Statistical method for revealing form-function relations in biological 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.
Additional Information
© 2011 National Academy of Sciences. Edited by Leslie Greengard, New York University, New York, NY, and approved November 12, 2010 (received for review June 25, 2010). Published online before print December 23, 2010. The authors thank Nicolas Buchler and William Bialek for useful conversations. A.M. was supported by National Science Foundation (NSF) Grant DGE-0742450; C.H.W. was supported by National Institutes of Health (NIH) Grants 1U54CA121852-01A1 and 5PN2EY016586-03. 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.Attached Files
Published - Mugler2011p13062P_Natl_Acad_Sci_Usa.pdf
Supplemental Material - Appendix.pdf
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Additional details
- PMCID
- PMC3021042
- Eprint ID
- 23077
- Resolver ID
- CaltechAUTHORS:20110323-115138147
- DGE-0742450
- NSF Graduate Research Fellowship
- 1U54CA121852-01A1
- NIH
- 5PN2EY016586-03
- NIH
- Created
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2011-03-24Created from EPrint's datestamp field
- Updated
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2021-11-09Created from EPrint's last_modified field