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Neural Network Identification of Run-and-Tumble Dynamics Governing Rules
Microorganisms such as E. coli and algae typically adjust their motility in response to external stimuli. While their behavioral response patterns are readily measurable, the underlying internal reaction dynamics are complex and more challenging to measure directly. Here, we propose a neural network approach that uncovers the governing rules from observable stimulus-response data, enabling the revelation of potential internal signaling structures. Lei et al. 2025
Image Credit: Shicong Lei and Min Tang
Citation: (2025) PLoS Computational Biology Issue Image | Vol. 21(8) September 2025. PLoS Comput Biol 21(8): ev21.i08. https://doi.org/10.1371/image.pcbi.v21.i08
Published: September 8, 2025
Copyright: © 2025 . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Microorganisms such as E. coli and algae typically adjust their motility in response to external stimuli. While their behavioral response patterns are readily measurable, the underlying internal reaction dynamics are complex and more challenging to measure directly. Here, we propose a neural network approach that uncovers the governing rules from observable stimulus-response data, enabling the revelation of potential internal signaling structures. Lei et al. 2025
Image Credit: Shicong Lei and Min Tang