Quantifying evolvability in small biological networks

Abstract

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.

8 pages, 3 figures

References in corpus (5)

• Analytical distributions for stochastic gene expression
• Information flow and optimization in transcriptional control
• Optimal signal processing in small stochastic biochemical networks
• Reliability of genetic networks is evolvable
• Serially-regulated biological networks fully realize a constrained set of functions

Cited by in corpus (5)

• Optimizing information flow in small genetic networks. II: Feed forward interactions
• Spectral solutions to stochastic models of gene expression with bursts and regulation
• Analytic methods for modeling stochastic regulatory networks
• Information theory and adaptation
• A statistical method for revealing form-function relations in biological networks