Optimized bacteria are environmental prediction engines
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 th...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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American Physical Society
2018
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| Online Access: | http://hdl.handle.net/1721.1/117060 |
| _version_ | 1826197056198279168 |
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| author | Marzen, Sarah E. Crutchfield, James P. |
| author2 | Massachusetts Institute of Technology. Department of Physics |
| author_facet | Massachusetts Institute of Technology. Department of Physics Marzen, Sarah E. Crutchfield, James P. |
| author_sort | Marzen, Sarah E. |
| collection | MIT |
| description | 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 prediction—or lossy approximations thereof. We propose new theoretical investigations into and new experiments on bacteria phenotypic bet-hedging in fluctuating complex environments. |
| first_indexed | 2024-09-23T10:41:43Z |
| format | Article |
| id | mit-1721.1/117060 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:41:43Z |
| publishDate | 2018 |
| publisher | American Physical Society |
| record_format | dspace |
| spelling | mit-1721.1/1170602022-09-30T22:21:50Z Optimized bacteria are environmental prediction engines Marzen, Sarah E. Crutchfield, James P. Massachusetts Institute of Technology. Department of Physics Marzen, Sarah E. 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 prediction—or lossy approximations thereof. We propose new theoretical investigations into and new experiments on bacteria phenotypic bet-hedging in fluctuating complex environments. Templeton Foundation (Grant 52095) Foundational Questions Institute (Grant FQXi-RFP-1609) United States. Army Research Office (Contract W911NF-13-1-0390) 2018-07-24T13:43:54Z 2018-07-24T13:43:54Z 2018-07 2018-06 2018-07-16T18:00:16Z Article http://purl.org/eprint/type/JournalArticle 2470-0045 2470-0053 http://hdl.handle.net/1721.1/117060 Marzen, Sarah E. and Crutchfield, James P. "Optimized bacteria are environmental prediction engines." Physical Review E 98, 1 (July 2018): 012408 © 2018 American Physical Society en http://dx.doi.org/10.1103/PhysRevE.98.012408 Physical Review E Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. American Physical Society application/pdf American Physical Society American Physical Society |
| spellingShingle | Marzen, Sarah E. Crutchfield, James P. Optimized bacteria are environmental prediction engines |
| title | Optimized bacteria are environmental prediction engines |
| title_full | Optimized bacteria are environmental prediction engines |
| title_fullStr | Optimized bacteria are environmental prediction engines |
| title_full_unstemmed | Optimized bacteria are environmental prediction engines |
| title_short | Optimized bacteria are environmental prediction engines |
| title_sort | optimized bacteria are environmental prediction engines |
| url | http://hdl.handle.net/1721.1/117060 |
| work_keys_str_mv | AT marzensarahe optimizedbacteriaareenvironmentalpredictionengines AT crutchfieldjamesp optimizedbacteriaareenvironmentalpredictionengines |