Optimal methylation noise for best chemotactic performance of {\sl E. coli}

Abstract

In response to a concentration gradient of nutrient, E. coli bacterium modulates the rotational bias of flagellar motors which control its run-and-tumble motion, to migrate towards regions of high nutrient concentration. Presence of stochastic noise in the biochemical pathway of the cell has important consequence on the switching mechanism of motor bias, which in turn affects the runs and tumbles of the cell. We model the intra-cellular reaction network in terms of coupled time-evolution of three stochastic variables, kinase activity, methylation level and CheY-P protein level, and study the effect of methylation noise on the chemotactic performance of the cell. In presence of a spatially varying nutrient concentration profile, a good chemotactic performance allows the cell to climb up the concentration gradient fast and localize in the nutrient-rich regions in the long time limit. Our simulations show that the best performance is obtained at an optimal noise strength. While it is expected that chemotaxis will be weaker for very large noise, it is counter-intuitive that the performance worsens even when noise level falls below a certain value. We explain this striking result by detailed analysis of CheY-P protein level statistics for different noise strengths. We show that when the CheY-P level falls below a certain (noise-dependent) threshold, the cell tends to move down the concentration gradient of the nutrient, which has a detrimental effect on its chemotactic response. This threshold value decreases as noise is increased, and this effect is responsible for noise-induced enhancement of chemotactic performance. In a harsh chemical environment, when the nutrient degrades with time, the amount of nutrient intercepted by the cell trajectory, is an effective performance criterion. In this case also, we find an optimum noise strength, depending on the nutrient lifetime.

References in corpus (8)

• Relationship between cellular response and behavioral variability in bacterial chemotaxis
• Limits of feedback control in bacterial chemotaxis
• Signalling noise enhances chemotactic drift of E. coli
• Chemotaxis When Bacteria Remember: Drift versus Diffusion
• Adaptation dynamics in densely clustered chemoreceptors
• Optimal search in E.coli chemotaxis
• Ultrasensitivity and Fluctuations in the Barkai-Leibler Model of Chemotaxis Receptors in {\it Escherichia coli}
• Steady-State Chemotactic Response in E. coli

Cited by in corpus (11)

• Run-to-Tumble Variability Controls the Surface Residence Times of $E.coli$ Bacteria
• Optimal sensing and control of run-and-tumble chemotaxis
• Effect of receptor clustering on chemotactic performance of Escherichia coli: sensing versus adaptation
• Run-and-tumble motion with step-like responses to a stochastic input
• Run-and-tumble chemotaxis using reinforcement learning
• Effect of switching time scale of receptor activity on chemotactic performance of Escherichia coli
• Effect of receptor cooperativity on methylation dynamics in bacterial chemotaxis with weak and strong gradient
• Short time extremal response to step stimulus for a single cell {\sl E. coli}
• Persistent exclusion process with time-periodic drive
• Bacterial Chemotaxis in a Traveling Wave Attractant Environment
• Run-and-tumble particle with saturating rates