Beyond Big Data: Making biomedical science scale through the use of in silico dynamic knowledge representation
Gary An, Ph.D.
Associate Professor of Surgery, co-Director of the Surgical Intensive Care Unit and a Senior Fellow of the Computation Institute at the University of Chicago
January 25, 2013
2:00 - 3:00 pm
Davis Center, Livak Ballroom
The greatest challenge in biomedical research is the translation of cellular-molecular level mechanistic knowledge into clinically effective interventions. As pathophysiological biocomplexity is manifest in increasingly dense data sets, thereis a need to develop means of augmenting knowledge generation and utilization with robust strategies for high-throughput hypothesis generation and evaluation, thereby shifting the scientific emphasis from data to knowledge. Agent-based modeling is particularly well suited to instantiating biomedical knowledge, and the development of methods for executable knowledge representation and the implementation of these methods in distributed and high-performance computing environments can pave the way for a scaleable, evolution-based paradigm for the propagation of biomedical knowledge.
Dr. Gary An is an Associate Professor of Surgery, co-Director of the Surgical Intensive Care Unit and a Senior Fellow of the Computation Institute at the University of Chicago. He is the founder and director of the Fellowship in Translational Systems Biology at the University of Chicago. 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, artificial intelligence systems for modular model construction, and community-wide meta-science environments, all with the goal of facilitating transformative scientific research.