Identification of the governing equation of stimulus-response data for run-and-tumble dynamics.

ABSTRACT: The run-and-tumble behavior is a simple yet powerful mechanism that enables microorganisms to efficiently navigate and adapt to their environment. These organisms run and tumble alternately, with transition rates modulated by intracellular chemical concentration. We introduce a neural network-based model capable of identifying the governing equations underlying run-and-tumble dynamics. This model accommodates the nonlinear functions describing movement responses to intracellular biochemical reactions by integrating the general structure of ODEs that represent these reactions, without requiring explicit reconstruction of the reaction mechanisms. It is trained on datasets of measured responses to simple, controllable signals. The resulting model is capable of predicting movement responses in

SUBMITTER: Lei S 

PROVIDER: S-EPMC12338844 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature