Optimal Regulation Strategy for Nonzero-Sum Games of the Immune System Using Adaptive Dynamic Programming

Area of Science:

• Computational Biology
• Immunology
• Control Theory

Background:

• Cancer treatment involves complex interactions between tumor cells and the immune system.
• Optimizing therapeutic interventions like chemotherapy and immunotherapy is crucial for patient outcomes.
• Nonzero-sum game theory provides a framework for modeling these competitive biological interactions.

Purpose of the Study:

• To develop an optimal control strategy for the immune system using adaptive dynamic programming (ADP).
• To approximate a Nash equilibrium between tumor and immune cells under drug influence.
• To minimize tumor growth rate and the dosage of chemotherapy and immunotherapy drugs.

Main Methods:

• Utilizing a novel intelligent nonzero-sum games-based ADP algorithm.
• Applying a mathematical growth model for tumor cell dynamics.
• Performing convergence analysis and employing an iterative ADP algorithm for feasibility.
• Conducting simulation examples to validate the methodology.

Main Results:

• The proposed ADP method effectively approximates the Nash equilibrium in the game model.
• Demonstrated reduction in tumor cell growth rate.
• Significant minimization of chemotherapy and immunotherapy drug dosages.
• Simulation results confirm the availability and effectiveness of the developed control strategy.

Conclusions:

• The intelligent nonzero-sum games-based ADP offers a feasible and effective approach for optimizing cancer treatment strategies.
• This methodology holds promise for personalized medicine by balancing therapeutic efficacy with reduced drug toxicity.
• Further research can explore more complex biological models and adaptive treatment adjustments.