1. 1 CIS 4930/6930 – Recent Advances in Bioinformatics Spring 2014 Network problems Tamer Kahveci

2. 2 What will we learn? Goal: Learn some of the key computational problems involving biological networks. Modeling network states/steady states Network construction Network alignment Motif finding Clustering/community structures Pathway identification Function identification

3. 3 Modeling network states Nodes and/or Edges can be used. Boolean (e.g. TRNs) –Each node has a state (1/0) –Each node has a state transition function x4 := x1 AND ~x2 –Network state = state of all nodes 1 2 4 3 Activate Inhibit

4. 4 Modeling network states Stoichiometric model (e.g. Metabolic networks) –Each compound and reaction has a state (real number) –Each reaction has an equation 2x1 + x2 => x4 –Stoichiometric matrix indicates transitions –Network state = state of all reactions (flow) S-systems –Xi’ = Vi + - Vi - –In – Out GMA –(Generalized Mass Action) 1 2 4 3 5

5. 5 Network Construction Answers various questions What are the nodes? What are the edges? Direction of edges? Activation or suppression? Clues Sequence similarity Gene expressions Known networks from other organisms 1 2 4 3

6. 6 Network Alignment R2 R3 R1 R4 R5 R6 R7 R8 R7R2 R1 R3 R4 R6 R5 Difficult problem (graph isomorphism) Global Alignment is GI-Complete Local Alignment is NP-Complete Issues Node similarity Topological similarity

7. Motif identification Subnetworks which appear significantly frequent in the given network data Issues –How frequent is significant? –Network characteristics Unlabeled graph: topology only Labeled graph: match nodes as well –Duplicity Single large network: motif appears many times in a single network A large number of networks: count each network once if it contains motif (even if it contains multiple copies) 7

8. Clustering/Community Structure 8 Issues Hard / soft clustering (nonoverlapping / overlapping) Optimization function