Biology – Quantitative Biology – Molecular Networks
Scientific paper
2008-11-18
IET Sys. Bio. 3 (2009) 379-387
Biology
Quantitative Biology
Molecular Networks
8 pages, 3 figures
Scientific paper
10.1049/iet-syb.2008.0165
We introduce a quantitative measure of the capacity of a small biological network to evolve. We apply our measure to a stochastic description of the experimental setup of Guet et al. (Science 296:1466, 2002), treating chemical inducers as functional inputs to biochemical networks and the expression of a reporter gene as the functional output. We take an information-theoretic approach, allowing the system to set parameters that optimize signal processing ability, thus enumerating each network's highest-fidelity functions. We find that all networks studied are highly evolvable by our measure, meaning that change in function has little dependence on change in parameters. Moreover, we find that each network's functions are connected by paths in the parameter space along which information is not significantly lowered, meaning a network may continuously change its functionality without losing it along the way. This property further underscores the evolvability of the networks.
Mugler Andrew
Nemenman Ilya
Wiggins Chris H.
Ziv Etay
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