A simple regulatory architecture allows learning the statistical structure of a changing environment

TL;DR

This paper demonstrates that a simple regulatory motif in bacteria can learn and predict environmental fluctuations by inferring their statistical structure, enabling adaptive responses in changing environments.

Contribution

It introduces a minimal regulatory circuit model that can learn environmental statistics and predict future changes, showing near-optimal performance.

Findings

A simple regulatory motif can learn environmental statistics.

The model enables predictive behavior in fluctuating environments.

The mechanism is plausible in natural bacterial circuits.

Abstract

Bacteria live in environments that are continuously fluctuating and changing. Exploiting any predictability of such fluctuations can lead to an increased fitness. On longer timescales bacteria can "learn" the structure of these fluctuations through evolution. However, on shorter timescales, inferring the statistics of the environment and acting upon this information would need to be accomplished by physiological mechanisms. Here, we use a model of metabolism to show that a simple generalization of a common regulatory motif (end-product inhibition) is sufficient both for learning continuous-valued features of the statistical structure of the environment and for translating this information into predictive behavior; moreover, it accomplishes these tasks near-optimally. We discuss plausible genetic circuits that could instantiate the mechanism we describe, including one similar to the architecture of two-component signaling, and argue that the key ingredients required for such predictive behavior are readily accessible to bacteria.