Gary An, Department of Surgery, University of Vermont, "Biological Heterogeneity and Parameter Space: Using agent-based models to unify knowledge regarding zoonitic transfer, vaccine development and in silico trials of multi-modal therapeutic strategies"
Gary An, Department of Surgery, University of Vermont,
December 3, 2020, IMAG/MSM WG on Multiscale Modeling for Viral Pandemics
Jacob Barhak Slides
Jacob Barhak Video
Gary An Slides
Gary An Video
Gary An Abstract: All too often biological heterogeneity is viewed as a challenge to be overcome. Rather, I propose that phenotypic heterogeneity represents an opportunity for using multi-scale models, and agent-based models (ABMs) in particular, as more generalizable, unifying knowledge representations. Specifically, focusing on the concept of the parameter space (as opposed to parameterization) of an ABM as a means to encompass heterogeneous data allows the ABM to serve as an instantiation of what is “similar” and conserved across different biological systems. I assert that this approach can bridge the gap between species and individuals and provide a useful approach to examining both fundamental aspects of zoonotic transfer of potential pandemic-generating viruses and as an in silico platform for testing biological countermeasures to novel agents. Integrating of machine learning, artificial intelligence and agent-based modeling can serve a critical role in a biothreat response strategy by discovering and evaluating multi-modal therapeutic regimens and vaccine strategies to accelerate and make more efficient their clinical implementation.