Optimized Bacteria are Environmental Prediction Engines

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.

arXiv comment7 pages, 1 figure; http://csc.ucdavis.edu/~cmg/compmech/pubs/obepe.htm

References in corpus (10)

• Deep Recurrent Q-Learning for Partially Observable MDPs
• Computational Mechanics: Pattern and Prediction, Structure and Simplicity
• The thermodynamics of prediction
• The Value of Information for Populations in Varying Environments
• Solving POMDPs by Searching the Space of Finite Policies
• Anatomy of a Bit: Information in a Time Series Observation
• Well-temperate phage: optimal bet-hedging against local environmental collapses
• Chaos Forgets and Remembers: Measuring Information Creation, Destruction, and Storage
• Nearly Maximally Predictive Features and Their Dimensions
• Beyond the Spectral Theorem: Spectrally Decomposing Arbitrary Functions of Nondiagonalizable Operators

Cited by in corpus (5)

• Emergence of local irreversibility in complex interacting systems
• Provenance of Lyfe: Chemical Autonomous Agents Surviving through Associative Learning
• Quantum adaptive agents with efficient long-term memories
• Information bounds production in replicator systems
• Energetic advantages for quantum agents in online execution of complex strategies