A simple regulatory architecture allows learning the statistical structure of a changing environment
Bacteria live in environments that are continuously fluctuating and changing. Exploiting any predictability of such fluctuations can lead to an increased fitness. On longer timescales, bacteria can ‘learn’ the structure of these fluctuations through evolution. However, on shorter timescales, inferri...
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eLife Sciences Publications Ltd
2021-09-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/67455 |
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doaj-f2d1af32df554ab4acd7eb319d0746662021-09-07T11:30:33ZengeLife Sciences Publications LtdeLife2050-084X2021-09-011010.7554/eLife.67455A simple regulatory architecture allows learning the statistical structure of a changing environmentStefan Landmann0Caroline M Holmes1https://orcid.org/0000-0001-9885-4933Mikhail Tikhonov2https://orcid.org/0000-0002-9558-1121Institute of Physics, Carl von Ossietzky University of Oldenburg, Oldenburg, GermanyDepartment of Physics, Princeton University, Princeton, United StatesDepartment of Physics, Center for Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, United StatesBacteria live in environments that are continuously fluctuating and changing. Exploiting any predictability of such fluctuations can lead to an increased fitness. On longer timescales, bacteria can ‘learn’ the structure of these fluctuations through evolution. However, on shorter timescales, inferring the statistics of the environment and acting upon this information would need to be accomplished by physiological mechanisms. Here, we use a model of metabolism to show that a simple generalization of a common regulatory motif (end-product inhibition) is sufficient both for learning continuous-valued features of the statistical structure of the environment and for translating this information into predictive behavior; moreover, it accomplishes these tasks near-optimally. We discuss plausible genetic circuits that could instantiate the mechanism we describe, including one similar to the architecture of two-component signaling, and argue that the key ingredients required for such predictive behavior are readily accessible to bacteria.https://elifesciences.org/articles/67455fluctuating environmentmetabolic regulationlearning |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Stefan Landmann Caroline M Holmes Mikhail Tikhonov |
| spellingShingle |
Stefan Landmann Caroline M Holmes Mikhail Tikhonov A simple regulatory architecture allows learning the statistical structure of a changing environment eLife fluctuating environment metabolic regulation learning |
| author_facet |
Stefan Landmann Caroline M Holmes Mikhail Tikhonov |
| author_sort |
Stefan Landmann |
| title |
A simple regulatory architecture allows learning the statistical structure of a changing environment |
| title_short |
A simple regulatory architecture allows learning the statistical structure of a changing environment |
| title_full |
A simple regulatory architecture allows learning the statistical structure of a changing environment |
| title_fullStr |
A simple regulatory architecture allows learning the statistical structure of a changing environment |
| title_full_unstemmed |
A simple regulatory architecture allows learning the statistical structure of a changing environment |
| title_sort |
simple regulatory architecture allows learning the statistical structure of a changing environment |
| publisher |
eLife Sciences Publications Ltd |
| series |
eLife |
| issn |
2050-084X |
| publishDate |
2021-09-01 |
| description |
Bacteria live in environments that are continuously fluctuating and changing. Exploiting any predictability of such fluctuations can lead to an increased fitness. On longer timescales, bacteria can ‘learn’ the structure of these fluctuations through evolution. However, on shorter timescales, inferring the statistics of the environment and acting upon this information would need to be accomplished by physiological mechanisms. Here, we use a model of metabolism to show that a simple generalization of a common regulatory motif (end-product inhibition) is sufficient both for learning continuous-valued features of the statistical structure of the environment and for translating this information into predictive behavior; moreover, it accomplishes these tasks near-optimally. We discuss plausible genetic circuits that could instantiate the mechanism we describe, including one similar to the architecture of two-component signaling, and argue that the key ingredients required for such predictive behavior are readily accessible to bacteria. |
| topic |
fluctuating environment metabolic regulation learning |
| url |
https://elifesciences.org/articles/67455 |
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