Cell decision-making through the lens of Bayesian learning

TL;DR

This paper proposes a Bayesian learning framework to understand how cells make decisions based on microenvironmental sensing, deriving a hierarchical Fokker-Planck model to analyze internal state evolution and the influence of environmental entropy.

Contribution

It introduces a novel Bayesian learning-based formalism for modeling cell decision-making, linking microenvironmental sensing to internal state dynamics without requiring detailed biochemical data.

Findings

Microenvironmental entropy changes influence cell state probabilities.

The hierarchical Fokker-Planck model captures cell decision dynamics.

Cell sensing impacts decision-making even with limited biochemical information.

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 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.