Optimized Bacteria are Environmental Prediction Engines

TL;DR

This paper models bacterial phenotypic variability as an adaptive prediction mechanism, showing that optimal epigenetic markers maximize growth by encoding minimal predictive information about future environments.

Contribution

It introduces a theoretical framework linking bacterial epigenetic markers to optimal prediction of environmental changes, highlighting the role of causal states under resource constraints.

Findings

Maximal expected log growth rate is linear in predictive information.

Optimal epigenetic markers are causal states, the minimal sufficient statistics.

Provides a basis for new experiments on bacterial bet-hedging.

Abstract

Experimentalists have observed phenotypic variability in isogenic bacteria populations. We explore the hypothesis that in fluctuating environments this variability is tuned to maximize a bacterium's expected log growth rate, potentially aided by epigenetic markers that store information about past environments. We show that, in a complex, memoryful environment, the maximal expected log growth rate is linear in the instantaneous predictive information---the mutual information between a bacterium's epigenetic markers and future environmental states. Hence, under resource constraints, optimal epigenetic markers are causal states---the minimal sufficient statistics for prediction. This is the minimal amount of information about the past needed to predict the future as well as possible. We suggest new theoretical investigations into and new experiments on bacteria phenotypic bet-hedging in fluctuating complex environments.