An optimal Bayesian intervention policy in response to unknown dynamic cell stimuli

Area of Science:

• Systems Biology
• Computational Biology
• Genomics

Background:

• Gene regulatory networks (GRNs) control cellular functions, but disruptions cause diseases like cancer.
• Current interventions often fail due to dynamic cellular responses to therapy.
• Developing adaptive strategies is crucial for effective disease treatment.

Purpose of the Study:

• To propose a novel Bayesian intervention policy for gene regulatory networks (GRNs).
• To address the challenge of dynamic cellular responses to therapeutic interventions.
• To develop an adaptive strategy that improves treatment efficacy.

Main Methods:

• Modeled GRNs using Boolean networks with perturbation (BNp).
• Formulated the cell-therapy interaction as a two-player zero-sum game.
• Developed a recursive Bayesian approach to estimate cell responses under incomplete information.
• Incorporated Nash equilibrium policies for adaptive decision-making.

Main Results:

• The proposed Bayesian intervention policy adaptively responds to cell dynamics.
• Demonstrated analytical superiority over existing intervention techniques.
• Numerical experiments confirmed convergence to optimal Nash equilibrium policies.
• Validated on p53-MDM2 and melanoma GRN models.

Conclusions:

• The Bayesian intervention policy offers a robust approach for dynamic GRN interventions.
• Adaptive strategies are essential for overcoming cellular resistance to therapy.
• This method holds promise for treating diseases driven by complex gene regulatory dynamics.