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A statistical method for revealing form-function relations in biological networks | | Andrew Mugler
; Boris Grinshpun
; Riley Franks
; Chris H. Wiggins
; | | Date: |
30 Nov 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 this http URL | | Source: | arXiv, 1012.0036 | | Services: | Forum | Review | PDF | Favorites | |
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