Evolutionary learning of microbial populations in partially predictable environments

TL;DR

This paper models how microbial populations adapt their resource allocation strategies in partially predictable environments, revealing a natural learning process that balances growth and adaptation by exploiting environmental patterns.

Contribution

It introduces a proteome allocation model showing how microbes evolve to encode environmental predictability without neural computation, linking resource allocation to information theory.

Findings

Populations evolve to minimize resource depletion time.

Evolved allocations reflect environmental transition probabilities.

Microbes use proteome as a distributed memory system.

Abstract

Populations evolving in fluctuating environments face the fundamental challenge of balancing adaptation to current conditions against preparation for uncertain futures. Here, we study microbial evolution in partially predictable environments using proteome allocation models that capture the trade-off between growth rate and lag time during environmental transitions. We demonstrate that evolution drives populations toward an evolutionary stable allocation strategy that minimizes resource depletion time, thereby balancing faster growth with shorter adaptation delays. In environments with temporal structure, populations evolve to learn the statistical patterns of environmental transitions through proteome pre-allocation, with the evolved allocations reflecting the transition probabilities between conditions. Our framework reveals how microbial populations can extract and exploit environmental predictability without explicit neural computation, using the proteome as a distributed memory system that encodes environmental patterns. This work demonstrates how information-theoretic principles govern cellular resource allocation and provides a mechanistic foundation for understanding learning-like behavior in evolving biological systems.