CS 4491/CS 7990 SPECIAL TOPICS IN

Mingon Kang, Ph.D Computer Science, Kennesaw State University Biological Networks

 Provide a mathematical representation of connections found in ecological, evolutionary, and physiological studies, such as neural networks

 Represent complex biological systems using graphs  Nodes: units in the network  Edges: interactions between the units Biological Networks

 Network analysis is increasingly recognized as a powerful approach to understanding biological organization and the function of cellular components and may help us to understand the principles deriving the of living organisms.

 Most and do not have a function on their own, rather their role is realized through a complex web of interactions with other proteins, genes, and biomolecules.

Reference: http://homepages.ulb.ac.be/~dgonze/TEACHING/network_motifs.pdf Biological Networks

regulatory network: two genes are connected if the expression of one gene modulates expression of another one by either activation of inhibition  interaction network: proteins that are connected in physical interactions or metabolic and signaling pathways of the cell;  Metabolic network: metabolic products and substrates that participate in one reaction;

Reference: http://dna.cs.byu.edu/bio465/slides/regulatory-network.ppt Biological Networks

 These networks can be obtained from  A collection of database  MIPS, String -> protein interaction network  A high-throughout experiments  Microarray -> regulation network  Large-scale bioinformatics predictions  Regulatory motif detection -> transcriptional network

Reference: http://homepages.ulb.ac.be/~dgonze/TEACHING/network_motifs.pdf What is Gene Regulatory Network?

 Gene regulatory networks (GRNs) are the on- off switches of a cell operating at the gene level.

 Two genes are connected if the expression of one gene modulates expression of another one by either activation or inhibition

Reference: http://dna.cs.byu.edu/bio465/slides/regulatory-network.ppt Why Study GRN?

 Genes are not independent  They regulate each other and act collectively  This collective behavior can be observed using microarray

 Some genes control the response of the cell to changes in the environment by regulating other genes

 Potential discovery of triggering mechanism and treatments for disease

Reference: http://dna.cs.byu.edu/bio465/slides/regulatory-network.ppt Background Knowledge

 Cell reproduction, metabolism, and responses to the environment are all controlled by proteins

 Each gene is responsible for constructing a single protein

 Some genes manufacture proteins which control the rate at which other genes manufacture proteins (either promoting or suppressing)

 Hence some genes regulate other genes (via the proteins they create)

Reference: http://dna.cs.byu.edu/bio465/slides/regulatory-network.ppt Learning Causal Relationships

 High-throughput genetic technologies empowers to study how genes interact with each other

 If gene A consistently turns on after Gene C, then gene C may be causing gene A to turn on

 We have to have a lot of carefully controlled data to infer this

Reference: http://dna.cs.byu.edu/bio465/slides/regulatory-network.ppt Biological Networks

 Represented by graphs  Nodes represent objects (genes, proteins)  Edges represent interactions (physical interaction between 2 proteins, metabolic reaction, regulation)  Directed/Undirected graph  If edges are oriented, as for chemical reactions or regulations, they are called arcs and the graph is said directed.  Otherwise undirected graph. Often correlation between genes are investigated.

Reference: http://homepages.ulb.ac.be/~dgonze/TEACHING/network_motifs.pdf Biological Networks

Reference: http://homepages.ulb.ac.be/~dgonze/TEACHING/network_motifs.pdf Kegg

 http://www.genome.jp/kegg/pathway.html

Reference: http://dna.cs.byu.edu/bio465/slides/regulatory-network.ppt Pathgen

Reference: http://dna.cs.byu.edu/bio465/slides/regulatory-network.ppt Microarray data

Samples Genes

 Gene up-regulate, down-regulate;

Reference: http://dna.cs.byu.edu/bio465/slides/regulatory-network.ppt Modules in networks

 Modules (sub-networks)  A common feature of large, complex biological networks  Connected molecular components

Reference: http://homepages.ulb.ac.be/~dgonze/TEACHING/network_motifs.pdf Modules in networks

 Examine their e.g. comparative analyses of structurally similar modules across different species

 Identify mutually shared functions, associate a modular structure with a new function, and provide insight into the evolution of various network structures.

Reference: http://homepages.ulb.ac.be/~dgonze/TEACHING/network_motifs.pdf Network Motifs

 Important local property of networks

 Patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks

Reference: http://homepages.ulb.ac.be/~dgonze/TEACHING/network_motifs.pdf Network Motifs

 Three types of Network Motifs  Feed-forward loop (FFL)  Single input motif (SIM)  Dense overlapping (DOR)

Reference: http://homepages.ulb.ac.be/~dgonze/TEACHING/network_motifs.pdf Feed-forward loop (FFL)

factor X that regulates a second Y, such that both X and Y jointly regulate on Z.  X: general transcription factor  Y: specific transcription factor  Z: effector operon

Reference: http://homepages.ulb.ac.be/~dgonze/TEACHING/network_motifs.pdf Single input motif (SIM)

 A set of that are controlled by a single transcription factor.

Reference: http://homepages.ulb.ac.be/~dgonze/TEACHING/network_motifs.pdf Dense overlapping regulons (DOR)

 A layer of overlapping interactions between operons and a group of input transcription factors that is much more dense than corresponding structures in randomized networks.

Reference: http://homepages.ulb.ac.be/~dgonze/TEACHING/network_motifs.pdf Network Motifs

Reference: http://homepages.ulb.ac.be/~dgonze/TEACHING/network_motifs.pdf Network Motifs

Reference: http://homepages.ulb.ac.be/~dgonze/TEACHING/network_motifs.pdf Network Analysis

 Maximum Spanning tree  A maximum spanning tree is a spanning tree of a weighted graph having maximum weight.

 Cliques  subset of vertices of an undirected graph such that its induced subgraph is complete