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

Seyed Hamid Hosseini¹, Mahdi Imani¹

¹Northeastern University, 360 Huntington Ave, Boston, MA, 02115, United States of America.

Information Sciences

|October 28, 2024

Summary

This study introduces an adaptive Bayesian intervention policy for gene regulatory networks (GRNs). It effectively manages dynamic cell responses to therapies, outperforming existing methods.

Keywords:

Bayesian intervention Boolean networks Gene regulatory networks Nash equilibrium Two-player zero-sum game

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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.