Rules for biological regulation based on error minimization

TL;DR

This paper proposes a rule-based framework for gene regulation that minimizes errors by favoring mechanisms where regulatory sites are mostly bound, explaining observed patterns like the Savageau demand rule and offering testable hypotheses.

Contribution

It introduces an error minimization principle to explain gene regulation modes and extends to multi-regulator systems, providing a unifying theoretical framework.

Findings

Regulatory mechanisms are chosen to minimize binding errors.

The Savageau demand rule is explained by error minimization.

Framework suggests testable hypotheses for biological regulation.

Abstract

The control of gene expression involves complex mechanisms that show large variation in design. For example, genes can be turned on either by the binding of an activator (positive control) or the unbinding of a repressor (negative control). What determines the choice of mode of control for each gene? This study proposes rules for gene regulation based on the assumption that free regulatory sites are exposed to nonspecific binding errors, whereas sites bound to their cognate regulators are protected from errors. Hence, the selected mechanisms keep the sites bound to their designated regulators for most of the time, thus minimizing fitness-reducing errors. This offers an explanation of the empirically demonstrated Savageau demand rule: Genes that are needed often in the natural environment tend to be regulated by activators, and rarely needed genes tend to be regulated by repressors; in both cases, sites are bound for most of the time, and errors are minimized. The fitness advantage of error minimization appears to be readily selectable. The present approach can also generate rules for multi-regulator systems. The error-minimization framework raises several experimentally testable hypotheses. It may also apply to other biological regulation systems, such as those involving protein-protein interactions.