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Bayesian Decision-Making Shapes Phenotypic Landscapes from Differentiation to Cancer.

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This summary is machine-generated.

Cells adapt to noisy environments using Bayesian decision-making, revealing distinct phenotypic regimes. This framework links cellular behavior to information processing, offering insights into development, homeostasis, and cancer.

Keywords:
Bayesian learningFokker-Planck equationcancercell decision-makingdifferentiationdynamical systemsphenotypic dynamics

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Area of Science:

  • Theoretical biology
  • Cellular and molecular biology
  • Systems biology

Background:

  • Cells must adapt to fluctuating microenvironments while maintaining stable functions.
  • Phenotypic adaptation is crucial for development, tissue homeostasis, and disease states like cancer.
  • Understanding the decision-making processes underlying cellular adaptation is a key challenge.

Purpose of the Study:

  • To develop a theoretical framework for cellular phenotypic adaptation.
  • To investigate the role of Bayesian decision-making, replication, and diffusion in phenotypic dynamics.
  • To identify distinct phenotypic regimes and their biological interpretations.

Main Methods:

  • Developed a coarse-grained theoretical framework.
  • Utilized an effective Fokker-Planck equation with an emergent fitness landscape.
  • Analyzed distinct phenotypic regimes including homeostatic fixation, bistable decision-making, critical switching, and runaway explosion.
  • Investigated the role of intrinsic-extrinsic state correlation and proliferation.

Main Results:

  • Identified four distinct phenotypic regimes: homeostatic fixation, bistable decision-making, critical switching, and runaway explosion.
  • Linked homeostatic and bistable landscapes to healthy differentiated cells, and explosive landscapes to stem-like or cancer-like behavior.
  • Showed that correlation between intrinsic and extrinsic states acts as a bifurcation parameter, influencing phenotypic plasticity.
  • Demonstrated that proliferation can either stabilize homeostasis or drive cancer-like phenotypic explosion, depending on environmental sensing.
  • Revealed a robustness-plasticity trade-off influenced by intrinsic-extrinsic correlations.

Conclusions:

  • Cellular phenotypic adaptation can be modeled as information-driven deformations of a Bayesian phenotypic fitness landscape.
  • The framework provides a unified view of development, tissue homeostasis, and carcinogenesis.
  • Insights into phenotypic plasticity and robustness offer potential applications in understanding and treating diseases.