movement based on the first- and second-order Markov dynamics. Following their investigations, they discovered that where the flow originates determines its course in all these networks; therefore, the second-order Markov dynamics is more capable to capture networks’ dynamic properties including community detection, spreading dynamics and ranking of components compared to the first-order Markov dynamics. In this study, we focused on the impact of memory on community detection problem in biological networks, since modular analysis provides a framework for understanding underlying biological functions. In a first-order Markov dynamics, the current position of a random walker determines its next destination (single-step memory); however, in second-order

Markov dynamics the next destination of the random walker depends on its previous and current positions (two-step memory).

How to model empirical pathways of signaling transduction pathways

The real biological pathways are required as raw data sets for constructing first- and second-order Markov models. These pathways indicate the actual rate and the distribution of the flow among different components of the network. The volume of the flow at each connection is proportional to the number of times that the flow passes through the connection. The ultimate goal is designing a flow-generative model that can properly estimate the empirical biological pathways in the network, in other words it can approximately model the direction of the real flow in the network. Also, considering that biological networks are huge, modeling the real flow for

41 long pathways is computationally expensive and impractical. Since, studying the first- and second order Markov dynamics requires pathways of length two and three respectively, a proper flow-generative model that can produce pathways of length three, is capable to capture the dynamics of both models.

In this chapter, I proposed two flow-generation models for signaling networks. Each approach views the modeling problem from its own perspective. One tries to solve the problem with a Data-mining solution and the other used state-transitions in a Boolean model. Kyoto

Encyclopedia of Genes and Genome (KEGG) website contains a large database of human signaling pathway maps. The first flow-generation model is an intuitive data mining approach, in which human signaling pathways are extracted from the KEGG website and breaks down to short pathways of length two to create a new database. The most important part is determining weights of short pathways in the new database. The weighting scheme is by counting the occurrence of these short pathways in the new database, since many of these short pathways are common in several signaling pathways (longer pathways) and more flow paths through them.

Second flow-generation model is constructed based on state-transitions in Boolean model of the

Mitogen-Activated Protein Kinase (MAPK) signaling pathway. Boolean model as a discrete dynamic model can provide insight about states of network’s component in response to stimuli, such that the state of each molecule is on or off at each time point. Short pathways are constructed by tracing the activation of genes in consecutive states, considering that the general output-input relationships of genes are given by the MAPK Boolean model.

Fundamental principles about memory mathematical representation

The dynamics of a random walk for first- and higher-order Markov models is explained in details in the paper by Rosvall and Esquivel et al (2013). In this section, I review briefly some

42 of the general concepts about modeling the dynamics as a stochastic process for first- and second-order Markov models. The state of the walker is denoted by 푋푡 at time 푡, which can get position values of nodes in the network from 푣 = 1,2, … , 푛. Generally, the probability that the walker visits node 푣 in the network at time 푡 + 1, depends on the full history of all previous nodes that the walker have visited up to now:

P(v; t + 1) ≡ P(Xt+1 = 푣푡+1) = P(Xt+1 = 푣푡+1|푋푡 = 푣푡, 푋푡−1 = 푣푡−1, … , 푋1 = 푣1, )

Where Xt+1is the state of flow at time t + 1, and 푣1, 푣2, … , 푣푡, 푣푡+1 ∈ 1,2, … , 푛. In a first- order Markov dynamics, the flow direction is determined based on its current state as follows:

P(v; t + 1) ≡ P(Xt+1 = 푣푡+1|푋푡 = 푣푡)

such that weight of directed links between nodes in the network is sufficient to describe the flow dynamics in the network. In second-order Markov model, the flow direction is given as:

P(v; t + 1) ≡ P(Xt+1 = 푣푡+1|푋푡 = 푣푡, 푋푡−1 = 푣푡−1)

To quantify weight of short pathways of length 3 for the second-order Markov model,

Rosvall, Esquivel et al(2013) constructed memory networks from the original networks, such that each node in the memory networks represents the current state of the walker and the previously visited nodes, in other words a memory node corresponds to a directed link in the original network. Also, a directed link in the memory network corresponds to a short pathway of length 3. For example, the weight of the memory link, 푣푤⃑⃑⃑⃑⃑ → 푤푢⃑⃑⃑⃑⃑ , between two memory nodes,

푣푤⃑⃑⃑⃑⃑ and ⃑푣푢⃑⃑⃑ , is given by the occurrence of the short pathway 푣 → 푤 → 푢 in the network, and is described as the number of times that the random walker travels from 푤 to 푢 if it arrived from node 푣 in the previous step. In this way, the walker’s next step is determined based on the currently and previously visited node. It is possible that the flow does not leave a node; in this case as long as it stays in the node, the state of the random walker does not change. In summary, 43 we generated bigrams and trigrams from our flow-generation models for a first- and second- order Markov models, respectively. Bigrams are directed links in the standard network. Trigrams are the short pathways of length three in the standard network, and correspond to the links in the memory networks.

Performance evaluation paramters

The performance of first- and second-order Markov dynamics are evaluated by investigating the quality of community detection algorithm and statistic parameters including the entropy rate of a random walker, module size, module assignement, and flow return rates.

Significant analysis with Bootstrap re-sampling

We need to perform bootstrap re-sampling on trigrams to verify that results are significant and calculated based on sufficient data. This is done by re-sampling all trigrams with replacement, where each replica is a collection of randomly selected trigrams with the same size as the original data. The underlying assumption is that trigrams in the original data set are independently generated. We repeat this process for 100 times, and create 100 bootstrap replicas.

Community detection algorithm is performed on all new generated data sets separately, and at the end the 90% bootstrap confidence interval for the results of bootstrap estimates is calculated.

To understand the significant memory in the analysis, confidence interval of the estimates of first- and second-order Markov dynamics should not overlap(Rosvall, Esquivel et al. 2013). If the estimates of the original trigrams are within the bootstrap confidence interval, it is concluded that the original data is rich and large of numbers of repeated trigrams can be found in the data set.

44

Community detection algorithm

Community detection can be influenced by considering memory in network dynamics modeling. Map equation framework is used for detecting communities in first- and second-order

Markov models(Rosvall and Bergstrom 2008) , since it is a flow-based approach and it is compatible with other assumption for modeling the dynamics in the network. This framework is an information-theoretic technique that detects communities by compressing the description of a trajectory of random walks in a network, such that the optimal partitioning of the network is achieved. The trajectory is defined as a map of vertices that visited by the random walker and can describe the network structure. This technique is capable of identifying overlapping modules in the network, which is really important for studying biological pathways and their interactions.

In the analysis by (Lancichinetti and Fortunato 2009) infomap illustrated the best performance among several community detection algorithms based on the LFR benchmark(Lancichinetti,

Fortunato et al. 2008, Lancichinetti and Fortunato 2009).

Entropy rate

Entropy rate of a random walker is defined as the average amount of required information for identifying the direction of the flow. Mathematically, entropy rate at each node is the conditional entropy where the uncertainty of the next destination of the random walker is calculated given its current state. This calculation is weighted by the stationary distribution of a random walk. More details about the distribution can be found in the paper(Rosvall, Esquivel et al. 2013). If the network is a memory network, the conditional entropy decreases in second-order

Markov dynamics, since more information is introduced by considering memory for calculating flow in the network.

45

Return rates, Module size and module assignment

Return rates, Module size and module assignment are other measures used for comparing results of first- and second- order Markov dynamics. Return rate is defined as the percentage of the flow that returns to the same node after a specific number of steps. The second-order Markov model can exactly measure the two-step return rate since the modeling technique has two-step memory. With a similar reasoning, three-step return rates can be captured by third-order Markov model. Our goal is to calculate both these rates for first- and second-order Markov model to understand more about system organization of the flow.

Module size is defined as the average of frequencies that a random walker visits each module. Average number of module assignment per each node defines module assignment measure. This gives an estimate of the level that modules overlap in a network (Rosvall,

Esquivel et al. 2013). Since, second-order Markov model use more detail information about the flow, if the memory effect is considerable, then the identified modules with this dynamics are smaller and considerably overlap with each other (Rosvall, Esquivel et al. 2013).

Flow-generation model for human signaling pathways

Human signaling pathways are extracted from KEGG website in a format of xml files.

We used KEGGREST to communicate with KEGG website and read xml files, KEGGREST is a package in R which provides a client interface to the KEGG REST server(Tenenbaum), and R is a free software programming language for statistical analysis on datasets(R Core Team 2013).

Each signaling pathway is broken into gene pairs (bigrams) using functions in KEGGgraph package in R(Zhang and Wiemann 2009). These gene pairs are combined to form a table of all gene pairs of human signaling pathways. For the second-order Markov model, we searched through all gene pairs to construct the short pathways of gene triplets (trigrams) and construct

46 trigram datasets. The frequency of bigrams and trigrams in their corresponding datasets determines the weight of links in the standard and memory networks, respectively. The trigram dataset has 567917 trigrams with various weights from 5123 total number of genes (nodes) in the standard graph. For the first-order Markov dynamics, we break the generated resulted trigram datasets into bigrams, this makes both trigram and bigram datasets have the same number of vertices. Finally, modules are identified in both datasets using the community detection algorithm, namely infomap. In result, Modules are sorted based on their share on flow volume in the whole network, and genes in each module are ranked based on their share on flow volume in the module. Overlapping genes distribute their flow volume among the modules that they belong to.

By considering memory for flow calculation (second-order Markov dynamics), community detection algorithm is able to detect 1565 modules, while it detects 1540 modules when memory is disregarded (first-order Markov dynamics). Also, modules overlap more in the memory network.

Bootstrap re-sampling of human pathway trigrams

For booststrap re-sampling, first we need to repeat the trigrams of human signaling pathways by their weights and generated an extended trigram dataset. The 100 bootstrap replicas are generated by re-sampling these trigrams by replacement. At the end, each trigram in the random-generated dataset is weighted by the number of its repetition. The community detection algorithm is implemented on all 100 bootstrap replicas. The 90% bootstrap confidence interval for the results of bootstrap estimates is calculated. Table 3 shows the statistic result of first- and second-order Markov dynamics with their confidence interval that is generated by bootstrap re- sampling. Since there are many trigrams with weight one in the original data, some parameters

47 lie outside of the bootstrap interval, but the memory effect is still present. This is due to non- overlapping confidence interval of first- and second-order Markov dynamics for all parameters.

Table 3. statistic results for first- and second-order Markov dynamics for human signaling network

Entropy rate Two-step return Three-step return Module Module size rate rate assignment first-order 3.8561 0.025975 0.016947 1.33484 0.105743 Markov (3.826-3.828) (0.0262- 0.0263) (0.01696-0.01708) (1.2569-1.2617) (0.1067-0.1072) dynamics Second- 3.4226 0.0310976 0.0220613 1.53861 0.103939 order (2.920-2.924) (0.0303-0.0311) (0.0216-0.0220) (1.5350-1.5398) (0.102782-0.103945) Markov dynamics

Analyzing memory effect on high flow-volume modules

As seen in the table 3, the module size is very small; this is due to the existence of many modules with only one gene in the network. To highlight the significance of the results, we need to remove all small modules and focused the analysis on the modules that dominate the flow volume in the network. After that pathways related to genes in high-volume modules can be found through pathway enrichment analysis. The biological function of each module can be shown by their underlying pathways. It is interesting to see which pathways are enriched in each module. The interrelation between theses pathways can also be studied by analyzing overlap genes between modules. To identify which modules to include, we cut the flow volume at %75 starting from the module with the highest flow volume in the network. This procedure is performed by summing up the flow volume from the top genes of the top module to the place that the accumulated flow reaches %75 of the total flow volume in the network. Since genes are sorted based on their share of volume flow of the module, there are some genes in each module that propagates very small amount of flow close to zero. It is required to choose a threshold for

48 the flow volume in each module to remove these genes. In our analysis, genes that participate in propagation of %95 of the flow volume are selected for pathway analysis in each module. The resultant modules have high flow volume in the network and contain genes with nonzero flow volume. With these assumptions, we end up with 17 and 16 modules for second- and first-order

Markov model, respectively.

Top modules of first-order Markov dynamics do not overlap in many genes (totally 5 genes in all modules), however, top modules of second-order Markov dynamics overlap in many genes

(totally 51 genes), particularly modules 3,8,17 and 16. The list of all modules and their containing genes are provided in the Appendix. Figure 9 shows the overlap genes between these four modules. Modules 3,8,17 and 16 have 61, 27, 35 and 14 genes, respectively. Also, top genes are shown in red in each module. Top genes in each module participate in %51 of the module’s flow volume. There are 21, 7, 6 and 3 top genes in the modules 3,8,17 and 16, respectively. It is important to note that some overlapping genes do not belong to the top gene list of their overlapping modules, or they only belong to the top gene list of only one module and not both.

For example PIK3R5, PIK3CA, PIK3CB, PIK3CD and PIK3CG are not in top gene list of modules 3 and 8, and PIK3R1, PIK3R2 and PIK3R3 are top genes in only module 8. The gene

GSK3B belongs to the top gene list of both modules 8 and 17. Genes PLCB1, PLCB2, PLCB3,

PLCB4 belongs to the top gene list of only module 3.

49

Module 3(61 genes) Module 8(27 genes) Module 16(14 genes)

PIK3R5 AKT3 AKT1 MAPK1 PTEN IMPAD1 PIK3R1 MAPK3 PIK3R2 AKT2 MTM1 IMPA2 CREB1 MAP2K1 PLCE1 IMPA1 PIK3R3 PLCG1 INPP1 PIK3CA PLCG2 PI4KA PIK3CB PLCD1 PI4KB PIK3CD PLCD3 PI4K2B PIK3CG PLCD4 PI4K2A PLCZ1 GSK3B

PLCB3

PLCB1 PLCB2

PLCB4 DVL2 DVL1

DVL3 VANGL2 Module 17(35 genes) VANGL1

Figure 9. Overlap and top genes in four modules of second-order Markov dynamics.

Red genes are top genes that dominate %51 flow volume of the module.

Pathway enrichment method

We used ToppGene(Chen, Bardes et al. 2009) to identify pathways enriched by the

genes found in each of these module. ToppGene is an online computational software that

performs several analysis including: gene list enrichment analysis, prioritizing candidate genes

based on their function, protein-protein interaction network analysis, and ranking the candidate

genes according to both functional annotation and PPI network. The statistically significance of

the identified pathways and functions is determined by Hypergeometric distribution and

50

Bonferroni correction. There are as many as 14 independent hypothesis testing analyzed simultaneously, therefore Bonferroni correction will generate a more accurate and conservative results. The cut-off p-value for pathway in each module is set to 0.001.

Overlapping genes and top genes in high-volume modules

We would like to test this hypothesis that flow of pathways propagated to other modules through their overlap genes between the overlapped modules, therefore we need to identify overlapping genes between modules and then identify pathways that contain these genes in overlapped modules. It hypothesized that these genes act as connectors of multiple pathways between the modules. The procedure for testing this hypothesis is explained in figure 10. First, pathway enrichment analysis is performed for all modules containing %75 of the flow volume of the whole network. In this stage, genes in each module propagate %95 of the total flow volume of their modules. Identified pathways are grouped in the group-one pathway, After that, top genes in each module which dominate the flow volume by propagating %51 of its flow volume are selected. Form group-one pathways, those that contain top genes are selected and form group-two pathways. So, group-two pathways contain one or more genes from the top genes of that module are selected. Common pathways between modules are identified by searching among group-two pathways. The overlapping genes are identified between modules containing genes with %95 of the module flow volume. Now, we can test the hypothesis by searching overlapped genes between common pathways.

There are 69, 139, 34 and155 enriched pathways in module 3, 8, 17and 16 with p-values less than 0.001. Top genes of module 3,8,17 and 16 are associated with 56, 136, 17 and 151 pathways out of 69, 139, 34 and 155 total signaling pathways for their modules.

51

Selecting genes that Selecting top genes that propagate %95 of the dominate %51 of the flow volume in each flow volume in each module module

Collecting pathways Selecting pathways from enriched by these genes. the list of enriched Cut-off p-value<0.001 pathways in each module ( group-one pathways) that contain top genes (group-two pathways)

Finding overlapping Finding common pathways genes between modules between modules containing genes with (Pathway searching among %95 of the module flow group-two pathways) volume

Checking whether common pathways contain overlapping genes between modules

Figure 10.The procedure for identifying overlapping genes that participate in enrichment pathways of top genes of each module.

In top genes of module 3, Overlapping genes with module 17 including PLCB2, PLCB4,

PLCB1 and PLCB3 have 24 related pathways among 56 pathways. Comparing pathways of top genes of module 3 and 8, there are 36 common pathways between module 3 and module 8 top

52 gene’s pathways. Figure 11 illustrates the van diagram of the number of enriched pathways containing top genes of modules 3 and 8. Each pathway in their common pathways (36 pathways) contains one of some of the overlapping genes between modules 3 and 8.

Figure 11. Van diagram of number of enriched pathways containing top genes of modules 3 and 8. Each pathway in their common pathways (36 pathways) contains one of some of the overlapping genes between modules 3 and 8.

There are two common pathways between modules 3,8 and 17 including Angiogenesis and MicroRNAs in cardiomyocyte hypertrophy. Angiogenesis pathway contains PIK3R1,

PIK3R2, PIK3R3, PIK3CA, PIK3Cb, Pik3CG, PIK3CD, GSK3B genes. The pathway related to

MicroRNAs in cardiomyocyte hypertrophy contains GSK3B, PLCB2, PIK3CA, PIK3CB,

PIK3CG, PIK3R1, PIK3CD, PIK3R2, PIK3R3 genes. So, common pathways among modules

3,8 and 17 contain one or some overlapping genes between 3 and 8 , overlapping genes between

53

8 and 17 and overlapping genes between 3 and 17. Figure 12 shows the intsection between pathways asscoated with top genes of modules 3,8 and 17. The list of pathways in intersections can be found in Appendix.

Figure 12. Van diagram of number of enriched pathways containing top genes of modules 3 and 8 and 17.

Top genes of module 16 are MAPK1, MAPK3 and MAP3K1. 20 pathways of the 56 common pathways between modules 8 and 16 contain CREB1, which is an only overlapping gene. Many of common pathways that do not have CREB1 are interrrelated with the common

54 pathways containing CREB1 such as vascular endothelial growth factor receptor 3 (VEGFR3)in lymphatic endothelium signaling pathway that contain CREB1 , and VEGFR signaling pathway that does not have CREB1 in its gene list. To improve the results, it is needed to find the parent pathways of all pathways and repeat the analysis, such that less number of pathways will be analyzed. Figure 13 shows the van diagram of number of enriched signaling pathways containg top genes of modules 8 and 16.

Figure 13. Van diagram of number of enriched pathways containing top genes of modules 8 and 16.

55

Biological interpretation of identified pathways in overlapping regions

In this section, common pathways between modules 3, 8,17 and 16 including angiogenesis and microRNAs in cardiomyocyte hypertrophy, Wnt signaling, Wnt/beta catenin pathways, will be studied.

Angiogenesis

Angiogenesis is the development process of new blood vessels from pre-existing vessels and is induced in many fundamental processes such as reproduction, development and wound healing (Fox, Gasparini et al. 2001). Excessive angiogenesis can be a feature of many disorders such as diabetes, arthritis, psoriasis, and more importantly cancer and infectious diseases(Carmeliet 2003).Insufficient angiogenesis in different organs of the body may be associated to different diseases such as stroke and Alzheimer disease in nervous system

(Krupinski, Kaluza et al. 1994, de la Torre 2002) or atherosclerosis, hypertension and diabetes in blood vessels(Van Belle, Rivard et al. 1997, Boudier 1999, Rivard, Silver et al. 1999).

Angiogenesis participates in the process of the tumor growth and spread (metastasis) in cancer (Folkman 1990, Fox, Gasparini et al. 2001). Endothelial Cells (ECs) are composed of cytokines, adhesion molecules, growth factors that are rarely divide in normal condition, but are capable to replicate rapidly in response to physiological stimulation for raising the blood supply for example in wound healing process(Polverini 2002). The problem with this rapid growth is that the duration of angiogenesis in disease state is much longer than that in normal condition(Carmeliet and Jain 2000).

There are different activators of the angiogenesis process. Vascular endothelial growth factor family(VEGF), as the key regulator of angiogenesis process, control different stages of the development of new blood vessels by promoting endothelial cell proliferation, migration and

56 formation(Veikkola and Alitalo 1999). Different growth factors and cytokines can control VEGF transcription such as epidermal growth factor (EGF), interleukin-1β (IL-1β), platelet-derived growth factor(PDGF) and transforming growth factor-β1 (TGF-β1)(Polverini 2002). VEGF, EGF and PDGF pathways are in the common pathway of module 3 and 8. IL-1 signaling pathway is in the list of top pathways of module 8. TGF-beta signaling pathway is in top pathways of module

16, where angiogenesis is also included in this list. Fibroblast growth factors (FGFs) can also induce signaling pathways related to cell proliferation, and cell migration in angiogenesis. These pathways are as follows: Ras pathway, Src family tyrosine kinases, phosphoinositide 3-kinase

(PI3K) and the PLC pathway(Cross and Claesson-Welsh 2001). FGF, Ras and PI3K signaling pathways are common pathways between modules 3 and 8. Top genes of module 3 contains all family members of phosphoplipase C (PLC) genes which some of them (PLCBs) are common between modules 3 and 17. MAPK-ERK signaling is thought that is responsible for angiogenesis(Mavria, Vercoulen et al. 2006). Interestingly module 8 and module 16 contain

MAPK signaling pathways among their top pathways. ERK signaling is in top pathway of module 16 where CREB, the overlapped gene, is part of the gene list of this pathway.

Wnt signaling pathways

Wnt signaling pathways regulate diverse biological processes including proliferation, apoptosis, polarity, differentiation(Clevers 2006).To activate these pathways, Wnt-protein ligands should bind to Frizzled (FZD) family receptors. Module 17 contains all Frizzled receptors in its gene list. Evidence of participation of Wnt signaling in angiogenesis is first observed when endothelial cells (ECs) express a number of Frizzled family receptors (Goodwin,

Sullivan et al. 2006). Researchers discovered that Wnt1,Wnt3 and Wnt5 control EC proliferation stage during angiogenesis and regulate angiogenesis in vitro(Wright, Aikawa et al. 1999,

57

Masckauchan, Agalliu et al. 2006, Newman and Hughes 2012). Also, variety of studies confirmed the activation of Wnt signaling during angiogenesis in EC in vivo. (Blankesteijn, van

Gijn et al. 2000, Daneman, Agalliu et al. 2009). Overlap pathway of modules 8 and 17 is

Wnt/beta catenin pathway. Overlap pathway of modules 3 and 17 is Wnt signaling pathway.

Wnt/beta catenin pathway is one of the three Wnt signaling pathways , where beta-catenin interact with transcription factors TCF/LEF family for regulating cell proliferation(Rao and Kühl

2010) . Goodwin and D’Amore confirmed the link between this pathways to angiogenesis(Goodwin and D'Amore 2002).

MicroRNAs in cardiomyocyte hypertrophy

Cardiac hypertrophy is the heart’s response to different types of external and internal stimuli that increase biomechanical stress. In physiology, Hypertrophy is defined as the enlargement of an organ or tissue from the increase in size of its cells. One of the essential features of hypertrophy is an increase in cardiomyocyte size(Frey and Olson 2003). Micro

Ribonucleic acids (miRNAs) are small non-coding RNAs that play important roles in gene regulation mechanism, including cardiac development and disease (da Costa Martins, Bourajjaj et al. 2008, Thum, Catalucci et al. 2008). Wang and Yang provides a good review about the regulation role of microRNAs expression on signaling pathways related to cardiac hypertrophy, to that extent, they illustrated that these microRNAs and their related pathways can be a potential targets for cardiac disease treatment(Wang and Yang 2012).

Cardiac miRNAs participates in complex regulatory networks and their dysfunction can impact a variety of cellular processes related to cardiac diseases, for example eight dysregulated miRNAs in human heart failure may influence 1716 predicted target genes related to other cardiac-related signaling networks(Naga Prasad, Duan et al. 2009). In a study by Mo, it is shown

58 that identical microRNA can regulate different types of pathways, and therefore it can contribute to various diseases(Mo 2012).

Biological functions of pathways in common regions and their interactions with other pathways inside each module illustrate the flow-based organization of communities. This suggests that second-order Markov model can capture flow information underlying the network dynamics. However, to get a better conclusion, we need to find parent pathways of all pathways in each module. Because some of the current pathways in modules are too specific and this causes the small number of common pathways between modules. Furthermore, this analysis provides a better understanding about the main functions in each module.

Flow-generation model for MAPK signaling pathway

Through Mitogen-Activated Protein Kinase (MAPK) signaling pathways, essential cellular activities are regulated such as growth, proliferation, differentiation, migration and apoptosis (Dhillon, Hagan et al. 2007). Deregulation of components in MAPK signaling pathways are observed in different types of cancers (Dhillon, Hagan et al. 2007, Wagner and

Nebreda 2009, Grieco, Calzone et al. 2013). MAPK network can be activated by both overlapped

Epidermal Growth Factor Receptor (EGFR) and Fibroblast Growth Factor Receptor 3 (FGFR3) pathways. Mutations of FGFR3 are identified in mild aggressive bladder cancers; but, they are less frequent in invasive tumors (Halawani, Mondeh et al. 2004, Ricote, García-Tuñón et al.

2006). Conversely, it has been found that over-expression of EGFR is closely correlated with tumor progression (Fan, Yang et al. 2005).

Boolean dynamic modeling of signaling pathways

59

Boolean model for biological networks is first introduced by Kauffman (Kauffman 1969,

Kauffman 1969)and, since then, many researchers have applied Boolean models for modeling signaling and regulatory networks (Kaufman, Andris et al. 1999, Li, Long et al. 2004, Saez-

Rodriguez, Simeoni et al. 2007, Zhang, Shah et al. 2008, Albert and Wang 2009, Sun and Albert

2013). Dynamics of a Boolean model can be described by assigning states, namely 0(inactive) or

1(active), to each molecule or biological phenomena of the network. Denoting the state of each molecule as x, a biological network with N molecules comprises a set of Boolean variables 푋 =

(푥1, 푥2, … , 푥푁). The logical dependencies between biologically related molecules can be described by transition functions containing Boolean operators AND (&), OR (|), NOT (!). For each molecule, a logical transition function can be defined to map a subset of Boolean variables in 푋 to a Boolean value (0 or 1).An example of these rules are described in below, where the first element in each row is a target molecule, and after comma the transition function is described using Boolean operators:

(1) 퐶, 퐴&퐵

(2) 퐷, 퐴|퐵

(3) 퐸, ! 퐴

(4) 퐴, 퐴

In this example, 퐶 is activated when both 퐴 and 퐵 are active; While, either activation of

퐴 or 퐵 will activate D. The inhibition of 퐸 by 퐴 is represented by Not (!) operator. The last rule indicates that the Boolean value of 퐴 does not change as time evolves. If the state of a network at time 푡 is defined as 푋(푡) = (푥1(푡), 푥2(푡), … , 푥푁(푡)), the state of the network at time 푡 + 1 can be obtained by applying transition functions on 푋(푡) (Müssel 2013). There are two options for updating the network state. In asynchronous model, all states are updated at the same time;

60 however, in asynchronous model, only the state of one molecule is chosen to be updated at the next time point. In a Boolean network with 푁 nodes, 2 푁 different network states are possible; however, in a biological network, a small number of these states are traversed to reach the steady state of the network. Sometimes instead of a single stable network state, the trajectory of these state transitions will fall into a cycle of states, called an attractor. It is discovered in several studies that phenotypes can be associated with attractors(Li, Long et al. 2004).

Data acquisition model: Trigram construction using state-transitions

The Boolean model of the MAPK signaling pathway is obtained from one of the maps, called red1 network, in (Grieco, Calzone et al. 2013) , where fully asynchronous updating rule is applied in state transitions process. The state transition process explains about the dynamics of a logical model. Gene Interaction Network simulation (GINsim) software is used for generating all state transitions(Naldi, Berenguier et al. 2009). This software provides state transitions of the

Boolean model with the capability of specifying initial conditions.

To simulate EGFR over-expression, EGFR is set to 1 throughout the whole simulation and all other inputs are zero.

Now, we can generate trigrams of active molecules from these state transitions. In Each state, activated molecules are represented by 1, and others are represented by 0. All codes for flow-generation of MAPK Boolean model is written in R language. To construct Boolean model

BoolNet package is used(Müssel 2013). In this package, logical relationship between molecules can be specified and it is possible to extract the list of activators/inhibitors for each molecule in the model. The Input of each molecule is determined as a set of its activators and inhibitors. The output of each molecule is considered as a set of its target molecules. To construct trigrams, we need to have three consecutive state transitions of the network. First we built trigrams of all state

61 transition of the network. For example if state 1 follows state 2, and state 2 follows state 3, then the state trigram will be (state1,state2,state3). The procedure of building the trigrams of active molecules is described as follows: At the current state of the network, all active molecules are selected. In the previous state of the network, the active inputs of those active molecules in the current state were chosen. In the next state of the network, the active outputs of the active molecules in the current state were chosen. The trigram of active molecules can be built for each molecule if its inputs in the previous state and its outputs in the next state are active. This brings an important question to our mind that whether making trigrams from bigrams does not introduce more information to the flow analysis in the next stage. A good answer to this question is that partial information is stored in trigram structure of states which illustrates the direction of the flow in the network.

Using different settings of initial conditions for the Boolean network, various trigram datasets are generated. Each of these trigram datasets contains specific flow of information in response to the underlying biological functions in the network. For the first-order Markov dynamics, the generated resulted trigram datasets are broken into bigrams, to have equal number of nodes for both first- and second-order Markov dynamics. Using Infomap, modules are detected in different conditions of network dynamics. In the results, flow is distributed between modules inhomogeneously. Such that, one module has the highest amount of the flow volume in the network, and is ranked as the top module in the module list. Other modules are sorted after the top module based on their flow volume in the network. Flow passing overlapping genes distribute between the modules that they are associated.

Bootstrap significance analysis is required to verify the significance of results of community detection algorithm and discover whether trigram datasets are sufficient for

62 describing the network dynamics. For each set of trigrams (network dynamics condition), 100 bootstrap replicas are generated through sampling with replacement. Each replica is selected randomly among trigrams of specific condition and the replica has the same size as the original trigram dataset. Then, community detection algorithm is performed on new generated replicas, and 90% bootstrap confidence interval of all estimates is computed. The original trigram datasets are rich, if the estimates of the original trigrams are within the bootstrap confidence interval.

Trigram datasets for 8 initial conditions of MAPK Boolean model are constructed based, community detection, bootstrap significance and biological interpretation of modules will be completed in future works.

CHAPTER FIVE: FUTURE WORKS

63

In the analysis of KEGG human signaling pathways, we study only four modules that overlapped together. Other modules should be studied to highlight the importance of considering memory in the analysis of network dynamics. More importantly, the parent of enriched biological pathways in each module should be found, because many detected pathways were part of a bigger biological pathway. In this way, the necessity of considering memory in analysis of network dynamics will be strongly confirmed.

The interdependence of pathways inside modules is not studied through biological analysis.

Identifying important pathways in each module and their communicate pattern through biological analysis will open doors for more deep understanding of the effect of memory in networks and may help us to identify what is lost when we only study the gene pairs in network analysis.

For MAPK signaling network, the flow-generation model is explained and series of trigram datasets are constructed in response to different initial conditions for certain genes in the network. After constructing trigram datasets for 8 initial conditions, we need to perform community detection analysis to identify modules and perform significance analysis. Moreover, biological analyses of the results are the next step in determining the significance of considering memory in network dynamics modeling.

After examining MAPK signaling networks, if the results shown significance of the memory in network dynamics, I am interested in modeling dynamics signaling cancer signaling pathways, and investigate whether crosstalks can be identified by overlapping genes between modules.

Crosstalk between signaling pathways occurs when these pathways share some components and enable pathways to perform specific functions while limiting the number of components in the pathway cascades, for example crosstalks in MAPK signaling pathways optimize the

64 performance of signaling by decreasing the number of required components for performing particular biological function(Junttila, Li et al. 2008). Deregulation of components in MAPK signaling pathways are observed in different types of cancers(Dhillon, Hagan et al. 2007,

Wagner and Nebreda 2009, Grieco, Calzone et al. 2013). Crosstalk in MAPK signaling pathways complicate their underlying molecular mechanism in cancer development and make the targeted cancer therapy inefficient(Wagner and Nebreda 2009).

65

APPENDIX

Table 4 – Selected 16 modules containing %75 of the flow volume of the network with first- order Markov dynamics. Genes in each module participate in propagation of %95 of the flow volume.

Module rank Rank in module Flow volume Gene name 1 1 0.007161 ENTPD8 1 2 0.007161 ENTPD1 1 3 0.007161 ENTPD3 1 4 0.00683 NUDT2 1 5 0.00658 ITPA 1 6 0.005737 NME6 1 7 0.005737 NME7 1 8 0.005737 NME1 1 9 0.005737 NME2 1 10 0.005737 NME3 1 11 0.005737 NME4 1 12 0.005737 NME1-NME2 1 13 0.005737 NME5 1 14 0.005511 PKLR 1 15 0.005491 PKM 1 16 0.004966 ADCY3 1 17 0.004712 ADCY5 1 18 0.004662 ADCY1 1 19 0.004659 ADCY8 1 20 0.004624 ADCY9 1 21 0.004543 ADCY2 1 22 0.004543 ADCY7 1 23 0.004542 ADCY4 1 24 0.004532 ADCY6 1 25 0.004118 ADCY10 1 26 0.003933 PNPT1 1 27 0.002542 CANT1 1 28 0.002542 ENTPD6 1 29 0.002542 ENTPD5 1 30 0.002542 ENTPD4

66

1 31 0.001975 LOC101060521 1 32 0.001975 POLR3F 1 33 0.001975 POLR3G 1 34 0.001975 POLR3C 1 35 0.001975 POLR3A 1 36 0.001975 POLR3H 1 37 0.001975 TWISTNB 1 38 0.001975 POLR2J2 1 39 0.001975 POLR1A 1 40 0.001975 ZNRD1 1 41 0.001975 POLR1D 1 42 0.001975 POLR3K 1 43 0.001975 POLR2A 1 44 0.001975 POLR2B 1 45 0.001975 POLR2C 1 46 0.001975 POLR2D 1 47 0.001975 POLR2E 1 48 0.001975 POLR2F 1 49 0.001975 POLR2G 1 50 0.001975 POLR2H 1 51 0.001975 POLR2I 1 52 0.001975 POLR2J 1 53 0.001975 POLR2K 1 54 0.001975 POLR2L 1 55 0.001975 POLR2J3 1 56 0.001975 POLR3B 1 57 0.001975 POLR3E 1 58 0.001975 POLR1E 1 59 0.001975 POLR3D 1 60 0.001975 POLR1B 1 61 0.001975 POLR3GL 1 62 0.001975 POLR1C 1 63 0.001685 GUK1 1 64 0.001533 RRM2B 1 65 0.001533 RRM2 1 66 0.001516 RRM1 1 67 0.001398 NTPCR 1 68 0.001363 ENTPD2

67

1 69 0.001261 POLD3 1 70 0.001261 POLE3 1 71 0.001261 POLD1 1 72 0.001261 POLD2 1 73 0.001261 POLE 1 74 0.001261 POLE2 1 75 0.001261 POLE4 1 76 0.001261 POLD4 1 77 0.001245 POLA2 1 78 0.001245 POLA1 1 79 0.001245 PRIM1 1 80 0.001245 PRIM2 1 81 0.001243 LOC100507855 1 82 0.001243 AK7 1 83 0.001243 AK1 1 84 0.001243 AK2 1 85 0.001243 AK4 1 86 0.001243 AK5 1 87 0.001243 LOC390877 1 88 0.001222 PDE1C 1 89 0.001199 PDE1A 1 90 0.001198 PDE1B 1 91 0.001126 PDE3A 1 92 0.001126 PDE3B 1 93 0.001104 PDE10A 1 94 0.001104 PDE7B 1 95 0.001104 PDE11A 1 96 0.001104 PDE2A 1 97 0.001104 PDE4A 1 98 0.001104 PDE4B 1 99 0.001104 PDE4C 1 100 0.001104 PDE4D 1 101 0.001104 PDE7A

Module rank Rank in module Flow volume Gene name 2 1 0.016705 PNP 2 2 0.008527 HPRT1 2 3 0.007063 ADA

68

2 4 0.005296 ADK 2 5 0.005263 NT5C1B-RDH14 2 6 0.005263 NT5C3B 2 7 0.005263 NT5C2 2 8 0.005263 NT5C 2 9 0.005263 NT5E 2 10 0.005263 NT5C3A 2 11 0.005263 NT5M 2 12 0.005263 NT5C1A 2 13 0.005263 NT5C1B 2 14 0.005096 ITPA 2 15 0.004857 IMPDH1 2 16 0.004857 IMPDH2 2 17 0.004137 GMPS 2 18 0.003312 APRT 2 19 0.00219 XDH 2 20 0.002096 DCTPP1 2 21 0.001972 GDA 2 22 0.001557 DCK

Module rank Rank in module Flow volume Gene name 3 1 0.002396 PLCB2 3 2 0.002395 PLCB1 3 3 0.002395 PLCB3 3 4 0.002395 PLCB4 3 5 0.001851 PLCG1 3 6 0.001841 PLCG2 3 7 0.001799 PLCE1 3 8 0.001774 MTM1 3 9 0.001684 PLCD3 3 10 0.001684 PLCD1 3 11 0.001684 PLCD4 3 12 0.001684 PLCZ1 3 13 0.001656 PTEN 3 14 0.001383 IMPAD1 3 15 0.001368 IMPA1 3 16 0.001368 IMPA2 3 17 0.00124 INPP1

69

3 18 0.001105 INPP5B 3 19 0.001105 OCRL 3 20 0.001105 INPP5E 3 21 0.001105 SYNJ1 3 22 0.001105 SYNJ2 3 23 0.001081 PI4KA 3 24 0.001081 PI4KB 3 25 0.001081 PI4K2B 3 26 0.001081 PI4K2A 3 27 0.000959 INPP4A 3 28 0.000959 INPP4B 3 29 0.000912 PIK3C3 3 30 0.000904 INPP5J 3 31 0.000904 INPP5A 3 32 0.000904 INPP5K 3 33 0.000857 PIKFYVE 3 34 0.000849 PIP5K1C 3 35 0.000845 PIP5K1A 3 36 0.000845 PIP5K1B 3 37 0.000827 PIP4K2A 3 38 0.000827 PIP4K2C 3 39 0.000827 PIP4K2B 3 40 0.000779 ITPKA 3 41 0.000779 ITPKB 3 42 0.000779 ITPKC 3 43 0.000729 PIK3C2A 3 44 0.000729 PIK3C2B 3 45 0.000729 PIK3C2G 3 46 0.000713 DGKA 3 47 0.000713 DGKB 3 48 0.000713 DGKG 3 49 0.000713 DGKH 3 50 0.000713 DGKQ 3 51 0.000713 DGKZ 3 52 0.000713 DGKE 3 53 0.000713 DGKD 3 54 0.000713 DGKI 3 55 0.000629 CDIPT

70

3 56 0.0006 ITPR3 3 57 0.000591 ITPR1 3 58 0.000588 ITPR2 3 59 0.000529 ITPK1 3 60 0.000454 INPP5D

Module rank Rank in module Flow volume Gene name 4 1 0.001896 CLDN11 4 2 0.001896 CLDN24 4 3 0.001896 CLDN16 4 4 0.001896 CLDN4 4 5 0.001896 CLDN3 4 6 0.001896 CLDN7 4 7 0.001896 CLDN23 4 8 0.001896 CLDN19 4 9 0.001896 CLDN14 4 10 0.001896 CLDN15 4 11 0.001896 CLDN17 4 12 0.001896 CLDN20 4 13 0.001896 CLDN18 4 14 0.001896 CLDN22 4 15 0.001896 CLDN25 4 16 0.001896 CLDN5 4 17 0.001896 CLDN10 4 18 0.001896 CLDN8 4 19 0.001896 CLDN6 4 20 0.001896 CLDN2 4 21 0.001896 CLDN1

Module rank Rank in module Flow volume Gene name 5 1 0.002069 LPCAT4 5 2 0.002014 PEMT 5 3 0.001459 CEPT1 5 4 0.001053 PLD1 5 5 0.001034 PLD2 5 6 0.001031 PLA2G15 5 7 0.000963 PLD4

71

5 8 0.000963 PLD3 5 9 0.00093 PLB1 5 10 0.000921 PISD 5 11 0.000913 PLA2G16 5 12 0.00086 CHPT1 5 13 0.000806 CYP2J2 5 14 0.00075 LPCAT2 5 15 0.00075 LPCAT1 5 16 0.000711 LPCAT3 5 17 0.000703 MBOAT2 5 18 0.000703 MBOAT1 5 19 0.000622 ALOX5 5 20 0.0006 EPT1 5 21 0.000555 PLA2G4B 5 22 0.000555 PLA2G4E 5 23 0.000555 PLA2G4F 5 24 0.000555 PLA2G4D 5 25 0.000555 PLA2G4A 5 26 0.000554 LYPLA1 5 27 0.000554 PNPLA6 5 28 0.000554 PNPLA7 5 29 0.000548 PLA2G4C 5 30 0.000548 JMJD7-PLA2G4B 5 31 0.000431 PLA2G6 5 32 0.000423 PLA2G2D 5 33 0.000423 PLA2G2E 5 34 0.000423 PLA2G2C 5 35 0.000423 PLA2G3 5 36 0.000423 PLA2G1B 5 37 0.000423 PLA2G2A 5 38 0.000423 PLA2G5 5 39 0.000423 PLA2G2F 5 40 0.000423 PLA2G12A 5 41 0.000423 PLA2G10 5 42 0.000423 PLA2G12B 5 43 0.000404 ENPP6 5 44 0.000347 CYP2E1 5 45 0.000337 CYP4A11

72

5 46 0.000302 PTDSS1

Module rank Rank in module Flow volume Gene name 6 1 0.001693 AKR1C3 6 2 0.001691 HSD17B3 6 3 0.001164 CYP3A5 6 4 0.001143 CYP1A1 6 5 0.001036 HSD17B6 6 6 0.001029 HSD3B1 6 7 0.001029 HSD3B2 6 8 0.000992 CYP19A1 6 9 0.000973 HSD17B2 6 10 0.000962 HSD17B8 6 11 0.000788 CYP2A13 6 12 0.000667 UGT2B11 6 13 0.000667 UGT2A1 6 14 0.000667 UGT2B28 6 15 0.000667 UGT1A10 6 16 0.000667 UGT1A8 6 17 0.000667 UGT1A7 6 18 0.000667 UGT1A6 6 19 0.000667 UGT1A5 6 20 0.000667 UGT1A9 6 21 0.000667 UGT1A4 6 22 0.000667 UGT1A1 6 23 0.000667 UGT1A3 6 24 0.000667 UGT2A2 6 25 0.000667 UGT2B4 6 26 0.000667 UGT2B7 6 27 0.000667 UGT2B10 6 28 0.000667 UGT2B15 6 29 0.000667 UGT2B17 6 30 0.000667 UGT2A3 6 31 0.000548 CYP3A4 6 32 0.0003 CYP2A6 6 33 0.000269 AOX1 6 34 0.000248 CYP2B6 6 35 0.00024 CYP2C8

73

6 36 0.000234 CYP3A7 6 37 0.000231 CYP17A1 6 38 0.000218 CYP4A11 6 39 0.000217 AKR1D1 6 40 0.000203 CYP26A1 6 41 0.000202 SRD5A1 6 42 0.000202 SRD5A2 6 43 0.000202 SRD5A3 6 44 0.0002 CYP26C1 6 45 0.0002 CYP26B1 6 46 0.000188 CYP2S1 6 47 0.000182 CYP21A2 6 48 0.000172 CYP2C18 6 49 0.000167 CYP11A1 6 50 0.000156 CYP11B2 6 51 0.000153 STS 6 52 0.00015 CYP7A1 6 53 0.000147 AKR1C4 6 54 0.000147 CYP2E1

Module rank Rank in module Flow volume Gene name 7 1 0.002594 AKT3 7 2 0.002594 AKT1 7 3 0.002594 AKT2 7 4 0.002066 PIK3R2 7 5 0.002066 PIK3R1 7 6 0.002066 PIK3R3 7 7 0.001914 PIK3R5 7 8 0.001729 PIK3CA 7 9 0.001728 PIK3CB 7 10 0.001728 PIK3CD 7 11 0.001586 PIK3CG 7 12 0.000781 KRAS 7 13 0.000777 NRAS 7 14 0.000777 HRAS 7 15 0.000674 PDPK1 7 16 0.000553 PRKCZ 7 17 0.0005 PTK2

74

7 18 0.00046 FOXO3 7 19 0.000286 GSK3A 7 20 0.000273 IRS1

Module rank Rank in module Flow volume Gene name 8 1 0.001021 EPHX1 8 2 0.000942 CYP2C9 8 3 0.000916 GSTO2 8 4 0.000916 GSTA5 8 5 0.000916 GSTA1 8 6 0.000916 GSTA2 8 7 0.000916 GSTA3 8 8 0.000916 GSTA4 8 9 0.000916 GSTM1 8 10 0.000916 GSTM2 8 11 0.000916 GSTM3 8 12 0.000916 GSTM4 8 13 0.000916 GSTM5 8 14 0.000916 GSTP1 8 15 0.000916 GSTT1 8 16 0.000916 GSTT2 8 17 0.000916 GSTK1 8 18 0.000916 MGST1 8 19 0.000916 MGST2 8 20 0.000916 MGST3 8 21 0.000916 GSTT2B 8 22 0.000916 GSTO1 8 23 0.000909 CYP1B1 8 24 0.000884 CYP1A2 8 25 0.000809 CYP3A4 8 26 0.000769 CYP2E1

Module rank Rank in module Flow volume Gene name 9 1 0.003674 RB1 9 2 0.001533 CCND1 9 3 0.001515 CCNE1 9 4 0.001515 CCNE2 9 5 0.001506 CDKN1B

75

9 6 0.00146 MYC 9 7 0.001094 E2F3 9 8 0.001076 E2F1 9 9 0.001036 E2F2 9 10 0.000962 CCND2 9 11 0.000817 RBL2 9 12 0.000809 CDKN1C 9 13 0.000753 CCND3 9 14 0.000747 CDKN1A 9 15 0.000683 RBL1 9 16 0.000563 TFDP1 9 17 0.000537 TFDP2 9 18 0.000528 CDK4 9 19 0.000446 CDK6 9 20 0.000362 CDK2 9 21 0.000297 E2F4 9 22 0.000297 E2F5

Module rank Rank in module Flow volume Gene name 10 1 0.001785 ADH1A 10 2 0.001785 ADH1B 10 3 0.001785 ADH1C 10 4 0.001785 ADH4 10 5 0.001785 ADH5 10 6 0.001785 ADH6 10 7 0.001785 ADH7 10 8 0.00163 ALDH3A1 10 9 0.00163 ALDH1A3 10 10 0.00163 ALDH3B1 10 11 0.00163 ALDH3B2 10 12 0.000641 MAOA 10 13 0.000641 MAOB 10 14 0.000555 COMT

Module rank Rank in module Flow volume Gene name 11 1 0.002941 TYMP 11 2 0.002322 UMPS 11 3 0.002285 UPP2

76

11 4 0.002285 UPP1 11 5 0.002005 DPYD 11 6 0.001951 CDA 11 7 0.001041 DHODH 11 8 0.00099 ITPA 11 9 0.000915 UPRT 11 10 0.000603 TK1 11 11 0.000603 TK2 11 12 0.000468 UCKL1 11 13 0.000468 UCK2

Module rank Rank in module Flow volume Gene name 12 1 0.001652 CDK1 12 2 0.001621 PLK1 12 3 0.000991 ESPL1 12 4 0.000861 CPEB1 12 5 0.000846 CCNB2 12 6 0.000813 CDC25C 12 7 0.000776 CCNB1 12 8 0.000743 PTTG2 12 9 0.000743 PTTG1 12 10 0.00072 FBXO43 12 11 0.000674 CDC20 12 12 0.000431 CCNB3 12 13 0.000425 PKMYT1 12 14 0.000389 CDC25B 12 15 0.000289 SLK 12 16 0.000236 REC8 12 17 0.000224 SMC1B 12 18 0.000224 SMC1A 12 19 0.000224 SMC3 12 20 0.000224 STAG1 12 21 0.000224 STAG2 12 22 0.000224 RAD21 12 23 0.000146 ANAPC10 12 24 0.000146 CDC26 12 25 0.000146 ANAPC2 12 26 0.000146 ANAPC4

77

12 27 0.000146 ANAPC5 12 28 0.000146 ANAPC7 12 29 0.000146 ANAPC11 12 30 0.000146 ANAPC1 12 31 0.000146 CDC23 12 32 0.000146 CDC16 12 33 0.000146 CDC27 12 34 0.000146 ANAPC13 12 35 0.000138 GADD45G

Module rank Rank in module Flow volume Gene name 13 1 0.000628 LOC100507709 13 2 0.000628 LOC100507714 13 3 0.000628 LOC100509457 13 4 0.000628 LOC101060835 13 5 0.000628 HLA-DOA 13 6 0.000628 HLA-DOB 13 7 0.000628 HLA-DPA1 13 8 0.000628 HLA-DPB1 13 9 0.000628 HLA-DQA1 13 10 0.000628 HLA-DQA2 13 11 0.000628 HLA-DQB1 13 12 0.000628 HLA-DRA 13 13 0.000628 HLA-DRB1 13 14 0.000628 HLA-DRB3 13 15 0.000628 HLA-DRB4 13 16 0.000628 HLA-DRB5 13 17 0.000628 HLA-DMB

Module rank Rank in module Flow volume Gene name 14 1 0.001493 ACTB 14 2 0.001484 ACTG1 14 3 0.000758 TJP1 14 4 0.000686 CGN 14 5 0.000392 MYL2 14 6 0.000386 MYL9 14 7 0.000301 MYL12B 14 8 0.000301 MYL12A

78

14 9 0.000301 MYLPF 14 10 0.000301 MYL5 14 11 0.000301 MYL7 14 12 0.000301 MYL10 14 13 0.000289 MYLK4 14 14 0.000289 MYLK 14 15 0.000289 MYLK2 14 16 0.000289 MYLK3 14 17 0.000223 CTNNA1 14 18 0.000223 CTNNA2 14 19 0.000223 CTNNA3 14 20 0.00021 ACTN4 14 21 0.00021 ACTN1 14 22 0.00021 ACTN2 14 23 0.00021 ACTN3 14 24 0.000128 VCL 14 25 9.43E-05 RHOA 14 26 8.37E-05 MYH6 14 27 7.77E-05 IQGAP1 14 28 7.20E-05 MYH7 14 29 6.20E-05 MYH11 14 30 6.20E-05 MYH15 14 31 6.20E-05 MYH1 14 32 6.20E-05 MYH2 14 33 6.20E-05 MYH3 14 34 6.20E-05 MYH4 14 35 6.20E-05 MYH8

Module rank Rank in module Flow volume Gene name 15 1 0.001569 NAMPT 15 2 0.001542 BST1 15 3 0.001542 CD38 15 4 0.001422 NNMT 15 5 0.001221 AOX1 15 6 0.000869 NNT

Module rank Rank in module Flow volume Gene name 16 1 0.001234 ATF4

79

16 2 0.000922 CREB1 16 3 0.000813 CRTC1 16 4 0.000813 CRTC3 16 5 0.000813 CRTC2 16 6 0.000744 ATF2 16 7 0.000535 PRKACB 16 8 0.000535 PRKACG 16 9 0.000535 PRKX 16 10 0.000535 PRKACA 16 11 0.000252 XBP1 16 12 0.000252 CREM 16 13 0.000252 ATF1 16 14 0.000252 ATF3

Table 5– Selected 17 modules containing %75 of the flow volume of the network with second- order Markov dynamics. Genes in each module participate in propagation of %95 of the flow volume.

Module rank Rank in module Flow volume Gene name 1 1 0.0085 NUDT2 1 2 0.007964 ITPA 1 3 0.006894 ENTPD8 1 4 0.006894 ENTPD1 1 5 0.006894 ENTPD3 1 6 0.005525 NME6 1 7 0.005525 NME7 1 8 0.005525 NME1 1 9 0.005525 NME2 1 10 0.005525 NME3 1 11 0.005525 NME4 1 12 0.005525 NME1-NME2 1 13 0.005525 NME5 1 14 0.005405 PKLR 1 15 0.005378 PKM 1 16 0.004471 ADCY5 1 17 0.004368 ADCY9

80

1 18 0.004341 ADCY2 1 19 0.004341 ADCY7 1 20 0.004336 ADCY4 1 21 0.004329 ADCY6 1 22 0.004316 ADCY3 1 23 0.004258 ADCY8 1 24 0.004247 ADCY1 1 25 0.003955 ADCY10 1 26 0.003747 PNPT1 1 27 0.002522 CANT1 1 28 0.002522 ENTPD6 1 29 0.002522 ENTPD5 1 30 0.002522 ENTPD4 1 31 0.001885 LOC101060521 1 32 0.001885 POLR3F 1 33 0.001885 POLR3G 1 34 0.001885 POLR3C 1 35 0.001885 POLR3A 1 36 0.001885 POLR3H 1 37 0.001885 TWISTNB 1 38 0.001885 POLR2J2 1 39 0.001885 POLR1A 1 40 0.001885 ZNRD1 1 41 0.001885 POLR1D 1 42 0.001885 POLR3K 1 43 0.001885 POLR2A 1 44 0.001885 POLR2B 1 45 0.001885 POLR2C 1 46 0.001885 POLR2D 1 47 0.001885 POLR2E 1 48 0.001885 POLR2F 1 49 0.001885 POLR2G 1 50 0.001885 POLR2H 1 51 0.001885 POLR2I 1 52 0.001885 POLR2J 1 53 0.001885 POLR2K 1 54 0.001885 POLR2L 1 55 0.001885 POLR2J3

81

1 56 0.001885 POLR3B 1 57 0.001885 POLR3E 1 58 0.001885 POLR1E 1 59 0.001885 POLR3D 1 60 0.001885 POLR1B 1 61 0.001885 POLR3GL 1 62 0.001885 POLR1C 1 63 0.001875 RRM2B 1 64 0.001875 RRM2 1 65 0.00186 RRM1 1 66 0.001652 GUK1 1 67 0.001333 NTPCR 1 68 0.001302 ENTPD2 1 69 0.001226 POLD3 1 70 0.001226 POLA2 1 71 0.001226 POLE3 1 72 0.001226 POLA1 1 73 0.001226 POLD1 1 74 0.001226 POLD2 1 75 0.001226 POLE 1 76 0.001226 POLE2 1 77 0.001226 PRIM1 1 78 0.001226 PRIM2 1 79 0.001226 POLE4 1 80 0.001226 POLD4 1 81 0.001187 LOC100507855 1 82 0.001187 AK7 1 83 0.001187 AK1 1 84 0.001187 AK2 1 85 0.001187 AK4 1 86 0.001187 AK5 1 87 0.001187 LOC390877 1 88 0.001051 PDE10A 1 89 0.001051 PDE7B 1 90 0.001051 PDE11A 1 91 0.001051 PDE1A 1 92 0.001051 PDE1C 1 93 0.001051 PDE2A

82

1 94 0.001051 PDE3A 1 95 0.001051 PDE3B 1 96 0.001051 PDE4A 1 97 0.001051 PDE4B 1 98 0.001051 PDE4C 1 99 0.001051 PDE4D 1 100 0.001051 PDE7A 1 101 0.001051 PDE8A

Module rank Rank in module Flow volume Gene name 2 1 0.019426 PNP 2 2 0.010161 HPRT1 2 3 0.008701 ADA 2 4 0.00627 ADK 2 5 0.005823 IMPDH1 2 6 0.005823 IMPDH2 2 7 0.005721 ITPA 2 8 0.005482 NT5C1B-RDH14 2 9 0.005482 NT5C3B 2 10 0.005482 NT5C2 2 11 0.005482 NT5C 2 12 0.005482 NT5E 2 13 0.005482 NT5C3A 2 14 0.005482 NT5M 2 15 0.005482 NT5C1A 2 16 0.005482 NT5C1B 2 17 0.005108 GMPS 2 18 0.003637 APRT 2 19 0.002488 GDA

Module rank Rank in module Flow volume Gene name 3 1 0.003216 PTEN 3 2 0.002297 PLCB2 3 3 0.002297 PLCB4 3 4 0.002297 PLCB1 3 5 0.002297 PLCB3 3 6 0.002124 MTM1 3 7 0.001936 PLCE1

83

3 8 0.001835 PLCG1 3 9 0.001835 PLCG2 3 10 0.001797 PLCD1 3 11 0.001797 PLCD3 3 12 0.001797 PLCD4 3 13 0.001797 PLCZ1 3 14 0.001642 PI4KA 3 15 0.001642 PI4KB 3 16 0.001642 PI4K2B 3 17 0.001642 PI4K2A 3 18 0.001626 IMPAD1 3 19 0.001623 IMPA2 3 20 0.001623 IMPA1 3 21 0.001568 INPP1 3 22 0.001263 INPP4A 3 23 0.001263 INPP4B 3 24 0.001233 INPP5B 3 25 0.001233 OCRL 3 26 0.001233 INPP5E 3 27 0.001233 SYNJ1 3 28 0.001233 SYNJ2 3 29 0.001072 INPP5J 3 30 0.001072 INPP5A 3 31 0.001072 INPP5K 3 32 0.001049 PIP4K2A 3 33 0.001049 PIP4K2C 3 34 0.001049 PIP4K2B 3 35 0.001014 PIK3C3 3 36 0.000956 PIK3R5 3 37 0.000956 PIK3R1 3 38 0.000956 PIK3R2 3 39 0.000956 PIK3R3 3 40 0.000938 PIKFYVE 3 41 0.000912 PIP5K1C 3 42 0.000912 PIP5K1A 3 43 0.000912 PIP5K1B 3 44 0.000886 ITPKA 3 45 0.000886 ITPKB 84

3 46 0.000886 ITPKC 3 47 0.000835 PIK3C2A 3 48 0.000835 PIK3C2B 3 49 0.000835 PIK3C2G 3 50 0.000726 CDIPT 3 51 0.000654 ITPK1 3 52 0.000616 INPP5D 3 53 0.000616 INPPL1 3 54 0.000592 ITPR3 3 55 0.000591 ITPR1 3 56 0.000587 ITPR2 3 57 0.000498 PIK3CA 3 58 0.000498 PIK3CB 3 59 0.000498 PIK3CD 3 60 0.000498 PIK3CG 3 61 0.000421 DGKA

Module rank Rank in module Flow volume Gene name 4 1 0.001865 CLDN11 4 2 0.001865 CLDN24 4 3 0.001865 CLDN16 4 4 0.001865 CLDN4 4 5 0.001865 CLDN3 4 6 0.001865 CLDN7 4 7 0.001865 CLDN23 4 8 0.001865 CLDN19 4 9 0.001865 CLDN14 4 10 0.001865 CLDN15 4 11 0.001865 CLDN17 4 12 0.001865 CLDN20 4 13 0.001865 CLDN18 4 14 0.001865 CLDN22 4 15 0.001865 CLDN25 4 16 0.001865 CLDN5 4 17 0.001865 CLDN10 4 18 0.001865 CLDN8 4 19 0.001865 CLDN6 4 20 0.001865 CLDN2

85

4 21 0.001865 CLDN1

Module rank Rank in module Flow volume Gene name 5 1 0.001176 EPHX1 5 2 0.001046 GSTM3 5 3 0.001046 GSTM4 5 4 0.001046 GSTM5 5 5 0.001046 GSTP1 5 6 0.001046 GSTT1 5 7 0.001046 GSTT2 5 8 0.001046 GSTK1 5 9 0.001046 MGST1 5 10 0.001046 MGST2 5 11 0.001046 MGST3 5 12 0.001046 GSTT2B 5 13 0.001046 GSTO1 5 14 0.001046 GSTO2 5 15 0.001046 GSTA5 5 16 0.001046 GSTA1 5 17 0.001046 GSTA2 5 18 0.001046 GSTA3 5 19 0.001046 GSTA4 5 20 0.001046 GSTM1 5 21 0.001046 GSTM2 5 22 0.000965 CYP1A1 5 23 0.00096 CYP3A4 5 24 0.000898 CYP2A13 5 25 0.000871 CYP1A2 5 26 0.000869 CYP2E1

Module rank Rank in module Flow volume Gene name 6 1 0.00333 RB1 6 2 0.00174 CCND1 6 3 0.001533 CDKN2B 6 4 0.001362 CCNE1 6 5 0.001362 CCNE2 6 6 0.001327 CDKN1B 6 7 0.001182 CCND2

86

6 8 0.001157 E2F3 6 9 0.001157 E2F1 6 10 0.001156 E2F2 6 11 0.001113 MYC 6 12 0.000976 RBL2 6 13 0.000975 CCND3 6 14 0.000898 CDKN1A 6 15 0.000822 RBL1 6 16 0.000777 CDKN1C 6 17 0.000616 TFDP1 6 18 0.000599 CDK4 6 19 0.000597 TFDP2 6 20 0.000564 ZBTB17 6 21 0.000526 CDK6

Module rank Rank in module Flow volume Gene name 7 1 0.00154 AKR1C3 7 2 0.001538 HSD17B3 7 3 0.001057 HSD3B1 7 4 0.001057 HSD3B2 7 5 0.000927 CYP19A1 7 6 0.000917 CYP3A5 7 7 0.000914 HSD17B2 7 8 0.000912 HSD17B8 7 9 0.000912 HSD17B6 7 10 0.00089 CYP1A1 7 11 0.000482 UGT2B11 7 12 0.000482 UGT2A1 7 13 0.000482 UGT2B28 7 14 0.000482 UGT1A10 7 15 0.000482 UGT1A8 7 16 0.000482 UGT1A7 7 17 0.000482 UGT1A6 7 18 0.000482 UGT1A5 7 19 0.000482 UGT1A9 7 20 0.000482 UGT1A4 7 21 0.000482 UGT1A1 7 22 0.000482 UGT1A3

87

7 23 0.000482 UGT2A2 7 24 0.000482 UGT2B4 7 25 0.000482 UGT2B7 7 26 0.000482 UGT2B10 7 27 0.000482 UGT2B15 7 28 0.000482 UGT2B17 7 29 0.000482 UGT2A3 7 30 0.000362 CYP3A4 7 31 0.000288 CYP17A1 7 32 0.000244 CYP7A1 7 33 0.000225 AKR1D1 7 34 0.000207 SRD5A1 7 35 0.000207 SRD5A2 7 36 0.000207 SRD5A3 7 37 0.00019 CYP21A2 7 38 0.000174 CYP1A2 7 39 0.000173 CYP11A1 7 40 0.000163 CYP11B2 7 41 0.000162 CYP1B1 7 42 0.000159 STS 7 43 0.000148 SULT2B1 7 44 0.000146 CYP11B1 7 45 0.000145 CYP2E1 7 46 0.000143 CYP3A7 7 47 0.000137 SULT1E1 7 48 0.000116 CYP2C8 7 49 0.000114 CYP2B6 7 50 0.000112 CYP4A11 7 51 0.0001 CYP26A1

Module rank Rank in module Flow volume Gene name 8 1 0.00223 AKT3 8 2 0.00223 AKT1 8 3 0.00223 AKT2 8 4 0.001229 GSK3B 8 5 0.001043 PIK3R2 8 6 0.001043 PIK3R1 8 7 0.001043 PIK3R3

88

8 8 0.001037 PIK3CA 8 9 0.001036 PIK3CB 8 10 0.000958 PIK3CD 8 11 0.000933 PIK3CG 8 12 0.000931 PIK3R5 8 13 0.000856 CHUK 8 14 0.000796 IKBKB 8 15 0.000608 PDPK1 8 16 0.000594 MTOR 8 17 0.000549 FOXO3 8 18 0.000533 IRS1 8 19 0.000406 PRKCZ 8 20 0.000356 NFKB1 8 21 0.000256 GSK3A 8 22 0.000219 FOXO1 8 23 0.000187 IRS2 8 24 0.000176 IRS4 8 25 0.000112 ATF4 8 26 0.000112 CREB3 8 27 0.000112 CREB1

Module rank Rank in module Flow volume Gene name 9 1 0.001842 LPCAT4 9 2 0.001637 PEMT 9 3 0.001013 CEPT1 9 4 0.000827 PLD1 9 5 0.000811 PLD2 9 6 0.000776 PLA2G15 9 7 0.000752 PISD 9 8 0.000737 PLD4 9 9 0.000737 PLD3 9 10 0.000585 PLB1 9 11 0.000584 MBOAT2 9 12 0.000584 MBOAT1 9 13 0.000577 PLA2G16 9 14 0.00056 LPCAT3 9 15 0.000546 CHPT1 9 16 0.00049 LPCAT2

89

9 17 0.00049 LPCAT1 9 18 0.000468 EPT1 9 19 0.000416 LYPLA1 9 20 0.000416 PNPLA6 9 21 0.000416 PNPLA7 9 22 0.000247 PTDSS1 9 23 0.000218 ENPP6 9 24 0.000215 PLA2G2D 9 25 0.000215 PLA2G2E 9 26 0.000215 PLA2G2C 9 27 0.000215 PLA2G3 9 28 0.000215 PLA2G1B 9 29 0.000215 PLA2G2A 9 30 0.000215 PLA2G5 9 31 0.000215 PLA2G2F 9 32 0.000215 PLA2G12A 9 33 0.000215 PLA2G10 9 34 0.000215 PLA2G12B 9 35 0.00019 PLA2G4B 9 36 0.00019 PLA2G4E 9 37 0.00019 PLA2G4F 9 38 0.00019 PLA2G4D 9 39 0.00019 PLA2G4A 9 40 0.00019 PLA2G6 9 41 0.00019 PLA2G4C 9 42 0.00019 JMJD7-PLA2G4B 9 43 0.00017 LCAT 9 44 0.000144 AGPAT1

Module rank Rank in module Flow volume Gene name 10 1 0.001467 ADH1A 10 2 0.001467 ADH1B 10 3 0.001467 ADH1C 10 4 0.001467 ADH4 10 5 0.001467 ADH5 10 6 0.001467 ADH6 10 7 0.001467 ADH7 10 8 0.001031 ALDH3A1

90

10 9 0.001031 ALDH1A3 10 10 0.001031 ALDH3B1 10 11 0.001031 ALDH3B2 10 12 0.00083 MAOA 10 13 0.00083 MAOB 10 14 0.000811 COMT 10 15 0.000215 DBH 10 16 0.000175 AKR1C2 10 17 0.000167 PNMT 10 18 0.000102 CYP2A6 10 19 9.46E-05 UGT2B11 10 20 9.46E-05 UGT2A1 10 21 9.46E-05 UGT2B28 10 22 9.46E-05 UGT1A10 10 23 9.46E-05 UGT1A8 10 24 9.46E-05 UGT1A7 10 25 9.46E-05 UGT1A6 10 26 9.46E-05 UGT1A5 10 27 9.46E-05 UGT1A9 10 28 9.46E-05 UGT1A4

Module rank Rank in module Flow volume Gene name 11 1 0.001905 CDK1 11 2 0.001462 PLK1 11 3 0.000972 ESPL1 11 4 0.000927 CCNB2 11 5 0.000917 CPEB1 11 6 0.000863 CCNB1 11 7 0.00082 CDC25C 11 8 0.000784 FBXO43 11 9 0.000706 CDC20 11 10 0.000684 PTTG2 11 11 0.000684 PTTG1 11 12 0.000507 CCNB3 11 13 0.000505 PKMYT1 11 14 0.00039 SLK 11 15 0.00024 REC8 11 16 0.00022 SMC1B

91

11 17 0.00022 SMC1A 11 18 0.00022 SMC3 11 19 0.00022 STAG1 11 20 0.00022 STAG2 11 21 0.00022 RAD21 11 22 0.000219 CDC25B 11 23 0.000136 ANAPC4 11 24 0.000136 ANAPC7 11 25 0.000136 ANAPC1 11 26 0.000136 CDC16 11 27 0.000136 CDC27 11 28 0.000136 ANAPC10 11 29 0.000136 CDC26 11 30 0.000136 ANAPC2

Module rank Rank in module Flow volume Gene name 12 1 0.002356 FAM213B 12 2 0.001231 CYP2J2 12 3 0.001203 PTGS2 12 4 0.001132 ALOX5 12 5 0.001001 PTGS1 12 6 0.000619 CYP2E1 12 7 0.000486 CYP4A11 12 8 0.000441 ALOX15 12 9 0.000409 HPGDS 12 10 0.000409 PTGDS 12 11 0.0004 ALOX15B 12 12 0.000317 TBXAS1 12 13 0.000221 PTGES3 12 14 0.000221 PTGES2 12 15 0.000221 PTGES 12 16 0.000197 PTGIS 12 17 0.000171 LTA4H 12 18 0.000162 PLA2G16 12 19 0.000162 PLA2G4B 12 20 0.000162 PLA2G4E 12 21 0.000162 PLB1 12 22 0.000162 PLA2G4F

92

12 23 0.000162 PLA2G2D 12 24 0.000162 PLA2G4D 12 25 0.000162 PLA2G2E 12 26 0.000162 PLA2G2C 12 27 0.000162 PLA2G3 12 28 0.000162 PLA2G1B 12 29 0.000162 PLA2G2A 12 30 0.000162 PLA2G4A 12 31 0.000162 PLA2G5 12 32 0.000162 PLA2G2F 12 33 0.000162 PLA2G12A 12 34 0.000162 PLA2G6 12 35 0.000162 PLA2G10 12 36 0.000162 PLA2G12B 12 37 0.000162 PLA2G4C 12 38 0.000162 JMJD7-PLA2G4B 12 39 0.000124 CYP4F3 12 40 0.000124 CYP4F2

Module rank Rank in module Flow volume Gene name 13 1 0.002508 TYMP 13 2 0.001912 UPP2 13 3 0.001912 UPP1 13 4 0.001591 DPYD 13 5 0.001521 CDA 13 6 0.001138 UMPS 13 7 0.000807 UPRT 13 8 0.00061 UCKL1 13 9 0.00061 UCK2 13 10 0.00061 UCK1 13 11 0.000539 TK1

Module rank Rank in module Flow volume Gene name 14 1 0.000595 HLA-DMA 14 2 0.000595 HLA-DMB 14 3 0.000595 LOC100507709 14 4 0.000595 LOC100507714 14 5 0.000595 LOC100509457

93

14 6 0.000595 LOC101060835 14 7 0.000595 HLA-DOB 14 8 0.000595 HLA-DPA1 14 9 0.000595 HLA-DPB1 14 10 0.000595 HLA-DQA1 14 11 0.000595 HLA-DQA2 14 12 0.000595 HLA-DQB1 14 13 0.000595 HLA-DRA 14 14 0.000595 HLA-DRB1 14 15 0.000595 HLA-DRB3 14 16 0.000595 HLA-DRB4 14 17 0.000595 HLA-DRB5

Module rank Rank in module Flow volume Gene name 15 1 0.001189 JAK2 15 2 0.001126 JAK3 15 3 0.000668 JAK1 15 4 0.000655 STAT1 15 5 0.000647 TYK2 15 6 0.000574 STAT3 15 7 0.000567 STAT2 15 8 0.000565 STAT5B 15 9 0.000305 PIM1 15 10 0.000275 STAT5A 15 11 0.000202 STAT4 15 12 0.000202 STAT6 15 13 0.000167 SOCS3 15 14 0.000161 SOCS1 15 15 0.000161 SOCS4 15 16 0.000161 SOCS7 15 17 0.000161 SOCS2 15 18 0.000161 SOCS5 15 19 9.79E-05 PRLR 15 20 5.25E-05 IFNAR1 15 21 5.25E-05 IFNAR2 15 22 4.82E-05 CSF3R 15 23 4.54E-05 IL10RB 15 24 4.44E-05 IL10RA

94

15 25 4.00E-05 LEPR 15 26 3.98E-05 CSF2RB 15 27 3.90E-05 IFNGR1 15 28 3.90E-05 IFNGR2 15 29 3.90E-05 IL3RA 15 30 3.90E-05 IL22RA2 15 31 3.90E-05 CNTFR 15 32 3.90E-05 CSF2RA 15 33 3.90E-05 IL23R 15 34 3.90E-05 IFNLR1 15 35 3.90E-05 EPOR 15 36 3.90E-05 GHR 15 37 3.90E-05 IL2RA 15 38 3.90E-05 IL2RB 15 39 3.90E-05 IL2RG 15 40 3.90E-05 IL4R 15 41 3.90E-05 IL5RA 15 42 3.90E-05 IL6ST 15 43 3.90E-05 IL7R 15 44 3.90E-05 IL9R 15 45 3.90E-05 IL11RA 15 46 3.90E-05 IL12RB1 15 47 3.90E-05 IL12RB2 15 48 3.90E-05 IL13RA1

Module rank Rank in module Flow volume Gene name 16 1 0.001405 MAPK1 16 2 0.001405 MAPK3 16 3 0.001219 MAP2K1 16 4 0.00096 RAF1 16 5 0.000779 MAP2K2 16 6 0.000647 BRAF 16 7 0.000476 HRAS 16 8 0.000476 KRAS 16 9 0.000476 NRAS 16 10 0.000229 ARAF 16 11 8.68E-05 CREB1 16 12 8.41E-05 RPS6KA6

95

16 13 8.41E-05 RPS6KA1 16 14 8.41E-05 RPS6KA2

Module rank Rank in module Flow volume Gene name 17 1 0.001022 GSK3B 17 2 0.000784 DVL1 17 3 0.000784 DVL2 17 4 0.000784 DVL3 17 5 0.000435 VANGL2 17 6 0.000435 VANGL1 17 7 0.000354 APC2 17 8 0.000354 APC 17 9 0.000306 GNAO1 17 10 0.000305 GNAQ 17 11 0.00022 AXIN1 17 12 0.00022 AXIN2 17 13 0.000152 PLCB1 17 14 0.000152 PLCB2 17 15 0.000152 PLCB3 17 16 0.000152 PLCB4 17 17 0.000129 FZD3 17 18 0.000129 FZD10 17 19 0.000129 FZD2 17 20 0.000129 FZD5 17 21 0.000129 FZD1 17 22 0.000129 FZD4 17 23 0.000129 FZD6 17 24 0.000129 FZD7 17 25 0.000129 FZD8 17 26 0.000129 FZD9 17 27 0.000126 PRICKLE1 17 28 0.000126 PRICKLE2 17 29 4.62E-05 WNT1 17 30 4.31E-05 FRAT1 17 31 4.31E-05 FRAT2 17 32 4.21E-05 WNT4 17 33 3.48E-05 LRP6 17 34 3.48E-05 LRP5

96

17 35 2.75E-05 WNT3

Table 6 – Enriched pathways in 16 modules of the network with first-order Markov dynamics.

Module Pathway P-value Hit in gene list

POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL MAP00230 R2H,POLR2J2,POLR2I,POLR2J,POLD1,POLD2,POLE,POLR2A,POL 1 Purine 9.75E-53 R2B,POLA1,ADCY2,ADCY3,ADCY1,PDE4D,ADCY9,ADCY8,ADCY7, metabolism ADCY6,PDE1A,PDE4A,PDE4B,PDE4C,PKLR,PKM,AK2,POLR1B,AK 1,RRM2,NME2,RRM1,NME1

protein PDE11A,ADCY10,ADCY4,PDE1B,ADCY5,ADCY2,ADCY3,ADCY1,P kinase A 1 1.53E-33 DE4D,ADCY9,ADCY8,PDE7A,ADCY7,ADCY6,PDE1A,PDE1C,PDE2 (PKA) A,PDE3A,PDE3B,PDE4A,PDE4B,PDE4C,PDE7B,PDE10A signaling

ADCY4,POLR2F,POLR2G,PDE1B,ADCY5,ADCY2,PDE4D,ADCY8,A purine 1 1.47E-30 DCY6,PDE1A,PDE2A,PDE3A,PDE4A,PDE4B,ENTPD1,PDE7B,PKLR metabolic ,PKM,PDE10A,AK2,AK1,NME3

MAP00240 POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 Pyrimidine 2.92E-30 R2H,POLR2J2,POLR2I,POLR2J,POLD1,POLD2,POLE,POLR2A,POL metabolism R2B,POLA1,POLR1B,RRM2,NME2,RRM1,NME1

Eukaryotic POLR3K,POLR2K,POLR2C,POLR2E,POLR2F,POLR2G,POLR2H,POL 1 Transcription 9.64E-25 R2I,POLR2J,POLR2A,POLR2B,POLR1D,POLR1A,POLR3E,POLR3B, Initiation POLR3D,POLR1B,POLR3H,POLR1E

MAP03020 POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 RNA 2.76E-23 R2H,POLR2J2,POLR2I,POLR2J,POLR2A,POLR2B,POLR1B polymerase

97

Genes involved in POLD3,POLD4,POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2 1 Transcription 2.44E-22 F,POLR2G,POLR2H,POLR2I,POLR2J,POLD1,POLD2,POLE,POLE2, -coupled NER POLR2A,POLR2B (TC-NER)

De novo GUK1,AK5,NME7,NME1- 1 purine 4.65E-22 NME2,AK4,AK2,AK1,RRM2,NME2,RRM1,NME1,RRM2B,NME6, biosynthesis NME3,NME4,NME5

Genes involved in POLD3,POLD4,POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2 1 Nucleotide 2.63E-21 F,POLR2G,POLR2H,POLR2I,POLR2J,POLD1,POLD2,POLE,POLE2, Excision POLR2A,POLR2B Repair Genes involved in Viral POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 4.06E-20 Messenger R2H,POLR2I,POLR2J,POLR2A,POLR2B RNA Synthesis Genes involved in G PDE11A,ADCY10,ADCY4,PDE1B,ADCY5,ADCY2,ADCY3,ADCY1,P 1 alpha (s) 5.65E-20 DE4D,ADCY9,ADCY8,PDE7A,ADCY7,ADCY6,PDE1A,PDE2A,PDE3 signalling A,PDE3B,PDE4A,PDE4B,PDE4C,PDE7B,PDE10A events Genes involved in RNA POLR2L,POLR2K,POLR2E,POLR2F,POLR2H,POLR3E,POLR3A,POL 1 5.53E-19 Polymerase R3B,POLR3D,POLR3F,POLR3H III Chain Elongation purine nucleotides GUK1,AK5,AK7,NME7,AK2,AK1,RRM2,NME2,RRM1,NME1,RRM 1 de 1.48E-18 2B,NME6,NME3,NME4 novo biosynthesis I Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 7.99E-18 MicroRNA R2H,POLR2I,POLR2J,POLR2A,POLR2B biogenesis Genes involved in ADCY10,ADCY4,PDE1B,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,AD 1 4.06E-17 CaM CY8,ADCY7,ADCY6,PDE1A,PDE1C pathway

98

Genes involved in Adenylate ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,AD 1 5.12E-17 cyclase CY7,ADCY6 activating pathway his+purine+p GUK1,AK5,AK7,NME7,AK2,AK1,RRM2,NME2,RRM1,NME1,RRM 1 yrimidine 2.08E-16 2B,NME6,NME3,NME4 biosynthesis Genes involved in RNA POLR2L,POLR2K,POLR2E,POLR2F,POLR2H,POLR3E,POLR3A,POL 1 Polymerase 5.45E-16 R3B,POLR3D,POLR3F,POLR3H III Transcription Termination Genes involved in Abortive elongation of POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 5.57E-16 HIV-1 R2H,POLR2I,POLR2J,POLR2A,POLR2B transcript in the absence of Tat

G Protein ADCY4,PDE1B,ADCY5,ADCY2,ADCY3,ADCY1,PDE4D,ADCY9,ADC 1 Signaling 1.71E-15 Y8,PDE7A,ADCY7,ADCY6,PDE1A,PDE1C,PDE4A,PDE4B,PDE4C,P Pathways DE7B

Genes involved in ADCY10,ADCY4,PDE1B,ADCY5,ADCY2,ADCY3,ADCY1,PDE4D,AD 1 3.81E-15 Opioid CY9,ADCY8,ADCY7,ADCY6,PDE1A,PDE1C,PDE4A,PDE4B,PDE4C Signalling Genes involved in RNA Pol II CTD POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 3.87E-15 phosphorylat R2H,POLR2I,POLR2J,POLR2A,POLR2B ion and interaction with CE Genes involved in G ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,AD 1 alpha (z) 4.54E-15 CY7,ADCY6 signalling events 99

Genes involved in ADCY10,ADCY4,PDE1B,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,AD 1 5.32E-15 PLC-gamma1 CY8,ADCY7,ADCY6,PDE1A,PDE1C signalling Genes involved in RNA Polymerase POLR2L,POLR2K,POLR2E,POLR2F,POLR2H,POLR3E,POLR3A,POL 1 III 7.21E-15 R3B,POLR3D,POLR3F,POLR3H Transcription Initiation From Type 2 Promoter

Genes POLD3,POLD4,POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2 1 involved in 9.48E-15 F,POLR2G,POLR2H,POLR2I,POLR2J,POLD1,POLD2,POLE,POLE2, DNA Repair POLR2A,POLR2B

Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 Dual incision 1.20E-14 R2H,POLR2I,POLR2J,POLR2A,POLR2B reaction in TC-NER Genes involved in ADCY10,ADCY4,PDE1B,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,AD 1 PLC beta 1.90E-14 CY8,ADCY7,ADCY6,PDE1A,PDE1C mediated events Genes involved in RNA Polymerase POLR2L,POLR2K,POLR2E,POLR2F,POLR2H,POLR3E,POLR3A,POL 1 III 2.97E-14 R3B,POLR3D,POLR3F,POLR3H Transcription Initiation From Type 3 Promoter

Genes POLR1C,POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POL 1 involved in 3.19E-14 R2G,POLR2H,POLR2I,POLR2J,POLR2A,POLR2B,POLR1D,POLR1A Transcription ,POLR3E,POLR3A,POLR3B,POLR3D,POLR3F,POLR1B,POLR3H

Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 3.35E-14 Tat-mediated R2H,POLR2I,POLR2J,POLR2A,POLR2B HIV-1

100

elongation arrest and recovery Genes involved in ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,AD 1 8.58E-14 PKA CY7,ADCY6 activation Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 8.58E-14 mRNA R2H,POLR2I,POLR2J,POLR2A,POLR2B Processing Genes involved in Formation of POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 8.58E-14 the Early R2H,POLR2I,POLR2J,POLR2A,POLR2B Elongation Complex Genes involved in Synthesis and interconversi 1 on of 1.91E-13 GUK1,AK5,AK2,AK1,RRM2,NME2,RRM1,NME1,RRM2B,NME4 nucleotide di- and triphosphate s de novo biosynthesis 1 of pyrimidine 3.23E-13 NME7,RRM2,NME2,RRM1,NME1,RRM2B,NME6,NME3,NME4 deoxyribonuc leotides Genes involved in RNA POLR2L,POLR2K,POLR2E,POLR2F,POLR2H,POLR3E,POLR3A,POL 1 Polymerase 1.37E-12 R3B,POLR3D,POLR3F,POLR3H III Transcription Initiation Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 HIV-1 1.40E-12 R2H,POLR2I,POLR2J,POLR2A,POLR2B Transcription Initiation LPA4- ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY7,ADC 1 2.22E-12 mediated Y6

101

signaling events Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 HIV-1 2.77E-12 R2H,POLR2I,POLR2J,POLR2A,POLR2B Transcription Elongation Genes involved in mRNA POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 3.84E-12 Splicing - R2H,POLR2I,POLR2J,POLR2A,POLR2B Minor Pathway Genes involved in RNA POLR2L,POLR2K,POLR2E,POLR2F,POLR2H,POLR3E,POLR3A,POL 1 1.08E-11 Polymerase R3B,POLR3D,POLR3F,POLR3H III Transcription G protein signaling via ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY7,ADC 1 2.10E-11 Galphas Y6 family Genes involved in G(s)-alpha ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,AD 1 mediated 3.40E-11 CY7,ADCY6 events in glucagon signalling Genes involved in POLD3,POLA2,POLD4,POLD1,POLD2,POLE,POLE2,POLA1,PRIM1 1 5.24E-11 Extension of ,PRIM2 Telomeres De novo pyrimidine 1 deoxyribonuc 1.31E-10 NME1-NME2,RRM2,NME2,RRM1,NME1,RRM2B,NME3,NME4 leotide biosynthesis Genes involved in 1 1.31E-10 POLD3,POLA2,POLD4,POLD1,POLD2,POLA1,PRIM1,PRIM2 Polymerase switching Genes 1 involved in 1.31E-10 POLD3,POLA2,POLD4,POLD1,POLD2,POLA1,PRIM1,PRIM2 Removal of

102

the Flap Intermediate Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 Transcription 3.34E-10 R2H,POLR2I,POLR2J,POLR2A,POLR2B of the HIV genome Genes involved in Glucagon ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,AD 1 4.96E-10 signaling in CY7,ADCY6 metabolic regulation Genes involved in RNA Polymerase I, POLR1C,POLR2L,POLR2K,POLR2E,POLR2F,POLR2H,POLR1D,POL RNA 1 7.37E-10 R1A,POLR3E,POLR3A,POLR3B,POLR3D,POLR3F,POLR1B,POLR3 Polymerase H III, and Mitochondria l Transcription DNA POLD3,POLA2,POLD4,POLD1,POLD2,POLE,POLE2,POLA1,PRIM1 1 3.98E-09 Replication ,PRIM2 Genes involved in 1 Lagging 5.21E-09 POLD3,POLA2,POLD4,POLD1,POLD2,POLA1,PRIM1,PRIM2 Strand Synthesis Genes involved in TRKA ADCY10,ADCY4,PDE1B,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,AD 1 signalling 2.11E-08 CY8,ADCY7,ADCY6,PDE1A,PDE1C from the plasma membrane salvages of pyrimidine 1 3.52E-08 NME7,NME2,NME1,NME6,NME3,NME4 ribonucleotid es Pyrimidine 1 4.28E-08 CANT1,PNPT1,ITPA,POLR3C,POLR3G,POLR3F,RRM1,NME6 Metabolism

103

Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 6.19E-08 Late Phase of R2H,POLR2I,POLR2J,POLR2A,POLR2B HIV Life Cycle Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 RNA 8.05E-08 R2H,POLR2I,POLR2J,POLR2A,POLR2B Polymerase II Transcription Genes involved in PDE11A,ADCY10,ADCY4,PDE1B,ADCY5,ADCY2,ADCY3,ADCY1,P Downstream 1 9.25E-08 DE4D,ADCY9,ADCY8,PDE7A,ADCY7,ADCY6,PDE1A,PDE2A,PDE3 events in A,PDE3B,PDE4A,PDE4B,PDE4C,PDE7B,PDE10A GPCR signaling de novo biosynthesis 1 of pyrimidine 1.90E-07 NME7,NME2,NME1,NME6,NME3,NME4 ribonucleotid es Genes involved in Influenza POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 Viral RNA 2.17E-07 R2H,POLR2I,POLR2J,POLR2A,POLR2B Transcription and Replication Genes involved in 1 2.95E-07 POLD3,POLA2,POLD4,POLD1,POLD2,POLA1,PRIM1,PRIM2 DNA strand elongation Genes POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 involved in 3.07E-07 R2H,POLR2I,POLR2J,POLR2A,POLR2B HIV Life Cycle Nucleotide 1 3.09E-07 POLD1,POLA1,NME1-NME2,RRM2,NME2,RRM1,RRM2B Metabolism Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 4.81E-07 mRNA R2H,POLR2I,POLR2J,POLR2A,POLR2B Splicing Genes involved in 1 1.25E-06 GUK1,AK5,AK2,AK1,RRM2,NME2,RRM1,NME1,RRM2B,NME4 Metablism of nucleotides

104

GABA-B 1 receptor II 1.72E-06 ADCY4,ADCY5,ADCY2,ADCY1,ADCY9,ADCY8,ADCY7,ADCY6 signaling Genes involved in Repair synthesis of 1 1.99E-06 POLD3,POLD4,POLD1,POLD2,POLE,POLE2 patch ~27-30 bases long by DNA polymerase Endothelin ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,AD 1 signaling 2.16E-06 CY7,ADCY6 pathway Genes involved in POLD3,POLA2,POLD4,POLD1,POLD2,POLE,POLE2,POLA1,PRIM1 1 2.81E-06 Telomere ,PRIM2 Maintenance Purine 1 3.32E-06 GUK1,ADCY1,PDE7B,PDE10A,AK1,RRM1,NME6 Metabolism Genes involved in Elongation POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 and 6.41E-06 R2H,POLR2I,POLR2J,POLR2A,POLR2B Processing of Capped Transcripts ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY7,ADC 1 Endothelins 6.79E-06 Y6 Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 8.23E-06 Influenza Life R2H,POLR2I,POLR2J,POLR2A,POLR2B Cycle Genes involved in Processing of POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 Capped 8.93E-06 R2H,POLR2I,POLR2J,POLR2A,POLR2B Intron- Containing Pre-mRNA LPA receptor ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY7,ADC 1 mediated 1.03E-05 Y6 events Genes POLD3,POLA2,POLD4,POLD1,POLD2,POLE,POLE2,POLA1,PRIM1 1 1.17E-05 involved in ,PRIM2

105

Synthesis of DNA Genes involved in RNA 1 1.47E-05 POLR1C,POLR2K,POLR2H,POLR1D,POLR1A,POLR1B Polymerase I Promoter Escape Genes involved in RNA 1 2.04E-05 POLR1C,POLR2K,POLR2H,POLR1D,POLR1A,POLR1B PolymeraseT ranscription Termination Genes involved in Formation POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 and 2.63E-05 R2H,POLR2I,POLR2J,POLR2A,POLR2B Maturation of mRNA Transcript De novo pyrimidine 1 ribonucleotid 3.04E-05 NME1-NME2,NME2,NME1,NME3,NME4 es biosythesis Genes POLD3,POLA2,POLD4,POLD1,POLD2,POLE,POLE2,POLA1,PRIM1 1 involved in S 4.78E-05 ,PRIM2 Phase Genes involved in RNA 1 4.93E-05 POLR1C,POLR2K,POLR2H,POLR1D,POLR1A,POLR1B PolymeraseT ranscription Initiation Genes involved in ADCY10,ADCY4,PDE1B,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,AD 1 1.68E-04 Signalling by CY8,ADCY7,ADCY6,PDE1A,PDE1C NGF Genes POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POL 1 involved in 1.99E-04 R2H,POLR2I,POLR2J,POLR2A,POLR2B HIV Infection Genes involved in 1 2.06E-04 POLA2,POLE,POLE2,POLA1,PRIM1,PRIM2 Activation of the pre- 106

replicative complex

Myometrial Relaxation ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,PDE4D,ADCY9,ADCY8,ADC 1 and 2.74E-04 Y7,ADCY6,PDE4B Contraction Pathways Genes involved in 1 Global 3.74E-04 POLD3,POLD4,POLD1,POLD2,POLE,POLE2 Genomic NER (GG-NER) Genes involved in RNA 1 4.50E-04 POLR1C,POLR2K,POLR2H,POLR1D,POLR1A,POLR1B PolymeraseC hain Elongation salvages of purine and 1 5.54E-04 GUK1,AK5,AK7,AK2,AK1 pyrimidine nucleotides Genes involved in Removal of 1 the Flap 7.61E-04 POLD3,POLD4,POLD1,POLD2 Intermediate from the C- strand Genes involved in NT5C,HPRT1,IMPDH2,NT5C1A,ADA,IMPDH1,XDH,NT5C2,DCK,A 2 1.09E-36 Purine DK,GMPS,APRT,PNP,NT5C1B,GDA,NT5E metabolism Genes involved in NT5C,HPRT1,IMPDH2,NT5C1A,ADA,IMPDH1,XDH,NT5C2,DCK,N 2 2.43E-35 Metablism of T5C3A,ADK,GMPS,NT5M,APRT,PNP,NT5C1B,GDA,NT5E nucleotides MAP00230 HPRT1,IMPDH2,ADA,IMPDH1,XDH,DCK,ADK,GMPS,APRT,PNP, 2 Purine 3.13E-18 NT5C1B,GDA metabolism Purine 2 2.66E-14 HPRT1,ADA,IMPDH1,XDH,NT5C2,GMPS,APRT,PNP Metabolism Genes 2 involved in 4.14E-12 HPRT1,ADA,DCK,ADK,APRT,PNP Purine 107

salvage reactions Adenine and hypoxanthin 2 5.12E-11 HPRT1,ADA,XDH,APRT,PNP e salvage pathway Genes involved in 2 3.29E-10 NT5C,NT5C1A,DCK,NT5C3A,NT5M,NT5E Pyrimidine metabolism Genes involved in 2 6.12E-10 NT5C,NT5C1A,NT5C3A,NT5M,NT5E Pyrimidine catabolism purine 2 2.22E-06 HPRT1,ADA,ADK,GDA,NT5E metabolic Xanthine and guanine 2 2.34E-06 HPRT1,PNP,GDA salvage pathway Purine 2 2.34E-06 XDH,GDA,NT5E metabolism purine nucleotides de 2 9.55E-05 IMPDH2,IMPDH1,GMPS novo biosynthesis II Genes involved in Purine ribonucleosid 2 9.55E-05 IMPDH2,IMPDH1,GMPS e monophosph ate biosynthesis MAP00562 Inositol ITPKB,ITPKA,PIP4K2B,INPP4B,PLCD1,PLCG1,PLCB2,PLCG2,PIK3C 3 1.01E-27 phosphate 2A,PIK3C2B,PIK3C2G,INPP4A,PI4KA,INPP1,IMPA1 metabolism phosphatidyli nositol 3- ITPKA,DGKZ,DGKG,PTEN,DGKB,PLCB4,PLCG1,PLCB3,PLCB2,PLC 3 2.84E-24 kinase-Akt G2,SYNJ2,PIK3C3,INPP5D,PI4KA,PLCB1,IMPA1 signaling Genes ITPR3,ITPR2,ITPR1,DGKZ,DGKE,DGKD,DGKG,DGKQ,DGKA,DGKB 3 2.78E-21 involved in ,DGKI,DGKH 108

Effects of PIP2 hydrolysis inositol ITPKA,PTEN,PLCB4,PLCG1,PLCB3,PLCB2,PLCG2,SYNJ2,PIK3C3,PI 3 phosphate 9.58E-19 4KA,PLCB1,IMPA1 metabolic Histamine H1 receptor PLCE1,ITPR3,ITPR2,ITPR1,PLCD1,PLCB4,PLCG1,PLCB3,PLCB2,PL 3 mediated 2.26E-18 CG2,PLCZ1,PLCB1,PLCD3,PLCD4 signaling pathway Inositol ITPKA,ITPK1,INPP5J,PIK3C3,INPP4A,PI4KA,INPP1,PIKFYVE,IMPA 3 1.32E-16 Metabolism 1,PLCD3 PLCE1,PLCD1,PLCB4,PLCG1,PLCB3,PLCB2,PLCG2,PLCZ1,PLCB1,P 3 lipases 3.48E-13 LCD4 Genes involved in G ITPR3,ITPR2,ITPR1,DGKZ,DGKE,DGKD,DGKG,DGKQ,DGKA,DGKB 3 alpha (q) 2.08E-12 ,PLCB4,DGKI,PLCB3,PLCB2,PLCB1,DGKH signalling events Oxytocin receptor PLCE1,PLCD1,PLCB4,PLCG1,PLCB3,PLCB2,PLCG2,PLCZ1,PLCB1,P 3 mediated 2.54E-11 LCD3,PLCD4 signaling pathway Thyrotropin- releasing hormone PLCE1,PLCD1,PLCB4,PLCG1,PLCB3,PLCB2,PLCG2,PLCZ1,PLCB1,P 3 3.88E-11 receptor LCD3,PLCD4 signaling pathway 5HT2 type receptor PLCE1,PLCD1,PLCB4,PLCG1,PLCB3,PLCB2,PLCG2,PLCZ1,PLCB1,P 3 mediated 1.51E-10 LCD3,PLCD4 signaling pathway G alpha i 3 3.41E-10 ITPKB,ITPKA,ITPR3,ITPR2,ITPR1,PLCB4,PLCB3,PLCB2,PLCB1 Pathway Inflammation mediated by chemokine PLCE1,ITPR3,ITPR2,ITPR1,PTEN,PLCD1,PLCB4,PLCG1,PLCB3,PLC 3 1.66E-09 and cytokine B2,PLCG2,PLCZ1,PLCB1,PLCD3,PLCD4 signaling pathway

109

Endothelin ITPR3,ITPR2,ITPR1,PLCB4,PLCB3,PLCB2,PIK3C2A,PIK3C2B,PIK3C 3 signaling 2.64E-08 3,PLCB1 pathway Genes involved in ITPR3,ITPR2,ITPR1,DGKZ,DGKE,DGKD,DGKG,DGKQ,DGKA,DGKB 3 3.11E-08 Platelet ,DGKI,PLCG2,DGKH Activation Genes involved in ITPR3,ITPR2,ITPR1,DGKZ,DGKE,DGKD,DGKG,DGKQ,DGKA,DGKB 3 1.20E-07 Formation of ,DGKI,PLCG2,DGKH Platelet plug Genes ITPR3,ITPR2,ITPR1,DGKZ,DGKE,DGKD,DGKG,DGKQ,DGKA,DGKB 3 involved in 1.38E-07 ,PLCG1,DGKI,PLCG2,INPP5D,DGKH Hemostasis Genes involved in Regulation of 3 Insulin 1.05E-06 ITPR3,ITPR2,ITPR1,PLCB3,PLCB2,PLCB1 Secretion by Free Fatty Acids Alpha adrenergic 3 receptor 1.47E-06 PLCE1,ITPR1,PLCB4,PLCB3,PLCB2,PLCB1 signaling pathway Genes involved in Regulation of 3 2.01E-06 ITPR3,ITPR2,ITPR1,PLCB3,PLCB2,PLCB1 Insulin Secretion by Acetylcholine Genes involved in 3 PLC beta 2.02E-06 ITPR3,ITPR2,ITPR1,PLCB4,PLCB3,PLCB2,PLCB1 mediated events Genes involved in Downstream ITPR3,ITPR2,ITPR1,DGKZ,DGKE,DGKD,DGKG,DGKQ,DGKA,DGKB 3 1.57E-05 events in ,PLCB4,DGKI,PLCB3,PLCB2,PLCB1,DGKH GPCR signaling Regulation of PIP4K2B,PIP5K1B,PIP5K1A,PIK3C2A,PIK3C2B,PIK3C2G,PIK3C3,P 3 Actin 1.68E-05 IP4K2A,PIP4K2C,PIP5K1C Cytoskeleton 110

3 Wnt signaling 2.80E-05 PLCB4,PLCG1,PLCB3,PLCB2,PLCG2,PLCB1

Wnt/Ca2+/cy 3 clic GMP 4.80E-05 ITPKB,ITPKA,ITPR3,ITPR2,ITPR1 signaling. glycerophosp 3 holipid 9.66E-05 DGKZ,DGKG,DGKB metabolic G alpha q 3 3.19E-04 ITPKB,ITPKA,ITPR3,ITPR2,ITPR1 Pathway Alzheimers 3 3.69E-04 ITPR3,ITPR2,ITPR1,PLCB4,PLCB3,PLCB2,PLCB1 Disease Genes involved in 3 5.17E-04 ITPR3,ITPR2,ITPR1,PLCB4,PLCB3,PLCB2,PLCB1 Opioid Signalling Nongenotrop 3 ic Androgen 6.56E-04 PLCG1,PLCB3,PLCB2,PLCG2,PLCB1 signaling Genes related to 3 PIP3 9.07E-04 ITPR3,ITPR2,ITPR1,PTEN,PLCG2 signaling in B lymphocytes Genes involved in CLDN2,CLDN20,CLDN6,CLDN19,CLDN8,CLDN1,CLDN23,CLDN7, 4 Tight 1.03E-48 CLDN3,CLDN4,CLDN17,CLDN11,CLDN15,CLDN18,CLDN16,CLDN junction 14,CLDN5,CLDN10,CLDN22 interactions Genes involved in CLDN2,CLDN20,CLDN6,CLDN19,CLDN8,CLDN1,CLDN23,CLDN7, 4 Cell-cell 2.64E-41 CLDN3,CLDN4,CLDN17,CLDN11,CLDN15,CLDN18,CLDN16,CLDN adhesion 14,CLDN5,CLDN10,CLDN22 systems Genes CLDN2,CLDN20,CLDN6,CLDN19,CLDN8,CLDN1,CLDN23,CLDN7, involved in 4 6.25E-38 CLDN3,CLDN4,CLDN17,CLDN11,CLDN15,CLDN18,CLDN16,CLDN Cell junction 14,CLDN5,CLDN10,CLDN22 organization

PLA2G10,PLA2G6,PLA2G4C,PLA2G2D,PLA2G3,PLA2G1B,PLA2G 5 lipases 5.53E-25 2F,PLA2G2A,PLA2G4A,PLA2G5,JMJD7-PLA2G4B,PLD1,PLD2

Phospholipid 5 7.15E-13 PLA2G2D,PLA2G15,PTDSS1,LYPLA1,PISD,CHPT1,PEMT,PLD2 Biosynthesis

111

phospholipid 5 3.74E-11 PLA2G10,PLA2G1B,PLA2G2A,PLA2G5,PLD2 metabolic eicosanoids 5 3.11E-07 PLA2G10,PLA2G1B,PLA2G2A,PLA2G5,ALOX5 metabolic MAP00590 Prostaglandi 5 n and 4.21E-07 PLA2G1B,PLA2G2A,PLA2G4A,PLA2G5,ALOX5 leukotriene metabolism glycerolipid 5 5.03E-06 PLA2G10,PLA2G1B,PLA2G2A,PLA2G5,PLD2 metabolic MAP00561 5 Glycerolipid 5.21E-05 PISD,PLA2G1B,PLA2G2A,PLA2G4A,PLA2G5 metabolism Arachidonic 5 Acid 6.02E-05 CYP4A11,PLA2G2D,CYP2J2,CYP2E1 Metabolism mitogen activated 5 protein 1.31E-04 PLA2G10,PLA2G1B,PLA2G2A,PLA2G5 kinase signaling phospholipid 5 biosynthesis 5.02E-04 CEPT1,PISD,CHPT1 II

CYP26C1,UGT2B11,UGT2B7,UGT2B10,UGT2B15,UGT2B17,UGT Genes 2B28,UGT2B4,UGT1A9,CYP11A1,UGT2A1,CYP7A1,UGT1A8,CYP involved in 4A11,UGT1A7,CYP3A4,UGT1A6,UGT1A5,CYP3A5,CYP2E1,CYP2 6 8.05E-55 Biological 6A1,CYP21A2,CYP19A1,CYP17A1,CYP11B2,CYP2A6,CYP3A7,CYP oxidations 1A1,CYP2S1,CYP2C18,CYP2C8,UGT1A1,CYP2A13,CYP26B1,UGT 1A3,CYP2B6,UGT1A4

UGT2A2,AKR1C4,AKR1C3,CYP26C1,AKR1D1,UGT2B11,UGT2B7, metapathwa UGT2A3,UGT2B17,UGT2B28,UGT2B4,UGT1A9,CYP11A1,UGT2A y 1,CYP7A1,UGT1A7,CYP3A4,UGT1A6,UGT1A5,CYP3A5,CYP2E1,C 6 3.18E-51 biotransform YP26A1,CYP21A2,UGT1A10,CYP19A1,CYP17A1,CYP11B2,CYP3A ation 7,CYP1A1,CYP2S1,CYP2C18,CYP2C8,UGT1A1,CYP2A13,CYP26B1 ,UGT1A3,CYP2B6,UGT1A4

112

UGT2A2,UGT2B11,UGT2B7,UGT2A3,UGT2B10,UGT2B15,UGT2 Glucuronidati 6 8.86E-36 B17,UGT2B28,UGT2B4,UGT1A9,UGT2A1,UGT1A8,UGT1A7,UGT on 1A6,UGT1A5,UGT1A10,UGT1A1,UGT1A3,UGT1A4

Genes involved in Cytochrome CYP26C1,CYP11A1,CYP7A1,CYP4A11,CYP3A4,CYP3A5,CYP2E1,C 6 P450 - 8.43E-33 YP26A1,CYP21A2,CYP19A1,CYP17A1,CYP11B2,CYP2A6,CYP3A7, arranged by CYP1A1,CYP2S1,CYP2C18,CYP2C8,CYP2A13,CYP26B1,CYP2B6 substrate type Genes UGT2B11,UGT2B7,UGT2B10,UGT2B15,UGT2B17,UGT2B28,UG involved in 6 1.46E-31 T2B4,UGT1A9,UGT2A1,UGT1A8,UGT1A7,UGT1A6,UGT1A5,UGT Glucuronidati 1A1,UGT1A3,UGT1A4 on Genes involved in CYP26C1,CYP11A1,CYP7A1,CYP4A11,CYP3A4,CYP3A5,CYP2E1,C Phase 1 - 6 2.62E-29 YP26A1,CYP21A2,CYP19A1,CYP17A1,CYP11B2,CYP2A6,CYP3A7, Functionaliza CYP1A1,CYP2S1,CYP2C18,CYP2C8,CYP2A13,CYP26B1,CYP2B6 tion of compounds

CYP26C1,CYP11A1,CYP7A1,CYP4A11,CYP3A4,CYP3A5,CYP2E1,C cytochrome 6 3.11E-28 YP26A1,CYP21A2,CYP19A1,CYP17A1,CYP11B2,CYP3A7,CYP1A1, P450 CYP2S1,CYP2C18,CYP2C8,CYP2A13,CYP26B1,CYP2B6

Genes UGT2B11,UGT2B7,UGT2B10,UGT2B15,UGT2B17,UGT2B28,UG involved in 6 1.89E-20 T2B4,UGT1A9,UGT2A1,UGT1A8,UGT1A7,UGT1A6,UGT1A5,UGT Phase II 1A1,UGT1A3,UGT1A4 conjugation MAP00150 Androgen HSD3B2,STS,HSD3B1,HSD17B3,HSD17B2,AKR1D1,SRD5A2,SRD 6 5.81E-19 and estrogen 5A1,UGT2B15,UGT2B4,CYP11B2 metabolism Genes CYP3A4,CYP3A5,CYP2E1,CYP2A6,CYP3A7,CYP1A1,CYP2C18,CYP 6 involved in 2.62E-17 2C8,CYP2A13,CYP2B6 Xenobiotics Tamoxifen UGT2B7,UGT1A8,CYP3A4,CYP3A5,CYP2E1,UGT1A10,CYP1A1,C 6 1.42E-13 metabolism YP2C8,UGT1A4

113

MAP00361 gamma CYP3A4,CYP3A5,CYP2E1,CYP19A1,CYP2A6,CYP3A7,CYP1A1,CYP 6 Hexachlorocy 1.63E-13 2C18,CYP2C8,CYP2B6 clohexane degradation MAP00071 CYP4A11,CYP3A4,CYP3A5,CYP2E1,CYP19A1,CYP2A6,CYP3A7,CY 6 Fatty acid 5.44E-13 P1A1,CYP2C18,CYP2C8,CYP2B6 metabolism MAP00380 AOX1,CYP3A4,CYP3A5,CYP2E1,CYP19A1,CYP2A6,CYP3A7,CYP1 6 Tryptophan 5.44E-13 A1,CYP2C18,CYP2C8,CYP2B6 metabolism Genes involved in HSD3B2,HSD3B1,HSD17B3,CYP11A1,CYP21A2,CYP19A1,CYP17 6 Steroid 8.27E-13 A1,CYP11B2 hormone biosynthesis Androgen 6 and Estrogen 1.74E-12 STS,HSD3B1,HSD17B3,AKR1D1,SRD5A1,UGT2B11,CYP17A1 Metabolism MAP00140 C21 Steroid HSD3B2,HSD3B1,AKR1D1,CYP11A1,CYP21A2,CYP17A1,CYP11B 6 5.78E-12 hormone 2 metabolism Estrogen STS,UGT2B7,UGT1A9,CYP3A4,UGT1A6,CYP1A1,UGT1A1,UGT1A 6 6.60E-12 metabolism 3 Genes involved in HSD3B2,HSD3B1,HSD17B3,AKR1C4,AKR1D1,CYP11A1,CYP7A1, 6 1.92E-11 Steroid CYP21A2,CYP19A1,CYP17A1,CYP11B2 metabolism Genes involved in HSD3B2,HSD3B1,HSD17B3,CYP11A1,CYP21A2,CYP19A1,CYP17 6 5.41E-11 Steroid A1,CYP11B2 hormones Steroidogene 6 2.28E-10 HSD3B1,AKR1C4,AKR1D1,CYP11A1,CYP21A2,CYP17A1 sis Glucocorticoi d & 6 Mineralcortic 2.28E-10 HSD3B2,HSD3B1,CYP11A1,CYP21A2,CYP17A1,CYP11B2 oid Metabolism Nicotine 6 8.11E-09 AOX1,UGT1A9,CYP2A6,CYP2B6,UGT1A4 metabolism Irinotecan 6 1.37E-08 UGT1A9,CYP3A4,UGT1A6,CYP3A5,UGT1A10,UGT1A1 Pathway

114

Genes involved in HSD3B2,HSD3B1,HSD17B3,AKR1C4,AKR1D1,UGT2B4,UGT1A9,C 6 Metabolism 1.92E-08 YP11A1,CYP7A1,CYP4A11,CYP21A2,CYP19A1,CYP17A1,CYP11B of lipids and 2 lipoproteins Genes involved in 6 2.38E-08 CYP11A1,CYP7A1,CYP21A2,CYP19A1,CYP17A1,CYP11B2 Endogenous sterols Genes involved in HSD3B2,HSD3B1,HSD17B3,CYP11A1,CYP21A2,CYP19A1,CYP17 6 1.71E-07 Hormone A1,CYP11B2 biosynthesis Steroid 6 3.34E-07 HSD3B2,HSD3B1,HSD17B3,HSD17B2,CYP17A1 Biosynthesis Tryptophan 6 2.15E-06 AOX1,CYP3A4,CYP2E1,CYP19A1,CYP1A1,CYP2C18,CYP2A13 metabolism Genes involved in 6 Phase 1 3.88E-06 CYP4A11,CYP3A4,CYP2E1,CYP2A6,CYP1A1 functionalizat ion Fatty Acid 6 Omega 3.88E-06 CYP4A11,CYP3A4,CYP2E1,CYP2A6,CYP1A1 Oxidation Nuclear receptors in 6 lipid 7.95E-06 CYP7A1,CYP4A11,CYP3A4,CYP2E1,CYP26A1,CYP2B6 metabolism and toxicity Benzo(a)pyre 6 ne 2.69E-05 AKR1C4,AKR1C3,CYP3A4,CYP1A1 metabolism nicotine 6 degradation 3.46E-05 AOX1,CYP2A6,UGT1A4 III androgen 6 and estrogen 6.99E-05 STS,SRD5A2,UGT2A1,CYP11B2 metabolic statin pharmacokin 6 1.50E-04 CYP3A4,CYP2C8,UGT1A1,UGT1A3 etics pathway nicotine 6 degradation 3.43E-04 AOX1,CYP2A6,UGT1A4 II 115

p53 pathway PIK3R5,AKT1,PIK3R3,PIK3CA,PIK3CB,NRAS,KRAS,PIK3CG,PIK3R1 7 feedback 3.77E-28 ,AKT3,PIK3CD,PIK3R2,HRAS,PDPK1,AKT2 loops 2 Insulin/IGF pathway- protein PIK3R5,AKT1,PIK3R3,FOXO3,PIK3CA,PIK3CB,IRS1,PIK3CG,PIK3R 7 1.47E-27 kinase B 1,PIK3CD,GSK3A,PIK3R2,PDPK1,AKT2 signaling cascade

PI3 kinase PIK3R5,AKT1,PIK3R3,FOXO3,PIK3CA,PIK3CB,IRS1,NRAS,KRAS,PI 7 1.59E-27 pathway K3R1,AKT3,PIK3R2,HRAS,PDPK1,AKT2

Class I PI3K PIK3R5,PIK3R3,FOXO3,PIK3CA,PIK3CB,NRAS,KRAS,PIK3CG,PIK3 7 signaling 7.47E-22 R1,PIK3CD,PIK3R2,HRAS,PDPK1 events VEGF PRKCZ,AKT1,PIK3R3,PIK3CA,PIK3CB,NRAS,KRAS,PIK3CG,PIK3R1, 7 signaling 3.46E-21 PIK3CD,PIK3R2,HRAS,PTK2 pathway Genes involved in TRKA AKT1,FOXO3,PIK3CA,PIK3CB,IRS1,NRAS,KRAS,PIK3R1,AKT3,GSK 7 signalling 4.16E-20 3A,PIK3R2,HRAS,PDPK1,AKT2 from the plasma membrane

Insulin PRKCZ,AKT1,PIK3R3,FOXO3,PIK3CA,PIK3CB,IRS1,PIK3CG,PIK3R1 7 1.90E-19 Signaling ,PIK3CD,GSK3A,PIK3R2,HRAS,PDPK1,AKT2

T cell AKT1,PIK3R3,PIK3CA,PIK3CB,NRAS,KRAS,PIK3CG,PIK3R1,AKT3, 7 3.48E-19 activation PIK3CD,PIK3R2,HRAS,AKT2

Genes involved in AKT1,FOXO3,PIK3CA,PIK3CB,IRS1,PIK3R1,AKT3,GSK3A,PIK3R2,P 7 4.62E-19 PI3K/AKT DPK1,AKT2 signalling Genes involved in Collagen- PRKCZ,PIK3R5,AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,AKT3,PDPK 7 4.75E-19 mediated 1,AKT2 activation cascade

116

PDGF PIK3R5,PIK3R3,PIK3CA,PIK3CB,NRAS,KRAS,PIK3CG,PIK3R1,PIK3 7 signaling 1.62E-18 CD,GSK3A,PIK3R2,HRAS,PDPK1,AKT2 pathway Hypoxia response via AKT1,PIK3R3,PIK3CA,PIK3CB,PIK3CG,PIK3R1,AKT3,PIK3CD,PIK3 7 2.20E-18 HIF R2,AKT2 activation CXCR3- mediated AKT1,PIK3R3,PIK3CA,PIK3CB,NRAS,KRAS,PIK3R1,PIK3CD,PIK3R2 7 3.09E-18 signaling ,HRAS,PDPK1 events Endothelin PRKCZ,PIK3R5,AKT1,PIK3R3,PIK3CA,PIK3CB,PIK3CG,PIK3R1,AKT 7 signaling 1.62E-17 3,PIK3CD,PIK3R2,AKT2 pathway

PRKCZ,AKT1,PIK3R3,PIK3CA,PIK3CB,NRAS,KRAS,PIK3CG,PIK3R1, 7 Angiogenesis 1.68E-17 AKT3,PIK3CD,PIK3R2,HRAS,PTK2

PIK3R5,AKT1,PIK3R3,PIK3CA,PIK3CB,PIK3CG,PIK3R1,AKT3,PIK3 7 p53 pathway 3.73E-17 CD,PIK3R2,PDPK1,AKT2

ErbB1 PRKCZ,AKT1,PIK3R3,FOXO3,PIK3CA,PIK3CB,NRAS,KRAS,PIK3R1, 7 downstream 4.44E-17 PIK3CD,PIK3R2,HRAS,PDPK1 signaling phosphatidyli nositol 3- AKT1,PIK3R3,PIK3CA,PIK3CB,PIK3R1,AKT3,PIK3CD,PIK3R2,PDPK 7 5.37E-17 kinase-Akt 1,AKT2 signaling EGF receptor (ErbB1) PIK3R3,PIK3CA,PIK3CB,NRAS,KRAS,PIK3R1,PIK3CD,PIK3R2,HRA 7 5.37E-17 signaling S,PTK2 pathway Internalizatio PIK3R3,PIK3CA,PIK3CB,NRAS,KRAS,PIK3R1,PIK3CD,PIK3R2,HRA 7 3.45E-16 n of ErbB1 S,PTK2 CXCR4- mediated PRKCZ,PIK3R5,AKT1,PIK3R3,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK 7 4.94E-16 signaling 3CD,PIK3R2,PDPK1,PTK2 events ErbB2/ErbB3 AKT1,PIK3R3,PIK3CA,PIK3CB,NRAS,KRAS,PIK3R1,PIK3CD,PIK3R2 7 signaling 7.77E-16 ,HRAS events Genes PRKCZ,PIK3R5,AKT1,PIK3CA,PIK3CB,NRAS,KRAS,PIK3CG,PIK3R1, 7 involved in 8.28E-16 AKT3,PIK3R2,HRAS,PDPK1,PTK2,AKT2 Hemostasis

117

AKT1,PIK3CA,PIK3CB,NRAS,KRAS,PIK3CG,AKT3,PIK3CD,GSK3A, 7 Ras Pathway 9.52E-16 HRAS,PDPK1 Genes involved in AKT1,FOXO3,PIK3CA,PIK3CB,IRS1,NRAS,KRAS,PIK3R1,AKT3,GSK 7 1.86E-15 Signalling by 3A,PIK3R2,HRAS,PDPK1,AKT2 NGF EGF receptor PRKCZ,PIK3R5,AKT1,PIK3CA,PIK3CB,NRAS,KRAS,PIK3CG,AKT3,PI 7 signaling 5.08E-15 K3CD,HRAS,AKT2 pathway Genes involved in PRKCZ,PIK3R5,AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,AKT3,PDPK 7 Platelet 2.50E-14 1,AKT2 activation triggers Toll-like receptor PIK3R5,AKT1,PIK3R3,PIK3CA,PIK3CB,PIK3CG,PIK3R1,AKT3,PIK3 7 1.00E-13 signaling CD,PIK3R2,AKT2 pathway Insulin 7 1.57E-13 PRKCZ,AKT1,FOXO3,PIK3CA,IRS1,PIK3R1,HRAS,PDPK1,AKT2 Pathway FGF signaling PRKCZ,AKT1,PIK3CA,PIK3CB,NRAS,KRAS,PIK3CG,AKT3,PIK3CD,H 7 2.36E-13 pathway RAS,AKT2 Axon guidance 7 2.79E-13 PIK3R5,PIK3R3,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2 mediated by netrin Genes related to IL4 7 rceptor 3.96E-13 AKT1,PIK3CA,IRS1,PIK3R1,AKT3,PIK3CD,GSK3A,AKT2 signaling in B lymphocytes Genes involved in PIK3CA,PIK3CB,IRS1,NRAS,KRAS,PIK3R1,PIK3R2,HRAS,PDPK1,A 7 5.59E-13 IRS-related KT2 events IGF1 7 1.04E-12 PRKCZ,AKT1,PIK3CA,IRS1,PIK3R1,HRAS,PDPK1,PTK2 pathway MicroRNAs in cardiomyocyt AKT1,PIK3R3,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,PD 7 1.21E-12 e PK1,AKT2 hypertrophy

Focal PIK3R5,AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,AKT3,PIK3CD,PIK3 7 1.30E-12 Adhesion R2,PDPK1,PTK2,AKT2

118

Nephrin/Nep h1 signaling 7 1.87E-12 PRKCZ,AKT1,PIK3R3,PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2 in the kidney podocyte Genes involved in 7 3.35E-12 PIK3CA,PIK3CB,NRAS,KRAS,PIK3R1,PIK3R2,HRAS Tie2 Signaling Regulation of toll-like PIK3R5,AKT1,PIK3R3,PIK3CA,PIK3CB,PIK3CG,PIK3R1,AKT3,PIK3 7 receptor 3.55E-12 CD,PIK3R2,AKT2 signaling pathway Inflammation mediated by chemokine PRKCZ,AKT1,PIK3CA,PIK3CB,NRAS,KRAS,PIK3CG,AKT3,PIK3CD,H 7 3.56E-12 and cytokine RAS,PDPK1,AKT2 signaling pathway Trk receptor signaling 7 mediated by 4.16E-12 AKT1,FOXO3,PIK3CA,NRAS,KRAS,PIK3R1,HRAS,PDPK1 PI3K and PLC- gamma Regulation of PIK3R5,PIK3R3,PIK3CA,PIK3CB,NRAS,KRAS,PIK3CG,PIK3R1,PIK3 7 Actin 4.50E-12 CD,PIK3R2,PTK2 Cytoskeleton FAS (CD95) 7 signaling 8.60E-12 AKT1,PIK3R3,PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,PDPK1 pathway AMPK AKT1,PIK3R3,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,AK 7 1.33E-11 signaling T2 Integrin PIK3R3,PIK3CA,PIK3CB,NRAS,KRAS,PIK3CG,PIK3R1,PIK3CD,PIK3 7 signalling 1.37E-11 R2,HRAS,PTK2 pathway Prolactin 7 Signaling 3.35E-11 AKT1,PIK3CA,PIK3CB,IRS1,PIK3CG,PIK3R1,PIK3R2,HRAS,PTK2 Pathway Genes related to 7 the insulin 7.81E-11 AKT1,PIK3CA,IRS1,PIK3R1,AKT3,PIK3CD,GSK3A,AKT2 receptor pathway Genes PRKCZ,PIK3R5,AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,AKT3,PDPK 7 7.84E-11 involved in 1,PTK2,AKT2

119

Formation of Platelet plug The TrkA receptor binds nerve growth factor to 7 2.86E-10 AKT1,PIK3CA,AKT3,PIK3CD,HRAS,AKT2 activate MAP kinase pathways and promote cell growth. The IGF-1 7 Receptor and 2.86E-10 AKT1,FOXO3,PIK3CA,PIK3CG,PIK3R1,HRAS Longevity Interleukin 7 signaling 3.70E-10 AKT1,FOXO3,PIK3CA,PIK3CB,IRS1,NRAS,AKT3,PDPK1,AKT2 pathway Role of nicotinic acetylcholine 7 receptors in 4.57E-10 AKT1,FOXO3,PIK3CA,PIK3CG,PIK3R1,PTK2 the regulation of apoptosis Genes related to PIP3 7 6.57E-10 AKT1,PIK3CA,IRS1,AKT3,PIK3CD,GSK3A,PTK2,AKT2 signaling in cardiac myocytes Genes involved in 7 Down-stream 6.90E-10 PIK3CA,PIK3CB,NRAS,KRAS,PIK3R1,PIK3R2,HRAS signal transduction PTEN dependent 7 cell cycle 1.06E-09 AKT1,FOXO3,PIK3CA,PIK3R1,PDPK1,PTK2 arrest and apoptosis Genes involved in PRKCZ,PIK3R5,AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,AKT3,PDPK 7 1.23E-09 Platelet 1,AKT2 Activation

120

Genes involved in CD28 7 1.54E-09 AKT1,PIK3CA,PIK3R1,AKT3,PDPK1,AKT2 dependent PI3K/Akt signaling IL-5 signaling 7 1.90E-09 AKT1,FOXO3,KRAS,PIK3CG,PIK3R1,GSK3A,PIK3R2 pathway Trefoil Factors 7 Initiate 3.07E-09 AKT1,PIK3CA,PIK3CG,PIK3R1,HRAS,PTK2 Mucosal Healing Signaling events mediated by Hepatocyte 7 3.50E-09 PRKCZ,AKT1,PIK3CA,PIK3R1,HRAS,PDPK1,PTK2,AKT2 Growth Factor Receptor (c- Met) Genes 7 related to 3.88E-09 AKT1,PIK3CA,PIK3CG,PIK3R1,AKT3,PIK3CD,AKT2 chemotaxis AKT Signaling 7 4.21E-09 AKT1,FOXO3,PIK3CA,PIK3CG,PIK3R1,PDPK1 Pathway Members of the BCR 7 5.40E-09 AKT1,PIK3CA,PIK3R1,AKT3,PIK3CD,GSK3A,AKT2 signaling pathway Genes involved in G beta:gamma 7 9.95E-09 PIK3R5,AKT1,PIK3CG,AKT3,PDPK1,AKT2 signalling through PI3Kgamma TRAIL 7 signaling 2.11E-08 PIK3R3,PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2 pathway Genes involved in 7 G-protein 2.11E-08 PIK3R5,AKT1,PIK3CG,AKT3,PDPK1,AKT2 beta:gamma signalling Genes 7 2.65E-08 AKT1,PIK3CA,PIK3R1,AKT3,PDPK1,AKT2 involved in 121

CD28 co- stimulation Alpha 6 Beta 7 4 signaling 4.10E-08 AKT1,IRS1,PIK3R1,PIK3R2,HRAS,PTK2 pathway Genes involved in AKT 7 5.77E-08 AKT1,AKT3,GSK3A,PDPK1,AKT2 phosphorylat es targets in the cytosol Genes involved in 7 6.10E-08 PIK3CA,PIK3CB,NRAS,KRAS,PIK3R1,PIK3R2,HRAS Signaling by PDGF altered phosphatody 7 linositol 3- 6.93E-08 AKT1,PIK3CA,AKT3,AKT2 kinase-Akt signaling Class I PI3K signaling 7 events 7.45E-08 AKT1,FOXO3,AKT3,GSK3A,PDPK1,AKT2 mediated by Akt ErbB4 7 signaling 8.98E-08 PIK3R3,PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2 events PTEN is a tumor suppressor that dephosphory 7 1.26E-07 AKT1,PIK3CA,AKT3,PIK3CD,AKT2 lates the lipid messenger phosphatidyli nositol triphosphate. Genes involved in AKT1,PIK3CA,PIK3CB,NRAS,KRAS,PIK3R1,AKT3,PIK3R2,HRAS,PD 7 Signaling in 1.27E-07 PK1,AKT2 Immune system EPHB 7 forward 1.52E-07 PIK3CA,NRAS,KRAS,PIK3R1,HRAS,PTK2 signaling

122

IL-2 Receptor Beta Chain in 7 1.52E-07 AKT1,PIK3CA,IRS1,PIK3CG,PIK3R1,HRAS T cell Activation B Cell 7 Antigen 1.79E-07 AKT1,PIK3CA,PIK3R1,AKT3,PIK3CD,AKT2 Receptor Plasma membrane 7 estrogen 2.11E-07 AKT1,PIK3CA,NRAS,KRAS,PIK3R1,HRAS receptor signaling Role of Erk5 7 in Neuronal 2.46E-07 AKT1,PIK3CA,PIK3CG,PIK3R1,HRAS Survival IGF-1 7 Signaling 5.80E-07 PIK3CA,IRS1,PIK3CG,PIK3R1,HRAS Pathway Insulin 7 Signaling 7.50E-07 PIK3CA,IRS1,PIK3CG,PIK3R1,HRAS Pathway B Cell Receptor 7 8.24E-07 AKT1,PIK3CG,PIK3R1,GSK3A,PIK3R2,HRAS,PDPK1 Signaling Pathway Inhibition of Cellular 7 9.56E-07 AKT1,PIK3CA,PIK3CG,PIK3R1,HRAS Proliferation by Gleevec Ras Signaling 7 9.56E-07 AKT1,PIK3CA,PIK3CG,PIK3R1,HRAS Pathway Multiple antiapoptotic pathways from IGF-1R 7 9.56E-07 AKT1,PIK3CA,IRS1,PIK3R1,HRAS signaling lead to BAD phosphorylat ion Genes involved in Cell surface 7 9.60E-07 PIK3CA,PIK3CB,NRAS,KRAS,PIK3R1,PIK3R2,HRAS interactions at the vascular wall

123

IL-4 signaling 7 9.71E-07 AKT1,PIK3CA,IRS1,PIK3R1,PIK3CD,PIK3R2 pathway Signaling events mediated by 7 Stem cell 1.10E-06 AKT1,FOXO3,PIK3CA,PIK3R1,HRAS,PDPK1 factor receptor (c- Kit) Regulation of eIF4e and 7 1.21E-06 AKT1,PIK3CA,IRS1,PIK3R1,PDPK1 p70 S6 Kinase Erk and PI-3 Kinase Are Necessary for 7 Collagen 1.21E-06 PIK3CA,PIK3CG,PIK3R1,HRAS,PTK2 Binding in Corneal Epithelia Integrin- 7 mediated cell 1.39E-06 AKT1,AKT3,PIK3R2,HRAS,PDPK1,PTK2,AKT2 adhesion IL2-mediated 7 signaling 1.55E-06 PIK3CA,IRS1,NRAS,KRAS,PIK3R1,HRAS events Phospholipid s as signalling 7 2.28E-06 AKT1,PIK3CA,PIK3CG,PIK3R1,PTK2 intermediarie s Apoptosis 7 signaling 2.56E-06 AKT1,PIK3CA,PIK3CB,PIK3CG,AKT3,PIK3CD,AKT2 pathway Growth Hormone 7 2.77E-06 PIK3CA,IRS1,PIK3CG,PIK3R1,HRAS Signaling Pathway B cell 7 2.95E-06 PIK3CA,PIK3CB,NRAS,PIK3CG,PIK3CD,HRAS activation Neurotrophic factor- 7 mediated Trk 3.26E-06 PRKCZ,PIK3CA,NRAS,KRAS,PIK3R1,HRAS receptor signaling

124

VEGF, 7 Hypoxia, and 3.34E-06 PIK3CA,PIK3CG,PIK3R1,HRAS,PTK2 Angiogenesis Signaling events 7 mediated by 3.96E-06 AKT1,PIK3CA,PIK3R1,HRAS,PDPK1,PTK2 VEGFR1 and VEGFR2 TSH signaling 7 4.36E-06 AKT1,PIK3CA,PIK3R1,PIK3R2,HRAS,PDPK1 pathway Nongenotrop 7 ic Androgen 4.77E-06 AKT1,PIK3CA,PIK3R1,HRAS,PTK2 signaling Intracellular Signalling Through 7 Adenosine 6.63E-06 PRKCZ,AKT1,PIK3CA,HRAS,PDPK1 Receptor A2a and Adenosine 7 PI3K Pathway 6.63E-06 AKT1,PIK3CA,AKT3,GSK3A,AKT2 Trka Receptor 7 6.78E-06 AKT1,PIK3CA,PIK3R1,HRAS Signaling Pathway Genes involved in 7 Costimulatio 6.86E-06 AKT1,PIK3CA,PIK3R1,AKT3,PDPK1,AKT2 n by the CD28 family mTOR 7 signaling 6.86E-06 AKT1,IRS1,NRAS,KRAS,HRAS,PDPK1 pathway Intracellular Signalling Through 7 Adenosine 7.76E-06 PRKCZ,AKT1,PIK3CA,HRAS,PDPK1 Receptor A2b and Adenosine G alpha 13 7 9.04E-06 AKT1,PIK3CB,AKT3,PTK2,AKT2 Pathway Genes 7 involved in 9.77E-06 IRS1,NRAS,KRAS,HRAS SOS-

125

mediated signalling Signaling of Hepatocyte 7 Growth 1.21E-05 PIK3CA,PIK3CG,PIK3R1,HRAS,PTK2 Factor Receptor Integrin 7 Signaling 1.33E-05 AKT1,PIK3CA,PIK3CB,AKT3,PTK2,AKT2 Pathway IL2 signaling events 7 1.39E-05 PRKCZ,AKT1,FOXO3,PIK3CA,PIK3R1 mediated by PI3K Genes 7 involved in 1.39E-05 PIK3CA,PIK3CB,IRS1,PIK3R1,PIK3R2 PI-3K cascade Signaling events 7 regulated by 1.59E-05 PIK3CA,IRS1,PIK3R1,HRAS,PTK2 Ret tyrosine kinase EGF/EGFR 7 Signaling 1.65E-05 PRKCZ,AKT1,KRAS,PIK3R1,PIK3R2,PDPK1,PTK2 Pathway B Cell 7 Survival 2.47E-05 AKT1,PIK3CA,PIK3CG,PIK3R1 Pathway Genes involved in Downstream 7 2.65E-05 PIK3CA,PIK3CB,IRS1,PIK3R1,PIK3R2 signaling of activated FGFR Human Cytomegalovi 7 rus and Map 3.23E-05 AKT1,PIK3CA,PIK3CG,PIK3R1 Kinase Pathways Nerve growth 7 factor 4.15E-05 PIK3CA,PIK3CG,PIK3R1,HRAS pathway (NGF) Genes 7 4.19E-05 PIK3CA,PIK3CB,PIK3R1,PIK3R2,PDPK1 involved in 126

Downstream TCR signaling Genes involved in 7 4.67E-05 PIK3CA,NRAS,KRAS,PIK3R1,HRAS Signaling by EGFR Integrins in 7 5.19E-05 AKT1,PIK3CA,IRS1,PIK3R1,PTK2 angiogenesis Skeletal muscle hypertrophy 7 is regulated 6.54E-05 AKT1,PIK3CA,PIK3R1,PDPK1 via AKT/mTOR pathway Corticosteroi ds and 7 6.54E-05 AKT1,PIK3CA,PIK3CG,PIK3R1 cardioprotect ion TCR signaling 7 in naïve CD8+ 7.04E-05 AKT1,NRAS,KRAS,HRAS,PDPK1 T cells E-cadherin 7 signaling in 8.07E-05 AKT1,PIK3CA,PIK3R1,AKT2 keratinocytes Genes involved in 7 CTLA4 8.07E-05 AKT1,AKT3,PDPK1,AKT2 inhibitory signaling NFAT and Hypertrophy of the heart 7 (Transcriptio 8.54E-05 AKT1,PIK3CA,PIK3CG,PIK3R1,HRAS n in the broken heart) RANKL/RANK 7 Signaling 8.54E-05 AKT1,PIK3R1,PIK3R2,PTK2,AKT2 Pathway Kit receptor 7 signaling 8.54E-05 AKT1,FOXO3,PIK3R1,PIK3R2,HRAS pathway ErbB 7 signaling 9.38E-05 PIK3R5,KRAS,AKT3,HRAS,PTK2 pathway

127

Role of ERBB2 in Signal 7 9.84E-05 PIK3CA,PIK3CG,PIK3R1,HRAS Transduction and Oncology Phosphoinosi tides and 7 their 1.19E-04 PRKCZ,AKT1,GSK3A,PDPK1 downstream targets. mTOR 7 Signaling 1.19E-04 AKT1,PIK3CA,PIK3R1,PDPK1 Pathway Fc-epsilon receptor I 7 1.34E-04 AKT1,PIK3CA,PIK3R1,HRAS,PTK2 signaling in mast cells TPO Signaling 7 1.43E-04 PIK3CA,PIK3CG,PIK3R1,HRAS Pathway CXCR4 7 Signaling 1.43E-04 PIK3CA,PIK3R1,HRAS,PTK2 Pathway Leptin 7 signaling 1.59E-04 AKT1,IRS1,PIK3R1,PIK3R2,HRAS pathway TCR signaling 7 in naïve CD4+ 1.87E-04 AKT1,NRAS,KRAS,HRAS,PDPK1 T cells Interleukin 4 7 (IL-4) 2.00E-04 AKT1,PIK3CA,AKT3,AKT2 Pathway Influence of Ras and Rho 7 proteins on 2.00E-04 AKT1,PIK3CA,PIK3R1,HRAS G1 to S Transition Regulation of BAD 7 2.00E-04 AKT1,PIK3CA,PIK3CG,PIK3R1 phosphorylat ion Genes 7 involved in 2.02E-04 PIK3CA,PIK3CB,PIK3R1,PIK3R2,PDPK1 TCR signaling G alpha q 7 2.34E-04 AKT1,PIK3CB,AKT3,AKT2 Pathway

128

Transcription factor CREB 7 and its 2.34E-04 AKT1,PIK3CA,PIK3R1,HRAS extracellular signals Inactivation of Gsk3 by AKT causes 7 accumulation 2.34E-04 AKT1,PIK3CA,PIK3R1,PDPK1 of b-catenin in Alveolar Macrophages BCR signaling 7 2.37E-04 AKT1,PIK3CA,PIK3R1,HRAS,PDPK1 pathway LPA receptor 7 mediated 2.37E-04 AKT1,PIK3CB,PIK3R1,HRAS,PTK2 events VEGFR1 7 specific 3.16E-04 AKT1,PIK3CA,PIK3R1,PDPK1 signals Control of skeletal myogenesis by HDAC and 7 calcium/calm 3.64E-04 AKT1,PIK3CA,PIK3CG,PIK3R1 odulin- dependent kinase (CaMK) Wnt/beta- 7 catenin 4.17E-04 AKT1,AKT3,GSK3A,AKT2 Pathway PDGF 7 Signaling 4.76E-04 PIK3CA,PIK3CG,PIK3R1,HRAS Pathway Genes related to 7 PIP3 5.41E-04 AKT1,PIK3CA,AKT3,AKT2 signaling in B lymphocytes G alpha i 7 6.12E-04 AKT1,PIK3CB,AKT3,AKT2 Pathway Adrenergic 7 6.12E-04 AKT1,PIK3CA,PIK3R1,PIK3CD Pathway Insulin 7 7.74E-04 PRKCZ,AKT1,IRS1,HRAS Signalling

129

MAPK 7 signaling 9.60E-04 PRKCZ,AKT1,NRAS,KRAS,AKT3,AKT2 pathway

metapathwa GSTO2,GSTK1,CYP3A4,MGST2,MGST3,GSTT2B,MGST1,CYP2E1, y 8 1.52E-36 GSTA5,EPHX1,GSTA4,GSTA3,GSTO1,CYP1A2,CYP1B1,GSTT2,GS biotransform TT1,GSTP1,GSTM5,GSTM4,GSTM3,GSTM2,GSTM1,CYP2C9 ation

Genes involved in GSTO2,MGST2,MGST3,MGST1,GSTA5,GSTA2,GSTA1,GSTA4,GS 8 1.52E-32 Glutathione TA3,GSTO1,GSTP1,GSTM5,GSTM4,GSTM1 conjugation Genes GSTO2,CYP3A4,MGST2,MGST3,MGST1,CYP2E1,GSTA5,GSTA2,G involved in 8 1.45E-27 STA1,GSTA4,GSTA3,GSTO1,CYP1A2,CYP1B1,GSTP1,GSTM5,GST Biological M4,GSTM1,CYP2C9 oxidations Genes involved in GSTO2,MGST2,MGST3,MGST1,GSTA5,GSTA2,GSTA1,GSTA4,GS 8 3.63E-22 Phase II TA3,GSTO1,GSTP1,GSTM5,GSTM4,GSTM1 conjugation glutathione GSTO2,GSTA2,GSTA1,GSTA4,GSTA3,GSTT1,GSTP1,GSTM2,GST 8 7.52E-22 conjugation M1 MAP00480 MGST1,GSTA2,GSTT2,GSTT1,GSTP1,GSTM5,GSTM4,GSTM3,GS 8 Glutathione 1.07E-18 TM2,GSTM1 metabolism Glutathione 8 2.40E-12 GSTT2B,GSTA5,GSTA1,GSTT2,GSTT1,GSTM2,GSTM1 metabolism Aflatoxin B1 8 9.62E-10 CYP3A4,EPHX1,CYP1A2,GSTT1,GSTM1 metabolism Tamoxifen 8 1.95E-07 CYP3A4,CYP2E1,CYP1A2,CYP1B1,CYP2C9 metabolism Genes 8 involved in 7.93E-06 CYP3A4,CYP2E1,CYP1A2,CYP2C9 Xenobiotics Estrogen 8 1.38E-05 CYP3A4,CYP1A2,CYP1B1,GSTM1 metabolism Genes involved in Cytochrome 8 P450 - 2.98E-05 CYP3A4,CYP2E1,CYP1A2,CYP1B1,CYP2C9 arranged by substrate type

130

cytochrome 8 9.04E-05 CYP3A4,CYP2E1,CYP1A2,CYP1B1,CYP2C9 P450 MAP00361 gamma 8 Hexachlorocy 1.34E-04 CYP3A4,CYP2E1,CYP1A2,CYP2C9 clohexane degradation Genes involved in Phase 1 - 8 1.45E-04 CYP3A4,CYP2E1,CYP1A2,CYP1B1,CYP2C9 Functionaliza tion of compounds Benzo(a)pyre 8 ne 1.63E-04 CYP3A4,EPHX1,CYP1B1 metabolism Nuclear receptors in 8 lipid 2.28E-04 CYP3A4,CYP2E1,CYP1A2,CYP2C9 metabolism and toxicity Fatty Acid 8 Omega 8.70E-04 CYP3A4,CYP2E1,CYP1A2 Oxidation Genes involved in 8 Phase 1 8.70E-04 CYP3A4,CYP2E1,CYP1A2 functionalizat ion MAP00380 8 Tryptophan 8.82E-04 CYP3A4,CYP2E1,CYP1A2,CYP2C9 metabolism Tryptophan 8 8.82E-04 CYP3A4,CYP2E1,CYP1A2,CYP1B1 metabolism MAP00071 8 Fatty acid 8.82E-04 CYP3A4,CYP2E1,CYP1A2,CYP2C9 metabolism

CDK2,CDK4,MYC,CDK6,E2F5,E2F4,CDKN1A,CDKN1B,CDKN1C,C G1 to S cell 9 8.41E-42 CND2,RBL1,CCNE2,CCND1,TFDP1,E2F2,TFDP2,E2F3,E2F1,RB1,C cycle control CNE1,CCND3

Genes CDK4,CDK6,E2F5,E2F4,CDKN1A,CCND2,CCND1,TFDP1,E2F2,E2F 9 involved in 1.08E-30 3,E2F1,RB1,CCND3 G1 Phase

131

E2F transcription CDK2,MYC,E2F5,E2F4,CDKN1A,CDKN1B,RBL1,CCNE2,TFDP1,RB 9 1.79E-29 factor L2,E2F2,TFDP2,E2F3,E2F1,RB1,CCNE1,CCND3 network Cyclins and CDK2,CDK4,CDK6,CDKN1A,CDKN1B,CCND2,RBL1,CCND1,TFDP1 9 Cell Cycle 2.19E-27 ,E2F1,RB1,CCNE1,CCND3 Regulation

CDK2,CDK4,CDK6,E2F5,E2F4,CDKN1A,CDKN1B,CCND2,RBL1,CC 9 Cell cycle 1.13E-26 NE2,TFDP1,E2F2,E2F3,E2F1,RB1,CCNE1,CCND3

Regulation of CDK2,CDK4,CDK6,E2F4,CDKN1A,CDKN1B,CCND2,CCND1,TFDP1 9 retinoblasto 3.72E-25 ,E2F2,E2F3,E2F1,RB1,CCNE1,CCND3 ma protein

DNA damage CDK2,CDK4,MYC,CDK6,CDKN1A,CDKN1B,CCND2,CCNE2,CCND1 9 4.63E-20 response ,E2F1,RB1,CCNE1,CCND3

miRNA regulation of CDK2,CDK4,MYC,CDK6,CDKN1A,CDKN1B,CCND2,CCNE2,CCND1 9 8.64E-20 DNA Damage ,E2F1,RB1,CCNE1,CCND3 Response Expression of cyclins regulates progression through the 9 2.65E-19 CDK2,CDK4,E2F4,CDKN1B,CCNE2,CCND1,E2F2,E2F1,CCNE1 cell cycle by activating cyclin- dependent kinases. Influence of Ras and Rho CDK2,CDK4,CDK6,CDKN1A,CDKN1B,CCND1,TFDP1,E2F1,RB1,CC 9 proteins on 3.32E-18 NE1 G1 to S Transition Cell Cycle: CDK2,CDK4,CDK6,CDKN1A,CDKN1B,CCND1,TFDP1,E2F1,RB1,CC 9 G1/S Check 8.17E-18 NE1 Point Genes involved in CDK2,CDK4,CDK6,E2F5,E2F4,CDKN1A,CCND2,CCNE2,CCND1,TF 9 3.42E-16 Cell Cycle, DP1,E2F2,E2F3,E2F1,RB1,CCNE1,CCND3 Mitotic miRNAs 9 involved in 1.28E-15 MYC,CDK6,CDKN1A,CDKN1B,CCND1,E2F1,CCNE1,CCND3 DDR 132

Cdk2, 4, and 6 bind cyclin D in G1, while 9 6.46E-13 CDK2,CDK4,CDKN1A,CDKN1B,CCND1,E2F2,E2F1 cdk2/cyclin E promotes the G1/S transition. p53 Signaling 9 1.15E-12 CDK2,CDK4,CDKN1A,CCND1,E2F1,RB1,CCNE1 Pathway TSH signaling 9 1.09E-11 CDK2,CDK4,MYC,CDKN1B,RBL2,E2F1,RB1,CCNE1,CCND3 pathway Regulation of p27 Phosphorylat 9 8.18E-11 CDK2,CDKN1B,TFDP1,E2F1,RB1,CCNE1 ion during Cell Cycle Progression Genes involved in 9 7.38E-10 CDK2,CDKN1A,CCNE2,TFDP1,E2F2,E2F3,E2F1,RB1,CCNE1 G1/S Transition E2F1 9 Destruction 5.37E-09 CDK2,TFDP1,E2F1,RB1,CCNE1 Pathway Regulation of nuclear 9 5.75E-09 CDK2,CDK4,MYC,E2F5,E2F4,CDKN1A,RBL1,TFDP1 SMAD2/3 signaling E2F/MIRHG1 9 feedback- 9.07E-09 MYC,E2F2,E2F3,E2F1 loop Genes involved in E2F 9 mediated 5.09E-08 TFDP1,E2F2,E2F3,E2F1,RB1,CCNE1 regulation of DNA replication ID signaling 9 9.21E-08 CDK2,RBL1,RBL2,RB1,CCNE1 pathway FOXM1 transcription 9 2.04E-07 CDK2,CDK4,MYC,CCND1,RB1,CCNE1 factor network

133

DNA damage response 9 3.40E-07 MYC,CDKN1A,CDKN1B,CCND2,CCND1,RBL2,CCND3 (only ATM dependent) p53 pathway 9 feedback 4.81E-07 CDK2,MYC,CDKN1A,RBL1,RB1,CCNE1 loops 2 Genes involved in E2F 9 5.49E-07 TFDP1,E2F2,E2F3,E2F1,CCNE1 transcription al targets at G1/S Genes 9 involved in S 1.71E-06 CDK2,CDK4,CDKN1A,CCNE2,CCND1,RB1,CCNE1 Phase 9 G1 phase 3.62E-06 CDK4,E2F5,CCND1 TGF beta 9 Signaling 3.70E-06 MYC,E2F5,E2F4,RBL1,CCND1,TFDP1,RBL2 Pathway 9 Cell cycle 1.61E-05 CCND2,CCND1,CCNE1,CCND3 METS affect on 9 Macrophage 2.70E-05 E2F4,RBL1,RBL2,E2F1 Differentiatio n Genes involved in Cyclin E 9 associated 8.93E-05 CDK2,CDKN1A,CCNE2,RB1,CCNE1 events during G1/S transition Validated targets of C- 9 MYC 1.36E-04 MYC,CDKN1A,CDKN1B,RBL1,CCND1 transcription al repression IL2 signaling events 9 2.37E-04 MYC,CDK6,CCND2,CCND3 mediated by STAT5 9 Adipogenesis 2.63E-04 E2F4,CDKN1A,RBL1,RBL2,E2F1,RB1 Direct p53 9 3.13E-04 CDKN1A,TFDP1,E2F2,E2F3,E2F1,RB1 effectors

134

Integrated 9 Cancer 4.48E-04 CDK2,CDK4,E2F1,RB1 pathway C-MYB transcription 9 5.08E-04 MYC,CDK6,CDKN1A,CDKN1B,CCND1 factor network Integrated Breast 9 5.73E-04 CDK2,CDK4,MYC,CCND1,E2F1,RB1 Cancer Pathway Genes involved in CDC6 9 association 5.88E-04 E2F2,E2F3,E2F1 with the ORC:origin complex Genes involved in Association of licensing 9 7.83E-04 E2F2,E2F3,E2F1 factors with the pre- replicative complex MAP00350 MAOB,MAOA,ADH5,ADH7,ADH6,ALDH1A3,ALDH3B1,ALDH3B2 10 Tyrosine 3.49E-34 ,ALDH3A1,COMT,ADH4,ADH1C,ADH1B,ADH1A metabolism MAP00010 Glycolysis ADH5,ADH7,ADH6,ALDH1A3,ALDH3B1,ALDH3B2,ALDH3A1,AD 10 1.04E-20 Gluconeogen H4,ADH1C,ADH1B,ADH1A esis Genes involved in 10 1.36E-13 ADH7,ADH6,ADH4,ADH1C,ADH1B,ADH1A Ethanol oxidation MAP00120 10 Bile acid 1.65E-13 ADH5,ADH7,ADH6,ADH4,ADH1C,ADH1B,ADH1A biosynthesis Fatty Acid 10 Omega 3.21E-12 ADH7,ADH6,ADH4,ADH1C,ADH1B,ADH1A Oxidation Genes 10 involved in 3.95E-12 MAOB,MAOA,ADH7,ADH6,ADH4,ADH1C,ADH1B,ADH1A Phase 1 - 135

Functionaliza tion of compounds MAP00360 10 Phenylalanin 5.14E-12 MAOB,MAOA,ALDH1A3,ALDH3B1,ALDH3B2,ALDH3A1 e metabolism Genes involved in 10 8.25E-12 MAOB,MAOA,ADH7,ADH6,COMT,ADH4,ADH1C,ADH1B,ADH1A Biological oxidations 5- Hydroxytrypt 10 1.74E-11 MAOB,MAOA,ALDH1A3,ALDH3B1,ALDH3B2,ALDH3A1 amine degredation MAP00340 10 Histidine 2.48E-11 MAOB,MAOA,ALDH1A3,ALDH3B1,ALDH3B2,ALDH3A1 metabolism MAP00071 10 Fatty acid 3.54E-11 ADH5,ADH7,ADH6,ADH4,ADH1C,ADH1B,ADH1A metabolism MAP00561 10 Glycerolipid 4.16E-11 ADH5,ADH7,ADH6,ADH4,ADH1C,ADH1B,ADH1A metabolism tyrosine 10 7.43E-07 MAOB,ALDH3A1,COMT,ADH4 metabolic Tyrosine 10 4.30E-06 MAOA,ALDH3A1,COMT,ADH1A Metabolism Adrenaline and 10 noradrenalin 9.09E-04 MAOB,MAOA,COMT e biosynthesis Genes involved in 11 4.64E-20 TK2,TK1,UPP1,CDA,DPYD,UMPS,TYMP,DHODH,UCK2 Pyrimidine metabolism Pyrimidine 11 1.52E-16 ITPA,TK1,CDA,UPP2,DPYD,UCKL1,TYMP,DHODH Metabolism Fluoropyrimi 11 1.95E-15 TK1,UPP1,CDA,UPP2,DPYD,UMPS,TYMP,UCK2 dine Activity Genes involved in 11 6.79E-15 TK2,TK1,UPP1,CDA,DPYD,UMPS,TYMP,DHODH,UCK2 Metablism of nucleotides

136

Salvage pyrimidine 11 1.63E-14 UPP1,CDA,UPP2,UCKL1,UPRT,UCK2 ribonucleotid es salvages of pyrimidine 11 7.77E-10 TK2,TK1,CDA,TYMP deoxyribonuc leotides MAP00240 11 Pyrimidine 1.33E-09 TK1,CDA,DPYD,UMPS,TYMP,DHODH metabolism salvages of purine and 11 7.42E-07 TK2,TK1,CDA,TYMP pyrimidine nucleotides Genes involved in 11 1.41E-05 UPP1,DPYD,TYMP Pyrimidine catabolism Salvage pyrimidine 11 2.44E-04 TK1,CDA deoxyribonuc leotides (deoxy)ribose 11 phosphate 8.11E-04 CDA,TYMP degradation Genes involved in ANAPC11,ANAPC10,PLK1,CCNB1,CDC26,ANAPC1,ANAPC4,CDC 12 Phosphorylat 5.97E-31 27,CDC16,ANAPC2,CDC23,CDK1,ANAPC7,ANAPC5 ion of the APC/C

Genes ANAPC11,ANAPC10,PLK1,CCNB1,CDC26,CCNB2,ANAPC1,ANAP involved in C4,SMC3,CDC25C,CDC25B,PTTG1,CDC27,CDC16,ANAPC2,STAG 12 2.19E-30 Cell Cycle, 2,CDC20,CDC23,CDK1,STAG1,SMC1A,ANAPC7,ANAPC5,REC8,P Mitotic KMYT1,RAD21

Genes involved in APC/C:Cdc20 ANAPC11,ANAPC10,CCNB1,CDC26,ANAPC1,ANAPC4,CDC27,CD 12 2.68E-30 mediated C16,ANAPC2,CDC20,CDC23,CDK1,ANAPC7,ANAPC5 degradation of Cyclin B

137

Genes involved in Conversion from ANAPC11,ANAPC10,CDC26,ANAPC1,ANAPC4,CDC27,CDC16,AN 12 APC/C:Cdc20 9.06E-25 APC2,CDC20,CDC23,ANAPC7,ANAPC5 to APC/C:Cdh1 in late anaphase Genes involved in Inactivation of APC/C via ANAPC11,ANAPC10,CDC26,ANAPC1,ANAPC4,CDC27,CDC16,AN 12 2.71E-24 direct APC2,CDC20,CDC23,ANAPC7,ANAPC5 inhibition of the APC/C complex Genes involved in Regulation of APC/C ANAPC11,ANAPC10,PLK1,CCNB1,CDC26,ANAPC1,ANAPC4,CDC 12 activators 1.62E-21 27,CDC16,ANAPC2,CDC20,CDC23,CDK1,ANAPC7,ANAPC5 between G1/S and early anaphase Genes ANAPC11,ANAPC10,CCNB1,CDC26,CCNB2,ANAPC1,ANAPC4,CD involved in 12 2.45E-20 C25C,CDC27,CDC16,ANAPC2,CDC20,CDC23,CDK1,ANAPC7,ANA Cell Cycle PC5 Checkpoints Genes involved in Cdc20:Phosp ANAPC11,ANAPC10,CDC26,ANAPC1,ANAPC4,CDC27,CDC16,AN 12 ho-APC/C 3.48E-18 APC2,CDC20,CDC23,CDK1,ANAPC7,ANAPC5 mediated degradation of Cyclin A

PLK1,CCNB1,CCNB2,CDC25C,CDC25B,PTTG1,CDC20,CDK1,SMC 12 Cell cycle 2.18E-15 1A,ESPL1,PTTG2,PKMYT1,CCNB3

Genes involved in Autodegrada ANAPC11,ANAPC10,CDC26,ANAPC1,ANAPC4,CDC27,CDC16,AN 12 9.26E-15 tion of Cdh1 APC2,CDC23,ANAPC7,ANAPC5 by Cdh1:APC/C 138

Genes involved in Cyclin A1 12 associated 4.57E-12 PLK1,CCNB1,CCNB2,CDC25C,CDC25B,CDK1,PKMYT1 events during G2/M transition PLK1 12 signaling 2.51E-08 PLK1,CCNB1,CDC25C,CDC25B,STAG2,CDC20,CDK1 events Genes involved in 12 Mitotic 1.70E-07 PLK1,SMC3,STAG2,CDC20,STAG1,SMC1A,REC8,RAD21 Prometaphas e DNA damage 12 5.34E-07 CCNB1,CCNB2,CDC25C,GADD45G,CDK1,SMC1A,CCNB3 response miRNA regulation of 12 7.30E-07 CCNB1,CCNB2,CDC25C,GADD45G,CDK1,SMC1A,CCNB3 DNA Damage Response Cell Cycle: 12 G2/M 2.58E-06 PLK1,CCNB1,CDC25C,CDC25B,CDK1 Checkpoint Genes involved in 12 2.65E-06 PLK1,CCNB1,CCNB2,CDC25C,CDC25B,CDK1,PKMYT1 G2/M Transition Activation of Src by Protein- 12 5.83E-06 CCNB1,CDC25C,CDC25B,CDK1 tyrosine phosphatase alpha Sonic Hedgehog (SHH) 12 Receptor 5.83E-06 CCNB1,CDC25C,CDC25B,CDK1 Ptc1 Regulates cell cycle Genes involved in 12 1.17E-05 PLK1,SMC3,STAG2,CDC20,STAG1,SMC1A,REC8,RAD21 Mitotic M- M/G1 phases

139

FOXM1 transcription 12 4.34E-05 PLK1,CCNB1,CCNB2,CDC25B,CDK1 factor network Genes involved in HLA-DRB1,HLA-DRA,HLA-DRB5,HLA-DRB3,HLA-DPB1,HLA- Translocation 13 7.90E-31 DOB,HLA-DPA1,HLA-DQA2,HLA-DQB1,HLA-DQA1,HLA- of ZAP-70 to DOA,HLA-DMB Immunologic al synapse Genes involved in HLA-DRB1,HLA-DRA,HLA-DRB5,HLA-DRB3,HLA-DPB1,HLA- Phosphorylat 13 2.82E-30 DOB,HLA-DPA1,HLA-DQA2,HLA-DQB1,HLA-DQA1,HLA- ion of CD3 DOA,HLA-DMB and TCR zeta chains Genes HLA-DRB1,HLA-DRA,HLA-DRB5,HLA-DRB3,HLA-DPB1,HLA- involved in 13 1.52E-29 DOB,HLA-DPA1,HLA-DQA2,HLA-DQB1,HLA-DQA1,HLA- PD-1 DOA,HLA-DMB signaling Genes involved in HLA-DRB1,HLA-DRA,HLA-DRB5,HLA-DRB3,HLA-DPB1,HLA- Generation 13 5.41E-28 DOB,HLA-DPA1,HLA-DQA2,HLA-DQB1,HLA-DQA1,HLA- of second DOA,HLA-DMB messenger molecules Genes HLA-DRB1,HLA-DRA,HLA-DRB5,HLA-DRB3,HLA-DPB1,HLA- involved in 13 1.53E-26 DOB,HLA-DPA1,HLA-DQA2,HLA-DQB1,HLA-DQA1,HLA- Downstream DOA,HLA-DMB TCR signaling

Genes HLA-DRB1,HLA-DRA,HLA-DRB5,HLA-DRB3,HLA-DPB1,HLA- 13 involved in 9.60E-25 DOB,HLA-DPA1,HLA-DQA2,HLA-DQB1,HLA-DQA1,HLA- TCR signaling DOA,HLA-DMB

Genes involved in HLA-DRB1,HLA-DRA,HLA-DRB5,HLA-DRB3,HLA-DPB1,HLA- 13 Costimulatio 3.11E-24 DOB,HLA-DPA1,HLA-DQA2,HLA-DQB1,HLA-DQA1,HLA- n by the DOA,HLA-DMB CD28 family Genes involved in HLA-DRB1,HLA-DRA,HLA-DRB5,HLA-DRB3,HLA-DPB1,HLA- 13 Signaling in 2.94E-15 DOB,HLA-DPA1,HLA-DQA2,HLA-DQB1,HLA-DQA1,HLA- Immune DOA,HLA-DMB system

140

T cell 13 2.28E-06 HLA-DPA1,HLA-DQA2,HLA-DQA1,HLA-DOA,HLA-DMB activation Cytoskeletal ACTG1,MYH3,MYH2,MYH4,MYH1,MYH11,MYH6,MYH7,MYH8, 14 regulation by 2.96E-17 MYLK,MYLK3,MYLK2,ACTB Rho GTPase Genes involved in VCL,MYL9,MYH3,MYL12B,MYH11,MYH6,MYH8,MYLK,ACTN3,A 14 3.44E-15 Muscle CTN2,MYL2 contraction Inflammation mediated by chemokine ACTG1,MYH3,MYH2,MYH4,MYH1,MYH11,MYH6,MYH7,MYH8, 14 9.26E-13 and cytokine RHOA,MYLK,MYLK3,MYLK2,ACTB signaling pathway Striated 14 Muscle 1.64E-12 ACTG1,MYL9,MYH3,MYH6,ACTN4,MYH8,ACTN3,ACTN2,MYL2 Contraction Nicotinic acetylcholine ACTG1,MYH3,MYH2,MYH4,MYH1,MYH11,MYH6,MYH7,MYH8, 14 receptor 2.42E-12 MYH15,ACTB signaling pathway Cell to Cell 14 Adhesion 4.14E-12 CTNNA3,VCL,CTNNA1,CTNNA2,ACTN1,ACTN3,ACTN2 Signaling Genes involved in 14 Striated 2.67E-07 MYH3,MYH6,MYH8,ACTN3,ACTN2,MYL2 Muscle Contraction Genes involved in 14 Smooth 4.75E-06 VCL,MYL9,MYL12B,MYH11,MYLK Muscle Contraction Integrin 14 signalling 2.14E-05 VCL,ACTG1,ACTN1,ACTN4,RHOA,ACTN3,ACTN2,ACTB pathway Wnt signaling CTNNA3,CTNNA1,MYH3,MYH2,MYH4,MYH1,CTNNA2,MYH6,M 14 2.14E-05 pathway YH7,MYH8 Integrin 14 Signaling 5.40E-05 VCL,ACTN1,RHOA,ACTN3,ACTN2 Pathway

141

Stabilization and expansion of 14 the E- 8.00E-05 VCL,CTNNA1,ACTN1,RHOA,MYL2 cadherin adherens junction uCalpain and 14 friends in Cell 1.01E-04 ACTN1,RHOA,ACTN3,ACTN2 spread Regulation of 14 Actin 1.87E-04 VCL,ACTG1,ACTN1,RHOA,MYLK,IQGAP1,ACTB Cytoskeleton Genes involved in Sema4D 14 induced cell 3.46E-04 MYL9,MYL12B,MYH11,RHOA migration and growth- cone collapse Genes involved in 14 Sema4D in 7.62E-04 MYL9,MYL12B,MYH11,RHOA semaphorin signaling Nicotinate and 15 1.93E-09 NAMPT,BST1,NNT,NNMT Nicotinamide Metabolism pyridine 15 nucleotide 1.88E-05 CD38,BST1 cycling Myometrial Relaxation 16 and 1.94E-07 ATF4,ATF1,ATF3,PRKACB,PRKACA,CREB1,ATF2 Contraction Pathways Repression of Pain Sensation by 16 the 7.34E-07 PRKACB,CREM,CREB1,PRKACG Transcription al Regulator DREAM Apoptosis 16 signaling 1.29E-06 ATF4,ATF1,ATF3,CREM,CREB1,ATF2 pathway 142

Histamine H2 receptor 16 mediated 7.72E-06 PRKACB,PRKACA,PRKACG,PRKX signaling pathway Metabotropi c glutamate 16 receptor 9.19E-06 PRKACB,PRKACA,PRKACG,PRKX group I pathway Genes involved in 16 1.09E-05 PRKACB,PRKACA,CREB1,PRKACG CaM pathway Genes involved in 16 3.77E-05 PRKACB,PRKACA,CREB1,PRKACG PLC-gamma1 signalling Genes involved in 16 PLC beta 5.30E-05 PRKACB,PRKACA,CREB1,PRKACG mediated events Beta2 adrenergic 16 receptor 8.83E-05 PRKACB,PRKACA,PRKACG,PRKX signaling pathway 5HT1 type receptor 16 mediated 8.83E-05 PRKACB,PRKACA,PRKACG,PRKX signaling pathway Beta1 adrenergic 16 receptor 8.83E-05 PRKACB,PRKACA,PRKACG,PRKX signaling pathway Metabotropi c glutamate 16 receptor 1.16E-04 PRKACB,PRKACA,PRKACG,PRKX group II pathway

143

Genes involved in Hormone- sensitive 16 1.36E-04 PRKACB,PRKACA,PRKACG lipase (HSL)- mediated triacylglycero l hydrolysis Transcription Regulation by 16 1.77E-04 PRKACB,CREB1,PRKACG Methyltransf erase of CARM1 Muscarinic acetylcholine receptor 2 16 2.79E-04 PRKACB,PRKACA,PRKACG,PRKX and 4 signaling pathway Genes involved in 16 4.18E-04 PRKACB,PRKACA,PRKACG PKA activation Heterotrimer ic G-protein signaling pathway-Gi 16 4.70E-04 PRKACB,PRKACA,CREM,CREB1,PRKACG alpha and Gs alpha mediated pathway Metabotropi c glutamate 16 receptor 4.75E-04 PRKACB,PRKACA,PRKACG,PRKX group III pathway Genes involved in 16 Unfolded 5.95E-04 XBP1,ATF4,ATF3 Protein Response

144

Table 7 –Enriched pathways in 17 modules of the network with second-order Markov dynamics.

module Pathway P-value Hit in gene list

POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR MAP00230 2H,POLR2J2,POLR2I,POLR2J,POLD1,POLD2,POLE,POLR2A,POLR2B 1 Purine 9.75E-53 ,POLA1,ADCY2,ADCY3,ADCY1,PDE4D,ADCY9,ADCY8,ADCY7,ADCY metabolism 6,PDE1A,PDE4A,PDE4B,PDE4C,PKLR,PKM,AK2,POLR1B,AK1,RRM2 ,NME2,RRM1,NME1

protein kinase PDE11A,ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,PDE4D,AD 1 A (PKA) 1.53E-33 CY9,ADCY8,PDE7A,ADCY7,PDE8A,ADCY6,PDE1A,PDE1C,PDE2A,P signaling DE3A,PDE3B,PDE4A,PDE4B,PDE4C,PDE7B,PDE10A

145

MAP00240 POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 Pyrimidine 2.92E-30 2H,POLR2J2,POLR2I,POLR2J,POLD1,POLD2,POLE,POLR2A,POLR2B metabolism ,POLA1,POLR1B,RRM2,NME2,RRM1,NME1

ADCY4,POLR2F,POLR2G,ADCY5,ADCY2,PDE4D,ADCY8,ADCY6,PDE purine 1 1.72E-28 1A,PDE2A,PDE3A,PDE4A,PDE4B,ENTPD1,PDE7B,PKLR,PKM,PDE1 metabolic 0A,AK2,AK1,NME3

Eukaryotic POLR3K,POLR2K,POLR2C,POLR2E,POLR2F,POLR2G,POLR2H,POLR 1 Transcription 9.64E-25 2I,POLR2J,POLR2A,POLR2B,POLR1D,POLR1A,POLR3E,POLR3B,PO Initiation LR3D,POLR1B,POLR3H,POLR1E

MAP03020 POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 RNA 2.76E-23 2H,POLR2J2,POLR2I,POLR2J,POLR2A,POLR2B,POLR1B polymerase

Genes involved in POLD3,POLD4,POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F, 1 Transcription- 2.44E-22 POLR2G,POLR2H,POLR2I,POLR2J,POLD1,POLD2,POLE,POLE2,POL coupled NER R2A,POLR2B (TC-NER)

146

De novo GUK1,AK5,NME7,NME1- 1 purine 4.65E-22 NME2,AK4,AK2,AK1,RRM2,NME2,RRM1,NME1,RRM2B,NME6,N biosynthesis ME3,NME4,NME5

Genes involved in POLD3,POLD4,POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F, 1 Nucleotide 2.63E-21 POLR2G,POLR2H,POLR2I,POLR2J,POLD1,POLD2,POLE,POLE2,POL Excision R2A,POLR2B Repair

Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 Viral 4.06E-20 2H,POLR2I,POLR2J,POLR2A,POLR2B Messenger RNA Synthesis

Genes involved in G PDE11A,ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,PDE4D,AD 1 alpha (s) 5.65E-20 CY9,ADCY8,PDE7A,ADCY7,PDE8A,ADCY6,PDE1A,PDE2A,PDE3A,P signalling DE3B,PDE4A,PDE4B,PDE4C,PDE7B,PDE10A events

Genes involved in RNA POLR2L,POLR2K,POLR2E,POLR2F,POLR2H,POLR3E,POLR3A,POLR 1 5.53E-19 Polymerase III 3B,POLR3D,POLR3F,POLR3H Chain Elongation

purine nucleotides GUK1,AK5,AK7,NME7,AK2,AK1,RRM2,NME2,RRM1,NME1,RRM2 1 de 1.48E-18 B,NME6,NME3,NME4 novo biosynthesis I

147

Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 7.99E-18 MicroRNA 2H,POLR2I,POLR2J,POLR2A,POLR2B biogenesis

Genes involved in Adenylate ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY 1 5.12E-17 cyclase 7,ADCY6 activating pathway

his+purine+py GUK1,AK5,AK7,NME7,AK2,AK1,RRM2,NME2,RRM1,NME1,RRM2 1 rimidine 2.08E-16 B,NME6,NME3,NME4 biosynthesis

Genes involved in RNA POLR2L,POLR2K,POLR2E,POLR2F,POLR2H,POLR3E,POLR3A,POLR 1 5.45E-16 Polymerase III 3B,POLR3D,POLR3F,POLR3H Transcription Termination

Genes involved in Abortive elongation of POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 5.57E-16 HIV-1 2H,POLR2I,POLR2J,POLR2A,POLR2B transcript in the absence of Tat

148

G Protein ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,PDE4D,ADCY9,ADCY8,PDE7A 1 Signaling 1.71E-15 ,ADCY7,PDE8A,ADCY6,PDE1A,PDE1C,PDE4A,PDE4B,PDE4C,PDE7B Pathways

Genes involved in RNA Pol II CTD POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 phosphorylati 3.87E-15 2H,POLR2I,POLR2J,POLR2A,POLR2B on and interaction with CE

Genes ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY 1 involved in 3.87E-15 7,ADCY6,PDE1A,PDE1C CaM pathway

Genes involved in G ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY 1 alpha (z) 4.54E-15 7,ADCY6 signalling events

Genes involved in RNA Polymerase III POLR2L,POLR2K,POLR2E,POLR2F,POLR2H,POLR3E,POLR3A,POLR 1 7.21E-15 Transcription 3B,POLR3D,POLR3F,POLR3H Initiation From Type 2 Promoter

149

Genes POLD3,POLD4,POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F, 1 involved in 9.48E-15 POLR2G,POLR2H,POLR2I,POLR2J,POLD1,POLD2,POLE,POLE2,POL DNA Repair R2A,POLR2B

Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 Dual incision 1.20E-14 2H,POLR2I,POLR2J,POLR2A,POLR2B reaction in TC- NER

Genes involved in RNA Polymerase III POLR2L,POLR2K,POLR2E,POLR2F,POLR2H,POLR3E,POLR3A,POLR 1 2.97E-14 Transcription 3B,POLR3D,POLR3F,POLR3H Initiation From Type 3 Promoter

Genes POLR1C,POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2 1 involved in 3.19E-14 G,POLR2H,POLR2I,POLR2J,POLR2A,POLR2B,POLR1D,POLR1A,POL Transcription R3E,POLR3A,POLR3B,POLR3D,POLR3F,POLR1B,POLR3H

150

Genes involved in Tat-mediated POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 HIV-1 3.35E-14 2H,POLR2I,POLR2J,POLR2A,POLR2B elongation arrest and recovery

Genes ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY 1 involved in 8.58E-14 7,ADCY6 PKA activation

Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 8.58E-14 mRNA 2H,POLR2I,POLR2J,POLR2A,POLR2B Processing

Genes involved in Formation of POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 8.58E-14 the Early 2H,POLR2I,POLR2J,POLR2A,POLR2B Elongation Complex

Genes involved in ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,PDE4D,ADCY9,ADCY 1 1.05E-13 Opioid 8,ADCY7,ADCY6,PDE1A,PDE1C,PDE4A,PDE4B,PDE4C Signalling

151

Genes involved in Synthesis and interconversio 1 1.91E-13 GUK1,AK5,AK2,AK1,RRM2,NME2,RRM1,NME1,RRM2B,NME4 n of nucleotide di- and triphosphates

Genes involved in ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY 1 3.09E-13 PLC-gamma1 7,ADCY6,PDE1A,PDE1C signalling

de novo biosynthesis 1 of pyrimidine 3.23E-13 NME7,RRM2,NME2,RRM1,NME1,RRM2B,NME6,NME3,NME4 deoxyribonucl eotides

Genes involved in ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY 1 PLC beta 9.75E-13 7,ADCY6,PDE1A,PDE1C mediated events

Genes involved in RNA POLR2L,POLR2K,POLR2E,POLR2F,POLR2H,POLR3E,POLR3A,POLR 1 1.37E-12 Polymerase III 3B,POLR3D,POLR3F,POLR3H Transcription Initiation

152

Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 HIV-1 1.40E-12 2H,POLR2I,POLR2J,POLR2A,POLR2B Transcription Initiation

LPA4- mediated 1 2.22E-12 ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY7,ADCY6 signaling events

Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 HIV-1 2.77E-12 2H,POLR2I,POLR2J,POLR2A,POLR2B Transcription Elongation

Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 mRNA Splicing 3.84E-12 2H,POLR2I,POLR2J,POLR2A,POLR2B - Minor Pathway

Genes involved in POLR2L,POLR2K,POLR2E,POLR2F,POLR2H,POLR3E,POLR3A,POLR 1 RNA 1.08E-11 3B,POLR3D,POLR3F,POLR3H Polymerase III Transcription

G protein signaling via 1 2.10E-11 ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY7,ADCY6 Galphas family

153

Genes involved in G(s)-alpha ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY 1 mediated 3.40E-11 7,ADCY6 events in glucagon signalling

Genes involved in POLD3,POLA2,POLD4,POLD1,POLD2,POLE,POLE2,POLA1,PRIM1,P 1 5.24E-11 Extension of RIM2 Telomeres

De novo pyrimidine 1 deoxyribonucl 1.31E-10 NME1-NME2,RRM2,NME2,RRM1,NME1,RRM2B,NME3,NME4 eotide biosynthesis

Genes involved in 1 1.31E-10 POLD3,POLA2,POLD4,POLD1,POLD2,POLA1,PRIM1,PRIM2 Polymerase switching

Genes involved in 1 Removal of 1.31E-10 POLD3,POLA2,POLD4,POLD1,POLD2,POLA1,PRIM1,PRIM2 the Flap Intermediate

154

Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 Transcription 3.34E-10 2H,POLR2I,POLR2J,POLR2A,POLR2B of the HIV genome

Genes involved in Glucagon ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY 1 4.96E-10 signaling in 7,ADCY6 metabolic regulation

Genes involved in RNA Polymerase I, POLR1C,POLR2L,POLR2K,POLR2E,POLR2F,POLR2H,POLR1D,POLR 1 RNA 7.37E-10 1A,POLR3E,POLR3A,POLR3B,POLR3D,POLR3F,POLR1B,POLR3H Polymerase III, and Mitochondrial Transcription

DNA POLD3,POLA2,POLD4,POLD1,POLD2,POLE,POLE2,POLA1,PRIM1,P 1 3.98E-09 Replication RIM2

Genes involved in 1 5.21E-09 POLD3,POLA2,POLD4,POLD1,POLD2,POLA1,PRIM1,PRIM2 Lagging Strand Synthesis

155

salvages of pyrimidine 1 3.52E-08 NME7,NME2,NME1,NME6,NME3,NME4 ribonucleotide s

Pyrimidine 1 4.28E-08 CANT1,PNPT1,ITPA,POLR3C,POLR3G,POLR3F,RRM1,NME6 Metabolism

Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 6.19E-08 Late Phase of 2H,POLR2I,POLR2J,POLR2A,POLR2B HIV Life Cycle

Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 RNA 8.05E-08 2H,POLR2I,POLR2J,POLR2A,POLR2B Polymerase II Transcription

Genes involved in PDE11A,ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,PDE4D,AD Downstream 1 9.25E-08 CY9,ADCY8,PDE7A,ADCY7,PDE8A,ADCY6,PDE1A,PDE2A,PDE3A,P events in DE3B,PDE4A,PDE4B,PDE4C,PDE7B,PDE10A GPCR signaling

de novo biosynthesis 1 of pyrimidine 1.90E-07 NME7,NME2,NME1,NME6,NME3,NME4 ribonucleotide s

156

Genes involved in Influenza Viral POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 RNA 2.17E-07 2H,POLR2I,POLR2J,POLR2A,POLR2B Transcription and Replication

Genes involved in 1 2.95E-07 POLD3,POLA2,POLD4,POLD1,POLD2,POLA1,PRIM1,PRIM2 DNA strand elongation

Genes POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 involved in 3.07E-07 2H,POLR2I,POLR2J,POLR2A,POLR2B HIV Life Cycle

Genes involved in TRKA ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY 1 signalling 3.07E-07 7,ADCY6,PDE1A,PDE1C from the plasma membrane

Nucleotide 1 3.09E-07 POLD1,POLA1,NME1-NME2,RRM2,NME2,RRM1,RRM2B Metabolism

Genes POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 involved in 4.81E-07 2H,POLR2I,POLR2J,POLR2A,POLR2B mRNA Splicing

157

Genes involved in 1 1.25E-06 GUK1,AK5,AK2,AK1,RRM2,NME2,RRM1,NME1,RRM2B,NME4 Metablism of nucleotides

GABA-B 1 receptor II 1.72E-06 ADCY4,ADCY5,ADCY2,ADCY1,ADCY9,ADCY8,ADCY7,ADCY6 signaling

Genes involved in Repair synthesis of 1 1.99E-06 POLD3,POLD4,POLD1,POLD2,POLE,POLE2 patch ~27-30 bases long by DNA polymerase

Endothelin ADCY10,ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY 1 signaling 2.16E-06 7,ADCY6 pathway

Genes involved in POLD3,POLA2,POLD4,POLD1,POLD2,POLE,POLE2,POLA1,PRIM1,P 1 2.81E-06 Telomere RIM2 Maintenance

Purine 1 3.32E-06 GUK1,ADCY1,PDE7B,PDE10A,AK1,RRM1,NME6 Metabolism

158

Genes involved in Elongation POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 and 6.41E-06 2H,POLR2I,POLR2J,POLR2A,POLR2B Processing of Capped Transcripts

1 Endothelins 6.79E-06 ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY7,ADCY6

Genes involved in POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 8.23E-06 Influenza Life 2H,POLR2I,POLR2J,POLR2A,POLR2B Cycle

Genes involved in Processing of POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 Capped 8.93E-06 2H,POLR2I,POLR2J,POLR2A,POLR2B Intron- Containing Pre-mRNA

LPA receptor 1 mediated 1.03E-05 ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,ADCY9,ADCY8,ADCY7,ADCY6 events

Genes involved in POLD3,POLA2,POLD4,POLD1,POLD2,POLE,POLE2,POLA1,PRIM1,P 1 1.17E-05 Synthesis of RIM2 DNA

159

Genes involved in RNA 1 1.47E-05 POLR1C,POLR2K,POLR2H,POLR1D,POLR1A,POLR1B Polymerase I Promoter Escape

Genes involved in RNA 1 2.04E-05 POLR1C,POLR2K,POLR2H,POLR1D,POLR1A,POLR1B PolymeraseTr anscription Termination

Genes involved in Formation POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 and 2.63E-05 2H,POLR2I,POLR2J,POLR2A,POLR2B Maturation of mRNA Transcript

De novo pyrimidine 1 3.04E-05 NME1-NME2,NME2,NME1,NME3,NME4 ribonucleotide s biosythesis

Genes POLD3,POLA2,POLD4,POLD1,POLD2,POLE,POLE2,POLA1,PRIM1,P 1 involved in S 4.78E-05 RIM2 Phase

160

Genes involved in RNA 1 4.93E-05 POLR1C,POLR2K,POLR2H,POLR1D,POLR1A,POLR1B PolymeraseTr anscription Initiation

Genes POLR2L,POLR2K,POLR2C,POLR2D,POLR2E,POLR2F,POLR2G,POLR 1 involved in 1.99E-04 2H,POLR2I,POLR2J,POLR2A,POLR2B HIV Infection

Genes involved in Activation of 1 2.06E-04 POLA2,POLE,POLE2,POLA1,PRIM1,PRIM2 the pre- replicative complex

Myometrial Relaxation ADCY4,ADCY5,ADCY2,ADCY3,ADCY1,PDE4D,ADCY9,ADCY8,ADCY7 1 and 2.74E-04 ,ADCY6,PDE4B Contraction Pathways

Genes involved in 1 Global 3.74E-04 POLD3,POLD4,POLD1,POLD2,POLE,POLE2 Genomic NER (GG-NER)

Genes involved in 1 RNA 4.50E-04 POLR1C,POLR2K,POLR2H,POLR1D,POLR1A,POLR1B PolymeraseCh ain Elongation

161

salvages of purine and 1 5.54E-04 GUK1,AK5,AK7,AK2,AK1 pyrimidine nucleotides

Genes involved in Removal of 1 the Flap 7.61E-04 POLD3,POLD4,POLD1,POLD2 Intermediate from the C- strand

Genes involved in NT5C,HPRT1,NT5C1A,IMPDH2,ADA,IMPDH1,NT5C2,ADK,GMPS,A 2 1.53E-31 Purine PRT,PNP,NT5C1B,GDA,NT5E metabolism

Genes involved in NT5C,HPRT1,NT5C1A,IMPDH2,ADA,IMPDH1,NT5C2,NT5C3A,ADK 2 3.53E-31 Metablism of ,GMPS,NT5M,APRT,PNP,NT5C1B,GDA,NT5E nucleotides

MAP00230 2 Purine 1.08E-14 HPRT1,IMPDH2,ADA,IMPDH1,ADK,GMPS,APRT,PNP,NT5C1B,GDA metabolism

Purine 2 2.18E-12 HPRT1,ADA,IMPDH1,NT5C2,GMPS,APRT,PNP Metabolism

162

Genes involved in 2 2.74E-10 NT5C,NT5C1A,NT5C3A,NT5M,NT5E Pyrimidine catabolism

Genes involved in 2 8.59E-10 HPRT1,ADA,ADK,APRT,PNP Purine salvage reactions

Adenine and hypoxanthine 2 2.23E-08 HPRT1,ADA,APRT,PNP salvage pathway

Genes involved in 2 2.82E-08 NT5C,NT5C1A,NT5C3A,NT5M,NT5E Pyrimidine metabolism

purine 2 1.00E-06 HPRT1,ADA,ADK,GDA,NT5E metabolic

Xanthine and guanine 2 1.38E-06 HPRT1,PNP,GDA salvage pathway

purine nucleotides 2 de 5.64E-05 IMPDH2,IMPDH1,GMPS novo biosynthesis II

163

Genes involved in Purine ribonucleosid 2 5.64E-05 IMPDH2,IMPDH1,GMPS e monophospha te biosynthesis

Pyrimidine 2 7.72E-04 ITPA,NT5C2,PNP Metabolism

MAP00562 ITPKB,ITPKA,PIP4K2B,INPP4B,PLCD1,PLCG1,PLCB2,PLCG2,PIK3C Inositol 3 3.59E-40 2A,PIK3C2B,PIK3CA,PIK3CB,PIK3C2G,INPPL1,PIK3CG,INPP4A,INPP phosphate 1,PI4KA,IMPA1 metabolism

phosphatidyli ITPKA,PTEN,PLCB4,PLCG1,PLCB3,PLCB2,PLCG2,PIK3CA,PIK3CB,SY nositol 3- 3 2.30E-33 NJ2,INPPL1,PIK3C3,INPP5D,PIK3R1,PIK3CD,PI4KA,PIK3R2,PLCB1,I kinase-Akt MPA1,PIK3R3 signaling

Endothelin ITPR3,ITPR2,ITPR1,PLCB4,PLCB3,PLCB2,PIK3C2A,PIK3C2B,PIK3CA, 3 signaling 5.21E-21 PIK3CB,PIK3C3,PIK3CG,PIK3R1,PIK3CD,PIK3R2,PLCB1,PIK3R5,PIK3 pathway R3

inositol ITPKA,PTEN,PLCB4,PLCG1,PLCB3,PLCB2,PLCG2,PIK3CB,SYNJ2,PI 3 phosphate 7.32E-21 K3C3,PI4KA,PLCB1,IMPA1 metabolic

164

Inositol ITPKA,ITPK1,INPP5J,PIK3CA,PIK3C3,INPP4A,INPP1,PI4KA,PIKFY 3 5.97E-19 Metabolism VE,IMPA1,PLCD3

Histamine H1 receptor PLCE1,ITPR3,ITPR2,ITPR1,PLCD1,PLCB4,PLCG1,PLCB3,PLCB2,PLCG 3 mediated 4.00E-18 2,PLCZ1,PLCB1,PLCD3,PLCD4 signaling pathway

Insulin/IGF pathway- PTEN,PIK3C2A,PIK3C2B,PIK3CA,PIK3CB,INPPL1,PIK3C3,PIK3CG,PI 3 protein kinase 2.03E-17 K3R1,PIK3CD,PIK3R2,PIK3R5,PIK3R3 B signaling cascade

Axon guidance PLCG1,PLCG2,PIK3C2A,PIK3C2B,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PI 3 mediated by 2.38E-17 K3CD,PIK3R2,PIK3R5,PIK3R3 netrin

Inflammation mediated by PLCE1,ITPR3,ITPR2,ITPR1,PTEN,PLCD1,PLCB4,PLCG1,PLCB3,PLCB chemokine 3 1.37E-15 2,PLCG2,PIK3CA,PIK3CB,INPPL1,PIK3CG,PIK3CD,PLCZ1,PLCB1,PL and cytokine CD3,PLCD4 signaling pathway

Regulation of PIP4K2B,PIP5K1B,PIP5K1A,PIK3C2A,PIK3C2B,PIK3CA,PIK3CB,PIK3 3 Actin 1.43E-15 C2G,PIK3C3,PIK3CG,PIK3R1,PIK3CD,PIK3R2,PIP4K2A,PIP4K2C,PIP Cytoskeleton 5K1C,PIK3R5,PIK3R3

165

p53 pathway PTEN,PIK3C2A,PIK3C2B,PIK3CA,PIK3CB,PIK3C2G,PIK3C3,PIK3CG,P 3 feedback 1.81E-15 IK3R1,PIK3CD,PIK3R2,PIK3R5,PIK3R3 loops 2

Hypoxia PTEN,PIK3C2A,PIK3C2B,PIK3CA,PIK3CB,PIK3C3,PIK3CG,PIK3R1,PI 3 response via 3.18E-15 K3CD,PIK3R2,PIK3R3 HIF activation

PLCE1,PLCD1,PLCB4,PLCG1,PLCB3,PLCB2,PLCG2,PLCZ1,PLCB1,PL 3 lipases 5.65E-13 CD4

Class I PI3K PTEN,PLCG1,PLCG2,PIK3CA,PIK3CB,INPPL1,PIK3CG,PIK3R1,PIK3C 3 signaling 7.26E-13 D,PIK3R2,PIK3R5,PIK3R3 events

VEGF signaling PLCG1,PLCG2,PIK3C2A,PIK3C2B,PIK3CA,PIK3CB,PIK3C3,PIK3CG,PI 3 2.87E-12 pathway K3R1,PIK3CD,PIK3R2,PIK3R3

PTEN,PIK3C2A,PIK3C2B,PIK3CA,PIK3CB,PIK3C2G,PIK3C3,PIK3CG,P 3 p53 pathway 3.41E-12 IK3R1,PIK3CD,PIK3R2,PIK3R5,PIK3R3

G alpha i ITPKB,ITPKA,ITPR3,ITPR2,ITPR1,PLCB4,PLCB3,PLCB2,PIK3CB,PLC 3 8.45E-12 Pathway B1

Oxytocin receptor PLCE1,PLCD1,PLCB4,PLCG1,PLCB3,PLCB2,PLCG2,PLCZ1,PLCB1,PL 3 mediated 4.21E-11 CD3,PLCD4 signaling pathway

166

Thyrotropin- releasing hormone PLCE1,PLCD1,PLCB4,PLCG1,PLCB3,PLCB2,PLCG2,PLCZ1,PLCB1,PL 3 6.41E-11 receptor CD3,PLCD4 signaling pathway

PDGF ITPR3,ITPR2,ITPR1,PLCG1,PLCG2,PIK3CA,PIK3CB,PIK3C3,PIK3CG,P 3 signaling 1.30E-10 IK3R1,PIK3CD,PIK3R2,PIK3R5,PIK3R3 pathway

5HT2 type receptor PLCE1,PLCD1,PLCB4,PLCG1,PLCB3,PLCB2,PLCG2,PLCZ1,PLCB1,PL 3 mediated 2.49E-10 CD3,PLCD4 signaling pathway

CXCR4- mediated PTEN,PLCB3,PLCB2,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R 3 1.36E-09 signaling 2,PLCB1,PIK3R5,PIK3R3 events

Genes related 3 6.92E-09 ITPR3,ITPR2,ITPR1,PTEN,PIK3CA,INPPL1,PIK3CG,PIK3R1,PIK3CD to chemotaxis

PI3 kinase PTEN,PIK3CA,PIK3CB,INPPL1,PIK3R1,INPP5A,PIK3R2,PIK3R5,PIK3 3 2.85E-08 pathway R3

Insulin PTEN,PIK3C2A,PIK3CA,PIK3CB,PIK3C2G,INPPL1,PIK3C3,PIK3CG,PI 3 2.86E-08 Signaling K3R1,PIK3CD,INPP4A,PIK3R2,PIK3R3

Adrenergic 3 2.94E-08 ITPKB,ITPKA,ITPR3,ITPR2,ITPR1,PIK3CA,PIK3R1,PIK3CD Pathway

167

EGF receptor (ErbB1) 3 2.94E-08 PLCG1,PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,PIP5K1C,PIK3R3 signaling pathway

T cell ITPR1,PLCG1,PIK3CA,PIK3CB,PIK3C3,PIK3CG,PIK3R1,PIK3CD,PIK3 3 1.19E-07 activation R2,PIK3R3

PLCG1,PLCG2,PIK3C2A,PIK3C2B,PIK3CA,PIK3CB,PIK3C3,PIK3CG,PI 3 Angiogenesis 3.18E-07 K3R1,PIK3CD,PIK3R2,PIK3R3

Members of the BCR 3 3.95E-07 ITPR3,ITPR2,ITPR1,PLCG2,PIK3CA,INPP5D,PIK3R1,PIK3CD signaling pathway

AMPK PIK3CA,PIK3CB,PIK3C3,PIK3CG,PIK3R1,PIK3CD,PIK3R2,PLCB1,PIK 3 4.20E-07 signaling 3R3

Nongenotropi 3 c Androgen 6.65E-07 PLCG1,PLCB3,PLCB2,PLCG2,PIK3CA,PIK3R1,PLCB1 signaling

Nephrin/Neph 1 signaling in 3 8.47E-07 PLCG1,PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,PIK3R3 the kidney podocyte

168

Genes involved in Regulation of 3 Insulin 1.57E-06 ITPR3,ITPR2,ITPR1,PLCB3,PLCB2,PLCB1 Secretion by Free Fatty Acids

Alpha adrenergic 3 receptor 2.19E-06 PLCE1,ITPR1,PLCB4,PLCB3,PLCB2,PLCB1 signaling pathway

Genes involved in Regulation of 3 3.00E-06 ITPR3,ITPR2,ITPR1,PLCB3,PLCB2,PLCB1 Insulin Secretion by Acetylcholine

Genes involved in 3 PLC beta 3.07E-06 ITPR3,ITPR2,ITPR1,PLCB4,PLCB3,PLCB2,PLCB1 mediated events

EGF receptor PLCG1,PLCG2,PIK3C2A,PIK3C2B,PIK3CA,PIK3CB,PIK3C3,PIK3CG,PI 3 signaling 3.94E-06 K3CD,PIK3R5 pathway

169

Genes involved in Collagen- 3 4.03E-06 PLCG2,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3R5 mediated activation cascade

B cell 3 4.09E-06 ITPR3,ITPR2,ITPR1,PLCG2,PIK3CA,PIK3CB,PIK3CG,PIK3CD activation

Internalization 3 4.48E-06 PIK3CA,PIK3CB,PIK3R1,SYNJ1,PIK3CD,PIK3R2,PIK3R3 of ErbB1

B Cell Receptor PIP5K1B,PIP5K1A,PLCG1,PLCG2,PIK3CG,INPP5D,PIK3R1,PIK3R2,PI 3 6.47E-06 Signaling P5K1C Pathway G alpha q 3 1.16E-05 ITPKB,ITPKA,ITPR3,ITPR2,ITPR1,PIK3CB Pathway TRAIL 3 signaling 1.46E-05 PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,PIK3R3 pathway

Genes involved in TRKA 3 signalling 1.76E-05 ITPR3,ITPR2,ITPR1,PTEN,PLCG1,PIK3CA,PIK3CB,PIK3R1,PIK3R2 from the plasma membrane

Genes ITPR3,ITPR2,ITPR1,DGKA,PLCG1,PLCG2,PIK3CA,PIK3CB,PIK3CG,IN 3 involved in 2.25E-05 PP5D,PIK3R1,PIK3R2,PIK3R5 Hemostasis

FGF signaling PLCG1,PLCG2,PIK3C2A,PIK3C2B,PIK3CA,PIK3CB,PIK3C3,PIK3CG, 3 3.38E-05 pathway PIK3CD

170

Genes related to PIP3 3 4.18E-05 ITPR3,ITPR2,ITPR1,PTEN,PLCG2,PIK3CA signaling in B lymphocytes

3 Wnt signaling 4.18E-05 PLCB4,PLCG1,PLCB3,PLCB2,PLCG2,PLCB1

MicroRNAs in 3 cardiomyocyt 5.84E-05 PLCB2,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,PIK3R3 e hypertrophy

ErbB4 3 signaling 6.06E-05 PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,PIK3R3 events

Genes involved in G ITPR3,ITPR2,ITPR1,DGKA,PLCB4,PLCB3,PLCB2,PIK3CA,PIK3R1,PLC 3 alpha (q) 6.08E-05 B1 signalling events

Integrin PIK3C2A,PIK3C2B,PIK3CA,PIK3CB,PIK3C3,PIK3CG,PIK3R1,PIK3CD, 3 signalling 6.85E-05 PIK3R2,PIK3R3 pathway

Wnt/Ca2+/cyc 3 lic GMP 7.03E-05 ITPKB,ITPKA,ITPR3,ITPR2,ITPR1 signaling.

FAS (CD95) 3 signaling 1.01E-04 PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,PIK3R3 pathway

171

Genes involved in ITPR3,ITPR2,ITPR1,DGKA,PLCG2,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PI 3 1.09E-04 Platelet K3R5 Activation

B Cell Antigen 3 1.19E-04 ITPKB,ITPKA,PLCG2,PIK3CA,PIK3R1,PIK3CD Receptor

Genes 3 involved in 1.30E-04 PTEN,PLCG1,PIK3CA,PIK3CB,INPP5D,PIK3R1,PIK3R2 TCR signaling

CXCR3- mediated 3 2.17E-04 PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,PIK3R3 signaling events

ErbB2/ErbB3 3 signaling 2.17E-04 PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,PIK3R3 events

Genes involved in Downstream 3 2.17E-04 PLCG1,PIK3CA,PIK3CB,PIK3C3,PIK3R1,PIK3R2 signaling of activated FGFR

Toll-like receptor 3 2.60E-04 PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,PIK3R5,PIK3R3 signaling pathway

EGF/EGFR PLCE1,PTEN,PLCG1,PIK3C2B,INPPL1,INPP5D,PIK3R1,SYNJ1,PIK3R 3 Signaling 2.65E-04 2 Pathway

172

Genes involved in ITPR3,ITPR2,ITPR1,DGKA,PLCG2,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PI 3 2.95E-04 Formation of K3R5 Platelet plug

Genes involved in 3 3.74E-04 PTEN,PIK3CA,PIK3CB,INPP5D,PIK3R1,PIK3R2 Downstream TCR signaling

Alzheimers 3 5.59E-04 ITPR3,ITPR2,ITPR1,PLCB4,PLCB3,PLCB2,PLCB1 Disease

Genes involved in G- 3 protein 5.65E-04 PLCB3,PLCB2,PIK3CG,PLCB1,PIK3R5 beta:gamma signalling

Genes involved in 3 7.82E-04 ITPR3,ITPR2,ITPR1,PLCB4,PLCB3,PLCB2,PLCB1 Opioid Signalling

Genes CLDN2,CLDN20,CLDN6,CLDN19,CLDN8,CLDN1,CLDN23,CLDN7,CL involved in 4 1.03E-48 DN3,CLDN4,CLDN17,CLDN11,CLDN15,CLDN18,CLDN16,CLDN14,C Tight junction LDN5,CLDN10,CLDN22 interactions

Genes involved in CLDN2,CLDN20,CLDN6,CLDN19,CLDN8,CLDN1,CLDN23,CLDN7,CL 4 Cell-cell 2.64E-41 DN3,CLDN4,CLDN17,CLDN11,CLDN15,CLDN18,CLDN16,CLDN14,C adhesion LDN5,CLDN10,CLDN22 systems

173

Genes CLDN2,CLDN20,CLDN6,CLDN19,CLDN8,CLDN1,CLDN23,CLDN7,CL involved in 4 6.25E-38 DN3,CLDN4,CLDN17,CLDN11,CLDN15,CLDN18,CLDN16,CLDN14,C Cell junction LDN5,CLDN10,CLDN22 organization

metapathway GSTO2,GSTK1,CYP3A4,MGST2,MGST3,GSTT2B,MGST1,CYP2E1,G 5 biotransforma 1.49E-36 STA5,EPHX1,GSTA4,GSTA3,GSTO1,CYP1A2,GSTT2,GSTT1,GSTP1, tion GSTM5,CYP1A1,GSTM4,GSTM3,GSTM2,GSTM1,CYP2A13

Genes involved in GSTO2,MGST2,MGST3,MGST1,GSTA5,GSTA2,GSTA1,GSTA4,GSTA 5 1.49E-32 Glutathione 3,GSTO1,GSTP1,GSTM5,GSTM4,GSTM1 conjugation

Genes GSTO2,CYP3A4,MGST2,MGST3,MGST1,CYP2E1,GSTA5,GSTA2,GS involved in 5 1.43E-27 TA1,GSTA4,GSTA3,GSTO1,CYP1A2,GSTP1,GSTM5,CYP1A1,GSTM4 Biological ,GSTM1,CYP2A13 oxidations

Genes involved in GSTO2,MGST2,MGST3,MGST1,GSTA5,GSTA2,GSTA1,GSTA4,GSTA 5 3.56E-22 Phase II 3,GSTO1,GSTP1,GSTM5,GSTM4,GSTM1 conjugation

glutathione 5 7.39E-22 GSTO2,GSTA2,GSTA1,GSTA4,GSTA3,GSTT1,GSTP1,GSTM2,GSTM1 conjugation

174

MAP00480 MGST1,GSTA2,GSTT2,GSTT1,GSTP1,GSTM5,GSTM4,GSTM3,GST 5 Glutathione 1.05E-18 M2,GSTM1 metabolism

Aflatoxin B1 5 1.31E-12 CYP3A4,EPHX1,CYP1A2,GSTT1,GSTM1,CYP2A13 metabolism

Glutathione 5 2.36E-12 GSTT2B,GSTA5,GSTA1,GSTT2,GSTT1,GSTM2,GSTM1 metabolism

Genes 5 involved in 4.98E-08 CYP3A4,CYP2E1,CYP1A2,CYP1A1,CYP2A13 Xenobiotics

Fatty Acid 5 Omega 7.79E-06 CYP3A4,CYP2E1,CYP1A2,CYP1A1 Oxidation

Genes involved in 5 Phase 1 7.79E-06 CYP3A4,CYP2E1,CYP1A2,CYP1A1 functionalizati on

Estrogen 5 1.35E-05 CYP3A4,CYP1A2,CYP1A1,GSTM1 metabolism

Tryptophan 5 2.12E-05 CYP3A4,CYP2E1,CYP1A2,CYP1A1,CYP2A13 metabolism

Tamoxifen 5 2.19E-05 CYP3A4,CYP2E1,CYP1A2,CYP1A1 metabolism

175

Genes involved in Cytochrome 5 2.92E-05 CYP3A4,CYP2E1,CYP1A2,CYP1A1,CYP2A13 P450 - arranged by substrate type

cytochrome 5 8.87E-05 CYP3A4,CYP2E1,CYP1A2,CYP1A1,CYP2A13 P450 Oxidative 5 9.74E-05 GSTT2B,MGST1,GSTT2,CYP1A1 Stress

MAP00361 gamma 5 Hexachlorocyc 1.31E-04 CYP3A4,CYP2E1,CYP1A2,CYP1A1 lohexane degradation

Genes involved in Phase 1 - 5 1.42E-04 CYP3A4,CYP2E1,CYP1A2,CYP1A1,CYP2A13 Functionalizati on of compounds

Benzo(a)pyren 5 1.60E-04 CYP3A4,EPHX1,CYP1A1 e metabolism

MAP00380 5 Tryptophan 8.66E-04 CYP3A4,CYP2E1,CYP1A2,CYP1A1 metabolism

176

MAP00071 5 Fatty acid 8.66E-04 CYP3A4,CYP2E1,CYP1A2,CYP1A1 metabolism

CDK4,MYC,CDK6,CDKN1A,CDKN1B,CDKN1C,CDKN2B,CCND2,RBL G1 to S cell 6 1.95E-36 1,CCNE2,CCND1,TFDP1,E2F2,TFDP2,E2F3,E2F1,RB1,CCNE1,CCND cycle control 3

Cyclins and CDK4,CDK6,CDKN1A,CDKN1B,CDKN2B,CCND2,RBL1,CCND1,TFDP 6 Cell Cycle 8.67E-28 1,E2F1,RB1,CCNE1,CCND3 Regulation

Genes CDK4,CDK6,CDKN1A,CCND2,CCND1,TFDP1,E2F2,E2F3,E2F1,RB1,C 6 involved in G1 2.12E-24 CND3 Phase

E2F transcription MYC,CDKN1A,CDKN1B,RBL1,CCNE2,TFDP1,RBL2,E2F2,TFDP2,E2F 6 1.85E-22 factor 3,E2F1,RB1,CCNE1,CCND3 network

Regulation of CDK4,CDK6,CDKN1A,CDKN1B,CCND2,CCND1,TFDP1,E2F2,E2F3,E 6 retinoblastom 9.56E-21 2F1,RB1,CCNE1,CCND3 a protein

CDK4,CDK6,CDKN1A,CDKN1B,CCND2,RBL1,CCNE2,TFDP1,E2F2,E2 6 Cell cycle 3.32E-20 F3,E2F1,RB1,CCNE1,CCND3

DNA damage CDK4,MYC,CDK6,CDKN1A,CDKN1B,CCND2,CCNE2,CCND1,E2F1,R 6 3.64E-18 response B1,CCNE1,CCND3

177

Cell Cycle: CDK4,CDK6,CDKN1A,CDKN1B,CDKN2B,CCND1,TFDP1,E2F1,RB1,C 6 G1/S Check 4.31E-18 CNE1 Point

miRNA regulation of CDK4,MYC,CDK6,CDKN1A,CDKN1B,CCND2,CCNE2,CCND1,E2F1,R 6 6.43E-18 DNA Damage B1,CCNE1,CCND3 Response

Influence of Ras and Rho 6 proteins on 6.47E-16 CDK4,CDK6,CDKN1A,CDKN1B,CCND1,TFDP1,E2F1,RB1,CCNE1 G1 to S Transition

miRNAs 6 involved in 7.88E-16 MYC,CDK6,CDKN1A,CDKN1B,CCND1,E2F1,CCNE1,CCND3 DDR

Expression of cyclins regulates progression through the 6 1.14E-13 CDK4,CDKN1B,CCNE2,CCND1,E2F2,E2F1,CCNE1 cell cycle by activating cyclin- dependent kinases.

Genes involved in CDK4,CDK6,CDKN1A,CCND2,CCNE2,CCND1,TFDP1,E2F2,E2F3,E2F 6 1.46E-11 Cell Cycle, 1,RB1,CCNE1,CCND3 Mitotic

178

Cdk2, 4, and 6 bind cyclin D in G1, while 6 cdk2/cyclin E 1.67E-10 CDK4,CDKN1A,CDKN1B,CCND1,E2F2,E2F1 promotes the G1/S transition.

p53 Signaling 6 2.67E-10 CDK4,CDKN1A,CCND1,E2F1,RB1,CCNE1 Pathway

TSH signaling 6 5.73E-10 CDK4,MYC,CDKN1B,RBL2,E2F1,RB1,CCNE1,CCND3 pathway

E2F/MIRHG1 6 7.16E-09 MYC,E2F2,E2F3,E2F1 feedback-loop

Regulation of p27 Phosphorylati 6 2.04E-08 CDKN1B,TFDP1,E2F1,RB1,CCNE1 on during Cell Cycle Progression

Genes involved in 6 2.35E-08 CDKN1A,CCNE2,TFDP1,E2F2,E2F3,E2F1,RB1,CCNE1 G1/S Transition

179

Validated targets of C- 6 MYC 3.39E-08 ZBTB17,MYC,CDKN1A,CDKN1B,CDKN2B,RBL1,CCND1 transcriptional repression

Genes involved in E2F mediated 6 3.58E-08 TFDP1,E2F2,E2F3,E2F1,RB1,CCNE1 regulation of DNA replication

Regulation of nuclear 6 2.07E-07 ZBTB17,CDK4,MYC,CDKN1A,CDKN2B,RBL1,TFDP1 SMAD2/3 signaling

DNA damage response (only 6 2.26E-07 MYC,CDKN1A,CDKN1B,CCND2,CCND1,RBL2,CCND3 ATM dependent)

Genes involved in E2F 6 4.10E-07 TFDP1,E2F2,E2F3,E2F1,CCNE1 transcriptional targets at G1/S

E2F1 6 Destruction 1.49E-06 TFDP1,E2F1,RB1,CCNE1 Pathway

180

FOXM1 transcription 6 1.13E-05 CDK4,MYC,CCND1,RB1,CCNE1 factor network

6 Cell cycle 1.28E-05 CCND2,CCND1,CCNE1,CCND3

ID signaling 6 1.28E-05 RBL1,RBL2,RB1,CCNE1 pathway

p53 pathway 6 feedback 2.29E-05 MYC,CDKN1A,RBL1,RB1,CCNE1 loops 2

Genes 6 involved in S 4.11E-05 CDK4,CDKN1A,CCNE2,CCND1,RB1,CCNE1 Phase

IL2 signaling events 6 1.87E-04 MYC,CDK6,CCND2,CCND3 mediated by STAT5

Direct p53 6 2.23E-04 CDKN1A,TFDP1,E2F2,E2F3,E2F1,RB1 effectors

C-MYB transcription 6 3.82E-04 MYC,CDK6,CDKN1A,CDKN1B,CCND1 factor network

Genes involved in CDC6 6 association 4.91E-04 E2F2,E2F3,E2F1 with the ORC:origin complex

181

Genes involved in Association of licensing 6 6.53E-04 E2F2,E2F3,E2F1 factors with the pre- replicative complex

UGT2B11,UGT2B7,UGT2B10,UGT2B15,UGT2B17,UGT2B28,UGT2 Genes B4,SULT2B1,UGT1A9,CYP11A1,UGT2A1,CYP7A1,UGT1A8,CYP4A1 involved in 1,UGT1A7,CYP3A4,UGT1A6,UGT1A5,CYP3A5,CYP2E1,CYP26A1,C 7 3.56E-54 Biological YP21A2,CYP19A1,CYP17A1,CYP11B2,CYP11B1,CYP3A7,CYP1A2,C oxidations YP1B1,CYP1A1,SULT1E1,CYP2C8,UGT1A1,UGT1A3,CYP2B6,UGT1 A4

182

UGT2A2,AKR1C3,AKR1D1,UGT2B11,UGT2B7,UGT2A3,UGT2B17, UGT2B28,UGT2B4,SULT2B1,UGT1A9,CYP11A1,UGT2A1,CYP7A1, metapathway UGT1A7,CYP3A4,UGT1A6,UGT1A5,CYP3A5,CYP2E1,CYP26A1,CYP 7 biotransforma 8.33E-51 21A2,UGT1A10,CYP19A1,CYP17A1,CYP11B2,CYP11B1,CYP3A7,CY tion P1A2,CYP1B1,CYP1A1,SULT1E1,CYP2C8,UGT1A1,UGT1A3,CYP2B6 ,UGT1A4

UGT2A2,UGT2B11,UGT2B7,UGT2A3,UGT2B10,UGT2B15,UGT2B1 Glucuronidati 7 2.05E-36 7,UGT2B28,UGT2B4,UGT1A9,UGT2A1,UGT1A8,UGT1A7,UGT1A6, on UGT1A5,UGT1A10,UGT1A1,UGT1A3,UGT1A4

Genes UGT2B11,UGT2B7,UGT2B10,UGT2B15,UGT2B17,UGT2B28,UGT2 involved in 7 4.46E-32 B4,UGT1A9,UGT2A1,UGT1A8,UGT1A7,UGT1A6,UGT1A5,UGT1A1 Glucuronidati ,UGT1A3,UGT1A4 on

Genes involved in CYP11A1,CYP7A1,CYP4A11,CYP3A4,CYP3A5,CYP2E1,CYP26A1,CY Cytochrome 7 8.28E-27 P21A2,CYP19A1,CYP17A1,CYP11B2,CYP11B1,CYP3A7,CYP1A2,CY P450 - P1B1,CYP1A1,CYP2C8,CYP2B6 arranged by substrate type

183

Genes UGT2B11,UGT2B7,UGT2B10,UGT2B15,UGT2B17,UGT2B28,UGT2 involved in 7 6.36E-25 B4,SULT2B1,UGT1A9,UGT2A1,UGT1A8,UGT1A7,UGT1A6,UGT1A5 Phase II ,SULT1E1,UGT1A1,UGT1A3,UGT1A4 conjugation

CYP11A1,CYP7A1,CYP4A11,CYP3A4,CYP3A5,CYP2E1,CYP26A1,CY cytochrome 7 8.99E-25 P21A2,CYP19A1,CYP17A1,CYP11B2,CYP11B1,CYP3A7,CYP1A2,CY P450 P1B1,CYP1A1,CYP2C8,CYP2B6

Genes involved in CYP11A1,CYP7A1,CYP4A11,CYP3A4,CYP3A5,CYP2E1,CYP26A1,CY Phase 1 - 7 6.27E-24 P21A2,CYP19A1,CYP17A1,CYP11B2,CYP11B1,CYP3A7,CYP1A2,CY Functionalizati P1B1,CYP1A1,CYP2C8,CYP2B6 on of compounds

MAP00150 Androgen and HSD3B2,STS,HSD3B1,HSD17B3,HSD17B2,AKR1D1,SRD5A2,SRD5A 7 6.55E-22 estrogen 1,UGT2B15,UGT2B4,CYP11B2,CYP11B1 metabolism

Tamoxifen UGT2B7,UGT1A8,CYP3A4,CYP3A5,CYP2E1,UGT1A10,CYP1A2,CYP 7 5.29E-21 metabolism 1B1,CYP1A1,SULT1E1,CYP2C8,UGT1A4

Estrogen STS,UGT2B7,UGT1A9,CYP3A4,UGT1A6,CYP1A2,CYP1B1,CYP1A1,S 7 2.67E-19 metabolism ULT1E1,UGT1A1,UGT1A3

184

Androgen and STS,HSD3B1,HSD17B3,AKR1D1,SRD5A1,UGT2B11,SULT2B1,CYP1 7 Estrogen 1.46E-15 7A1 Metabolism

Genes involved in HSD3B2,HSD3B1,HSD17B3,CYP11A1,CYP21A2,CYP19A1,CYP17A1 7 Steroid 1.68E-15 ,CYP11B2,CYP11B1 hormone biosynthesis

MAP00140 C21 Steroid HSD3B2,HSD3B1,AKR1D1,CYP11A1,CYP21A2,CYP17A1,CYP11B2, 7 7.26E-15 hormone CYP11B1 metabolism

Genes involved in HSD3B2,HSD3B1,HSD17B3,CYP11A1,CYP21A2,CYP19A1,CYP17A1 7 2.39E-13 Steroid ,CYP11B2,CYP11B1 hormones

Genes involved in CYP11A1,CYP7A1,CYP21A2,CYP19A1,CYP17A1,CYP11B2,CYP11B1 7 4.75E-13 Endogenous ,CYP1B1 sterols

Genes CYP3A4,CYP3A5,CYP2E1,CYP3A7,CYP1A2,CYP1A1,CYP2C8,CYP2B 7 involved in 1.01E-12 6 Xenobiotics

MAP00361 gamma CYP3A4,CYP3A5,CYP2E1,CYP19A1,CYP3A7,CYP1A2,CYP1A1,CYP2 7 Hexachlorocyc 7.84E-12 C8,CYP2B6 lohexane degradation

185

Genes involved in HSD3B2,HSD3B1,HSD17B3,AKR1D1,CYP11A1,CYP7A1,CYP21A2,C 7 8.96E-12 Steroid YP19A1,CYP17A1,CYP11B2,CYP11B1 metabolism

MAP00071 CYP4A11,CYP3A4,CYP3A5,CYP2E1,CYP19A1,CYP3A7,CYP1A2,CYP 7 Fatty acid 1.53E-11 1A1,CYP2C8,CYP2B6 metabolism

Steroidogenes 7 1.51E-10 HSD3B1,AKR1D1,CYP11A1,CYP21A2,CYP17A1,CYP11B1 is

Glucocorticoid & 7 Mineralcortic 1.51E-10 HSD3B2,HSD3B1,CYP11A1,CYP21A2,CYP17A1,CYP11B2 oid Metabolism

MAP00380 CYP3A4,CYP3A5,CYP2E1,CYP19A1,CYP3A7,CYP1A2,CYP1A1,CYP2 7 Tryptophan 7.97E-10 C8,CYP2B6 metabolism

Genes involved in HSD3B2,HSD3B1,HSD17B3,CYP11A1,CYP21A2,CYP19A1,CYP17A1 7 2.59E-09 Hormone ,CYP11B2,CYP11B1 biosynthesis

Genes involved in HSD3B2,HSD3B1,HSD17B3,AKR1D1,UGT2B4,UGT1A9,CYP11A1,C 7 Metabolism of 7.50E-09 YP7A1,CYP4A11,CYP21A2,CYP19A1,CYP17A1,CYP11B2,CYP11B1 lipids and lipoproteins

186

Irinotecan 7 9.01E-09 UGT1A9,CYP3A4,UGT1A6,CYP3A5,UGT1A10,UGT1A1 Pathway

Nuclear receptors in 7 lipid 1.14E-07 CYP7A1,CYP4A11,CYP3A4,CYP2E1,CYP26A1,CYP1A2,CYP2B6 metabolism and toxicity

Steroid 7 2.36E-07 HSD3B2,HSD3B1,HSD17B3,HSD17B2,CYP17A1 Biosynthesis

androgen and 7 estrogen 4.30E-07 STS,SRD5A2,UGT2A1,CYP11B2,CYP11B1 metabolic

Genes involved in 7 Phase 1 2.74E-06 CYP4A11,CYP3A4,CYP2E1,CYP1A2,CYP1A1 functionalizati on

Fatty Acid 7 Omega 2.74E-06 CYP4A11,CYP3A4,CYP2E1,CYP1A2,CYP1A1 Oxidation

Benzo(a)pyren 7 2.03E-05 AKR1C3,CYP3A4,CYP1B1,CYP1A1 e metabolism

Tryptophan 7 4.21E-05 CYP3A4,CYP2E1,CYP19A1,CYP1A2,CYP1B1,CYP1A1 metabolism

statin 7 pharmacokine 1.13E-04 CYP3A4,CYP2C8,UGT1A1,UGT1A3 tics pathway

Clopidogrel 7 4.73E-04 CYP3A4,CYP3A5,CYP1A2,CYP2B6 Pathway

187

bioactivation via 7 5.49E-04 CYP1A2,CYP1B1,CYP1A1 cytochrome P450

Nicotine 7 5.49E-04 UGT1A9,CYP2B6,UGT1A4 metabolism

Arylamine 7 9.56E-04 UGT1A9,CYP1A2,UGT1A4 metabolism

Insulin/IGF pathway- AKT1,IRS4,FOXO3,IRS2,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,GS 8 protein kinase 6.82E-33 K3A,PIK3R2,GSK3B,PDPK1,AKT2,PIK3R5,PIK3R3,IRS1 B signaling cascade

Genes involved in AKT1,FOXO1,FOXO3,IRS2,PIK3CA,PIK3CB,PIK3R1,GSK3A,PIK3R2, 8 2.38E-32 PI3K/AKT GSK3B,PDPK1,AKT2,MTOR,IRS1,AKT3,CHUK,CREB1 signalling

AKT1,IRS4,FOXO1,FOXO3,IRS2,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK Insulin 8 2.84E-26 3CD,GSK3A,PIK3R2,GSK3B,PDPK1,IKBKB,AKT2,PRKCZ,MTOR,PIK3 Signaling R3,IRS1

Genes involved in TRKA AKT1,FOXO1,FOXO3,IRS2,PIK3CA,PIK3CB,PIK3R1,GSK3A,PIK3R2, 8 signalling 1.59E-23 GSK3B,PDPK1,AKT2,MTOR,IRS1,AKT3,CHUK,CREB1 from the plasma membrane

188

AKT1,FOXO1,FOXO3,PIK3CA,PIK3CB,PIK3R1,PIK3R2,GSK3B,PDPK 8 PI3K Pathway 4.74E-22 1,AKT2,PIK3R5,PIK3R3,IRS1,AKT3

Genes involved in AKT1,FOXO1,FOXO3,IRS2,PIK3CA,PIK3CB,PIK3R1,GSK3A,PIK3R2, 8 1.48E-21 Signalling by GSK3B,PDPK1,IKBKB,AKT2,MTOR,IRS1,AKT3,CHUK,CREB1,NFKB1 NGF

MicroRNAs in AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,GSK3B,PDPK 8 cardiomyocyt 1.08E-20 1,IKBKB,AKT2,MTOR,PIK3R3,CHUK,NFKB1 e hypertrophy

Hypoxia AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,AKT2,MTOR, 8 response via 3.77E-19 PIK3R3,AKT3 HIF activation

p53 pathway AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,PDPK1,AKT2, 8 feedback 5.21E-18 PIK3R5,PIK3R3,AKT3 loops 2

AKT Signaling AKT1,FOXO1,FOXO3,PIK3CA,PIK3CG,PIK3R1,PDPK1,IKBKB,CHUK, 8 1.41E-17 Pathway NFKB1

Toll-like receptor AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,IKBKB,AKT2, 8 2.32E-17 signaling PIK3R5,PIK3R3,AKT3,CHUK,NFKB1 pathway

189

Genes involved in Collagen- AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PDPK1,AKT2,PRKCZ,PIK3R5, 8 2.50E-17 mediated AKT3 activation cascade

T cell AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,IKBKB,AKT2, 8 9.86E-17 activation PIK3R3,AKT3,CHUK,NFKB1

Genes related to IL4 rceptor 8 1.83E-16 AKT1,IRS2,PIK3CA,PIK3R1,PIK3CD,GSK3A,GSK3B,AKT2,IRS1,AKT3 signaling in B lymphocytes

Genes related to PIP3 AKT1,IRS4,IRS2,PIK3CA,PIK3CD,GSK3A,GSK3B,AKT2,CREB3,IRS1,A 8 signaling in 2.58E-16 KT3,CREB1 cardiac myocytes

Interleukin AKT1,FOXO3,IRS2,PIK3CA,PIK3CB,GSK3B,PDPK1,IKBKB,AKT2,MTO 8 signaling 8.48E-16 R,IRS1,AKT3,CHUK pathway

CXCR4- mediated AKT1,FOXO1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,PDPK 8 9.76E-16 signaling 1,PRKCZ,PIK3R5,MTOR,PIK3R3 events

Genes related to the insulin AKT1,IRS4,IRS2,PIK3CA,PIK3R1,PIK3CD,GSK3A,GSK3B,AKT2,IRS1, 8 1.36E-15 receptor AKT3 pathway

190

Regulation of toll-like AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,IKBKB,AKT2, 8 receptor 2.29E-15 PIK3R5,PIK3R3,AKT3,CHUK,NFKB1 signaling pathway

Endothelin AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,AKT2,PRKCZ, 8 signaling 2.47E-15 PIK3R5,PIK3R3,AKT3 pathway

Class I PI3K signaling AKT1,FOXO1,FOXO3,GSK3A,GSK3B,PDPK1,AKT2,MTOR,AKT3,CH 8 events 2.80E-15 UK mediated by Akt

phosphatidyli nositol 3- AKT1,PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,PDPK1,AKT2,PIK3R3, 8 2.80E-15 kinase-Akt AKT3 signaling

AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,PDPK1,AKT2, 8 p53 pathway 5.65E-15 PIK3R5,PIK3R3,AKT3

FAS (CD95) AKT1,PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,PDPK1,IKBKB,PIK3R3 8 signaling 1.00E-14 ,CHUK pathway

ErbB1 AKT1,FOXO1,FOXO3,PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,PDPK 8 downstream 1.22E-14 1,PRKCZ,MTOR,PIK3R3,CREB1 signaling

B Cell Receptor AKT1,FOXO1,PIK3CG,PIK3R1,GSK3A,PIK3R2,GSK3B,PDPK1,IKBKB, 8 3.33E-14 Signaling CHUK,CREB1,NFKB1 Pathway

191

Prolactin AKT1,IRS2,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3R2,GSK3B,MTOR,I 8 Signaling 2.18E-13 RS1,NFKB1 Pathway

Apoptosis AKT1,PIK3CA,PIK3CB,ATF4,PIK3CG,PIK3CD,IKBKB,AKT2,AKT3,CHU 8 signaling 2.48E-13 K,CREB1,NFKB1 pathway

IL-4 signaling AKT1,IRS2,PIK3CA,PIK3R1,PIK3CD,PIK3R2,IKBKB,IRS1,CHUK,NFKB 8 2.63E-13 pathway 1

Class I PI3K FOXO3,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,PDPK1,PIK 8 signaling 4.90E-13 3R5,PIK3R3 events

Genes involved in AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PDPK1,AKT2,PRKCZ,PIK3R5, 8 Platelet 1.27E-12 AKT3 activation triggers

Leptin AKT1,FOXO1,PIK3R1,PIK3R2,IKBKB,MTOR,IRS1,CHUK,CREB1,NFK 8 signaling 1.82E-12 B1 pathway

PDGF PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,GSK3A,PIK3R2,GSK3B,PDP 8 signaling 2.97E-12 K1,AKT2,PIK3R5,PIK3R3 pathway

Genes involved in AKT 8 4.82E-12 AKT1,GSK3A,GSK3B,PDPK1,AKT2,AKT3,CHUK phosphorylate s targets in the cytosol

CXCR3- mediated AKT1,PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,PDPK1,MTOR,PIK3R 8 4.95E-12 signaling 3 events 192

Axon guidance 8 mediated by 5.65E-12 PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,PIK3R5,PIK3R3 netrin

AMPK AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,AKT2,MTOR, 8 5.79E-12 signaling PIK3R3

TRAIL 8 signaling 1.12E-11 PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,IKBKB,PIK3R3,CHUK pathway

Nephrin/Neph 1 signaling in 8 3.75E-11 AKT1,PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,PRKCZ,PIK3R3 the kidney podocyte

Intracellular Signalling Through 8 Adenosine 4.94E-11 AKT1,PIK3CA,PDPK1,IKBKB,PRKCZ,CHUK,CREB1,NFKB1 Receptor A2a and Adenosine

Intracellular Signalling Through 8 Adenosine 6.45E-11 AKT1,PIK3CA,PDPK1,IKBKB,PRKCZ,CHUK,CREB1,NFKB1 Receptor A2b and Adenosine

193

Genes involved in CD28 8 6.99E-11 AKT1,PIK3CA,PIK3R1,PDPK1,AKT2,MTOR,AKT3 dependent PI3K/Akt signaling

VEGF signaling AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,PRKCZ,PIK3R 8 1.25E-10 pathway 3

Focal AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,GSK3B,PDPK 8 1.80E-10 Adhesion 1,AKT2,PIK3R5,AKT3

Ras Signaling 8 4.70E-10 AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3CD,GSK3A,GSK3B,PDPK1,AKT3 Pathway

ErbB2/ErbB3 8 signaling 5.05E-10 AKT1,PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,MTOR,PIK3R3 events

Insulin 8 5.05E-10 AKT1,FOXO3,PIK3CA,PIK3R1,PDPK1,AKT2,PRKCZ,IRS1 Pathway

AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,GSK3B,PRKC 8 Angiogenesis 8.21E-10 Z,PIK3R3,AKT3

Members of the BCR 8 9.02E-10 AKT1,PIK3CA,PIK3R1,PIK3CD,GSK3A,GSK3B,AKT2,AKT3 signaling pathway

Genes involved in 8 1.09E-09 PIK3CA,PIK3CB,PIK3R1,PIK3R2,PDPK1,IKBKB,CHUK,NFKB1 Downstream TCR signaling

194

Genes involved in 8 1.67E-09 IRS2,PIK3CA,PIK3CB,PIK3R1,PIK3R2,PDPK1,AKT2,MTOR,IRS1 IRS-related events

Genes involved in 8 2.12E-09 AKT1,PIK3CA,PIK3R1,PDPK1,AKT2,MTOR,AKT3 CD28 co- stimulation

8 IGF1 pathway 2.75E-09 AKT1,IRS2,PIK3CA,PIK3R1,PDPK1,PRKCZ,IRS1

RANKL/RANK 8 Signaling 3.53E-09 AKT1,PIK3R1,PIK3R2,IKBKB,AKT2,MTOR,CHUK,NFKB1 Pathway

Human Cytomegalovir 8 us and Map 6.21E-09 AKT1,PIK3CA,PIK3CG,PIK3R1,CREB1,NFKB1 Kinase Pathways

Trk receptor signaling 8 mediated by 8.99E-09 AKT1,FOXO3,PIK3CA,PIK3R1,GSK3B,PDPK1,CREB1 PI3K and PLC- gamma

Genes 8 involved in 1.47E-08 PIK3CA,PIK3CB,PIK3R1,PIK3R2,PDPK1,IKBKB,CHUK,NFKB1 TCR signaling

IL2 signaling events 8 1.68E-08 AKT1,FOXO3,PIK3CA,PIK3R1,PRKCZ,MTOR,NFKB1 mediated by PI3K

195

Skeletal muscle hypertrophy is 8 1.93E-08 AKT1,PIK3CA,PIK3R1,GSK3B,PDPK1,MTOR regulated via AKT/mTOR pathway

B Cell Antigen 8 2.04E-08 AKT1,PIK3CA,PIK3R1,PIK3CD,AKT2,AKT3,NFKB1 Receptor

IL-5 signaling 8 2.46E-08 AKT1,FOXO3,PIK3CG,PIK3R1,GSK3A,PIK3R2,GSK3B pathway

Genes involved in AKT1,PIK3CA,PIK3CB,PIK3R1,PIK3R2,PDPK1,IKBKB,AKT2,MTOR,A 8 Signaling in 3.10E-08 KT3,CHUK,CREB1,NFKB1 Immune system

Ras Signaling 8 4.99E-08 AKT1,PIK3CA,PIK3CG,PIK3R1,CHUK,NFKB1 Pathway

Genes related 8 5.02E-08 AKT1,PIK3CA,PIK3CG,PIK3R1,PIK3CD,AKT2,AKT3 to chemotaxis

Genes involved in AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PDPK1,AKT2,PRKCZ,PIK3R5, 8 5.73E-08 Platelet AKT3 Activation

Regulation of 8 eIF4e and p70 6.64E-08 AKT1,PIK3CA,PIK3R1,PDPK1,MTOR,IRS1 S6 Kinase

196

EGF receptor 8 signaling 6.67E-08 AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3CD,AKT2,PRKCZ,PIK3R5,AKT3 pathway

Genes involved in G beta:gamma 8 8.72E-08 AKT1,PIK3CG,PDPK1,AKT2,PIK3R5,AKT3 signalling through PI3Kgamma

Influence of Ras and Rho 8 proteins on 1.13E-07 AKT1,PIK3CA,PIK3R1,IKBKB,CHUK,NFKB1 G1 to S Transition

Inactivation of Gsk3 by AKT causes 8 accumulation 1.45E-07 AKT1,PIK3CA,PIK3R1,GSK3B,PDPK1,NFKB1 of b-catenin in Alveolar Macrophages

IL-1 signaling 8 1.49E-07 AKT1,PIK3R1,PIK3R2,IKBKB,PRKCZ,CHUK,NFKB1 pathway

197

Genes involved in AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PDPK1,AKT2,PRKCZ,PIK3R5, 8 1.67E-07 Formation of AKT3 Platelet plug

Genes involved in G- 8 protein 1.84E-07 AKT1,PIK3CG,PDPK1,AKT2,PIK3R5,AKT3 beta:gamma signalling

TCR Signaling 8 2.22E-07 AKT1,PIK3R1,PIK3R2,PDPK1,IKBKB,CHUK,CREB1,NFKB1 Pathway

altered phosphatodyli 8 nositol 3- 2.92E-07 AKT1,PIK3CA,AKT2,AKT3 kinase-Akt signaling

Alpha 6 Beta 4 8 signaling 3.57E-07 AKT1,IRS2,PIK3R1,PIK3R2,MTOR,IRS1 pathway

Inflammation mediated by chemokine AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3CD,PDPK1,IKBKB,AKT2,PRKCZ,A 8 3.57E-07 and cytokine KT3 signaling pathway

198

Genes AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3R2,PDPK1,AKT2,PRKCZ, 8 involved in 4.01E-07 PIK3R5,AKT3 Hemostasis

The IGF-1 8 Receptor and 5.20E-07 AKT1,FOXO3,PIK3CA,PIK3CG,PIK3R1 Longevity

The TrkA receptor binds nerve growth factor to 8 activate MAP 5.20E-07 AKT1,PIK3CA,PIK3CD,AKT2,AKT3 kinase pathways and promote cell growth.

8 PI3K Pathway 5.35E-07 AKT1,PIK3CA,GSK3A,GSK3B,AKT2,AKT3

EGF receptor (ErbB1) 8 6.48E-07 PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,PIK3R3 signaling pathway

Integrated 8 Breast Cancer 6.50E-07 AKT1,FOXO1,GSK3A,PIK3R2,MTOR,IRS1,CHUK,CREB1,NFKB1 Pathway

B Cell Survival 8 7.55E-07 AKT1,PIK3CA,PIK3CG,PIK3R1,MTOR Pathway

199

PTEN is a tumor suppressor that dephosphoryl 8 7.55E-07 AKT1,PIK3CA,PIK3CD,AKT2,AKT3 ates the lipid messenger phosphatidyli nositol triphosphate.

Role of nicotinic acetylcholine 8 7.55E-07 AKT1,FOXO3,PIK3CA,PIK3CG,PIK3R1 receptors in the regulation of apoptosis

ErbB4 8 signaling 7.80E-07 PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,PIK3R3 events

IL1-mediated 8 signaling 7.80E-07 PIK3CA,PIK3R1,IKBKB,PRKCZ,CHUK,NFKB1 events

TSH signaling 8 8.68E-07 AKT1,PIK3CA,PIK3R1,PIK3R2,PDPK1,MTOR,CREB1 pathway

Insulin 8 9.34E-07 AKT1,IRS2,GSK3B,PRKCZ,MTOR,IRS1 Signaling

BCR signaling 8 9.69E-07 AKT1,PIK3CA,PIK3R1,PDPK1,IKBKB,CHUK,NFKB1 pathway

200

FGF signaling 8 1.32E-06 AKT1,PIK3CA,PIK3CB,PIK3CG,PIK3CD,AKT2,PRKCZ,AKT3 pathway

Genes 8 involved in PI- 1.32E-06 IRS2,PIK3CA,PIK3CB,PIK3R1,PIK3R2,IRS1 3K cascade

PTEN dependent 8 cell cycle 1.47E-06 AKT1,FOXO3,PIK3CA,PIK3R1,PDPK1 arrest and apoptosis

Role of Erk5 in 8 Neuronal 1.47E-06 AKT1,PIK3CA,PIK3CG,PIK3R1,CREB1 Survival

Genes involved in 8 Costimulation 1.48E-06 AKT1,PIK3CA,PIK3R1,PDPK1,AKT2,MTOR,AKT3 by the CD28 family

Internalization 8 1.82E-06 PIK3CA,PIK3CB,PIK3R1,PIK3CD,PIK3R2,PIK3R3 of ErbB1

Differentiatio n Pathway in PC12 Cells; this is a 8 2.48E-06 AKT1,PIK3CA,PIK3R1,PIK3CD,CREB3,CREB1 specific case of PAC1 Receptor Pathway.

201

TWEAK 8 Signaling 2.48E-06 AKT1,GSK3B,IKBKB,AKT2,CHUK,NFKB1 Pathway

Corticosteroid s and 8 2.65E-06 AKT1,PIK3CA,PIK3CG,PIK3R1,NFKB1 cardioprotecti on

Genes involved in Downstream 8 2.88E-06 IRS2,PIK3CA,PIK3CB,PIK3R1,PIK3R2,IRS1 signaling of activated FGFR

Signaling events mediated by 8 Hepatocyte 2.92E-06 AKT1,PIK3CA,PIK3R1,PDPK1,AKT2,PRKCZ,MTOR Growth Factor Receptor (c- Met)

Type II 8 diabetes 3.48E-06 IKBKB,PRKCZ,PIK3R5,MTOR,IRS1 mellitus mTOR 8 Signaling 5.72E-06 AKT1,PIK3CA,PIK3R1,PDPK1,MTOR Pathway

Phosphoinosit ides and their 8 5.72E-06 AKT1,GSK3A,GSK3B,PDPK1,PRKCZ downstream targets.

202

Signaling events mediated by 8 Stem cell 9.40E-06 AKT1,FOXO3,PIK3CA,PIK3R1,GSK3B,PDPK1 factor receptor (c- Kit)

Regulation of 8 Actin 1.04E-05 PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,PIK3R5,PIK3R3 Cytoskeleton

G alpha q 8 1.36E-05 AKT1,PIK3CB,AKT2,AKT3,NFKB1 Pathway

Fc-epsilon receptor I 8 2.05E-05 AKT1,PIK3CA,PIK3R1,IKBKB,CHUK,NFKB1 signaling in mast cells

IL4-mediated 8 signaling 2.27E-05 AKT1,IRS2,PIK3CA,PIK3R1,MTOR,IRS1 events

B cell 8 2.51E-05 PIK3CA,PIK3CB,PIK3CG,PIK3CD,IKBKB,CHUK activation Wnt/beta- 8 catenin 2.83E-05 AKT1,GSK3A,GSK3B,AKT2,AKT3 Pathway p75(NTR)- 8 mediated 4.45E-05 AKT1,PIK3CA,PIK3R1,IKBKB,PRKCZ,CHUK signaling

G alpha 13 8 5.36E-05 AKT1,PIK3CB,AKT2,AKT3,NFKB1 Pathway

IL-2 Receptor Beta Chain in 8 8.23E-05 AKT1,PIK3CA,PIK3CG,PIK3R1,IRS1 T cell Activation

203

Signaling events 8 regulated by 9.41E-05 IRS2,PIK3CA,PIK3R1,IRS1,CREB1 Ret tyrosine kinase

Atypical NF- 8 kappaB 1.35E-04 PIK3CA,PIK3R1,IKBKB,NFKB1 pathway

Genes 8 involved in 1.73E-04 PIK3CA,PIK3CB,PIK3R1,PIK3R2 Tie2 Signaling

Androgen receptor 8 1.87E-04 AKT1,FOXO1,PIK3R1,PIK3R2,GSK3B,CREB1 signaling pathway

EGF/EGFR 8 Signaling 1.98E-04 AKT1,FOXO1,PIK3R1,PIK3R2,PDPK1,PRKCZ,CREB1 Pathway

Estrogen 8 signaling 2.73E-04 AKT1,PIK3CA,CREB1,NFKB1 pathway

Angiopoietin receptor Tie2- 8 3.04E-04 AKT1,FOXO1,PIK3CA,PIK3R1,NFKB1 mediated signaling

FoxO family 8 3.04E-04 AKT1,FOXO1,FOXO3,IKBKB,CHUK signaling

E-cadherin 8 signaling in 3.36E-04 AKT1,PIK3CA,PIK3R1,AKT2 keratinocytes

204

Genes involved in 8 CTLA4 3.36E-04 AKT1,PDPK1,AKT2,AKT3 inhibitory signaling

IGF-1 8 Signaling 3.36E-04 PIK3CA,PIK3CG,PIK3R1,IRS1 Pathway

Trefoil Factors Initiate 8 3.36E-04 AKT1,PIK3CA,PIK3CG,PIK3R1 Mucosal Healing

Insulin 8 4.10E-04 PIK3CA,PIK3CG,PIK3R1,IRS1 Signaling Integrin 8 signalling 4.47E-04 PIK3CA,PIK3CB,PIK3CG,PIK3R1,PIK3CD,PIK3R2,PIK3R3 pathway MAPK 8 signaling 4.66E-04 AKT1,ATF4,IKBKB,AKT2,PRKCZ,AKT3,NFKB1 pathway

CTCF: First 8 Multivalent 4.95E-04 PIK3CA,PIK3CG,PIK3R1,MTOR Nuclear Factor

Inhibition of Cellular 8 4.95E-04 AKT1,PIK3CA,PIK3CG,PIK3R1 Proliferation by Gleevec

205

Multiple antiapoptotic pathways from IGF-1R 8 4.95E-04 AKT1,PIK3CA,PIK3R1,IRS1 signaling lead to BAD phosphorylati on

VEGFR3 signaling in 8 4.95E-04 AKT1,PIK3CA,PIK3R1,CREB1 lymphatic endothelium

NFAT and Hypertrophy of the heart 8 4.99E-04 AKT1,PIK3CA,PIK3CG,PIK3R1,GSK3B (Transcription in the broken heart)

ErbB signaling 8 5.47E-04 FOXO1,GSK3B,PIK3R5,MTOR,AKT3 pathway

IL-7 signaling 8 7.04E-04 AKT1,PIK3R1,PIK3R2,GSK3B pathway

EPO Receptor 8 8.29E-04 AKT1,IRS2,PIK3CG,IRS1 Signaling

Interleukin 4 8 8.29E-04 AKT1,PIK3CA,AKT2,AKT3 (IL-4) Pathway

206

Regulation of BAD 8 8.29E-04 AKT1,PIK3CA,PIK3CG,PIK3R1 phosphorylati on

Notch 8 Signaling 9.22E-04 AKT1,PIK3R1,PIK3R2,GSK3B,NFKB1 Pathway Insulin 8 9.71E-04 IRS2,PIK3R1,PDPK1,IRS1 Signaling

Phospholipids 8 as signalling 9.71E-04 AKT1,PIK3CA,PIK3CG,PIK3R1 intermediaries

Transcription factor CREB 8 and its 9.71E-04 AKT1,PIK3CA,PIK3R1,CREB1 extracellular signals

PLA2G10,PLA2G6,PLA2G4C,PLA2G2D,PLA2G3,PLA2G1B,PLA2G2F 9 lipases 1.15E-25 ,PLA2G2A,PLA2G4A,PLA2G5,JMJD7-PLA2G4B,PLD1,PLD2

Phospholipid PLA2G2D,PLA2G15,PTDSS1,LYPLA1,PISD,CHPT1,AGPAT1,PEMT,PL 9 1.17E-15 Biosynthesis D2

phospholipid 9 2.09E-11 PLA2G10,PLA2G1B,PLA2G2A,PLA2G5,PLD2 metabolic

glycerolipid 9 2.83E-06 PLA2G10,PLA2G1B,PLA2G2A,PLA2G5,PLD2 metabolic

eicosanoids 9 2.36E-05 PLA2G10,PLA2G1B,PLA2G2A,PLA2G5 metabolic

207

MAP00561 9 Glycerolipid 2.94E-05 PISD,PLA2G1B,PLA2G2A,PLA2G4A,PLA2G5 metabolism

MAP00590 Prostaglandin 9 and 2.98E-05 PLA2G1B,PLA2G2A,PLA2G4A,PLA2G5 leukotriene metabolism

mitogen activated 9 8.08E-05 PLA2G10,PLA2G1B,PLA2G2A,PLA2G5 protein kinase signaling

phospholipid 9 2.38E-04 PTDSS1,PISD,AGPAT1 biosynthesis I

phospholipid 9 3.39E-04 CEPT1,PISD,CHPT1 biosynthesis II

MAP00350 MAOB,MAOA,ADH5,ADH7,ADH6,ALDH1A3,ALDH3B1,ALDH3B2,A 10 Tyrosine 7.64E-32 LDH3A1,PNMT,COMT,ADH4,ADH1C,ADH1B,ADH1A,DBH metabolism

Genes UGT2A1,MAOB,MAOA,UGT1A8,UGT1A7,UGT1A6,UGT1A5,ADH7, involved in 10 2.70E-26 ADH6,CYP2A6,UGT2B11,UGT2B28,COMT,ADH4,ADH1C,ADH1B,U Biological GT1A9,ADH1A,UGT1A4 oxidations

208

Glucuronidati UGT2A1,UGT1A8,UGT1A7,UGT1A6,UGT1A5,UGT1A10,UGT2B11, 10 3.89E-17 on UGT2B28,UGT1A9,UGT1A4

Genes involved in UGT2A1,UGT1A8,UGT1A7,UGT1A6,UGT1A5,UGT2B11,UGT2B28, 10 2.73E-16 Glucuronidati UGT1A9,UGT1A4 on

MAP00010 Glycolysis ADH5,ADH7,ADH6,ALDH1A3,ALDH3B1,ALDH3B2,ALDH3A1,ADH4 10 1.00E-15 Gluconeogene ,ADH1C,ADH1B,ADH1A sis

Genes involved in UGT2A1,UGT1A8,UGT1A7,UGT1A6,UGT1A5,UGT2B11,UGT2B28, 10 5.13E-13 Phase II COMT,UGT1A9,UGT1A4 conjugation

Fatty Acid 10 Omega 2.61E-12 ADH7,ADH6,CYP2A6,ADH4,ADH1C,ADH1B,ADH1A Oxidation

Genes involved in 10 2.96E-11 ADH7,ADH6,ADH4,ADH1C,ADH1B,ADH1A Ethanol oxidation

MAP00120 10 Bile acid 9.75E-11 ADH5,ADH7,ADH6,ADH4,ADH1C,ADH1B,ADH1A biosynthesis

Genes involved in Phase 1 - 10 1.13E-10 MAOB,MAOA,ADH7,ADH6,CYP2A6,ADH4,ADH1C,ADH1B,ADH1A Functionalizati on of compounds

209

MAP00071 10 Fatty acid 2.73E-10 ADH5,ADH7,ADH6,CYP2A6,ADH4,ADH1C,ADH1B,ADH1A metabolism

metapathway UGT2A1,AKR1C2,UGT1A7,UGT1A6,UGT1A5,UGT1A10,UGT2B11, 10 biotransforma 1.03E-09 UGT2B28,COMT,UGT1A9,UGT1A4 tion

MAP00360 10 Phenylalanine 1.11E-09 MAOB,MAOA,ALDH1A3,ALDH3B1,ALDH3B2,ALDH3A1 metabolism

5- Hydroxytrypta 10 3.74E-09 MAOB,MAOA,ALDH1A3,ALDH3B1,ALDH3B2,ALDH3A1 mine degredation

MAP00340 10 Histidine 5.33E-09 MAOB,MAOA,ALDH1A3,ALDH3B1,ALDH3B2,ALDH3A1 metabolism

Tyrosine 10 1.83E-08 MAOA,ALDH3A1,PNMT,COMT,ADH1A,DBH Metabolism

MAP00561 10 Glycerolipid 2.36E-08 ADH5,ADH7,ADH6,ADH4,ADH1C,ADH1B,ADH1A metabolism

210

Adrenaline and 10 4.36E-06 MAOB,MAOA,PNMT,COMT,DBH noradrenaline biosynthesis

Biogenic 10 Amine 1.97E-05 MAOA,PNMT,COMT,DBH Synthesis tyrosine 10 2.62E-05 MAOB,ALDH3A1,COMT,ADH4 metabolic

Nicotine 10 8.94E-05 CYP2A6,UGT1A9,UGT1A4 metabolism

Genes ANAPC10,PLK1,CCNB1,CDC26,CCNB2,ANAPC1,ANAPC4,SMC3,CD involved in 11 4.40E-27 C25C,CDC25B,PTTG1,CDC27,CDC16,ANAPC2,STAG2,CDC20,CDK1, Cell Cycle, STAG1,SMC1A,ANAPC7,REC8,PKMYT1,RAD21 Mitotic

Genes involved in ANAPC10,PLK1,CCNB1,CDC26,ANAPC1,ANAPC4,CDC27,CDC16,A 11 Phosphorylati 1.03E-22 NAPC2,CDK1,ANAPC7 on of the APC/C

Genes involved in APC/C:Cdc20 ANAPC10,CCNB1,CDC26,ANAPC1,ANAPC4,CDC27,CDC16,ANAPC 11 2.64E-22 mediated 2,CDC20,CDK1,ANAPC7 degradation of Cyclin B

211

Genes involved in Conversion from ANAPC10,CDC26,ANAPC1,ANAPC4,CDC27,CDC16,ANAPC2,CDC20 11 3.78E-17 APC/C:Cdc20 ,ANAPC7 to APC/C:Cdh1 in late anaphase

Genes involved in Inactivation of APC/C via ANAPC10,CDC26,ANAPC1,ANAPC4,CDC27,CDC16,ANAPC2,CDC20 11 7.53E-17 direct ,ANAPC7 inhibition of the APC/C complex

Genes involved in Regulation of APC/C ANAPC10,PLK1,CCNB1,CDC26,ANAPC1,ANAPC4,CDC27,CDC16,A 11 2.04E-16 activators NAPC2,CDC20,CDK1,ANAPC7 between G1/S and early anaphase

PLK1,CCNB1,CCNB2,CDC25C,CDC25B,PTTG1,CDC20,CDK1,SMC1A 11 Cell cycle 2.15E-16 ,ESPL1,PTTG2,PKMYT1,CCNB3

212

Genes involved in ANAPC10,CCNB1,CDC26,CCNB2,ANAPC1,ANAPC4,CDC25C,CDC27 11 7.90E-16 Cell Cycle ,CDC16,ANAPC2,CDC20,CDK1,ANAPC7 Checkpoints

Genes involved in Cdc20:Phosph ANAPC10,CDC26,ANAPC1,ANAPC4,CDC27,CDC16,ANAPC2,CDC20 11 o-APC/C 4.30E-13 ,CDK1,ANAPC7 mediated degradation of Cyclin A

Genes involved in Cyclin A1 11 associated 1.51E-12 PLK1,CCNB1,CCNB2,CDC25C,CDC25B,CDK1,PKMYT1 events during G2/M transition

Genes involved in ANAPC10,CDC26,ANAPC1,ANAPC4,CDC27,CDC16,ANAPC2,ANAP 11 Autodegradati 9.33E-10 C7 on of Cdh1 by Cdh1:APC/C

PLK1 signaling 11 8.40E-09 PLK1,CCNB1,CDC25C,CDC25B,STAG2,CDC20,CDK1 events

213

Genes involved in 11 4.86E-08 PLK1,SMC3,STAG2,CDC20,STAG1,SMC1A,REC8,RAD21 Mitotic Prometaphase

Genes involved in 11 9.05E-07 PLK1,CCNB1,CCNB2,CDC25C,CDC25B,CDK1,PKMYT1 G2/M Transition

Cell Cycle: 11 G2/M 1.21E-06 PLK1,CCNB1,CDC25C,CDC25B,CDK1 Checkpoint

Activation of Src by Protein- 11 tyrosine 3.18E-06 CCNB1,CDC25C,CDC25B,CDK1 phosphatase alpha

Sonic Hedgehog (SHH) 11 3.18E-06 CCNB1,CDC25C,CDC25B,CDK1 Receptor Ptc1 Regulates cell cycle

Genes involved in 11 3.44E-06 PLK1,SMC3,STAG2,CDC20,STAG1,SMC1A,REC8,RAD21 Mitotic M- M/G1 phases

DNA damage 11 7.44E-06 CCNB1,CCNB2,CDC25C,CDK1,SMC1A,CCNB3 response

miRNA regulation of 11 9.71E-06 CCNB1,CCNB2,CDC25C,CDK1,SMC1A,CCNB3 DNA Damage Response

214

FOXM1 transcription 11 2.04E-05 PLK1,CCNB1,CCNB2,CDC25B,CDK1 factor network

RB Tumor Suppressor/C heckpoint 11 8.65E-04 CDC25C,CDC25B,CDK1 Signaling in response to DNA damage

G2/M 11 9.39E-04 CCNB1,CDK1 checkpoint

G2/M 11 9.39E-04 CCNB1,CDK1 transition

G2/M DNA 11 replication 9.39E-04 CCNB1,CDK1 checkpoint

MAP00590 Prostaglandin TBXAS1,CYP4F2,HPGDS,PLA2G1B,PTGS2,PTGS1,PTGIS,PTGDS,PLA 12 and 4.98E-33 2G2A,PLA2G4A,PLA2G5,CYP4F3,LTA4H,ALOX15,ALOX5 leukotriene metabolism

eicosanoids PLA2G10,TBXAS1,HPGDS,PTGES,PLA2G1B,PTGS2,PTGS1,PTGIS,PT 12 2.11E-24 metabolic GDS,PLA2G2A,PLA2G5,ALOX5

Eicosanoid PLA2G6,TBXAS1,PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A,LTA 12 5.70E-24 Synthesis 4H,ALOX15B,PTGES2,ALOX5

215

Arachidonic CYP4A11,PLA2G2D,CYP2J2,CYP2E1,PTGS1,PTGIS,CYP4F3,LTA4H,A 12 Acid 7.54E-21 LOX15,ALOX15B,PTGES2 Metabolism

PLA2G10,PLA2G6,PLA2G4C,PLA2G2D,PLA2G3,PLA2G1B,PLA2G2F 12 lipases 5.76E-19 ,PLA2G2A,PLA2G4A,PLA2G5,JMJD7-PLA2G4B

Bromfenac 12 5.67E-15 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A,PLA2G4A Pathway

Genes involved in 12 2.32E-13 TBXAS1,PTGS2,PTGS1,PTGIS,PTGES3,LTA4H,ALOX5 Prostanoid hormones

Ibuprofen 12 1.57E-12 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Pathway

Diclofenac 12 1.57E-12 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Pathway

Mefanamic 12 1.57E-12 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A acid Pathway

Ketoprofen 12 1.57E-12 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Pathway

Oxaprozin 12 1.57E-12 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Pathway

Indomethacin 12 1.57E-12 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Pathway

Diflunisal 12 1.57E-12 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Pathway Ketorolac 12 1.57E-12 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Pathway Naproxen 12 1.57E-12 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Pathway 216

Piroxicam 12 1.57E-12 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Pathway Sulindac 12 1.57E-12 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Pathway

Nabumetone 12 1.57E-12 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Pathway

Suprofen 12 1.57E-12 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Pathway

Genes involved in Phase 1 - TBXAS1,CYP4F2,CYP4A11,CYP2J2,CYP2E1,PTGS2,PTGS1,PTGIS,CY 12 2.32E-10 Functionalizati P4F3 on of compounds

Genes involved in 12 Phase 1 1.10E-09 TBXAS1,CYP4A11,CYP2E1,PTGS2,PTGS1,PTGIS functionalizati on

Acetylsalicylic 12 2.70E-09 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Acid Pathway

Celecoxib 12 8.38E-09 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Pathway Etodolac 12 8.38E-09 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Pathway Rofecoxib 12 8.38E-09 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Pathway

Meloxicam 12 8.38E-09 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Pathway

Valdecoxib 12 8.38E-09 PTGES,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G2A Pathway

217

Genes involved in Cytochrome 12 5.46E-08 TBXAS1,CYP4F2,CYP4A11,CYP2J2,CYP2E1,PTGIS,CYP4F3 P450 - arranged by substrate type

Genes involved in TBXAS1,CYP4F2,CYP4A11,CYP2J2,CYP2E1,PTGS2,PTGS1,PTGIS,CY 12 8.25E-08 Biological P4F3 oxidations

Genes involved in 12 8.44E-08 TBXAS1,PTGS2,PTGS1,PTGIS,PTGES3,LTA4H,ALOX5 Hormone biosynthesis

phospholipid 12 9.91E-08 PLA2G10,PLA2G1B,PLA2G2A,PLA2G5 metabolic

Prostaglandin 12 Synthesis and 1.55E-07 TBXAS1,PTGS2,PTGS1,PTGIS,PTGDS,PLA2G4A Regulation

lipoxygenase mediated of 12 arachidonic 1.38E-06 LTA4H,ALOX15,ALOX15B,ALOX5 acid metabolism

218

mitogen activated 12 2.00E-04 PLA2G10,PLA2G1B,PLA2G2A,PLA2G5 protein kinase signaling

cytochrome 12 3.47E-04 CYP4F2,CYP4A11,CYP2J2,CYP2E1,CYP4F3 P450

glycerolipid 12 5.07E-04 PLA2G10,PLA2G1B,PLA2G2A,PLA2G5 metabolic

Fluoropyrimidi 13 2.31E-19 DPYD,UCK1,TK1,UPP1,UMPS,CDA,TYMP,UPP2,UCK2 ne Activity

Salvage pyrimidine 13 1.04E-18 UCK1,UPP1,UCKL1,CDA,UPRT,UPP2,UCK2 ribonucleotide s

Genes involved in 13 4.84E-18 DPYD,UCK1,TK1,UPP1,UMPS,CDA,TYMP,UCK2 Pyrimidine metabolism

Genes involved in 13 1.58E-13 DPYD,UCK1,TK1,UPP1,UMPS,CDA,TYMP,UCK2 Metablism of nucleotides

Pyrimidine 13 7.65E-12 DPYD,TK1,UCKL1,CDA,TYMP,UPP2 Metabolism

219

MAP00240 13 Pyrimidine 5.84E-08 DPYD,TK1,UMPS,CDA,TYMP metabolism

salvages of purine and 13 2.86E-07 UCK1,TK1,CDA,TYMP pyrimidine nucleotides

salvages of pyrimidine 13 5.66E-07 TK1,CDA,TYMP deoxyribonucl eotides

Genes involved in 13 6.77E-06 DPYD,UPP1,TYMP Pyrimidine catabolism

Salvage pyrimidine 13 1.43E-04 TK1,CDA deoxyribonucl eotides

(deoxy)ribose 13 phosphate 4.77E-04 CDA,TYMP degradation

220

ribose and deoxyribose 13 7.14E-04 CDA,TYMP phosphate metabolism

Genes involved in HLA-DRB1,HLA-DRA,HLA-DRB5,HLA-DRB3,HLA-DPB1,HLA- Translocation 14 8.26E-31 DOB,HLA-DPA1,HLA-DQA2,HLA-DQB1,HLA-DQA1,HLA-DMA,HLA- of ZAP-70 to DMB Immunologica l synapse

Genes involved in HLA-DRB1,HLA-DRA,HLA-DRB5,HLA-DRB3,HLA-DPB1,HLA- Phosphorylati 14 2.95E-30 DOB,HLA-DPA1,HLA-DQA2,HLA-DQB1,HLA-DQA1,HLA-DMA,HLA- on of CD3 and DMB TCR zeta chains

Genes HLA-DRB1,HLA-DRA,HLA-DRB5,HLA-DRB3,HLA-DPB1,HLA- 14 involved in 1.59E-29 DOB,HLA-DPA1,HLA-DQA2,HLA-DQB1,HLA-DQA1,HLA-DMA,HLA- PD-1 signaling DMB

221

Genes involved in HLA-DRB1,HLA-DRA,HLA-DRB5,HLA-DRB3,HLA-DPB1,HLA- Generation of 14 5.66E-28 DOB,HLA-DPA1,HLA-DQA2,HLA-DQB1,HLA-DQA1,HLA-DMA,HLA- second DMB messenger molecules

Genes HLA-DRB1,HLA-DRA,HLA-DRB5,HLA-DRB3,HLA-DPB1,HLA- involved in 14 1.60E-26 DOB,HLA-DPA1,HLA-DQA2,HLA-DQB1,HLA-DQA1,HLA-DMA,HLA- Downstream DMB TCR signaling

Genes HLA-DRB1,HLA-DRA,HLA-DRB5,HLA-DRB3,HLA-DPB1,HLA- 14 involved in 1.00E-24 DOB,HLA-DPA1,HLA-DQA2,HLA-DQB1,HLA-DQA1,HLA-DMA,HLA- TCR signaling DMB

Genes involved in HLA-DRB1,HLA-DRA,HLA-DRB5,HLA-DRB3,HLA-DPB1,HLA- 14 Costimulation 3.25E-24 DOB,HLA-DPA1,HLA-DQA2,HLA-DQB1,HLA-DQA1,HLA-DMA,HLA- by the CD28 DMB family

Genes involved in HLA-DRB1,HLA-DRA,HLA-DRB5,HLA-DRB3,HLA-DPB1,HLA- 14 Signaling in 3.07E-15 DOB,HLA-DPA1,HLA-DQA2,HLA-DQB1,HLA-DQA1,HLA-DMA,HLA- Immune DMB system

222

T cell 14 2.38E-06 HLA-DPA1,HLA-DQA2,HLA-DQA1,HLA-DMA,HLA-DMB activation

IL22 Soluble Receptor SOCS3,IL22RA2,STAT3,STAT4,STAT1,STAT2,STAT6,STAT5A,STAT5 15 1.29E-28 Signaling B,JAK2,TYK2,JAK1,JAK3,IL10RA Pathway

JAK-STAT SOCS7,SOCS5,SOCS3,SOCS4,STAT3,STAT4,STAT1,STAT2,STAT6,ST 15 3.26E-25 signaling AT5A,STAT5B,JAK2,TYK2,JAK1,JAK3

Interleukin IL2RA,IL4R,IL2RB,IL3RA,IL6ST,IL5RA,STAT3,STAT4,STAT1,STAT2,S 15 signaling 6.51E-25 TAT6,STAT5A,STAT5B,IL13RA1,JAK3,IL12RB1,IL12RB2,IL10RB,IL11 pathway RA,IL10RA

JAK/STAT SOCS1,STAT3,STAT4,STAT1,STAT6,STAT5A,STAT5B,JAK2,JAK1,JAK 15 signaling 2.22E-17 3 pathway

IL-10 Anti- inflammatory STAT3,STAT4,STAT1,STAT2,STAT6,STAT5A,STAT5B,JAK1,IL10RB,IL 15 5.36E-17 Signaling 10RA Pathway

IL27-mediated IL6ST,STAT3,STAT4,STAT1,STAT2,STAT5A,JAK2,TYK2,JAK1,IL12RB 15 signaling 9.52E-17 1,IL12RB2 events

IL12-mediated SOCS1,IL2RA,IL2RG,IL2RB,STAT3,STAT4,STAT1,STAT6,STAT5A,JAK 15 signaling 2.20E-15 2,TYK2,IL12RB1,IL12RB2 events

IL-4 signaling SOCS1,SOCS5,IL4R,IL2RG,SOCS3,STAT3,STAT1,STAT6,JAK2,TYK2,J 15 7.98E-15 pathway AK1,JAK3

223

IL2-mediated SOCS1,IL2RA,IL2RG,IL2RB,SOCS3,STAT3,STAT1,STAT5A,STAT5B,S 15 signaling 2.17E-14 OCS2,JAK1,JAK3 events

Interferon- gamma SOCS7,SOCS5,IFNGR1,IFNGR2,SOCS3,SOCS4,STAT1,SOCS2,JAK2,J 15 5.25E-14 signaling AK1 pathway

IL4-mediated SOCS1,SOCS5,IL4R,IL2RG,SOCS3,STAT6,STAT5A,STAT5B,JAK2,IL13 15 signaling 6.78E-14 RA1,JAK1,JAK3 events

IL-7 signaling 15 1.14E-12 IL2RA,IL2RG,IL7R,STAT3,STAT1,STAT5A,STAT5B,JAK1,JAK3 pathway

IL-2 Signaling IL2RA,IL2RG,IL2RB,SOCS3,STAT3,STAT1,STAT5A,STAT5B,JAK1,JAK 15 3.65E-12 pathway 3

IL23-mediated 15 signaling 6.59E-11 IL23R,SOCS3,STAT3,STAT4,STAT1,STAT5A,JAK2,TYK2,IL12RB1 events

IL-2 Receptor Beta Chain in 15 8.60E-11 SOCS1,IL2RA,IL2RG,IL2RB,SOCS3,STAT5A,STAT5B,JAK1,JAK3 T cell Activation

Interleukin 4 15 1.77E-10 IL4R,IL2RG,STAT6,JAK2,IL13RA1,TYK2,JAK1,JAK3 (IL-4) Pathway

IL-9 signaling 15 2.68E-10 IL2RG,IL9R,STAT3,STAT1,STAT5B,JAK1,JAK3 pathway

EGF Signaling 15 8.73E-10 STAT3,STAT4,STAT1,STAT2,STAT6,STAT5A,STAT5B,JAK1 Pathway

PDGF 15 Signaling 1.16E-09 STAT3,STAT4,STAT1,STAT2,STAT6,STAT5A,STAT5B,JAK1 Pathway 224

Prolactin PRLR,SOCS1,SOCS3,STAT3,STAT1,STAT5A,STAT5B,SOCS2,JAK2,JA 15 Signaling 1.79E-09 K1 Pathway

IL 2 signaling 15 3.90E-09 IL2RA,IL2RG,IL2RB,STAT5A,STAT5B,JAK1,JAK3 pathway

IL-5 signaling 15 8.19E-09 IL5RA,CSF2RB,STAT3,STAT1,STAT5A,STAT5B,JAK2,JAK1 pathway

Leptin 15 signaling 8.29E-09 SOCS7,LEPR,SOCS3,STAT3,STAT1,STAT5B,SOCS2,JAK2,JAK1 pathway

EPO Receptor 15 1.48E-08 SOCS1,EPOR,STAT3,STAT1,STAT5A,STAT5B,JAK2 Signaling

Bioactive Peptide 15 Induced 1.52E-08 STAT3,STAT4,STAT1,STAT2,STAT6,STAT5A,STAT5B,JAK2 Signaling Pathway

Interleukin 13 15 (IL-13) 1.87E-08 IL4R,JAK2,IL13RA1,TYK2,JAK1 Pathway

Interleukin 13 15 (IL-13) 1.87E-08 IL4R,JAK2,IL13RA1,TYK2,JAK1 Pathway

IL-3 Signaling 15 2.25E-08 IL3RA,IL5RA,CSF2RB,STAT3,STAT5A,STAT5B,JAK2,JAK1 Pathway

PRLR,SOCS1,IL6ST,CNTFR,SOCS3,STAT3,STAT1,STAT2,STAT6,STAT 15 Adipogenesis 2.98E-08 5A,STAT5B

IL2 signaling events 15 4.49E-08 IL2RA,IL2RG,IL2RB,STAT5A,STAT5B,JAK1,JAK3 mediated by STAT5

225

Type I Interferon 15 4.97E-08 IFNAR1,STAT1,STAT2,TYK2,JAK1 (alpha/beta IFN) Pathway

Type II interferon 15 5.47E-08 SOCS1,IFNGR1,IFNGR2,SOCS3,STAT1,STAT2,JAK2,JAK1 signaling (IFNG)

IL-7 Signal 15 5.59E-08 IL2RG,IL7R,STAT5A,STAT5B,JAK1,JAK3 Transduction

Signaling events 15 6.46E-08 PRLR,LEPR,SOCS3,STAT3,STAT5A,STAT5B,JAK2,TYK2 mediated by PTP1B

Th1/Th2 15 Differentiatio 1.22E-07 IL2RA,IL4R,IFNGR1,IFNGR2,IL12RB1,IL12RB2 n

IL 6 signaling 15 3.30E-07 IL6ST,STAT3,JAK2,TYK2,JAK1,JAK3 pathway

IL 4 signaling 15 4.05E-07 IL4R,IL2RG,STAT6,JAK1,JAK3 pathway

PDGF STAT3,STAT4,STAT1,STAT2,STAT6,STAT5A,STAT5B,JAK2,JAK1,JAK 15 signaling 5.29E-07 3 pathway

IL-6 signaling 15 5.65E-07 IL6ST,SOCS3,STAT3,STAT1,JAK2,TYK2,JAK1 pathway

IL6-mediated 15 signaling 1.10E-06 IL6ST,SOCS3,STAT3,STAT1,JAK2,TYK2,JAK1 events

IL 3 signaling 15 2.58E-06 IL3RA,CSF2RB,STAT5A,STAT5B,JAK2 pathway

226

EPO signaling 15 4.66E-06 EPOR,SOCS3,STAT1,STAT5A,STAT5B,JAK2 pathway

NO2- dependent IL 15 5.28E-06 STAT4,JAK2,TYK2,IL12RB1,IL12RB2 12 Pathway in NK cells

Interferon 15 gamma 2.05E-05 IFNGR1,STAT1,JAK2,JAK1 pathway.

IFN-gamma 15 2.46E-05 SOCS1,IFNGR1,STAT3,STAT1,JAK2,JAK1 pathway

IL12 and Stat4 Dependent Signaling 15 2.80E-05 STAT4,JAK2,TYK2,IL12RB1,IL12RB2 Pathway in Th1 Development

TPO Signaling 15 3.52E-05 STAT3,STAT1,STAT5A,STAT5B,JAK2 Pathway

STAT3 15 5.33E-05 STAT3,JAK2,JAK1,JAK3 Pathway

Genes related to IL4 rceptor 15 6.59E-05 SOCS1,IL4R,STAT6,JAK1,JAK3 signaling in B lymphocytes

227

Signaling events mediated by 15 Stem cell 7.88E-05 SOCS1,EPOR,STAT3,STAT1,STAT5A,JAK2 factor receptor (c- Kit)

15 leptin system 7.95E-05 LEPR,SOCS3,STAT3,JAK2

Growth Hormone 15 7.99E-05 SOCS1,GHR,STAT5A,STAT5B,JAK2 Signaling Pathway

Selective expression of chemokine 15 7.99E-05 IL4R,IFNGR1,IFNGR2,IL12RB1,IL12RB2 receptors during T-cell polarization

Kit receptor 15 signaling 9.91E-05 SOCS1,STAT3,STAT1,STAT5A,STAT5B,JAK2 pathway

Inflammatory 15 Response 1.15E-04 IL2RA,IL4R,IL2RG,IL2RB,IL5RA Pathway

Genes involved in 15 Down-stream 2.56E-04 STAT3,STAT1,STAT6,STAT5A,STAT5B signal transduction

228

IL2 signaling events 15 3.90E-04 IL2RA,IL2RG,IL2RB,JAK1,JAK3 mediated by PI3K

Downstream signaling in 15 3.95E-04 IL2RA,IL2RG,IL2RB,IFNAR2,IFNAR1,STAT4 naïve CD8+ T cells

EPO Signaling 15 6.04E-04 EPOR,STAT5A,STAT5B,JAK2 Pathway

EGF receptor 15 signaling 7.89E-04 STAT3,STAT4,STAT1,STAT2,STAT6,STAT5A,STAT5B pathway

RPS6KA1,ARAF,RPS6KA2,NRAS,KRAS,MAPK3,MAPK1,BRAF,MAP2 16 Ras Pathway 3.99E-24 K1,MAP2K2,HRAS,RAF1,RPS6KA6

Genes involved in 16 Signalling to 5.70E-21 NRAS,KRAS,MAPK3,MAPK1,BRAF,MAP2K1,MAP2K2,HRAS,RAF1 p38 via RIT and RIN

The extracellular signal- RPS6KA1,RPS6KA2,KRAS,MAPK3,MAPK1,BRAF,MAP2K1,MAP2K2, 16 9.94E-21 regulated RAF1,RPS6KA6 RAF/MEK/ERK signaling

229

Genes involved in 16 3.26E-20 NRAS,KRAS,MAPK3,MAPK1,BRAF,MAP2K1,MAP2K2,HRAS,RAF1 Frs2-mediated activation

PDGF RPS6KA1,ARAF,RPS6KA2,NRAS,KRAS,MAPK3,MAPK1,BRAF,MAP2 16 signaling 3.30E-20 K1,MAP2K2,HRAS,RAF1,RPS6KA6 pathway

MAPK ARAF,NRAS,KRAS,MAPK3,MAPK1,BRAF,MAP2K1,MAP2K2,HRAS, 16 3.74E-20 Cascade RAF1

Trk receptor signaling RPS6KA1,NRAS,KRAS,MAPK3,MAPK1,BRAF,MAP2K1,HRAS,CREB1 16 mediated by 2.44E-19 ,RAF1 the MAPK pathway

Genes involved in 16 1.78E-18 NRAS,KRAS,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 SHC-mediated signalling

Genes involved in 16 4.63E-18 NRAS,KRAS,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 SOS-mediated signalling

Genes involved in 16 4.63E-18 NRAS,KRAS,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 Grb2 events in EGFR signaling

230

Genes involved in 16 1.08E-17 NRAS,KRAS,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 SHC-related events

Ras signaling 16 in the CD4+ 1.08E-17 NRAS,KRAS,MAPK3,MAPK1,BRAF,MAP2K1,HRAS,RAF1 TCR pathway

MAPKinase RPS6KA1,ARAF,RPS6KA2,MAPK3,MAPK1,BRAF,MAP2K1,MAP2K2, 16 Signaling 2.71E-17 HRAS,CREB1,RAF1 Pathway

Genes involved in 16 1.48E-16 NRAS,KRAS,MAPK3,MAPK1,BRAF,MAP2K1,MAP2K2,HRAS,RAF1 Signalling to ERKs

mTOR RPS6KA1,NRAS,KRAS,MAPK3,MAPK1,BRAF,MAP2K1,MAP2K2,HR 16 signaling 7.27E-16 AS,RAF1 pathway

T cell ARAF,NRAS,KRAS,MAPK3,MAPK1,BRAF,MAP2K1,MAP2K2,HRAS, 16 4.43E-15 activation RAF1

Genes involved in 16 5.57E-15 NRAS,KRAS,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 Signalling to RAS

Genes involved in RPS6KA1,RPS6KA2,NRAS,KRAS,MAPK3,MAPK1,BRAF,MAP2K1,M 16 5.58E-15 Signalling by AP2K2,HRAS,CREB1,RAF1 NGF

ERK1/ERK2 RPS6KA1,RPS6KA2,MAPK3,MAPK1,BRAF,MAP2K1,MAP2K2,CREB 16 MAPK 1.53E-14 1 Pathway

231

Insulin/IGF pathway- mitogen activated RPS6KA1,RPS6KA2,MAPK3,MAPK1,MAP2K1,MAP2K2,RAF1,RPS6 16 2.80E-14 protein kinase KA6 kinase/MAP kinase cascade

Genes involved in TRKA NRAS,KRAS,MAPK3,MAPK1,BRAF,MAP2K1,MAP2K2,HRAS,CREB1, 16 signalling 4.25E-14 RAF1 from the plasma membrane

Genes involved in 16 Down-stream 8.33E-14 NRAS,KRAS,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 signal transduction

Downstream signaling in 16 1.36E-13 NRAS,KRAS,MAPK3,MAPK1,BRAF,MAP2K1,MAP2K2,HRAS,RAF1 naïve CD8+ T cells

Genes involved in NCAM 16 1.56E-13 NRAS,KRAS,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,CREB1,RAF1 signaling for neurite out- growth

232

ErbB1 NRAS,KRAS,MAPK3,MAPK1,BRAF,MAP2K1,MAP2K2,HRAS,CREB1, 16 downstream 1.74E-13 RAF1 signaling

EGF receptor ARAF,NRAS,KRAS,MAPK3,MAPK1,BRAF,MAP2K1,MAP2K2,HRAS, 16 signaling 1.74E-13 RAF1 pathway

ErbB2/ErbB3 16 signaling 5.11E-13 NRAS,KRAS,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 events

CXCR3- mediated 16 5.11E-13 NRAS,KRAS,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 signaling events

Genes involved in 16 1.32E-12 NRAS,KRAS,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 Signaling by EGFR

Phosphorylati on of MEK1 by cdk5/p35 16 down 1.70E-12 MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 regulates the MAP kinase pathway

Genes involved in CREB 16 phophorylatio 2.53E-12 RPS6KA1,RPS6KA2,MAPK1,HRAS,CREB1,RAF1,RPS6KA6 n through the activation of Ras

233

Interleukin RPS6KA1,ARAF,RPS6KA2,NRAS,MAPK3,MAPK1,BRAF,RAF1,RPS6K 16 signaling 3.87E-12 A6 pathway

IL2-mediated 16 signaling 4.25E-12 NRAS,KRAS,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 events

Erk1/Erk2 Mapk 16 4.55E-12 RPS6KA1,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 Signaling pathway

VEGF signaling 16 8.90E-12 ARAF,NRAS,KRAS,MAPK3,MAPK1,BRAF,HRAS,RAF1 pathway

Nongenotropi 16 c Androgen 1.01E-11 MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,CREB1,RAF1 signaling

B cell 16 1.02E-11 ARAF,NRAS,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 activation

Genes involved in Post NMDA 16 1.29E-11 RPS6KA1,RPS6KA2,MAPK1,HRAS,CREB1,RAF1,RPS6KA6 receptor activation events

FGF signaling 16 1.36E-11 ARAF,NRAS,KRAS,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 pathway

Genes involved in 16 1.54E-11 NRAS,KRAS,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 Signaling by PDGF

TSH signaling 16 1.75E-11 RPS6KA1,MAPK3,MAPK1,BRAF,MAP2K1,HRAS,CREB1,RAF1 pathway

234

Roles of fl- arrestin- dependent 16 Recruitment 1.84E-11 MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 of Src Kinases in GPCR Signaling

Genes involved in Activation of NMDA 16 receptor upon 3.19E-11 RPS6KA1,RPS6KA2,MAPK1,HRAS,CREB1,RAF1,RPS6KA6 glutamate binding and postsynaptic events

IL-5 signaling 16 7.09E-11 RPS6KA1,KRAS,MAPK3,MAPK1,MAP2K1,MAP2K2,RAF1 pathway

Genes involved in 16 8.95E-11 NRAS,KRAS,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 IRS-related events

Role of MAL in Rho-Mediated 16 9.93E-11 MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 Activation of SRF

EGF/EGFR RPS6KA1,RPS6KA2,KRAS,MAPK1,BRAF,MAP2K1,MAP2K2,CREB1, 16 Signaling 1.24E-10 RAF1 Pathway

235

Genes involved in 16 MAP kinases 1.45E-10 RPS6KA1,RPS6KA2,MAPK3,MAPK1,MAP2K1,MAP2K2,CREB1 activation in TLR cascade

B Cell Receptor 16 3.17E-10 RPS6KA1,MAPK1,BRAF,MAP2K1,MAP2K2,HRAS,CREB1,RAF1 Signaling Pathway

Multiple antiapoptotic pathways from IGF-1R 16 3.68E-10 RPS6KA1,MAPK3,MAPK1,MAP2K1,HRAS,RAF1 signaling lead to BAD phosphorylati on

Insulin RPS6KA1,RPS6KA2,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1, 16 3.71E-10 Signaling RPS6KA6

Integrin 16 signalling 3.71E-10 ARAF,NRAS,KRAS,MAPK3,BRAF,MAP2K1,MAP2K2,HRAS,RAF1 pathway

Genes 16 involved in 4.16E-10 NRAS,KRAS,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,CREB1,RAF1 Axon guidance

236

Genes involved in TRAF6 Mediated 16 Induction of 5.80E-10 RPS6KA1,RPS6KA2,MAPK3,MAPK1,MAP2K1,MAP2K2,CREB1 the antiviral cytokine IFN- alphaeta cascade

Kit receptor 16 signaling 6.66E-10 RPS6KA1,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 pathway

Genes involved in Toll Like 16 1.28E-09 RPS6KA1,RPS6KA2,MAPK3,MAPK1,MAP2K1,MAP2K2,CREB1 Receptor 3 (TLR3) Cascade

Growth Hormone 16 1.37E-09 RPS6KA1,MAPK3,MAPK1,MAP2K1,HRAS,RAF1 Signaling Pathway

Leptin 16 signaling 1.63E-09 MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,CREB1,RAF1 pathway

Links between 16 Pyk2 and Map 1.72E-09 MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 Kinases

237

Role of fl- arrestins in the activation 16 2.45E-09 MAPK3,MAPK1,MAP2K1,MAP2K2,RAF1 and targeting of MAP kinases

Signaling of Hepatocyte 16 4.00E-09 MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 Growth Factor Receptor

Intracellular Signalling Through 16 Adenosine 4.00E-09 RPS6KA1,MAPK1,BRAF,MAP2K2,HRAS,CREB1 Receptor A2a and Adenosine

Intracellular Signalling Through 16 Adenosine 4.86E-09 RPS6KA1,MAPK1,BRAF,MAP2K2,HRAS,CREB1 Receptor A2b and Adenosine

238

Angiotensin II mediated activation of 16 JNK Pathway 4.86E-09 MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 via Pyk2 dependent signaling

Insulin 16 7.02E-09 RPS6KA1,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 Signalling Prolactin 16 Signaling 7.34E-09 RPS6KA2,MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 Pathway

fMLP induced chemokine 16 gene 8.37E-09 MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 expression in HMC-1 cells

Signaling of Hepatocyte 16 8.37E-09 MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 Growth Factor Receptor

EPHB forward 16 9.93E-09 NRAS,KRAS,MAPK3,MAPK1,MAP2K1,HRAS signaling

Integrin 16 Signaling 9.93E-09 MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 Pathway

Regulation of 16 Actin 1.23E-08 NRAS,KRAS,MAPK3,MAPK1,BRAF,MAP2K1,MAP2K2,RAF1 Cytoskeleton

239

Cadmium induces DNA synthesis and 16 1.35E-08 MAPK3,MAPK1,MAP2K1,HRAS,RAF1 proliferation in macrophages

Genes involved in RPS6KA1,RPS6KA2,NRAS,KRAS,MAPK3,MAPK1,MAP2K1,MAP2K2, 16 Signaling in 1.66E-08 HRAS,CREB1 Immune system

Genes involved in Neuroransmit ter Receptor Binding And 16 1.66E-08 RPS6KA1,RPS6KA2,MAPK1,HRAS,CREB1,RAF1,RPS6KA6 Downstream Transmission In The Postsynaptic Cell

IL-2 Signaling 16 1.88E-08 MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 pathway

Human Cytomegalovir 16 us and Map 1.91E-08 MAPK3,MAPK1,MAP2K1,MAP2K2,CREB1 Kinase Pathways

240

Genes involved in 16 1.97E-08 RPS6KA1,RPS6KA2,MAPK3,MAPK1,MAP2K1,MAP2K2,CREB1 Toll Receptor Cascades

Bioactive Peptide 16 Induced 2.18E-08 MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 Signaling Pathway

16 Angiogenesis 2.24E-08 ARAF,NRAS,KRAS,MAPK3,MAPK1,BRAF,HRAS,RAF1

TCR Signaling 16 2.52E-08 MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,CREB1,RAF1 Pathway

Serotonin Receptor 16 4/6/7 and 2.64E-08 MAPK3,BRAF,MAP2K1,MAP2K2,CREB1 NR3C Signaling

Role of Erk5 in 16 Neuronal 2.64E-08 RPS6KA1,MAPK3,MAPK1,HRAS,CREB1 Survival

Sprouty regulation of 16 2.64E-08 MAPK3,MAPK1,MAP2K1,HRAS,RAF1 tyrosine kinase signals

MAPK 16 signaling 2.89E-08 NRAS,KRAS,MAPK3,MAPK1,BRAF,MAP2K1,MAP2K2,RAF1 pathway

Integrin- 16 mediated cell 5.40E-08 ARAF,MAPK1,BRAF,MAP2K1,MAP2K2,HRAS,RAF1 adhesion

241

Aspirin Blocks Signaling Pathway 16 8.08E-08 MAPK3,MAPK1,MAP2K1,HRAS,RAF1 Involved in Platelet Activation

Role of ERBB2 in Signal 16 8.08E-08 MAPK3,MAPK1,MAP2K1,HRAS,RAF1 Transduction and Oncology

CCR3 signaling 16 1.03E-07 MAPK3,MAPK1,MAP2K1,HRAS,RAF1 in Eosinophils

Erk and PI-3 Kinase Are Necessary for 16 Collagen 1.30E-07 MAPK3,MAPK1,MAP2K1,HRAS,RAF1 Binding in Corneal Epithelia

Genes involved in Nuclear Events (kinase 16 1.30E-07 RPS6KA1,RPS6KA2,MAPK3,MAPK1,CREB1 and transcription factor activation)

242

Antigen binding to B cell receptors activates protein tyrosine 16 1.30E-07 RPS6KA1,MAPK3,MAPK1,MAP2K1,HRAS kinases, such as the Src family, which ultimate activate MAP kinases.

CXCR4 16 Signaling 1.30E-07 MAPK3,MAPK1,MAP2K1,HRAS,RAF1 Pathway

Fc-epsilon receptor I 16 1.59E-07 MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 signaling in mast cells

EPO Receptor 16 2.01E-07 MAPK3,MAPK1,MAP2K1,MAP2K2,RAF1 Signaling

Neurotrophic factor- 16 mediated Trk 2.16E-07 NRAS,KRAS,MAPK3,MAPK1,MAP2K1,HRAS receptor signaling

16 Endothelins 2.39E-07 MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1

243

Transcription factor CREB 16 and its 2.46E-07 RPS6KA1,MAPK3,MAPK1,HRAS,CREB1 extracellular signals

Genes involved in Transmission 16 3.74E-07 RPS6KA1,RPS6KA2,MAPK1,HRAS,CREB1,RAF1,RPS6KA6 across Chemical Synapses

Genes involved in MAPK 16 targets/Nucle 4.34E-07 RPS6KA1,RPS6KA2,MAPK3,MAPK1,CREB1 ar events mediated by MAP kinases

Genes involved in 16 Innate 5.14E-07 RPS6KA1,RPS6KA2,MAPK3,MAPK1,MAP2K1,MAP2K2,CREB1 Immunity Signaling

Hypothetical Network for 16 6.12E-07 MAPK3,MAPK1,MAP2K1,MAP2K2,CREB1 Drug Addiction

Serotonin HTR1 Group 16 6.12E-07 MAPK3,BRAF,MAP2K1,MAP2K2,CREB1 and FOS Pathway

244

Endothelin 16 signaling 7.00E-07 ARAF,MAPK3,MAPK1,MAP2K1,MAP2K2,RAF1 pathway

Signaling events mediated by 16 Hepatocyte 8.23E-07 MAPK3,MAPK1,MAP2K1,MAP2K2,HRAS,RAF1 Growth Factor Receptor (c- Met)

EGF receptor (ErbB1) 16 8.43E-07 NRAS,KRAS,MAPK3,MAPK1,HRAS signaling pathway

Signaling Pathway from 16 9.83E-07 MAPK3,MAP2K1,HRAS,CREB1,RAF1 G-Protein Families

G alpha s 16 1.16E-06 MAPK1,BRAF,CREB1,RAF1 Pathway

altered extracellular signal- 16 1.63E-06 KRAS,BRAF,RAF1 regulated RAF/MEK/ERK signaling

Fc Epsilon Receptor I 16 1.74E-06 MAPK3,MAPK1,MAP2K1,HRAS,RAF1 Signaling in Mast Cells

245

IL 3 signaling 16 3.19E-06 MAPK3,MAP2K1,HRAS,RAF1 pathway

IL-3 Signaling 16 3.66E-06 MAPK3,MAPK1,MAP2K1,HRAS,RAF1 Pathway

Ceramide 16 signaling 4.11E-06 MAPK3,MAPK1,MAP2K1,MAP2K2,RAF1 pathway

Keratinocyte 16 Differentiatio 4.11E-06 MAPK3,MAPK1,MAP2K1,HRAS,RAF1 n

IL-9 signaling 16 4.25E-06 MAPK3,MAPK1,MAP2K1,MAP2K2 pathway

Serotonin Receptor 2 16 and ELK- 4.25E-06 MAPK3,MAP2K1,MAP2K2,RAF1 SRF/GATA4 signaling

Genes related to the insulin 16 5.70E-06 RPS6KA1,RPS6KA2,MAPK3,MAPK1,RAF1 receptor pathway

Nerve growth factor 16 7.13E-06 MAPK3,MAP2K1,HRAS,RAF1 pathway (NGF)

Signaling events mediated by 16 Stem cell 7.74E-06 MAPK3,MAP2K1,MAP2K2,HRAS,RAF1 factor receptor (c- Kit)

246

Inflammation mediated by chemokine 16 7.89E-06 ARAF,NRAS,KRAS,MAPK3,MAPK1,HRAS,RAF1 and cytokine signaling pathway

EPO Signaling 16 9.03E-06 MAPK3,MAP2K1,HRAS,RAF1 Pathway

NFAT and Hypertrophy of the heart 16 9.40E-06 MAPK3,MAPK1,MAP2K1,HRAS,RAF1 (Transcription in the broken heart)

ErbB signaling 16 1.03E-05 ARAF,KRAS,MAPK1,MAP2K1,HRAS pathway

Estrogen 16 signaling 1.13E-05 MAPK1,BRAF,MAP2K1,CREB1 pathway

Genes involved in 16 1.39E-05 RPS6KA1,RPS6KA2,MAPK3,MAPK1 ERK/MAPK targets

IGF-1 16 Signaling 1.39E-05 MAPK3,MAP2K1,HRAS,RAF1 Pathway Ceramide 16 signaling 1.70E-05 MAPK3,MAPK1,MAP2K1,RAF1 pathway

247

IL 6 signaling 16 1.70E-05 MAPK3,MAP2K1,HRAS,RAF1 pathway

Insulin 16 Signaling 1.70E-05 MAPK3,MAP2K1,HRAS,RAF1 Pathway

IL 2 signaling 16 1.70E-05 MAPK3,MAP2K1,HRAS,RAF1 pathway

VEGFR3 signaling in 16 2.05E-05 RPS6KA1,MAPK3,MAPK1,CREB1 lymphatic endothelium

Inhibition of Cellular 16 2.05E-05 MAPK3,MAP2K1,HRAS,RAF1 Proliferation by Gleevec

Ras Signaling 16 2.05E-05 MAPK3,MAP2K1,HRAS,RAF1 Pathway

TPO Signaling 16 2.46E-05 MAPK3,MAP2K1,HRAS,RAF1 Pathway

BCR signaling 16 2.62E-05 MAPK3,MAPK1,MAP2K1,HRAS,RAF1 pathway

IL-7 signaling 16 2.93E-05 MAPK3,MAPK1,MAP2K1,MAP2K2 pathway

Influence of Ras and Rho 16 proteins on 3.46E-05 MAPK3,MAPK1,HRAS,RAF1 G1 to S Transition

248

CDC42 16 signaling 3.54E-05 MAPK3,MAPK1,BRAF,HRAS,RAF1 events

FSH signaling 16 4.72E-05 MAPK3,MAPK1,CREB1,RAF1 pathway

Cellular roles 16 of Anthrax 7.23E-05 MAPK3,MAPK1,MAP2K1,MAP2K2 toxin

EGF Signaling 16 7.23E-05 MAPK3,MAP2K1,HRAS,RAF1 Pathway

PDGF 16 Signaling 8.26E-05 MAPK3,MAP2K1,HRAS,RAF1 Pathway

MicroRNAs in 16 cardiomyocyt 9.45E-05 MAPK3,MAPK1,MAP2K1,MAP2K2,RAF1 e hypertrophy

How Progesterone 16 Initiates 1.06E-04 RPS6KA1,MAPK3,MAPK1,HRAS Oocyte Membrane

BCR signaling 16 1.20E-04 MAPK3,MAP2K1,HRAS,RAF1 pathway

Trk receptor signaling 16 mediated by 1.20E-04 NRAS,KRAS,HRAS,CREB1 PI3K and PLC- gamma

249

TGF-beta 16 signaling 1.57E-04 NRAS,KRAS,MAPK3,MAPK1,HRAS pathway Focal 16 1.61E-04 ARAF,MAPK1,BRAF,MAP2K1,MAP2K2,RAF1 Adhesion

IL-2 Receptor Beta Chain in 16 1.68E-04 MAPK3,MAPK1,HRAS,RAF1 T cell Activation

Signaling events 16 regulated by 1.88E-04 MAPK3,MAPK1,HRAS,CREB1 Ret tyrosine kinase

B Cell Antigen 16 1.88E-04 MAPK1,MAP2K1,MAP2K2,RAF1 Receptor

Senescence 16 and 2.03E-04 MAPK1,BRAF,MAP2K1,HRAS,RAF1 Autophagy

Internalization 16 2.08E-04 NRAS,KRAS,HRAS,RAF1 of ErbB1

Differentiatio n Pathway in PC12 Cells; this is a 16 2.54E-04 MAPK3,MAPK1,BRAF,CREB1 specific case of PAC1 Receptor Pathway.

250

IL-6 signaling 16 2.54E-04 MAPK3,MAPK1,MAP2K1,MAP2K2 pathway

Osteopontin 16 2.67E-04 MAPK3,MAPK1,MAP2K1 Signaling

T Cell Receptor 16 4.03E-04 MAPK3,MAP2K1,HRAS,RAF1 Signaling Pathway

Genes involved in 16 4.61E-04 NRAS,KRAS,HRAS p38MAPK events

IL-1 signaling 16 5.63E-04 MAPK3,MAPK1,MAP2K1,MAP2K2 pathway

The TrkA receptor binds nerve growth factor to 16 activate MAP 7.32E-04 MAP2K1,MAP2K2,HRAS kinase pathways and promote cell growth.

DVL1,WNT1,FZD6,FZD4,FZD1,FZD9,FZD8,FZD7,APC,GSK3B,PLCB1, Wnt signaling 17 2.26E-37 WNT3,FRAT1,GNAQ,FZD10,FZD3,APC2,LRP5,FZD2,LRP6,FRAT2,PL network CB4,PLCB3,PLCB2,WNT4,FZD5,DVL2,DVL3,AXIN2,AXIN1

251

Wnt Signaling DVL1,WNT1,FZD6,FZD4,FZD1,FZD9,FZD8,FZD7,APC,GSK3B,WNT3, 17 Pathway and 4.07E-35 FRAT1,FZD10,FZD3,LRP5,FZD2,LRP6,WNT4,FZD5,DVL2,DVL3,AXIN Pluripotency 2,AXIN1

Wnt signaling DVL1,WNT1,FZD6,FZD1,FZD9,FZD8,FZD7,APC,GSK3B,WNT3,FRAT 17 7.92E-31 network 1,FZD10,FZD3,FZD2,WNT4,FZD5,DVL2,DVL3,AXIN1

Genes related to Wnt- DVL1,WNT1,FZD6,FZD4,FZD1,FZD8,FZD7,APC,GSK3B,WNT3,FRAT 17 mediated 4.17E-27 1,FZD3,LRP5,FZD2,LRP6,WNT4,FZD5,DVL2,AXIN1 signal transduction

Alzheimer disease- DVL1,WNT1,FZD6,FZD4,FZD1,FZD9,FZD8,FZD7,GSK3B,WNT3,FZD 17 1.71E-24 presenilin 10,FZD3,LRP5,FZD2,LRP6,WNT4,FZD5,DVL2,DVL3 pathway

Wnt signaling WNT1,FZD6,FZD4,FZD1,FZD9,FZD8,FZD7,WNT3,FZD10,LRP5,FZD2 17 1.83E-22 network ,LRP6,FZD5

canonical Wnt DVL1,WNT1,FZD1,APC,GSK3B,WNT3,FZD10,LRP5,LRP6,WNT4,AXI 17 3.61E-17 signaling N2,AXIN1

Genes involved in Class B/2 WNT1,FZD6,FZD4,FZD1,FZD9,FZD8,FZD7,WNT3,FZD10,FZD3,FZD2 17 1.74E-15 (Secretin ,WNT4,FZD5 family receptors)

252

17 Wnt signaling 9.98E-15 DVL1,WNT1,PLCB1,WNT3,PLCB4,PLCB3,PLCB2,WNT4,DVL2,DVL3

the planar cell 17 polarity Wnt 6.94E-14 DVL1,FZD6,VANGL2,FZD9,FZD7,PRICKLE1,FZD3,DVL2 signaling

Cadherin WNT1,FZD6,FZD4,FZD1,FZD9,FZD8,FZD7,GSK3B,WNT3,FZD10,FZ 17 signaling 2.42E-13 D3,FZD2,WNT4,FZD5 pathway

Canonical Wnt 17 signaling 6.87E-13 DVL1,APC,GSK3B,LRP6,FZD5,DVL2,DVL3,AXIN2,AXIN1 pathway

DVL1,WNT1,FZD1,APC,GSK3B,FZD3,APC2,FZD2,FZD5,DVL2,DVL3, 17 Angiogenesis 5.64E-12 AXIN2,AXIN1

DNA damage response (only 17 2.01E-10 DVL1,WNT1,APC,GSK3B,WNT3,FRAT1,WNT4,DVL2,DVL3,AXIN1 ATM dependent)

Wnt signaling 17 1.99E-09 DVL1,WNT1,FZD1,APC,GSK3B,FRAT1,AXIN1 network

Presenilin action in 17 2.64E-09 DVL1,WNT1,FZD1,APC,GSK3B,AXIN1 Notch and Wnt signaling

253

Presenilin action in 17 3.09E-09 DVL1,WNT1,FZD1,APC,GSK3B,FRAT1,LRP6,AXIN1 Notch and Wnt signaling

Multi-step Regulation of 17 4.39E-09 DVL1,WNT1,FZD1,APC,GSK3B,AXIN1 Transcription by Pitx2

Inactivation of Gsk3 by AKT causes 17 accumulation 2.50E-07 DVL1,WNT1,FZD1,APC,GSK3B,AXIN1 of b-catenin in Alveolar Macrophages

altered 17 canonical Wnt 3.18E-07 APC,LRP5,AXIN2,AXIN1 signaling

Wnt/beta- 17 catenin 6.14E-07 DVL1,APC,GSK3B,FRAT1,AXIN2,AXIN1 Pathway

Neural Crest 17 Differentiatio 7.32E-07 DVL1,WNT1,GSK3B,FZD3,DVL2,DVL3,AXIN2,AXIN1 n

Genes involved in WNT1,FZD6,FZD4,FZD1,FZD9,FZD8,FZD7,WNT3,FZD10,FZD3,FZD2 17 7.36E-07 GPCR ligand ,WNT4,FZD5 binding

254

ALK in cardiac 17 1.90E-06 DVL1,WNT1,FZD1,APC,GSK3B,AXIN1 myocytes

Genes involved in Regulation of 17 Insulin 3.61E-06 PLCB1,GNAQ,GNAO1,PLCB3,PLCB2 Secretion by Free Fatty Acids

Genes involved in Regulation of 17 6.10E-06 PLCB1,GNAQ,GNAO1,PLCB3,PLCB2 Insulin Secretion by Acetylcholine

MicroRNAs in 17 cardiomyocyt 1.25E-05 DVL1,FZD1,GSK3B,LRP5,FZD2,LRP6,PLCB2 e hypertrophy

Genes involved in 17 PLC beta 1.11E-04 PLCB1,GNAQ,PLCB4,PLCB3,PLCB2 mediated events

255

Plasma membrane 17 estrogen 1.44E-04 PLCB1,GNAQ,GNAO1,PLCB3,PLCB2 receptor signaling

Histamine H1 receptor 17 mediated 1.85E-04 PLCB1,GNAQ,PLCB4,PLCB3,PLCB2 signaling pathway

Alzheimers 17 2.00E-04 GSK3B,PLCB1,GNAQ,PLCB4,PLCB3,PLCB2 Disease Genes involved in 17 2.69E-04 PLCB1,GNAQ,GNAO1,PLCB4,PLCB3,PLCB2 Opioid Signalling

2- arachidonoylgl 17 3.20E-04 PLCB1,PLCB3,PLCB2 ycerol biosynthesis

Alpha adrenergic 17 receptor 3.62E-04 PLCB1,PLCB4,PLCB3,PLCB2 signaling pathway

inositol 17 phosphate 4.42E-04 PLCB1,PLCB4,PLCB3,PLCB2 metabolic

Endogenous 17 cannabinoid 5.33E-04 PLCB1,GNAO1,PLCB3,PLCB2 signaling

256

Oxytocin receptor 17 mediated 7.27E-04 PLCB1,GNAQ,PLCB4,PLCB3,PLCB2 signaling pathway

Thyrotropin- releasing hormone 17 8.70E-04 PLCB1,GNAQ,PLCB4,PLCB3,PLCB2 receptor signaling pathway

Table 8 – Common Enriched pathway between modules 3, 8 and 17

Common pathways between modules 3, 8 and 17 Angiogenesis MicroRNAs in cardiomyocyte hypertrophy

Table 9- Common Enriched pathway between modules 3 and 8

Common Enriched pathway between modules 3 and 8 AMPK signaling Axon guidance mediated by netrin B Cell Antigen Receptor B Cell Receptor Signaling Pathway CXCR4-mediated signaling events Class I PI3K signaling events EGF receptor (ErbB1) signaling pathway EGF receptor signaling pathway EGF/EGFR Signaling Pathway Endothelin signaling pathway FGF signaling pathway Genes involved in Collagen-mediated activation cascade Genes involved in Downstream TCR signaling Genes involved in Downstream signaling of activated FGFR Genes involved in Formation of Platelet plug Genes involved in G-protein beta:gamma signalling Genes involved in Hemostasis Genes involved in Platelet Activation Genes involved in TCR signaling

257

Genes involved in TRKA signalling from the plasma membrane Genes related to chemotaxis Hypoxia response via HIF activation Inflammation mediated by chemokine and cytokine signaling pathway Insulin Signaling Insulin/IGF pathway-protein kinase B signaling cascade Members of the BCR signaling pathway Nephrin/Neph1 signaling in the kidney podocyte PDGF signaling pathway PI3 kinase pathway T cell activation VEGF signaling pathway p53 pathway p53 pathway feedback loops 2 phosphatidylinositol 3-kinase-Akt signaling

Table 10- Common enriched pathways between modules 8 and 17

Common pathway between md8&md17 Inactivation of Gsk3 by AKT causes accumulation of b-catenin in Alveolar Macrophages Wnt/beta-catenin Pathway

Table 11- Common enriched pathways between modules 3 and 17

Common pathways between modules 3 and 17 Alzheimers Disease Wnt signaling

Table 12– Common pathways between modules 8 and 16

Common pathways between modules 8 and 16 Angiogenesis B Cell Antigen Receptor B Cell Receptor Signaling Pathway BCR signaling pathway CXCR3-mediated signaling events Differentiation Pathway in PC12 Cells; this is a specific case of PAC1 Receptor Pathway. EGF receptor (ErbB1) signaling pathway EGF receptor signaling pathway EGF/EGFR Signaling Pathway

258

EPO Receptor Signaling Endothelin signaling pathway ErbB signaling pathway ErbB1 downstream signaling ErbB2/ErbB3 signaling events Estrogen signaling pathway FGF signaling pathway Fc-epsilon receptor I signaling in mast cells Focal Adhesion Genes involved in IRS-related events Genes involved in Signaling in Immune system Genes involved in Signalling by NGF Genes involved in TRKA signalling from the plasma membrane Genes related to the insulin receptor pathway Human Cytomegalovirus and Map Kinase Pathways IGF-1 Signaling Pathway IL-1 signaling pathway IL-2 Receptor Beta Chain in T cell Activation IL-5 signaling pathway IL-7 signaling pathway Inflammation mediated by chemokine and cytokine signaling pathway Influence of Ras and Rho proteins on G1 to S Transition Inhibition of Cellular Proliferation by Gleevec Integrin signalling pathway Interleukin signaling pathway Intracellular Signalling Through Adenosine Receptor A2a and Adenosine Intracellular Signalling Through Adenosine Receptor A2b and Adenosine Leptin signaling pathway MAPK signaling pathway MicroRNAs in cardiomyocyte hypertrophy Multiple antiapoptotic pathways from IGF-1R signaling lead to BAD phosphorylation NFAT and Hypertrophy of the heart (Transcription in the broken heart) PDGF signaling pathway Prolactin Signaling Pathway Ras Signaling Pathway Regulation of Actin Cytoskeleton Role of Erk5 in Neuronal Survival Signaling events mediated by Hepatocyte Growth Factor Receptor (c-Met) 259

Signaling events mediated by Stem cell factor receptor (c-Kit) Signaling events regulated by Ret tyrosine kinase T cell activation TCR Signaling Pathway TSH signaling pathway The TrkA receptor binds nerve growth factor to activate MAP kinase pathways and promote cell growth. Transcription factor CREB and its extracellular signals VEGF signaling pathway VEGFR3 signaling in lymphatic endothelium

260

REFERENCES

Adamcsek, B., et al. (2006). "CFinder: locating cliques and overlapping modules in biological networks." Bioinformatics 22(8): 1021-1023.

Albert, R., et al. (2000). "Error and attack tolerance of complex networks." Nature 406(6794): 378-382.

Albert, R. and R.-S. Wang (2009). Chapter 11 - Discrete Dynamic Modeling of Cellular Signaling Networks. Methods in Enzymology. L. J. Michael and B. Ludwig, Academic Press. Volume 467: 281-306.

Alon, U. (2003). "Biological networks: The tinkerer as an engineer." Science 301(5641): 1866- 1867.

Aluru, M., et al. (2013). "Reverse engineering and analysis of large genome-scale gene networks." Nucleic Acids Research 41(1): e24-e24.

Auyang, S. Y. (1998). Foundations of complex-system theories : in economics, evolutionary biology, and statistical physics. Cambridge, U.K. ; New York, Cambridge University Press.

Babu, M. M., et al. (2004). "Structure and evolution of transcriptional regulatory networks." Current opinion in structural biology 14(3): 283-291.

Barabási, A.-L. and Z. N. Oltvai (2004). "Network biology: understanding the cell's functional organization." Nature Reviews Genetics 5(2): 101-113.

Barabasi, A. L. and R. Albert (1999). "Emergence of scaling in random networks." Science 286(5439): 509-512.

Berg, J., et al. (2002). Biochemistry. New York, W H Freeman.

Blankesteijn, W. M., et al. (2000). "Beta-catenin, an inducer of uncontrolled cell proliferation and migration in malignancies, is localized in the cytoplasm of vascular endothelium during neovascularization after myocardial infarction." Am J Pathol 157(3): 877-883.

Borate, B., et al. (2009). "Comparison of threshold selection methods for microarray gene co- expression matrices." BMC Res Notes 2(1): 240.

Boudier, H. A. (1999). "Arteriolar and capillary remodelling in hypertension." Drugs 58 Spec No 1: 37-40.

261

Boulesteix, A.-L. and K. Strimmer (2005). "Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach." Theoretical Biology and Medical Modelling 2(1): 23.

Brandes, U. (2001). "A faster algorithm for betweenness centrality*." The Journal of Mathematical Sociology 25(2): 163-177.

Carmeliet, P. (2003). "Angiogenesis in health and disease." Nature medicine 9(6).

Carmeliet, P. and R. K. Jain (2000). "Angiogenesis in cancer and other diseases." Nature 407(6801): 249-257.

Chen, J., et al. (2009). "ToppGene Suite for gene list enrichment analysis and candidate gene prioritization." Nucleic Acids Research 37(suppl 2): W305-W311.

Chierichetti, F., et al. (2012). Are web users really Markovian? Proceedings of the 21st international conference on World Wide Web. Lyon, France, ACM: 609-618.

Clauset, A., et al. (2004). "Finding community structure in very large networks." Physical Review E 70(6): 066111.

Clevers, H. (2006). "Wnt/beta-catenin signaling in development and disease." Cell 127(3): 469- 480.

Conzelmann, H., et al. (2006). "A domain-oriented approach to the reduction of combinatorial complexity in signal transduction networks." BMC bioinformatics 7(1): 34.

Crick, F. (1970). "Central dogma of molecular biology." Nature 227(5258): 561-563.

Crick, F. H. (1958). "On protein synthesis." Symp Soc Exp Biol 12: 138-163.

Cross, M. J. and L. Claesson-Welsh (2001). "FGF and VEGF function in angiogenesis: signalling pathways, biological responses and therapeutic inhibition." Trends in Pharmacological Sciences 22(4): 201-207. da Costa Martins, P. A., et al. (2008). "Conditional dicer gene deletion in the postnatal myocardium provokes spontaneous cardiac remodeling." Circulation 118(15): 1567-1576.

Dandekar, T., et al. (1998). "Conservation of gene order: a fingerprint of proteins that physically interact." Trends in Biochemical Sciences 23(9): 324-328.

Daneman, R., et al. (2009). "Wnt/beta-catenin signaling is required for CNS, but not non-CNS, angiogenesis." Proc Natl Acad Sci U S A 106(2): 641-646. de la Torre, J. C. (2002). "Alzheimer's disease: how does it start?" J Alzheimers Dis 4(6): 497- 512.

262

del Sol, A. and P. O'Meara (2005). "Small-world network approach to identify key residues in protein–protein interaction." Proteins: Structure, Function, and Bioinformatics 58(3): 672-682.

Dhillon, A. S., et al. (2007). "MAP kinase signalling pathways in cancer." Oncogene 26(22): 3279-3290.

Dongen, S. and C. Abreu-Goodger (2012). Using MCL to Extract Clusters from Networks. Bacterial Molecular Networks. J. Helden, A. Toussaint and D. Thieffry, Springer New York. 804: 281-295.

Dongen, S. v. (2000). Graph Clustering by Flow Simulation, University of Utrecht.

Enright, A. J., et al. (2002). "An efficient algorithm for large-scale detection of protein families." Nucleic Acids Research 30(7): 1575-1584.

Erdos, P. and A. Rényi (1960). "On the evolution of random graphs." Publ. Math. Inst. Hungar. Acad. Sci 5: 17-61.

Fan, L., et al. (2005). "A Novel Role of p38α MAPK in Mitotic Progression Independent of Its Kinase Activity." Cell Cycle 4(11): 1616-1624.

Folkman, J. (1990). "What is the evidence that tumors are angiogenesis dependent?" Journal of the National Cancer Institute 82(1): 4-7.

Fortunato, S. (2010). "Community detection in graphs." Physics Reports-Review Section of Physics Letters 486(3-5): 75-174.

Fox, S. B., et al. (2001). "Angiogenesis: pathological, prognostic, and growth-factor pathways and their link to trial design and anticancer drugs." The Lancet Oncology 2(5): 278-289.

Freeman, L. C. (1977). "Set of Measures of Centrality Based on Betweenness." Sociometry 40(1): 35-41.

Freeman, L. C., et al. (1991). "Centrality in valued graphs: A measure of betweenness based on network flow." Social Networks 13(2): 141-154.

Frey, N. and E. N. Olson (2003). "Cardiac hypertrophy: the good, the bad, and the ugly." Annu Rev Physiol 65: 45-79.

Girvan, M. and M. E. Newman (2002). "Community structure in social and biological networks." Proceedings of the National Academy of Sciences 99(12): 7821-7826.

Goh, K.-I., et al. (2007). "The human disease network." Proceedings of the National Academy of Sciences 104(21): 8685-8690.

263

Goodwin, A. M. and P. A. D'Amore (2002). "Wnt signaling in the vasculature." Angiogenesis 5(1-2): 1-9.

Goodwin, A. M., et al. (2006). "Cultured endothelial cells display endogenous activation of the canonical Wnt signaling pathway and express multiple ligands, receptors, and secreted modulators of Wnt signaling." Dev Dyn 235(11): 3110-3120.

Grieco, L., et al. (2013). "Integrative Modelling of the Influence of MAPK Network on Cancer Cell Fate Decision." PLoS Comput Biol 9(10): e1003286.

Guare, J. (1992). Six degrees of separation, Dramatists Play Service.

Halawani, D., et al. (2004). "p38 MAP kinase signaling is necessary for rat chondrosarcoma cell proliferation." Oncogene 23(20): 3726-3731.

Hintze, A. and C. Adami (2008). "Evolution of complex modular biological networks." PLoS computational biology 4(2): e23.

Holme, P., et al. (2002). "Attack vulnerability of complex networks." Physical Review E 65(5): 056109.

Jacovitti, G. and G. Scarano (1993). "Discrete-Time Techniques for Time-Delay Estimation." Ieee Transactions on Signal Processing 41(2): 525-533.

Jeong, H., et al. (2002). "Prediction of protein essentiality based on genomic data." ComPlexUs 1(1): 19-28.

Jeong, H., et al. (2000). "The large-scale organization of metabolic networks." Nature 407(6804): 651-654.

Jiang, W., et al. (2008). "Constructing disease-specific gene networks using pair-wise relevance metric: application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements." BMC Systems Biology 2(1): 72.

Junttila, M. R., et al. (2008). "Phosphatase-mediated crosstalk between MAPK signaling pathways in the regulation of cell survival." The FASEB Journal 22(4): 954-965.

Kauffman, S. (1969). "Homeostasis and Differentiation in Random Genetic Control Networks." Nature 224(5215): 177-178.

Kauffman, S. A. (1969). "Metabolic stability and epigenesis in randomly constructed genetic nets." Journal of Theoretical Biology 22(3): 437-467.

Kaufman, M., et al. (1999). "A logical analysis of T cell activation and anergy." Proceedings of the National Academy of Sciences 96(7): 3894-3899.

264

Kochen, M. (1989). The small world, Ablex Norwood, NJ.

Koschützki, D. and F. Schreiber (2004). Comparison of Centralities for Biological Networks. German Conference on Bioinformatics.

Krupinski, J., et al. (1994). "Role of angiogenesis in patients with cerebral ischemic stroke." Stroke 25(9): 1794-1798.

Lancichinetti, A. and S. Fortunato (2009). "Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities." Phys Rev E Stat Nonlin Soft Matter Phys 80(1 Pt 2): 016118.

Lancichinetti, A. and S. Fortunato (2009). "Community detection algorithms: a comparative analysis." Phys Rev E Stat Nonlin Soft Matter Phys 80(5 Pt 2): 056117.

Lancichinetti, A., et al. (2008). "Benchmark graphs for testing community detection algorithms." Phys Rev E Stat Nonlin Soft Matter Phys 78(4 Pt 2): 046110.

Lee, T. I., et al. (2002). "Transcriptional Regulatory Networks in Saccharomyces cerevisiae." Science 298(5594): 799-804.

Levy, L. and C. S. Hill (2006). "Alterations in components of the TGF-β superfamily signaling pathways in human cancer." Cytokine & growth factor reviews 17(1): 41-58.

Li, F., et al. (2004). "The yeast cell-cycle network is robustly designed." Proceedings of the National Academy of Sciences of the United States of America 101(14): 4781-4786.

Li, X., et al. (2006). "Discovery of Time-Delayed Gene Regulatory Networks based on temporal gene expression profiling." BMC bioinformatics 7: 26.

Li, Z. and C. Chan (2004). "Extracting novel information from gene expression data." Trends in Biotechnology 22(8): 381-383.

Liu, G., et al. (2009). "Complex discovery from weighted PPI networks." Bioinformatics 25(15): 1891-1897.

Lodish, H., et al. (2000). Molecular Cell Biology. New York, W. H. Freeman.

Luce, R. D. and A. D. Perry (1949). "A Method of Matrix Analysis of Group Structure." Psychometrika 14(2): 95-116.

Managbanag, J. R., et al. (2008). "Shortest-Path Network Analysis Is a Useful Approach toward Identifying Genetic Determinants of Longevity." PLoS ONE 3(11): e3802.

Manfield, I. W., et al. (2006). "Arabidopsis Co-expression Tool (ACT): web server tools for microarray-based gene expression analysis." Nucleic Acids Research 34(suppl 2): W504-W509.

265

Masckauchan, T. N., et al. (2006). "Wnt5a signaling induces proliferation and survival of endothelial cells in vitro and expression of MMP-1 and Tie-2." Mol Biol Cell 17(12): 5163- 5172.

Maslov, S. and K. Sneppen (2002). "Specificity and stability in topology of protein networks." Science Signaling 296(5569): 910.

Mavria, G., et al. (2006). "ERK-MAPK signaling opposes Rho-kinase to promote endothelial cell survival and sprouting during angiogenesis." Cancer Cell 9(1): 33-44.

Michaelis, L. and M. Menten (1913). "Die Kinetik der Invertinwirkung." Biochemische Zeitschrift 49: 333 - 369.

Milgram, S. (1967). "The small world problem." Psychology today 2(1): 60-67.

Mo, Y. Y. (2012). "MicroRNA regulatory networks and human disease." Cell Mol Life Sci 69(21): 3529-3531.

Mohyedinbonab, E., et al. (2013). Time delay estimation in gene regulatory networks. System of Systems Engineering (SoSE), 2013 8th International Conference on.

Müssel, C., Hopfensitz, M., & Kestler, H.A. (2013). BoolNet package vignette.

Nabieva, E., et al. (2005). "Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps." Bioinformatics 21(suppl 1): i302-i310.

Naga Prasad, S. V., et al. (2009). "Unique MicroRNA Profile in End-stage Heart Failure Indicates Alterations in Specific Cardiovascular Signaling Networks." Journal of Biological Chemistry 284(40): 27487-27499.

Naldi, A., et al. (2009). "Logical modelling of regulatory networks with GINsim 2.3." Biosystems 97(2): 134-139.

Nepusz, T., et al. (2012). "Detecting overlapping protein complexes in protein-protein interaction networks." Nature methods 9(5): 471-472.

Newman, A. C. and C. C. Hughes (2012). "Macrophages and angiogenesis: a role for Wnt signaling." Vasc Cell 4(1): 13.

Newman, M. (2008). "The mathematics of networks." The new palgrave encyclopedia of economics 2: 1-12.

Newman, M. E. (2002). "Assortative mixing in networks." Phys Rev Lett 89(20): 208701.

266

Newman, M. E. (2004). "Fast algorithm for detecting community structure in networks." Physical Review E 69(6): 066133.

Newman, M. E. and M. Girvan (2004). "Finding and evaluating community structure in networks." Physical Review E 69(2): 026113.

Newman, M. E. J. (2001). "Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality." Physical Review E 64(1): 016132.

Newman, M. E. J. (2005). "A measure of betweenness centrality based on random walks." Social Networks 27(1): 39-54.

Newman, M. E. J. (2006). "Modularity and community structure in networks." Proceedings of the National Academy of Sciences 103(23): 8577-8582.

Özgür, A., et al. (2008). "Identifying gene-disease associations using centrality on a literature mined gene-interaction network." Bioinformatics 24(13): i277-i285.

Perkins, A. and M. Langston (2009). "Threshold selection in gene co-expression networks using spectral graph theory techniques." BMC bioinformatics 10(Suppl 11): S4.

Pfitzner, R., et al. (2013). "Betweenness preference: quantifying correlations in the topological dynamics of temporal networks." Physical review letters 110(19): 198701.

Pinney, J. W. and D. R. Westhead (2006). "Betweenness-based decomposition methods for social and biological networks." Interdisciplinary Statistics and Bioinformatics: 87-90.

Polverini, P. (2002). "Angiogenesis in health and disease: insights into basic mechanisms and therapeutic opportunities." Journal of dental education 66(8): 962-975.

Polverini, P. J. (2002). "Angiogenesis in health and disease: insights into basic mechanisms and therapeutic opportunities." Journal of dental education 66(8): 962-975.

Pons, P. and M. Latapy (2005). Computing Communities in Large Networks Using Random Walks. Computer and Information Sciences - ISCIS 2005. p. Yolum, T. Güngör, F. Gürgen and C. Özturan, Springer Berlin Heidelberg. 3733: 284-293.

Potapov, A. P., et al. (2005). "Topology of mammalian transcription networks." Genome Informatics Series 16(2): 270.

R Core Team (2013). R: A Language and Environment for Statistical Computing. R foundation for Statistical Computing.

Radicchi, F., et al. (2004). "Defining and identifying communities in networks." Proceedings of the National Academy of Sciences of the United States of America 101(9): 2658-2663.

267

Rao, T. P. and M. Kühl (2010). "An Updated Overview on Wnt Signaling Pathways: A Prelude for More." Circulation Research 106(12): 1798-1806.

Ravasz, E., et al. (2002). "Hierarchical Organization of Modularity in Metabolic Networks." Science 297(5586): 1551-1555.

Ren, B., et al. (2000). "Genome-Wide Location and Function of DNA Binding Proteins." Science 290(5500): 2306-2309.

Ricote, M., et al. (2006). "The p38 transduction pathway in prostatic neoplasia." The Journal of Pathology 208(3): 401-407.

Rivard, A., et al. (1999). "Rescue of diabetes-related impairment of angiogenesis by intramuscular gene therapy with adeno-VEGF." Am J Pathol 154(2): 355-363.

Rosvall, M. and C. T. Bergstrom (2008). "Maps of random walks on complex networks reveal community structure." Proceedings of the National Academy of Sciences 105(4): 1118-1123.

Rosvall, M., et al. (2013). "Networks with memory." arXiv preprint arXiv:1305.4807.

Saez-Rodriguez, J., et al. (2007). "A Logical Model Provides Insights into T Cell Receptor Signaling." PLoS Comput Biol 3(8): e163.

Schmith, J., et al. (2005). "Damage, connectivity and essentiality in protein–protein interaction networks." Physica A: Statistical Mechanics and its Applications 349(3): 675-684.

Sedgewick, R. (1988). Algorithms. Reading, Mass., Addison-Wesley.

Shannon, C. E. (2001). "A mathematical theory of communication." ACM SIGMOBILE Mobile Computing and Communications Review 5(1): 3-55.

Silvescu, A. and V. Honavar (2001) Temporal boolean network models of genetic networks and their inference from gene expression time series. Complex Systems 13, 54 - 75

Soinov, L. A., et al. (2003). "Towards reconstruction of gene networks from expression data by supervised learning." Genome Biol 4(1): R6.

Spyridopoulos, I., et al. (2002). "Divergence of Angiogenic and Vascular Permeability Signaling by VEGF: Inhibition of Protein Kinase C Suppresses VEGF-Induced Angiogenesis, but Promotes VEGF-Induced, NO-Dependent Vascular Permeability." Arteriosclerosis, Thrombosis, and Vascular Biology 22(6): 901-906.

Sun, Z. and R. Albert (2013). Chapter 10 - Boolean Models of Cellular Signaling Networks. Handbook of Systems Biology. A. J. M. Walhout, M. Vidal and J. Dekker. San Diego, Academic Press: 197-210.

268

Tenenbaum, D. KEGGREST: Client-side REST access to KEGG, R package version 1.2.2.

Thum, T., et al. (2008). "MicroRNAs: novel regulators in cardiac development and disease." Cardiovasc Res 79(4): 562-570.

Tong, A. H. Y., et al. (2004). "Global mapping of the yeast genetic interaction network." Science Signaling 303(5659): 808.

Van Belle, E., et al. (1997). "Hypercholesterolemia attenuates angiogenesis but does not preclude augmentation by angiogenic cytokines." Circulation 96(8): 2667-2674.

Veikkola, T. and K. Alitalo (1999). "VEGFs, receptors and angiogenesis." Semin Cancer Biol 9(3): 211-220.

Vespignani, A. (2012). "Modelling dynamical processes in complex socio-technical systems." Nat Phys 8(1): 32-39.

Vlasblom, J. and S. Wodak (2009). "Markov clustering versus affinity propagation for the partitioning of protein interaction graphs." BMC bioinformatics 10(1): 99.

Vogelstein, B., et al. (2000). "Surfing the p53 network." Nature 408(6810): 307-310.

Voy, B. H., et al. (2006). "Extracting Gene Networks for Low-Dose Radiation Using Graph Theoretical Algorithms." PLoS Comput Biol 2(7): e89.

Wagner, A. (2001). "How to reconstruct a large genetic network from n gene perturbations in fewer than n2 easy steps." Bioinformatics 17(12): 1183-1197.

Wagner, A. (2001). "The yeast protein interaction network evolves rapidly and contains few redundant duplicate genes." Molecular Biology and Evolution 18(7): 1283-1292.

Wagner, E. F. and A. R. Nebreda (2009). "Signal integration by JNK and p38 MAPK pathways in cancer development." Nat Rev Cancer 9(8): 537-549.

Wang, J. and X. Yang (2012). "The function of miRNA in cardiac hypertrophy." Cell Mol Life Sci 69(21): 3561-3570.

Watts, D. J. and S. H. Strogatz (1998). "Collective dynamics of 'small-world' networks." Nature 393(6684): 440-442.

Witten, T. M. and D. Bonchev (2007). "Predicting Aging/Longevity-Related Genes in the Nematode Caenorhabditis elegans." Chem Biodivers 4(11): 2639-2655.

Wolfe, C., et al. (2005). "Systematic survey reveals general applicability of "guilt-by- association" within gene coexpression networks." BMC bioinformatics 6(1): 227.

269

Wright, M., et al. (1999). "Identification of a Wnt-responsive signal transduction pathway in primary endothelial cells." Biochem Biophys Res Commun 263(2): 384-388.

Wuchty, S. (2001). "Scale-free behavior in protein domain networks." Molecular Biology and Evolution 18(9): 1694-1702.

Yook, S. H., et al. (2004). "Functional and topological characterization of protein interaction networks." Proteomics 4(4): 928-942.

Yoon, J., et al. (2006). "An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality." Bioinformatics 22(24): 3106-3108.

Yu, H. and M. Gerstein (2006). "Genomic analysis of the hierarchical structure of regulatory networks." Proceedings of the National Academy of Sciences 103(40): 14724-14731.

Yu, H., et al. (2004). "Genomic analysis of essentiality within protein networks." RNA 71: 817- 846.

Zhang, B. and S. Horvath (2005). "A general framework for weighted gene co-expression network analysis." Statistical applications in genetics and molecular biology 4(1): 1128.

Zhang, H., et al. (2010). "MIClique: an algorithm to identify differentially coexpressed disease gene subset from microarray data." BioMed Research International 2009.

Zhang, J. D. and S. Wiemann (2009). "KEGGgraph: a graph approach to KEGG PATHWAY in R and bioconductor." Bioinformatics 25(11): 1470-1471.

Zhang, R., et al. (2008). "Network model of survival signaling in large granular lymphocyte leukemia." Proceedings of the National Academy of Sciences 105(42): 16308-16313.

Zhou, H. J. (2003). "Distance, dissimilarity index, and network community structure." Physical Review E 67(6).

Zhou, H. J. and R. Lipowsky (2004). "Network Brownian motion: A new method to measure vertex-vertex proximity and to identify communities and subcommunities." Computational Science - Iccs 2004, Pt 3, Proceedings 3038: 1062-1069.

Ziv, E., et al. (2005). "Information-theoretic approach to network modularity." Physical Review E 71(4): 046117.

270

VITA

Indent paragraph. The Vita is required and is a short biographical summary of the student. Students typically include information such as where they are from, what they have studied in the past, degrees they have earned, and future endeavors. We ask that students not include any contact information, such as addresses, phone numbers, etc. Please do not include a resume or list any publications in the Vita.

Vita should be written in THIRD PERSON, paragraph style. The first line of each paragraph will be indented. The text should be double-spaced. There will be NO PAGE

NUMBER AT BOTTOM CENTER.

271