Learning run-and-tumble chemotaxis with support vector machines

Publikation: Beitrag in FachzeitschriftKurzartikel (Letter) / Leserbrief mit OriginaldatenBeigetragenBegutachtung

Abstract

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 D rot characterizing motility noise. A weak amount of motility noise slightly increases chemotactic performance.

Details

OriginalspracheEnglisch
Aufsatznummer47001
Seiten (von - bis)47001
FachzeitschriftEurophysics letters
Jahrgang142
Ausgabenummer4
PublikationsstatusVeröffentlicht - 1 Mai 2023
Peer-Review-StatusJa

Externe IDs

Scopus 85159643056

Schlagworte