Paterson, J., López-García, M. orcid.org/0000-0003-3833-8595, Gillard, J. et al. (3 more authors) (2021) Analysis of single-bacterium dynamics in a stochastic model of toxin-producing bacteria. In: Performance Engineering and Stochastic Modeling. ASMTA 2021, 09-14 Dec 2021, Virtual Event. Lecture Notes in Computer Science, 13104 . Springer Nature , Cham, Switzerland , pp. 210-225. ISBN: 978-3-030-91824-8 ISSN: 0302-9743 EISSN: 1611-3349
Abstract
We stochastically model two bacterial populations which can produce toxins. We propose to analyse this biological system by following the dynamics of a single bacterium during its lifetime, as well as its progeny. We study the lifespan of a single bacterium, the number of divisions that this bacterium undergoes, and the number of toxin molecules that it produces during its lifetime. We also compute the mean number of bacteria in the genealogy of the original bacterium and the number of toxin molecules produced by its genealogy. We illustrate the applicability of our methods by considering the bacteria Bacillus anthracis and antibiotic treatment, making use of in vitro experimental data. We quantify, for the first time, bacterial toxin production by exploiting an in vitro assay for the A16R strain, and make use of the resulting parameterised model to illustrate our techniques.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2021. This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. |
Keywords: | Bacteria, Toxins, Stochastic model, Continuous time, Markov chain, Single cell, Antibiotic |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Applied Mathematics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Jun 2025 14:33 |
Last Modified: | 12 Aug 2025 12:57 |
Published Version: | https://link.springer.com/chapter/10.1007/978-3-03... |
Status: | Published |
Publisher: | Springer Nature |
Series Name: | Lecture Notes in Computer Science |
Identification Number: | 10.1007/978-3-030-91825-5_13 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177687 |