Quantifying evolvability in small biological networks

TL;DR

This paper introduces an information-theoretic measure to quantify the evolvability of small biological networks, demonstrating that these networks can change functions with minimal parameter adjustments and maintain high signal fidelity.

Contribution

It presents a novel quantitative measure of evolvability for biological networks and applies it to experimental data, revealing their high capacity for functional change.

Findings

Networks are highly evolvable with little dependence on parameter changes

Functions are connected by paths in parameter space with minimal information loss

Networks can change functions continuously without losing their capabilities

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.