Title:

Modeling Biological Networks With Exponential Random Graph Models

Domain:

1. Life Sciences.
2. Computer Science and Applied Mathematics.

Authors:

Alex Stivala (Swinburne University of Technology and Universita della Svizzera italiana)
Maksym Byshkin (Universita della Svizzera italiana)
Antonietta Mira (Uiversita della Svizzera italiana and Universita dell'Insubria)
Garry Robins (University of Melbourne)
Alessandro Lomi (Universita della Svizzera italiana and University of Melbourne)

Abstract:

Much research in biological networks concerns "motifs", small
subgraphs which occur more frequently than by chance, which are
considered the building blocks of complex networks.

Exponential random graph models (ERGMs), a well-established class of
statistical models for network data which are widely used in social
network analysis, represent a principled statistical method of
determining whether a motif is over (or under) represented in a
network. Although the use of ERGMs for analyzing biological networks
was introduced into the bioinformatics literature ten years ago, the
use of ERGMs in biological network analysis has been very limited
since then due to problems with applying existing methods to such
networks, their size being typically far larger than those social
networks to which ERGMs are usually applied.

Here we use high performance computing to apply our recently developed
new techniques (snowball sampling, improved fixed density ERGM
sampling, scalable Equilibrium Expectation algorithm) for ERGM
estimation to several biological networks (protein-protein interaction
networks, gene regulatory networks, and a neural network), ranging in
size from a few hundred to over five thousand nodes.