Literature DB >> 1933037

Analysis of chemotactic bacterial distributions in population migration assays using a mathematical model applicable to steep or shallow attractant gradients.

R M Ford1, D A Lauffenburger.   

Abstract

The mathematical model developed by Rivero et al. (1989, Chem. Engng Sci. 44, 2881-2897) is applied to literature data measuring chemotactic bacterial population distributions in response to steep as well as shallow attractant gradients. This model is based on a fundamental picture of the sensing and response mechanisms of individual bacterial cells, and thus related individual cell properties such as swimming speed and tumbling frequency to population parameters such as the random motility coefficient and the chemotactic sensitivity coefficient. Numerical solution of the model equations generates predicted bacterial density and attractant concentration profiles for any given experimental assay. We have previously validated the mathematical model from experimental work involving a step change in the attractant gradient (Ford et al., 1991 Biotechnol. Bioengng, 37, 647-660; Ford and Lauffenburger, 1991, Biotechnol. Bioengng, 37, 661-672). Within the context of this experimental assay, effects of attractant diffusion and consumption, random motility, and chemotactic sensitivity on the shape of the profiles are explored to enhance our understanding of this complex phenomenon. We have applied this model to various other types of gradients with successful interpretation of data reported by Dalquist et al. (1972, Nature New Biol. 236, 120-123) for Salmonella typhimurium validating the mathematical model and supporting the involvement of high and low affinity receptors for serine chemotaxis by these cells.

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Year:  1991        PMID: 1933037     DOI: 10.1007/bf02461551

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


  37 in total

1.  Quantitation of the sensory response in bacterial chemotaxis.

Authors:  J L Spudich; D E Koshland
Journal:  Proc Natl Acad Sci U S A       Date:  1975-02       Impact factor: 11.205

2.  Role of chemotaxis in the ecology of denitrifiers.

Authors:  M J Kennedy; J G Lawless
Journal:  Appl Environ Microbiol       Date:  1985-01       Impact factor: 4.792

3.  Model for the chemotactic response of a bacterial population.

Authors:  I R Lapidus; R Schiller
Journal:  Biophys J       Date:  1976-07       Impact factor: 4.033

Review 4.  Chemotaxis as a model second-messenger system.

Authors:  D E Koshland
Journal:  Biochemistry       Date:  1988-08-09       Impact factor: 3.162

5.  Biomaterial-centered infection: microbial adhesion versus tissue integration.

Authors:  A G Gristina
Journal:  Science       Date:  1987-09-25       Impact factor: 47.728

6.  A method for measuring chemotaxis and use of the method to determine optimum conditions for chemotaxis by Escherichia coli.

Authors:  J Adler
Journal:  J Gen Microbiol       Date:  1973-01

7.  Model for chemotaxis.

Authors:  E F Keller; L A Segel
Journal:  J Theor Biol       Date:  1971-02       Impact factor: 2.691

8.  The effect of environmental conditions on the motility of Escherichia coli.

Authors:  J Adler; B Templeton
Journal:  J Gen Microbiol       Date:  1967-02

Review 9.  Protein phosphorylation in bacterial chemotaxis.

Authors:  J S Parkinson
Journal:  Cell       Date:  1988-04-08       Impact factor: 41.582

10.  Studies of bacterial chemotaxis in defined concentration gradients. A model for chemotaxis toward L-serine.

Authors:  F W Dahlquist; R A Elwell; P S Lovely
Journal:  J Supramol Struct       Date:  1976
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  12 in total

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Authors:  R B Marx; M D Aitken
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2.  Modeling of chemotactic steering of bacteria-based microrobot using a population-scale approach.

Authors:  Sunghoon Cho; Young Jin Choi; Shaohui Zheng; Jiwon Han; Seong Young Ko; Jong-Oh Park; Sukho Park
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3.  Stopped-flow chamber and image analysis system for quantitative characterization of bacterial population migration: Motility and chemotaxis ofEscherichia coli K12 to fucose.

Authors:  R M Ford; B R Phillips; J A Quinn; D A Lauffenburger
Journal:  Microb Ecol       Date:  1991-12       Impact factor: 4.552

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6.  Chemotaxis and chemokinesis in eukaryotic cells: the Keller-Segel equations as an approximation to a detailed model.

Authors:  J A Sherratt
Journal:  Bull Math Biol       Date:  1994-01       Impact factor: 1.758

7.  A minimal mechanism for bacterial pattern formation.

Authors:  R Tyson; S R Lubkin; J D Murray
Journal:  Proc Biol Sci       Date:  1999-02-07       Impact factor: 5.349

8.  Towards the Personalized Treatment of Glioblastoma: Integrating Patient-Specific Clinical Data in a Continuous Mechanical Model.

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9.  Formation of phage lysis patterns and implications on co-propagation of phages and motile host bacteria.

Authors:  Xiaochu Li; Floricel Gonzalez; Nathaniel Esteves; Birgit E Scharf; Jing Chen
Journal:  PLoS Comput Biol       Date:  2020-03-13       Impact factor: 4.475

10.  A hybrid model of the role of VEGF binding in endothelial cell migration and capillary formation.

Authors:  Harsh V Jain; Trachette L Jackson
Journal:  Front Oncol       Date:  2013-05-10       Impact factor: 6.244

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