CS 4491/CS 7990 SPECIAL TOPICS IN BIOINFORMATICS
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 evolution of living organisms.
Most genes and proteins 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
Gene regulatory network: two genes are connected if the expression of one gene modulates expression of another one by either activation of inhibition Protein 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 time series 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 modularity 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 regulons (DOR)
Reference: http://homepages.ulb.ac.be/~dgonze/TEACHING/network_motifs.pdf Feed-forward loop (FFL)
Transcription factor X that regulates a second transcription factor Y, such that both X and Y jointly regulate on operon 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 operons 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