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

• Published in: Journal of theoretical biology Vol. 326; pp. 58 - 69
• Main Authors: Li, Xiaobo, Lenaghan, Scott C., Zhang, Mingjun
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 07.06.2013
• Subjects: Adaptation, Biological; Adaptation, Biological - physiology; Algorithms; Animals; Antiprotozoal Agents; Antiprotozoal Agents - pharmacology; biological control; Biological Evolution; Biota; Computer Simulation; Control application; drug delivery systems; Drug Delivery Systems - methods; drug effects; drugs; Game Theory; Giardia lamblia; Giardia lamblia - drug effects; Giardia lamblia - growth & development; Giardia lamblia - pathogenicity; growth & development; Humans; mathematical models; methods; Models, Biological; Optimal control; pathogenicity; pathogens; pharmacology; physiology; Population dynamics; Population Dynamics - statistics & numerical data; statistics & numerical data; Strategy dynamics; synthetic biology; Systems Biology; Systems Biology - methods; Control application; Population dynamics; Optimal control; Strategy dynamics
• ISSN: 0022-5193; 1095-8541; 1095-8541
• DOI: 10.1016/j.jtbi.2012.12.022

Control engineering and analysis of biological systems have become increasingly important for systems and synthetic biology. Unfortunately, no widely accepted control framework is currently available for these systems, especially at the cell and molecular levels. This is partially due to the lack of appropriate mathematical models to describe the unique dynamics of biological systems, and the lack of implementation techniques, such as ultra-fast and ultra-small devices and corresponding control algorithms. This paper proposes a control framework for biological systems subject to dynamics that exhibit adaptive behavior under evolutionary pressures. The control framework was formulated based on evolutionary game based modeling, which integrates both the internal dynamics and the population dynamics. In the proposed control framework, the adaptive behavior was characterized as an internal dynamic, and the external environment was regarded as an external control input. The proposed open-interface control framework can be integrated with additional control algorithms for control of biological systems. To demonstrate the effectiveness of the proposed framework, an optimal control strategy was developed and validated for drug delivery using the pathogen Giardia lamblia as a test case. In principle, the proposed control framework can be applied to any biological system exhibiting adaptive behavior under evolutionary pressures. ► Proposed a control framework for a class of biological complex systems. ► Framework was formulated based on an evolutionary game and external control. ► Optimal control algorithm was embedded in the framework. ► Validated for drug delivery using the pathogen Giardia lamblia as a test case.