4.7 Article

Effect of receptor cooperativity on methylation dynamics in bacterial chemotaxis with weak and strong gradient

期刊

PHYSICAL REVIEW E
卷 105, 期 1, 页码 -

出版社

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevE.105.014411

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资金

  1. Council of Scientific and Industrial Research (CSIR), India [09/575(0122)/2019-EMR-I]
  2. [MTR/2019/000946]

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Explored methylation dynamics of chemoreceptors in Escherichia coli in the presence of varying chemoattractant environments. Found that methylation level is sensitive to receptor cluster size and gradient strength. Short runs lead to demethylation, while long runs have varied effects depending on receptor cooperativity or gradient strength.
We study methylation dynamics of the chemoreceptors as an Escherichia coli cell moves around in a spatially varying chemoattractant environment. We consider attractant concentration with strong and weak spatial gradient. During the uphill and downhill motion of the cell along the gradient, we measure the temporal variation of average methylation level of the receptor clusters. Our numerical simulations we show that the methylation dynamics depends sensitively on the size of the receptor clusters and also on the strength of the gradient. At short times after the beginning of a run, the methylation dynamics is mainly controlled by short runs which are generally associated with high receptor activity. This results in demethylation at short times. But for intermediate or large times, long runs play an important role and depending on receptor cooperativity or gradient strength, the qualitative variation of methylation can be completely different in this time regime. For weak gradient, both for uphill and downhill runs, after the initial demethylation, we find methylation level increases steadily with time for all cluster sizes. Similar qualitative behavior is observed for strong gradient during uphill runs as well. However, the methylation dynamics for downhill runs in strong gradient show highly nontrivial dependence on the receptor cluster size. We explain this behavior as a result of interplay between the sensing and adaptation modules of the signaling network.

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