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ToolsIhekwaba, Adaoha E. C., Mura, Ivan, Walshaw, John, Peck, Michael W. and Barker, Gary C. (2016) An Integrative Approach to Computational Modelling of the Gene Regulatory Network Controlling Clostridium botulinum Type A1 Toxin Production. PLoS Computational Biology, 12 (11). ISSN 1553-734X
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Abstract
Clostridium botulinum produces botulinum neurotoxins (BoNTs), highly potent substances responsible for botulism. Currently, mathematical models of C. botulinum growth and toxigenesis are largely aimed at risk assessment and do not include explicit genetic information beyond group level but integrate many component processes, such as signalling, membrane permeability and metabolic activity. In this paper we present a scheme for modelling neurotoxin production in C. botulinum Group I type A1, based on the integration of diverse information coming from experimental results available in the literature. Experiments show that production of BoNTs depends on the growth-phase and is under the control of positive and negative regulatory elements at the intracellular level. Toxins are released as large protein complexes and are associated with non-toxic components. Here, we systematically review and integrate those regulatory elements previously described in the literature for C. botulinum Group I type A1 into a population dynamics model, to build the very first computational model of toxin production at the molecular level. We conduct a validation of our model against several items of published experimental data for different wild type and mutant strains of C. botulinum Group I type A1. The result of this process underscores the potential of mathematical modelling at the cellular level, as a means of creating opportunities in developing new strategies that could be used to prevent botulism; and potentially contribute to improved methods for the production of toxin that is used for therapeutics.
| Item Type: | Article |
|---|---|
| Additional Information: | © 2016 Ihekwaba et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| Faculty \ School: | Faculty of Science > School of Computing Sciences Faculty of Science > School of Biological Sciences |
| Related URLs: | |
| Depositing User: | Pure Connector |
| Date Deposited: | 07 Dec 2016 00:06 |
| Last Modified: | 21 Mar 2024 01:37 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/61596 |
| DOI: | 10.1371/journal.pcbi.1005205 |
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