Learning the organizational principles of biological systems using AI

Artificial Intelligence (AI) is an increasingly important tool in the biological sciences. Here, we demonstrate our ongoing efforts to develop AI methods to directly decode the organizational principles of biological systems from large microscopy datasets. Examples include machine learning approaches that can be utilized to build multiscale maps of biological systems, to derive new quantitative insight from timelapse microscopy data and to build predictive models of cell fate in complex biological tissues. In exploring these ideas, we hope to enable a new platform for automated scientific hypothesis generation and directed experimental studies.