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CIS 4930/6930 – Recent Advances in Bioinformatics

Spring 2014

Network problems

Tamer Kahveci

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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

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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

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ActivateInhibit

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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)

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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

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Network Alignment

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• Difficult problem (graph isomorphism)• Global Alignment is GI-Complete• Local Alignment is NP-Complete

• Issues• Node similarity• Topological similarity

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

Clustering/Community Structure

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Issues

•Hard / soft clustering (nonoverlapping / overlapping)

•Optimization function