Network Motifs

apricotpigletAI and Robotics

Oct 19, 2013 (3 years and 10 months ago)

79 views

Network Motifs

Zach Saul

CS 289

Network 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