Effect of receptor clustering on chemotactic performance of Escherichia coli: sensing versus adaptation

Abstract

We show how the competition between sensing and adaptation can result in a performance peak in E.coli chemotaxis using extensive numerical simulations in a detailed theoretical model. Receptor clustering amplifies the input signal coming from ligand binding which enhances chemotactic efficiency. But large clusters also induce large fluctuations in total activity since the number of clusters go down. The activity and hence the run-tumble motility now gets controlled by methylation levels which are part of adaptation module, rather than ligand binding. This reduces chemotactic efficiency.

References in corpus (11)

• Limits of feedback control in bacterial chemotaxis
• Feedback Between Motion and Sensation Provides Nonlinear Boost in Run-and-tumble Navigation
• Signalling noise enhances chemotactic drift of E. coli
• Chemotaxis When Bacteria Remember: Drift versus Diffusion
• Optimal receptor-cluster size determined by intrinsic and extrinsic noise
• Adaptation dynamics in densely clustered chemoreceptors
• Polar Chemoreceptor Clustering by Coupled Trimers of Dimers
• Drift and behavior of E. coli cells
• Optimal methylation noise for best chemotactic performance of {\sl E. coli}
• Optimal search in E.coli chemotaxis
• Chemotactic drift speed for bacterial motility pattern with two alternating turning events

Cited by in corpus (9)

• Optimal sensing and control of run-and-tumble chemotaxis
• 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
• Persistent exclusion process with time-periodic drive
• Bacterial Chemotaxis in a Traveling Wave Attractant Environment
• Short time extremal response to step stimulus for a single cell {\sl E. coli}
• Dynamics of chemo-receptor activity with time-periodic attractant field
• Autotaxis of a Run-and-Tumble Agent via Reinforcement Learning