1. CSCI2950-C Lecture 13 Network Motifs; Network Integration
Ben Raphael
November 20, 2008

2. Biological Interaction Networks
Many types:
Protein-DNA (regulatory)
Protein-metabolite (metabolic)
Protein-protein (signaling)
RNA-RNA (regulatory)
Genetic interactions (gene knockouts)

3. Outline Network Motifs Network integration
Network alignment and querying: conserved complexes.

4. Network Motifs
Subnetworks with more occurrences than expected by chance.
How to find?
How to assess statistical significance?
Shen-Orr et al. 2002

5. 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.

6. Network Motifs
Subnetworks with more occurrences than expected by chance.
How to assess statistical significance?
Compare number of occurrences to random network.

7. Randomizing a Network
Occurrence of motifs depend strongly on network topology. What is an appropriate ensemble of random networks? (null model)

8. Random Networks
Occurrence of motifs depend strongly on network topology. What is an appropriate ensemble of random networks? (null model)

9. Random Networks
One parameter governing occurrence of motifs is degree distribution.

10. Preserving Degree Distribution
How to sample a graph with the same degree sequence?
Method of Newman, Strogatz and Watts (2001)
Assign indegree i(v) and outdegree o(v) to vertex v according to degree sequence.
Randomly pair o(v) and i(w).

11. Network Motifs Transcriptional regulatory network of E. coli:
116 transcription factors
~700 “genes” (operons)
577 interactions.
Shen-Orr et al. 2002

12. 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

13. 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.

14. Network Integration Given: G = (V,E) interaction network. V = genes
E = protein-DNA or protein-protein 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)

15. Network Integration Given: G = (V,E) interaction network. V = genes
E = protein-DNA or protein-protein 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)

16. Finding High-scoring subnetwork
Simulated Annealing:
Identify set of active nodes.
Gw = working subgraph induced by active nodes.

17. Finding High-scoring subnetwork
Modifications:
Search for M subnetworks simultaneously.
Reduce effect of high degree nodes.

18. Network Predictors of Cancer

19. Results

20. Questions
Are zij signed?
Should edge scores or topology be included?

21. 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.

22. Sources
Shen-Orr, S.S., Milo, R., Mangan, S., et al 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 Random graphs with arbitrary degree distributions and their applications. Phys. Rev. E 64, –
Ideker T, Ozier O, Schwikowski B, Siegel AF. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics. 2002;18 Suppl 1:S
Chuang HY, Lee E, Liu YT, Lee D, Ideker T Network-based classification of breast cancer metastasis. Mol Syst Biol. 2007;3:140.