Bayesian Decision-Making Shapes Phenotypic Landscapes from Differentiation to Cancer

TL;DR

This paper explores how cells use Bayesian decision-making to adapt their phenotypes in noisy environments, linking this process to healthy and cancerous behaviors.

Contribution

The paper introduces a novel theoretical framework modeling phenotypic adaptation as Bayesian decision-making, revealing how it shapes cellular behavior.

Findings

Bayesian decision-making generates distinct phenotypic regimes like homeostasis, bistability, and explosion.

Proliferation can stabilize homeostasis or drive cancer-like phenotypic explosion.

Negative correlations between intrinsic and extrinsic states reduce plasticity but increase robustness.

Abstract

Cells adapt their phenotypes in noisy microenvironments while maintaining robust decision-making. We develop a coarse-grained theoretical framework in which cellular phenotypic adaptation is described as Bayesian decision-making coupled to replication and diffusion. This leads to an effective Fokker-Planck equation with an emergent fitness landscape governing phenotypic dynamics. We identify distinct phenotypic regimes homeostatic fixation, bistable decision-making, critical switching, and runaway explosion and propose a biological interpretation in which homeostatic and bistable landscapes correspond to healthy differentiated cell states, whereas explosive landscapes capture stem-like or cancer-like behavior. In the Gaussian setting, the correlation between intrinsic and extrinsic states directly encodes mutual information and acts as a bifurcation parameter: high correlation produces shallow or explosive landscapes associated with phenotypic plasticity, while reduced correlation stabilizes differentiated fates by deepening potential wells. We further show that proliferation reshapes these landscapes in a nontrivial manner. Proliferation conditionally stabilizes local homeostasis without altering global confinement, or cooperates with biased environmental sensing to eliminate homeostasis/bistability and drive cancer-like phenotypic explosion even at high phenotypic fidelity. Finally, we show that negative intrinsic–extrinsic correlations suppress explosive dynamics but also reduce bistable plasticity, suggesting a robustness–plasticity trade-off. Together, our results suggest that development, tissue homeostasis, and carcinogenesis can be understood as information-driven deformations of a Bayesian phenotypic fitness landscape.

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