Optimized Bacteria are Environmental Prediction Engines

Sarah E. Marzen

Physics of Living Systems Group
Department of Physics
Massachusetts Institute of Technology
Cambridge, MA 02139


James P. Crutchfield

Complexity Sciences Center
Physics Department
University of California at Davis
Davis, CA 95616

ABSTRACT: Experimentalists observe phenotypic variability even 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 (all inheritable nongenetic) markers that store information about past environments. Crucially, we assume a time delay between sensing and action, so that a past epigenetic marker is used to generate the present phenotypic variability. 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 predictionmdash;or lossy approximations thereof. We propose new theoretical investigations into and new experiments on bacteria phenotypic bet-hedging in fluctuating complex environments.

Sarah E. Marzen and James P. Crutchfield, "Optimized Bacteria are Environmental Prediction Engines", Physical Review E 98 (2018) 012408. doi:10.1103/PhysRevE.98.012408.
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arXiv.org:1802.03105 [q-bio].