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Abstract: We introduce a quantitative measure of the capacity of a small biologicalnetwork to evolve. We apply our measure to a stochastic description of theexperimental setup of Guet et al. Science 296:1466, 2002, treating chemicalinducers as functional inputs to biochemical networks and the expression of areporter gene as the functional output. We take an information-theoreticapproach, allowing the system to set parameters that optimize signal processingability, thus enumerating each network-s highest-fidelity functions. We findthat all networks studied are highly evolvable by our measure, meaning thatchange in function has little dependence on change in parameters. Moreover, wefind that each network-s functions are connected by paths in the parameterspace along which information is not significantly lowered, meaning a networkmay continuously change its functionality without losing it along the way. Thisproperty further underscores the evolvability of the networks.



Author: Andrew Mugler, Etay Ziv, Ilya Nemenman, Chris H. Wiggins

Source: https://arxiv.org/







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