Analytical modeling and experimental characterization of chemotaxis in Serratia marcescens

Jiang Zhuang, Guopeng Wei, Rika Wright Carlsen, Matthew R. Edwards, Radu Marculescu, Paul Bogdan, and Metin Sitti
Phys. Rev. E 89, 052704 – Published 12 May 2014
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Abstract

This paper presents a modeling and experimental framework to characterize the chemotaxis of Serratia marcescens (S. marcescens) relying on two-dimensional and three-dimensional tracking of individual bacteria. Previous studies mainly characterized bacterial chemotaxis based on population density analysis. Instead, this study focuses on single-cell tracking and measuring the chemotactic drift velocity VC from the biased tumble rate of individual bacteria on exposure to a concentration gradient of l-aspartate. The chemotactic response of S. marcescens is quantified over a range of concentration gradients (103 to 5 mM/mm) and average concentrations (0.5×103 to 2.5 mM). Through the analysis of a large number of bacterial swimming trajectories, the tumble rate is found to have a significant bias with respect to the swimming direction. We also verify the relative gradient sensing mechanism in the chemotaxis of S. marcescens by measuring the change of VC with the average concentration and the gradient. The applied full pathway model with fitted parameters matches the experimental data. Finally, we show that our measurements based on individual bacteria lead to the determination of the motility coefficient μ (7.25×106 cm2/s) of a population. The experimental characterization and simulation results for the chemotaxis of this bacterial species contribute towards using S. marcescens in chemically controlled biohybrid systems.

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  • Received 11 December 2013

DOI:https://doi.org/10.1103/PhysRevE.89.052704

©2014 American Physical Society

Authors & Affiliations

Jiang Zhuang1,*, Guopeng Wei2,†, Rika Wright Carlsen1,‡, Matthew R. Edwards1,§, Radu Marculescu2,∥, Paul Bogdan3,¶, and Metin Sitti1,4,#

  • 1Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
  • 2Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
  • 3Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, California 90089, USA
  • 4Max-Planck Institute for Intelligent Systems, 70569 Stuttgart, Germany

  • *jzhuang@andrew.cmu.edu
  • guopengw@ece.cmu.edu
  • rikawc@cmu.edu
  • §mredward@princeton.edu
  • radum@ece.cmu.edu
  • pbogdan@usc.edu
  • #Corresponding author: sitti@cmu.edu

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Issue

Vol. 89, Iss. 5 — May 2014

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