Form, Function, and Information Processing in Small Stochastic Biological Networks

Etay Ziv, Andrew Mugler, Ilya Nemenman, and Chris Wiggins

Abstract

We present an information-theoretic approach to quantitative comparison of signal processing in small networks of varying topology. Restricting ourselves to computational experiments using the experimental setup employed by Guet, we first quantify the ability of a network to transduce chemical information into genetic information (and the extent to which topology thwarts or enhances this transduction). We then investigate the extent to which similar (either parametrically or topologically) networks perform similar function. A key component of our analysis is framing the question in a way that does not assume which particular function a particular network is designed for, but rather allows this to be determined by optimal information processing.

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