A statistical method for revealing form-function relations in biological networks

TL;DR

This paper introduces a statistical approach to uncover how the topology of small biological network motifs relates to their functional roles, validated on transcriptional regulatory networks.

Contribution

It formulates the form-function relationship as a statistical task and demonstrates its effectiveness by confirming known and discovering new correlations.

Findings

Revealed a form-function relationship previously predicted analytically.

Discovered a new correlation with an analytic interpretation.

Validated the method on experimental transcriptional networks.

Abstract

Over the past decade, a number of researchers in systems biology have sought to relate the function of biological systems to their network-level descriptions -- lists of the most important players and the pairwise interactions between them. Both for large networks (in which statistical analysis is often framed in terms of the abundance of repeated small subgraphs) and for small networks which can be analyzed in greater detail (or even synthesized in vivo and subjected to experiment), revealing the relationship between the topology of small subgraphs and their biological function has been a central goal. We here seek to pose this revelation as a statistical task, illustrated using a particular setup which has been constructed experimentally and for which parameterized models of transcriptional regulation have been studied extensively. The question "how does function follow form" is here mathematized by identifying which topological attributes correlate with the diverse possible information-processing tasks which a transcriptional regulatory network can realize. The resulting method reveals one form-function relationship which had earlier been predicted based on analytic results, and reveals a second for which we can provide an analytic interpretation. Resulting source code is distributed via http://formfunction.sourceforge.net.