Statistical parameter inference of bacterial swimming strategies
We provide a detailed stochastic description of the swimming motion of an E.coli bacterium in two dimension, where we resolve tumble events in time. For this purpose, we set up two Langevin equations for the orientation angle and speed dynamics. Calculating moments, distribution and autocorrelation...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2018-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/aae72c |
| _version_ | 1797750588111323136 |
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| author | Maximilian Seyrich Zahra Alirezaeizanjani Carsten Beta Holger Stark |
| author_facet | Maximilian Seyrich Zahra Alirezaeizanjani Carsten Beta Holger Stark |
| author_sort | Maximilian Seyrich |
| collection | DOAJ |
| description | We provide a detailed stochastic description of the swimming motion of an E.coli bacterium in two dimension, where we resolve tumble events in time. For this purpose, we set up two Langevin equations for the orientation angle and speed dynamics. Calculating moments, distribution and autocorrelation functions from both Langevin equations and matching them to the same quantities determined from data recorded in experiments, we infer the swimming parameters of E.coli . They are the tumble rate λ , the tumble time r ^−1 , the swimming speed v _0 , the strength of speed fluctuations σ , the relative height of speed jumps η , the thermal value for the rotational diffusion coefficient D _0 , and the enhanced rotational diffusivity during tumbling D _T . Conditioning the observables on the swimming direction relative to the gradient of a chemoattractant, we infer the chemotaxis strategies of E.coli . We confirm the classical strategy of a lower tumble rate for swimming up the gradient but also a smaller mean tumble angle (angle bias). The latter is realized by shorter tumbles as well as a slower diffusive reorientation. We also find that speed fluctuations are increased by about 30% when swimming up the gradient compared to the reversed direction. |
| first_indexed | 2024-03-12T16:34:56Z |
| format | Article |
| id | doaj.art-e6fb5ee698054e3690ded2354cd00d8d |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:34:56Z |
| publishDate | 2018-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-e6fb5ee698054e3690ded2354cd00d8d2023-08-08T14:56:17ZengIOP PublishingNew Journal of Physics1367-26302018-01-01201010303310.1088/1367-2630/aae72cStatistical parameter inference of bacterial swimming strategiesMaximilian Seyrich0https://orcid.org/0000-0002-8423-2276Zahra Alirezaeizanjani1https://orcid.org/0000-0001-8803-7390Carsten Beta2https://orcid.org/0000-0002-0100-1043Holger Stark3https://orcid.org/0000-0002-6388-5390Institut für Theoretische Physik, Technische Universität Berlin , Hardenbergstrasse 36, D-10623 Berlin, GermanyInstitut für Physik und Astronomie, Universität Potsdam , Karl-Liebknecht-Strasse 24/25, D-14476 Potsdam, GermanyInstitut für Physik und Astronomie, Universität Potsdam , Karl-Liebknecht-Strasse 24/25, D-14476 Potsdam, GermanyInstitut für Theoretische Physik, Technische Universität Berlin , Hardenbergstrasse 36, D-10623 Berlin, GermanyWe provide a detailed stochastic description of the swimming motion of an E.coli bacterium in two dimension, where we resolve tumble events in time. For this purpose, we set up two Langevin equations for the orientation angle and speed dynamics. Calculating moments, distribution and autocorrelation functions from both Langevin equations and matching them to the same quantities determined from data recorded in experiments, we infer the swimming parameters of E.coli . They are the tumble rate λ , the tumble time r ^−1 , the swimming speed v _0 , the strength of speed fluctuations σ , the relative height of speed jumps η , the thermal value for the rotational diffusion coefficient D _0 , and the enhanced rotational diffusivity during tumbling D _T . Conditioning the observables on the swimming direction relative to the gradient of a chemoattractant, we infer the chemotaxis strategies of E.coli . We confirm the classical strategy of a lower tumble rate for swimming up the gradient but also a smaller mean tumble angle (angle bias). The latter is realized by shorter tumbles as well as a slower diffusive reorientation. We also find that speed fluctuations are increased by about 30% when swimming up the gradient compared to the reversed direction.https://doi.org/10.1088/1367-2630/aae72cE.colirun and tumblechemotaxisstochastic processesbacterial swimming strategiesparameter inference |
| spellingShingle | Maximilian Seyrich Zahra Alirezaeizanjani Carsten Beta Holger Stark Statistical parameter inference of bacterial swimming strategies New Journal of Physics E.coli run and tumble chemotaxis stochastic processes bacterial swimming strategies parameter inference |
| title | Statistical parameter inference of bacterial swimming strategies |
| title_full | Statistical parameter inference of bacterial swimming strategies |
| title_fullStr | Statistical parameter inference of bacterial swimming strategies |
| title_full_unstemmed | Statistical parameter inference of bacterial swimming strategies |
| title_short | Statistical parameter inference of bacterial swimming strategies |
| title_sort | statistical parameter inference of bacterial swimming strategies |
| topic | E.coli run and tumble chemotaxis stochastic processes bacterial swimming strategies parameter inference |
| url | https://doi.org/10.1088/1367-2630/aae72c |
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