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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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. |
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Engineering::Mechanical engineering AI-2 Maltose Tan, Pei Yen Marcos Liu, Yu Modelling bacterial chemotaxis for indirectly binding attractants |
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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. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Tan, Pei Yen Marcos Liu, Yu |
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Article |
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Tan, Pei Yen Marcos Liu, Yu |
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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 |
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Modelling bacterial chemotaxis for indirectly binding attractants |
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modelling bacterial chemotaxis for indirectly binding attractants |
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2021 |
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https://hdl.handle.net/10356/153913 |
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