CBDS Seminar Series-Gary An presents: "An Agent-based Model of Host Response to Infection as a Proxy System for Control Discovery using Evolutionary Computation and Game-playing Artificial Intelligence"
Sepsis, which is brought about by the body’s host response to severe infection or injury, is one of the most prevalent causes of mortality in intensive care units (ICUs). Sepsis has a mortality rate of ~30-40% and a cost of more than $20 billion annually in the US. The fundamentals of sepsis care, antibiotics, fluid management and organ support, have not changed in nearly 30 years, and to date there is no single approved drug that targets the pathophysiological processes that drive the host-response that produces sepsis, this despite tens of billions of dollars spent on hundreds of failed clinical trials. We have proposed that the controllability of sepsis can be examined by using a previously validated agent-based model (ABM) of the host response to infection as a proxy model upon which different methods of control discovery have been applied. Specifically, we treat the search for an effective multi-modal treatment regimen as a control-optimization problem that manipulates the internal variables of the ABM with combinations of putative molecular-based interventions at different intervals. Given the combinatorial complexity of the high-dimensional potential control space we have applied both genetic algorithms and deep reinforcement learning (as used in the game-playing DeepMind artificial intelligence systems, e.g. AlphaGo and AlphaZero) to characterize the scale of the control problem. Implemented on high-performance computing environments and following the principle that clinical heterogeneity is a function of model parameter space, both approaches produced fairly generalizable solutions, but with acknowledged limitations in interpretability and potential clinical translation. We suggest that these technologies can be integrated with ABM development in an iterative workflow that can both continually refine the ABM as well as guide basic and translational research in sensor and drug design. This approach for multi-scale model-based control discovery is potentially applicable to any complex disease process.
Dr. Gary An is a Professor of Surgery and Vice-Chairman for Surgical Research in the Department of Surgery at the University of Vermont Larner College of Medicine. He is a graduate of the University of Miami, Florida School of Medicine, and did his surgical residency at Cook County Hospital/University of Illinois, Chicago. He is a founding member of the Society of Complexity in Acute Illness (SCAI) and past president of the Swarm Development Group, one of the original organizations promoting the use of agent-based modeling for scientific investigation. He has worked on the application of complex systems analysis to sepsis and inflammation since 1999, primarily using agent based modeling to create mechanistic models of various aspects of the acute inflammatory response, work that has evolved to the use of agent-based models as a means of dynamic knowledge representation to integrate multiple scales of biological phenomenon. The impetus for his work is the recognition that the Translational Dilemma has arisen from a bottleneck in the scientific cycle at the point of experiment and hypothesis evaluation. His research involves the development of: mechanism-based computer simulations in conjunction with biomedical research labs, high-performance/parallel computing architectures for agent-based models, and integration of machine learning and artificial intelligence with multi-scale simulation models for control discovery, all with the goal of facilitating transformative scientific research.
University of VermontGary An, MD, SurgeonComputational Biology, Mathmatical Modeling and Computer Simulation, Translational Systms Biology