Cell Decision Making through the Lens of Bayesian Learning
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/25/4/609 |
| _version_ | 1797605553657085952 |
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| author | Arnab Barua Haralampos Hatzikirou |
| author_facet | Arnab Barua Haralampos Hatzikirou |
| author_sort | Arnab Barua |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T05:02:44Z |
| format | Article |
| id | doaj.art-0f99a0a3c88e4ea2bcb2aff853f9456c |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T05:02:44Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-0f99a0a3c88e4ea2bcb2aff853f9456c2023-11-17T19:08:29ZengMDPI AGEntropy1099-43002023-04-0125460910.3390/e25040609Cell Decision Making through the Lens of Bayesian LearningArnab Barua0Haralampos Hatzikirou1Departement de Biochimie, Université de Montréal, Montréal, QC H3T 1C5, CanadaCenter for Information Services and High Performance Computing, Technische Univesität Dresden, 01062 Dresden, GermanyCell 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.https://www.mdpi.com/1099-4300/25/4/609cell decision makingBayesian learningleast microenvironmental uncertainty principle (LEUP)hierarchical Fokker–Planck equationcell sensing dynamicsmultiscale |
| spellingShingle | Arnab Barua Haralampos Hatzikirou Cell Decision Making through the Lens of Bayesian Learning Entropy cell decision making Bayesian learning least microenvironmental uncertainty principle (LEUP) hierarchical Fokker–Planck equation cell sensing dynamics multiscale |
| title | Cell Decision Making through the Lens of Bayesian Learning |
| title_full | Cell Decision Making through the Lens of Bayesian Learning |
| title_fullStr | Cell Decision Making through the Lens of Bayesian Learning |
| title_full_unstemmed | Cell Decision Making through the Lens of Bayesian Learning |
| title_short | Cell Decision Making through the Lens of Bayesian Learning |
| title_sort | cell decision making through the lens of bayesian learning |
| topic | cell decision making Bayesian learning least microenvironmental uncertainty principle (LEUP) hierarchical Fokker–Planck equation cell sensing dynamics multiscale |
| url | https://www.mdpi.com/1099-4300/25/4/609 |
| work_keys_str_mv | AT arnabbarua celldecisionmakingthroughthelensofbayesianlearning AT haralamposhatzikirou celldecisionmakingthroughthelensofbayesianlearning |