journal article Open Access Apr 03, 2023

Cell Decision Making through the Lens of Bayesian Learning

Entropy Vol. 25 No. 4 pp. 609 · MDPI AG
View at Publisher Save 10.3390/e25040609
Abstract
Cell decision making refers to the process by which cells gather information from their local microenvironment and regulate their internal states to create appropriate responses. Microenvironmental cell sensing plays a key role in this process. Our hypothesis is that cell decision-making regulation is dictated by Bayesian learning. In this article, we explore the implications of this hypothesis for internal state temporal evolution. By using a timescale separation between internal and external variables on the mesoscopic scale, we derive a hierarchical Fokker–Planck equation for cell-microenvironment dynamics. By combining this with the Bayesian learning hypothesis, we find that changes in microenvironmental entropy dominate the cell state probability distribution. Finally, we use these ideas to understand how cell sensing impacts cell decision making. Notably, our formalism allows us to understand cell state dynamics even without exact biochemical information about cell sensing processes by considering a few key parameters.
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Cited By
12
ACS Synthetic Biology
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Citations
48
References
Details
Published
Apr 03, 2023
Vol/Issue
25(4)
Pages
609
License
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Funding
Khalifa University Award: 96732
Bundes Ministerium für Bildung und Forschung Award: 96732
Cite This Article
Arnab Barua, Haralampos Hatzikirou (2023). Cell Decision Making through the Lens of Bayesian Learning. Entropy, 25(4), 609. https://doi.org/10.3390/e25040609
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