Statistical method for revealing form-function relations in biological networks
Andrew Mugler a,1,2, Boris Grinshpun b, Riley Franks c, and Chris H. Wiggins b,d
+Author Affiliations
aDepartment of Physics;
bDepartment of Applied Physics and Applied Mathematics;
dCenter for Computational Biology and Bioinformatics, Columbia University, New York, NY 10027; and
cDepartment of Applied and Computational Mathematics, California Institute of Technology, Pasadena, CA 91125
↵2Present address: Foundation for Fundamental Research on Matter (FOM), Institute for Atomic and Molecular Physics (AMOLF), Science Park 104, 1098 XG, Amsterdam, The Netherlands.
Edited* by Leslie Greengard, New York University, New York, NY, and approved November 12, 2010 (received for review June 25, 2010)
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.
form and function, information theory, dynamical systems, systems biology
Footnotes
1To whom correspondence should be addressed. E-mail: mugler@amolf.nl.
Author contributions: A.M., B.G., R.F., and C.H.W. designed research; A.M., B.G., and R.F. performed research; A.M. contributed new reagents/analytic tools; A.M. and B.G. analyzed data; and A.M. and C.H.W. wrote the paper.
The authors declare no conflict of interest.
*This Direct Submission article had a prearranged editor.
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†We use ⇾ to indicate up-regulation, ⊣ to indicate down-regulation, and → to indicate regulation whose sign is not specified; additionally we use ↝ to indicate inhibition by a small molecule.
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