A stochastic model for directional changes of swimming bacteria. Issue 18 (21st April 2017)

• Record Type: Journal Article
• Authors: Fier, G.; Hansmann, D.; Buceta, R. C.
• Is Part Of: Soft matter. Volume 13:Issue 18(2017)
• Page Start: 3385
• Page End: 3394
• Publication Date: 2017-04-21
• Subjects: Soft condensed matter -- Periodicals; 530.413
• DOI: 10.1039/c6sm02771g
• Languages: English
• ISSNs: 1744-683X

Abstract

Abstract : In this work we introduce a stochastic model to describe directional changes in the movement of swimming bacteria. Abstract : In this work we introduce a stochastic model to describe directional changes in the movement of swimming bacteria. We use the probability density function (PDF) of turn angles, measured on tumbling wild-type E. coli, to build a Langevin equation for the deflection of the bacterial body swimming in isotropic media. We have solved this equation analytically by means of the Green function method and shown that three parameters are sufficient to describe the movement: the characteristic time, the steady-state solution and the control parameter. We conclude that the tumble motion, which is manifested as abrupt turns, is primarily caused by the rotational boost generated by the flagellar motor and complementarily by the rotational diffusion introduced by noise. We show that in the tumble motion the deflection is a non-stationary stochastic process during times at which the tumbling occurs. By tuning the control parameter our model is able to explain small turns of the bacteria around their centres of mass along the run. We show that the deflection during the run is an Ornstein–Uhlenbeck process, which for typical run times is stationary. We conclude that, along the run, the rotational boosts do not exist and that only the rotational diffusion remains. Thus we have a single model to explain the turns of the bacterium during the run or tumble movements, through a control parameter that can be tuned through a critical value that can explain the transition between the two turn behaviours. This model is also able to explain in a very satisfactory way all available statistical experimental data, such as PDFs and average values of turning angles times, of both run and tumble motions.