Literature DB >> 18642048

Overview of mathematical approaches used to model bacterial chemotaxis I: the single cell.

M J Tindall1, S L Porter, P K Maini, G Gaglia, J P Armitage.   

Abstract

Mathematical modeling of bacterial chemotaxis systems has been influential and insightful in helping to understand experimental observations. We provide here a comprehensive overview of the range of mathematical approaches used for modeling, within a single bacterium, chemotactic processes caused by changes to external gradients in its environment. Specific areas of the bacterial system which have been studied and modeled are discussed in detail, including the modeling of adaptation in response to attractant gradients, the intracellular phosphorylation cascade, membrane receptor clustering, and spatial modeling of intracellular protein signal transduction. The importance of producing robust models that address adaptation, gain, and sensitivity are also discussed. This review highlights that while mathematical modeling has aided in understanding bacterial chemotaxis on the individual cell scale and guiding experimental design, no single model succeeds in robustly describing all of the basic elements of the cell. We conclude by discussing the importance of this and the future of modeling in this area.

Mesh:

Year:  2008        PMID: 18642048     DOI: 10.1007/s11538-008-9321-6

Source DB:  PubMed          Journal:  Bull Math Biol        ISSN: 0092-8240            Impact factor:   1.758


  32 in total

1.  Large mass self-similar solutions of the parabolic-parabolic Keller-Segel model of chemotaxis.

Authors:  Piotr Biler; Lucilla Corrias; Jean Dolbeault
Journal:  J Math Biol       Date:  2010-08-22       Impact factor: 2.259

2.  Dynamics of bacterial swarming.

Authors:  Nicholas C Darnton; Linda Turner; Svetlana Rojevsky; Howard C Berg
Journal:  Biophys J       Date:  2010-05-19       Impact factor: 4.033

3.  Attractant binding induces distinct structural changes to the polar and lateral signaling clusters in Bacillus subtilis chemotaxis.

Authors:  Kang Wu; Hanna E Walukiewicz; George D Glekas; George W Ordal; Christopher V Rao
Journal:  J Biol Chem       Date:  2010-11-22       Impact factor: 5.157

4.  Ultrasensitivity in independent multisite systems.

Authors:  Shane Ryerson; Germán A Enciso
Journal:  J Math Biol       Date:  2013-09-18       Impact factor: 2.259

5.  Adaptive response by state-dependent inactivation.

Authors:  Tamar Friedlander; Naama Brenner
Journal:  Proc Natl Acad Sci U S A       Date:  2009-12-15       Impact factor: 11.205

6.  Suspension biomechanics of swimming microbes.

Authors:  Takuji Ishikawa
Journal:  J R Soc Interface       Date:  2009-08-12       Impact factor: 4.118

Review 7.  Signal processing in complex chemotaxis pathways.

Authors:  Steven L Porter; George H Wadhams; Judith P Armitage
Journal:  Nat Rev Microbiol       Date:  2011-02-01       Impact factor: 60.633

8.  Quantitative analysis of transverse bacterial migration induced by chemotaxis in a packed column with structured physical heterogeneity.

Authors:  Meng Wang; Roseanne M Ford
Journal:  Environ Sci Technol       Date:  2010-01-15       Impact factor: 9.028

9.  Reverse engineering of bacterial chemotaxis pathway via frequency domain analysis.

Authors:  Junjie Luo; Jun Wang; Ting Martin Ma; Zhirong Sun
Journal:  PLoS One       Date:  2010-03-09       Impact factor: 3.240

10.  Using structural information to change the phosphotransfer specificity of a two-component chemotaxis signalling complex.

Authors:  Christian H Bell; Steven L Porter; Annabel Strawson; David I Stuart; Judith P Armitage
Journal:  PLoS Biol       Date:  2010-02-09       Impact factor: 8.029

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