Volume 142, Number 4, May 2023
|Number of page(s)||7|
|Section||Biological and soft matter physics|
|Published online||12 May 2023|
Learning run-and-tumble chemotaxis with support vector machines
1 cfaed, Technische Universität Dresden - 01069 Dresden, Germany
2 Cluster of Excellence “Physics of Life” - 01307 Dresden, Germany
(a) E-mail: email@example.com (corresponding author)
Received: 31 January 2023
Accepted: 27 April 2023
To navigate in spatial fields of sensory cues, bacterial cells employ gradient sensing by temporal comparison for run-and-tumble chemotaxis. Sensing and motility noise imply trade-off choices between precision and accuracy. To gain insight into these trade-offs, we learn optimal chemotactic decision filters using supervised machine learning, applying support vector machines to a biologically motivated training dataset. We discuss how the optimal filter depends on the level of sensing and motility noise, and derive an empirical power law for the optimal measurement time with as a function of the rotational diffusion coefficient Drot characterizing motility noise. A weak amount of motility noise slightly increases chemotactic performance.
© 2023 The author(s)
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