Research Notes: Gradient sensing in Bayesian chemotaxis

Abstract

Bayesian chemotaxis is an information-based target search problem inspired by biological chemotaxis. It is defined by a decision strategy coupled to the dynamic estimation of target position from detections of signaling molecules. We extend the case of a point-like agent previously introduced in [Vergassola et al., Nature 2007], which establishes concentration sensing as the dominant contribution to information processing, to the case of a circular agent of small finite size. We identify gradient sensing and a Laplacian correction to concentration sensing as the two leading-order expansion terms in the expected entropy variation. Numerically, we find that the impact of gradient sensing is most relevant because it provides direct directional information to break symmetry in likelihood distributions, which are generally circle-shaped by concentration sensing.

References in corpus (7)

• First-passage times in complex scale-invariant media
• Nonequilibrium Thermodynamics of Feedback Control
• Maxwell's demon in biochemical signal transduction with feedback loop
• Physical limit to concentration sensing in a changing environment
• On the trajectories and performance of Infotaxis, an information-based greedy search algorithm
• Bayesian gradient sensing in the presence of rotational diffusion
• Triangulation sensing: how cells recover a source from diffusing particles in three dimensions

Cited by in corpus (4)

• Cell decision-making through the lens of Bayesian learning
• Gradient sensing limit of a cell when controlling the elongating direction
• Theory for Optimal Estimation and Control under Resource Limitations and Its Applications to Biological Information Processing and Decision-Making
• Learning stochastic filtering