National Institute of General Medical Sciences 5R35GM149335-03

Data-driven and science-informed methods for the discovery of biomedical mechanisms and processes

This award supports research on advanced data-driven methods to discover biomedical mechanisms by extending the WSINDy method to handle noisy and sparse biological data, aiming to model cell migration, infectious disease dynamics, and stochastic systems.

Description

Data-driven discovery methods are a novel class of methodologies and computational approaches, revolutionizing the modeling, prediction, and control of complex systems, while remaining scientifically explainable and interpretable. These methods learn governing equations directly from data and have found considerable success in a wide range of applications including turbulence, climate, robotics, and autonomy. However, the first generation of these methods has proven poorly suited to the study of biomedical data. To realize the full potential of data-driven approaches, they must be extended and adapted to deal with the noise, sparsity, and variability intrinsic to experiments with living organisms. My group has extended the seminal Sparse Identification of Nonlinear Dynamics (SINDy) method to the Weak form SINDy (WSINDy). Weak form equations are a transform of the original data that enables learning of the equations even in the presence of substantial noise and sparsity. The approach effectively recasts scientific discovery from proposing and validating/refuting a single scientific hypothesis to simultaneously proposing (in many cases) more than 10^180 hypotheses and using sparse regressing to prune the hypotheses which are not supported by the data. Moreover, our approach currently takes on the order of minutes on a standard laptop. The overarching goals of this research are to use the WSINDy method to investigate the 1) individual cell-based drivers for collective cell migration and 2) data-driven inference for unobserved processes in infectious disease dynamics as well as 3) extend WSINDy to infer stochastic dynamical systems and discover critical, but hidden, compartments.

View original record