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Chin. Phys. B, 2012, Vol. 21(9): 098701    DOI: 10.1088/1674-1056/21/9/098701
INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY Prev   Next  

A population-level model from the microscopic dynamics in Escherichia coli chemotaxis via Langevin approximation

He Zhuo-Ran (贺卓然)a, Wu Tai-Lin (吴泰霖)a, Ouyang Qi (欧阳颀)a b, Tu Yu-Hai (涂豫海)c
a State Key Laboratory for Mesoscopic Physics, School of Physics, Peking University, Beijing 100871, China;
b Center for Theoretical Biology, Peking University, Beijing 100871, China;
c IBM T. J. Watson Research Center, Yorktown Heights, New York 10598, USA
Abstract  Recent extensive studies of Escherichia coli (E. coli) chemotaxis have achieved a deep understanding of its microscopic control dynamics. As a result, various quantitatively predictive models have been developed to describe the chemotactic behavior of E. coli motion. However, a population-level partial differential equation (PDE) that rationally incorporates such microscopic dynamics is still insufficient. Apart from the traditional Keller-Segel (K-S) equation, many existing population-level models developed from the microscopic dynamics are integro-PDEs. The difficulty comes mainly from cell tumbles which yield a velocity jumping process. Here, we propose a Langevin approximation method that avoids such a difficulty without appreciable loss of precision. The resulting model not only quantitatively reproduces the results of pathway-based single-cell simulators, but also provides new inside information on the mechanism of E. coli chemotaxis. Our study demonstrates a possible alternative in establishing a simple population-level model that allows for the complex microscopic mechanisms in bacterial chemotaxis.
Keywords:  bacterial chemotaxis      population-level model      Langevin approximation  
Received:  06 April 2012      Revised:  10 May 2012      Accepted manuscript online: 
PACS:  87.17.Jj (Cell locomotion, chemotaxis)  
  87.17.Aa (Modeling, computer simulation of cell processes)  
  87.18.Mp (Signal transduction networks)  
  87.18.Vf (Systems biology)  
Corresponding Authors:  Ouyang Qi     E-mail:  qi@pku.edu.cn

Cite this article: 

He Zhuo-Ran (贺卓然), Wu Tai-Lin (吴泰霖), Ouyang Qi (欧阳颀), Tu Yu-Hai (涂豫海) A population-level model from the microscopic dynamics in Escherichia coli chemotaxis via Langevin approximation 2012 Chin. Phys. B 21 098701

[1] Tindall M J, Porter S L, Maini P K, Gaglia G and Armitage J P 2008 Bull. Math. Biol. 70 1525
[2] Ahmed T and Stocker R 2008 Biophys. J. 95 4481
[3] Kalinin Y V, Jiang L, Tu Y and Wu M 2009 Biophys. J. 96 2439
[4] Amarie D, Glazier J A and Jacobson S C 2007 Anal. Chem. 79 9471
[5] Zhu X, Si G, Deng N, Ouyang Q, Wu T, He Z, Jiang L, Luo C and Tu Y 2012 Phys. Rev. Lett. 108 128101
[6] Keller E and Segel L 1971 J. Theor. Biol. 30 225
[7] Tindall M J, Maini P K, Porter S L and Armitage J P 2008 Bull. Math. Biol. 70 1570
[8] Macnab R M and Koshland D E 1972 Proc. Natl. Acad. Sci. 69 2509
[9] Segall J E, Block S M and Berg H C 1986 Proc. Natl. Acad. Sci. 83 8987
[10] Shimizu T S, Tu Y and Berg H C 2010 Mol. Syst. Biol. 6 382
[11] Alon U, Surette M G, Barkai N and Leibler S 1999 Nature 397 168
[12] Stewart R C, Jahreis K and Parkinson J S 2000 Biochemistry 39 13157
[13] Cluzel P, Surette M and Leibler S 2000 Science 287 1652
[14] Turner L, Ryu W S and Berg H C 2000 J. Bact. 182 2793
[15] Schnitzer M J 1993 Phys. Rev. E 48 2553
[16] Alt W 1980 J. Math. Biol. 9 147
[17] Berg H C and Brown D A 1972 Nature 239 500
[18] Vladimirov N, Lebiedz D and Sourjik V 2010 PLoS Comput. Biol. 6 e1000717
[19] Vladimirov N, Lovdok L, Lebiedz D and Sourjik V 2008 PLoS Comput. Biol. 4 e1000242
[20] Jiang L, Ouyang Q and Tu Y 2010 PLoS Comput. Biol. 6 e1000735
[21] Vladimirov N 2009 Multiscale Modeling of Bacterial Chemotaxis (Ph. D. Dissertation) (Germany: the Ruperto-Carola University of Heidelberg)
[22] Erban R and Othmer H G 2004 SIAM J. Appl. Math. 65 361
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