Modelling bacterial chemotaxis for indirectly binding attractants

In bacterial chemotaxis, chemoattractant molecules may bind either directly or indirectly with receptors within the cell periplasmic space. The indirect binding mechanism, which involves an intermediate periplasmic binding protein, has been reported to increase sensitivity to dilute attractant conce...

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Main Authors: Tan, Pei Yen, Marcos, Liu, Yu
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/153913
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1539132021-12-13T04:43:41Z Modelling bacterial chemotaxis for indirectly binding attractants Tan, Pei Yen Marcos Liu, Yu School of Mechanical and Aerospace Engineering School of Civil and Environmental Engineering Nanyang Environment and Water Research Institute Advanced Environmental Biotechnology Centre (AEBC) Engineering::Mechanical engineering AI-2 Maltose In bacterial chemotaxis, chemoattractant molecules may bind either directly or indirectly with receptors within the cell periplasmic space. The indirect binding mechanism, which involves an intermediate periplasmic binding protein, has been reported to increase sensitivity to dilute attractant concentrations as well as range of response. Current mathematical models for bacterial chemotaxis at the population scale do not appear to take the periplasmic binding protein (BP) concentration or the indirect binding mechanics into account. We formulate an indirect binding extension to the existing Rivero equation for chemotactic velocity based on fundamental reversible enzyme kinetics. The formulated indirect binding expression accounts for the periplasmic BP concentration and the dissociation constants for binding between attractant and periplasmic BP, as well as between BP and chemoreceptor. We validate the indirect-binding model using capillary assay simulations of the chemotactic responses of E. coli to the indirectly-binding attractants maltose and AI-2. The predicted response agrees well with experimental data from a number of maltose capillary assay studies conducted in previous literature. The model is also able to achieve good agreement with AI-2 capillary assay data of one study out of two tested. The chemotactic response of E. coli towards AI-2 appears to be of higher complexity due to reports of variable periplasmic BP concentration as well as the low concentration of periplasmic BP relative to the total receptor concentration. Our current model is thus suitable for indirect binding chemotactic response systems with constant periplasmic BP concentration that is significantly larger than the total receptor concentration, such as the response of E. coli towards maltose. Further considerations may be taken into account to model the chemotactic response towards AI-2 with greater accuracy. 2021-12-13T04:43:40Z 2021-12-13T04:43:40Z 2020 Journal Article Tan, P. Y., Marcos & Liu, Y. (2020). Modelling bacterial chemotaxis for indirectly binding attractants. Journal of Theoretical Biology, 487, 110120-. https://dx.doi.org/10.1016/j.jtbi.2019.110120 0022-5193 https://hdl.handle.net/10356/153913 10.1016/j.jtbi.2019.110120 31857084 2-s2.0-85076704397 487 110120 en Journal of Theoretical Biology © 2019 Elsevier Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Mechanical engineering
AI-2
Maltose
spellingShingle Engineering::Mechanical engineering
AI-2
Maltose
Tan, Pei Yen
Marcos
Liu, Yu
Modelling bacterial chemotaxis for indirectly binding attractants
description In bacterial chemotaxis, chemoattractant molecules may bind either directly or indirectly with receptors within the cell periplasmic space. The indirect binding mechanism, which involves an intermediate periplasmic binding protein, has been reported to increase sensitivity to dilute attractant concentrations as well as range of response. Current mathematical models for bacterial chemotaxis at the population scale do not appear to take the periplasmic binding protein (BP) concentration or the indirect binding mechanics into account. We formulate an indirect binding extension to the existing Rivero equation for chemotactic velocity based on fundamental reversible enzyme kinetics. The formulated indirect binding expression accounts for the periplasmic BP concentration and the dissociation constants for binding between attractant and periplasmic BP, as well as between BP and chemoreceptor. We validate the indirect-binding model using capillary assay simulations of the chemotactic responses of E. coli to the indirectly-binding attractants maltose and AI-2. The predicted response agrees well with experimental data from a number of maltose capillary assay studies conducted in previous literature. The model is also able to achieve good agreement with AI-2 capillary assay data of one study out of two tested. The chemotactic response of E. coli towards AI-2 appears to be of higher complexity due to reports of variable periplasmic BP concentration as well as the low concentration of periplasmic BP relative to the total receptor concentration. Our current model is thus suitable for indirect binding chemotactic response systems with constant periplasmic BP concentration that is significantly larger than the total receptor concentration, such as the response of E. coli towards maltose. Further considerations may be taken into account to model the chemotactic response towards AI-2 with greater accuracy.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Tan, Pei Yen
Marcos
Liu, Yu
format Article
author Tan, Pei Yen
Marcos
Liu, Yu
author_sort Tan, Pei Yen
title Modelling bacterial chemotaxis for indirectly binding attractants
title_short Modelling bacterial chemotaxis for indirectly binding attractants
title_full Modelling bacterial chemotaxis for indirectly binding attractants
title_fullStr Modelling bacterial chemotaxis for indirectly binding attractants
title_full_unstemmed Modelling bacterial chemotaxis for indirectly binding attractants
title_sort modelling bacterial chemotaxis for indirectly binding attractants
publishDate 2021
url https://hdl.handle.net/10356/153913
_version_ 1720447134627528704