Optimizing information flow in small genetic networks. I

TL;DR

This paper formulates a theoretical framework to optimize information transfer in simple genetic networks constrained by molecular noise and resource limits, revealing optimal solutions that resemble real biological regulatory strategies.

Contribution

It introduces a formal method to determine optimal gene regulation parameters under physical constraints, providing insights into the design principles of genetic networks.

Findings

Optimal solutions depend on physical molecule constraints.

Regulatory parameters are uniquely determined at optimality.

Optimal solutions resemble natural genetic network behaviors.

Abstract

In order to survive, reproduce and (in multicellular organisms) differentiate, cells must control the concentrations of the myriad different proteins that are encoded in the genome. The precision of this control is limited by the inevitable randomness of individual molecular events. Here we explore how cells can maximize their control power in the presence of these physical limits; formally, we solve the theoretical problem of maximizing the information transferred from inputs to outputs when the number of available molecules is held fixed. We start with the simplest version of the problem, in which a single transcription factor protein controls the readout of one or more genes by binding to DNA. We further simplify by assuming that this regulatory network operates in steady state, that the noise is small relative to the available dynamic range, and that the target genes do not interact. Even in this simple limit, we find a surprisingly rich set of optimal solutions. Importantly, for each locally optimal regulatory network, all parameters are determined once the physical constraints on the number of available molecules are specified. Although we are solving an over--simplified version of the problem facing real cells, we see parallels between the structure of these optimal solutions and the behavior of actual genetic regulatory networks. Subsequent papers will discuss more complete versions of the problem.