2023/04/03 by Arnab Barua, Haralampos Hatzikirou · 1 voice
Biochemistry, Genetics and Molecular Biology · Chemistry · Computer Science · #Artificial intelligence #Bayesian probability #Biological system #Biology #Computational Drug Discovery Methods #Computer science #Entropy (arrow of time) #Gene Regulatory Network Analysis #Mesoscopic physics #Physics #Principle of maximum entropy #State variable #Statistical physics #thermodynamics and calorimetric analyses
paper · pdf · doi:10.3390/e25040609
openalex publication_date 2023/04/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22
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.