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CSCI2950-C
Lecture 13
Network Motifs; Network
Integration
Ben Raphael
November 20, 2008
http://cs.brown.edu/courses/csci2950-c/
Biological Interaction Networks
Many types:
• Protein-DNA
(regulatory)
• Protein-metabolite
(metabolic)
• Protein-protein
(signaling)
• RNA-RNA
(regulatory)
• Genetic interactions
(gene knockouts)
Outline
1. Network Motifs
2. Network integration
3. Network alignment and querying: conserved
complexes.
Network Motifs
Subnetworks with more occurrences than
expected by chance.
• How to find?
• How to assess statistical significance?
Shen-Orr et al. 2002
Network Motifs
Subnetworks with more occurrences than
expected by chance.
• How to find?
1) Exhaustive: Count all n-node subgraphs.
2) Greedy and other heuristic methods.
Network Motifs
Subnetworks with more occurrences than
expected by chance.
• How to assess statistical significance?
– Compare number of occurrences to random
network.
Randomizing a Network
Occurrence of motifs depend strongly on
network topology.
What is an appropriate ensemble of random
networks? (null model)
Random Networks
Occurrence of motifs depend strongly on
network topology.
What is an appropriate ensemble of random
networks? (null model)
Random Networks
• One parameter governing occurrence of
motifs is degree distribution.
https://nwb.slis.indiana.edu/community/?n=CustomFillings.AnalysisOfBiologicalNetworks
Preserving Degree Distribution
• How to sample a graph with the same degree
sequence?
Method of Newman, Strogatz and Watts (2001)
1.Assign indegree i(v) and outdegree o(v) to
vertex v according to degree sequence.
2.Randomly pair o(v) and i(w).
Network Motifs
•
•
•
•
Transcriptional regulatory network of E. coli:
116 transcription factors
~700 “genes” (operons)
577 interactions.
Shen-Orr et al. 2002
E. coli Network Motifs
• Enumerated all 3 and 4 node
motifs.
• Looked for identical rows in
adjacency matrix (SIM)
• Used clustering algorithm to
identify DOR.
Shen-Orr et al. 2002
Importance of Network Motifs
• Building block of networks.
• Indicate modular structure of biological
networks.
• Appearance of some motifs might be
explained by particular dynamics (e.g.
feedforward and feedback loops)
• Some skepticism, particularly because data is
incomplete.
Network Integration
Given:
G = (V,E) interaction network.
V = genes
E = protein-DNA or proteinprotein interactions
Normalized expression
“z-score” zij for gene i in
condition/sample j.
Goal: Find “active” subnetworks.
Ideker, et al. (2002); Chuang et al. (2007)
Network Integration
Given:
G = (V,E) interaction network.
V = genes
E = protein-DNA or proteinprotein interactions
M = [ zij ] z-scores of
gene i in
condition/sample j.
Goal: Find A* = argmax rA
A: connected subgraph
Ideker, et al. (2002); Chuang et al. (2007)
Finding High-scoring subnetwork
Simulated Annealing:
Identify set of active nodes.
Gw = working subgraph induced by active nodes.
Finding High-scoring subnetwork
Modifications:
Search for M subnetworks simultaneously.
Reduce effect of high degree nodes.
Network Predictors of Cancer
Results
Questions
• Are zij signed?
• Should edge scores
or topology be
included?
Knockout Experiments & Reverse
Engineering
Input: Signal
Output: Gene expression.
Given input-output
relationship for normal (“wild
type”) and mutant
(“knockout”) cells, what can
one infer about the network?
• Topology (hard or
impossible de novo)
• New interactions or
signs of existing
interactions.
Sources
•
•
•
•
Shen-Orr, S.S., Milo, R., Mangan, S., et al. 2002. Network motifs in the
transcriptional regulation network of Escherichia coli. Nature Genetics 31, 64–68.
Newman, M.E.J., Strogatz, S.H., and Watts, D.J. 2001. Random graphs with
arbitrary degree distributions and their applications. Phys. Rev. E 64, 026118–
026134.
Ideker T, Ozier O, Schwikowski B, Siegel AF. Discovering regulatory and signalling
circuits in molecular interaction networks. Bioinformatics. 2002;18 Suppl 1:S23340.
Chuang HY, Lee E, Liu YT, Lee D, Ideker T. 2007. Network-based classification of
breast cancer metastasis. Mol Syst Biol. 2007;3:140.