Kinetic and macroscopic models for active particles exploring complex environments with an internal navigation control system
L. G\'omez-Nava, T. Goudon, F. Peruani

TL;DR
This paper develops a mathematical framework to model active particles with internal navigation controls, showing how their collective behavior can be described by convection-diffusion equations and how chemotactic responses emerge.
Contribution
It introduces a Markov chain-based model for internal dynamics of active particles and derives conditions for effective drift and diffusion in complex environments.
Findings
Drift depends on NCS transition rate asymmetry and position dependence.
Agents can exhibit chemotaxis without gradient sensing or memory.
Theoretical framework applies to various biological systems.
Abstract
A large number of biological systems - from bacteria to sheep - can be described as ensembles of self-propelled agents (active particles) with a complex internal dynamic that controls the agent's behavior: resting, moving slow, moving fast, feeding, etc. In this study, we assume that such a complex internal dynamic can be described by a Markov chain, which controls the moving direction, speed, and internal state of the agent. We refer to this Markov chain as the Navigation Control System (NCS). Furthermore, we model that agents sense the environment by considering that the transition rates of the NCS depend on local (scalar) measurement of the environment such as e.g. chemical concentrations, light intensity, or temperature. Here, we investigate under which conditions the (asymptotic) behavior of the agents can be reduced to an effective convection-diffusion equation for the density of…
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