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Modelling bacterial chemotaxis for indirectly binding attractants.

Pei Yen Tan1, Marcos2, Yu Liu3

  • 1Advanced Environmental Biotechnology Centre, Nanyang Environment & Water Research Institute, Nanyang Technological University, 1 Cleantech Loop, 637141, Singapore.

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|December 21, 2019
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Summary

This study introduces a new mathematical model for bacterial chemotaxis, incorporating indirect binding mechanisms via periplasmic binding proteins (BP). The model accurately predicts E. coli responses to maltose, enhancing our understanding of chemotaxis sensitivity.

Keywords:
AI-2Escherichia coliMaltose

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Area of Science:

  • Microbiology
  • Biophysics
  • Mathematical Biology

Background:

  • Bacterial chemotaxis involves chemoattractant binding to cell receptors, either directly or indirectly through periplasmic binding proteins (BP).
  • Indirect binding mechanisms are known to enhance sensitivity and response range in dilute attractant conditions.
  • Existing population-scale chemotaxis models often neglect the role of periplasmic BP concentration and indirect binding kinetics.

Purpose of the Study:

  • To formulate an extended mathematical model for bacterial chemotaxis that incorporates indirect binding mechanics.
  • To account for periplasmic binding protein concentration and relevant dissociation constants in chemotactic velocity calculations.
  • To validate the new model against experimental data for E. coli chemotaxis towards maltose and AI-2.

Main Methods:

  • Developed an indirect binding extension to the Rivero equation for chemotactic velocity, based on reversible enzyme kinetics.
  • Incorporated parameters for periplasmic BP concentration and dissociation constants for BP-attractant and BP-chemoreceptor binding.
  • Simulated chemotactic responses using capillary assay models for E. coli exposed to maltose and AI-2.

Main Results:

  • The indirect-binding model showed good agreement with experimental data for E. coli chemotaxis towards maltose.
  • The model achieved reasonable agreement with AI-2 chemotaxis data, though less precise than for maltose.
  • Model suitability is confirmed for systems with constant, high periplasmic BP concentration relative to receptor concentration.

Conclusions:

  • The developed model successfully extends existing chemotaxis equations to include indirect binding mechanisms.
  • The model provides accurate predictions for E. coli chemotaxis to maltose, highlighting the importance of BP concentration.
  • Further refinements are needed to accurately model complex chemotaxis responses like that to AI-2, which involves variable BP concentrations.