Quantifying evolvability in small biological networks

Dept. of Phys., Columbia Univ., New York, NY, USA
IET Systems Biology (Impact Factor: 1.06). 10/2009; 3(5):379 - 387. DOI: 10.1049/iet-syb.2008.0165
Source: IEEE Xplore


The authors introduce a quantitative measure of the capacity of a small biological network to evolve. The measure is applied to a stochastic description of the experimental setup of Guet et al. ( Science 2002, 296, pp. 1466), treating chemical inducers as functional inputs to biochemical networks and the expression of a reporter gene as the functional output. The authors take an information-theoretic approach, allowing the system to set parameters that optimise signal processing ability, thus enumerating each network's highest-fidelity functions. All networks studied are highly evolvable by the measure, meaning that change in function has little dependence on change in parameters. Moreover, 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 completely losing it along the way. This property further underscores the evolvability of the networks.

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    • "Within a mathematical model, such behavior follows straightforwardly from considering the behavior of a given dynamical system at different points in the space of quantitative parameters [12]. Revealing such degeneracy of functions by exploring the parameter space given a topology (and given an algebraic expression modeling the regulatory interactions among the genes) may be recast as one of optimizing — locally in parameter space — the mutual information between input and output over this space [13] [14] [15]. Mutual information (MI) as a cost function is advantageous both biologically (in that many natural systems including transcriptional regulatory networks are known to operate near their information-optimal constraints [16] [17] [18]) and mathematically (in that by optimizing MI we can identify parameter settings which are functional without demanding in advance the particular input-output functions we seek). "
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