Network Motifs Zach Saul CS 289 Network Motifs: Simple Building Blocks of Complex Networks R. Milo...
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Transcript of Network Motifs Zach Saul CS 289 Network Motifs: Simple Building Blocks of Complex Networks R. Milo...
Network Motifs
Zach Saul
CS 289Network Motifs: Simple Building Blocks of Complex Networks
R. Milo et al.
Network Models
• Interactions are represented as directed nodes (as presented in class)
• Example problems include gene networks, neural nets, ecological models and computer networking models
Network Motifs
• Patterns that appear more often in real networks than in randomly generated networks
• Many notions of a random network– Naïve algorithm– Erdos-Renyi random graphs– Scale free networks– Even more specialized?
Random Graphs
• Three node motifs– Preserve degree for each node
• Four node motifs– Preserve degree for each node– Preserve the number of three node motifs
Example Motif
Method
• Using brute force, searched target network for every possible subgraph, counting results
• Similarly, searched random network• Motifs are patterns that occur greater or equal
number of times in random networks more than 1% of the time.
Results
Results (cont.)
Gene/Neural Net Analysis
• The nematode neural net and the gene net both contain similar structures– Feed forward– Bi-fan
• Both are information processing networks with sensory and acting components– Sensory neurons/transcription factors
regulated by biochemical signals– Motor neurons/structural genes
Food Web Analysis
• Food Webs do not show feed-forward motifs– Suggests that direct interaction between
species at a separation of two layers selected against (e.g. Omnivores)
• Bi-parallel suggests that prey of same predator share prey
Electronic Circuit Analysis
• Circuits can be classified by function using network motifs
• Circuits from benchmark set showed different motifs for each functional class
• Some info processing circuits show similar motifs to biological info processing circuits
Web Analysis
• Network of hyperlinks
• Many more bidirectional links
• Motifs indicate a design that allows the shortest path among sets of related pages
Conclusions
• Technique robust to data errors
• Motifs can indicate common function
• ..or could indicate similar evolutionary constraints
• Scalability to other types of networks possible
• Scalability to larger subgraphs difficult