Evolutionary game based control for biological systems with applications in drug delivery

Area of Science:

• Control engineering
• Systems biology
• Synthetic biology

Background:

• Lack of a unified control framework for biological systems at cellular and molecular levels.
• Challenges in modeling unique biological dynamics and implementing control strategies.
• Need for methods addressing adaptive behavior under evolutionary pressures.

Purpose of the Study:

• To propose a novel control framework for biological systems exhibiting adaptive behavior.
• To integrate internal and population dynamics using evolutionary game-based modeling.
• To provide an open-interface framework adaptable for various control algorithms.

Main Methods:

• Formulation of a control framework based on evolutionary game theory.
• Characterization of adaptive behavior as internal dynamics.
• Modeling the external environment as a control input.

Main Results:

• Development and validation of an optimal control strategy for drug delivery using Giardia lamblia.
• Demonstration of the framework's effectiveness in a practical biological control scenario.
• Successful integration of internal and population dynamics for adaptive control.

Conclusions:

• The proposed control framework provides a robust approach for managing adaptive biological systems.
• The framework is applicable to diverse biological systems under evolutionary pressures.
• This work advances control engineering principles for systems and synthetic biology applications.