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Probabilistic adaptation in changing microbial environments

Yarden Katz, Michael Springer
doi: https://doi.org/10.1101/065243
Yarden Katz
aDept. of Systems Biology, Harvard Medical School, Boston, MA
bBerkman Klein Center for Internet & Society, Harvard University, Cambridge, MA
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Michael Springer
aDept. of Systems Biology, Harvard Medical School, Boston, MA
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Abstract

Microbes growing in animal host environments face fluctuations that have elements of both randomness and predictability. In the mammalian gut, fluctuations in nutrient levels and other physiological parameters are structured by the animal host’s behavior, diet, health and microbiota composition. Microbial cells that are able to anticipate these fluctuations by exploiting this structure would likely gain a fitness advantage, by adapting their internal state in advance. We propose that the problem of adaptive growth in these structured changing environments can be viewed as probabilistic inference. We analyze environments that are “meta-changing”: where there are changes in the way the environment fluctuates, governed by a mechanism unobservable to cells. We develop a dynamic Bayesian model of these environments and show that a real-time inference algorithm (particle filtering) for this model can be used as a microbial growth strategy implementable in molecular circuits. The growth strategy suggested by our model outperforms heuristic strategies, and points to a class of algorithms that could support real-time probabilistic inference in natural or synthetic cellular circuits.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 22, 2016.
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Probabilistic adaptation in changing microbial environments
Yarden Katz, Michael Springer
bioRxiv 065243; doi: https://doi.org/10.1101/065243
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Probabilistic adaptation in changing microbial environments
Yarden Katz, Michael Springer
bioRxiv 065243; doi: https://doi.org/10.1101/065243

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