Optimizing Information Transmission in Large Genetic Networks

Abstract

A central challenge in biophysics is to find the general principles underlying universal biological structures. Here, we focus on genetic networks, fundamental regulatory units that control gene expression. Inherent in the notion of control is the idea that genetic networks transmit information, and, in some limits, this can be quantified using information theory. A large body of theoretical and experimental work suggests that some genetic networks are tuned close to optimality, such that they transmit the maximum possible amount of information given a set of physical constraints and irreducible sources of noise. Following this principle, here we optimize information transmission in a class of idealized genetic networks. We explore the optimal network structures and derive a scaling relation for the information transmission in terms of the relevant biological parameters. Counterintuitively, this scaling suggests that the optimal strategy is to deploy a large number of noisy channels. Additionally, we find that the system should have a preponderance of weak binding interactions. Next, we show that the optimal solutions are surprisingly stable to a wide range of biologically relevant perturbations. Finally, we connect the predictions of the model to experimental data. Our model represents a step towards understanding the structure and reliability of large genetic networks.