We are interested in revealing design principles that underlie information processing, dynamic division of labor, computation, decision-making, and self-organization in multicellular biological systems. We develop algorithms for spatiotemporal inference of collective behavior and communication in biological systems based on information encoded by its components (like cells in a tissue) based on single-cell data. We also study the problem of reconstructing the structure of biological networks (gene regulation networks and cell-cell communication networks) based on the dynamics of its components, as well as the mathematical and algorithmic connections between dynamical systems and machine learning.
Our work involves a mix of theory, modeling and simulations, and data analysis. We approach these questions by developing algorithms and mathematical frameworks based on concepts derived from diverse fields, including dynamical systems, machine learning, and computational systems biology.