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
