From heterogeneous datasets to predictive models of embryonic development

Abstract

Modern studies of embryogenesis are increasingly quantitative, powered by rapid advances in imaging, sequencing, and genome manipulation technologies. Deriving mechanistic insights from the complex datasets generated by these new tools requires systematic approaches for data-driven analysis of the underlying developmental processes. Here we use data from our work on signal-dependent gene repression in the fruit fly, Drosophila melanogaster, to illustrate how computational models can compactly summarize quantitative results of live imaging, chromatin immunoprecipitation, and optogenetic perturbation experiments. The presented computational approach is ideally suited for integrating rapidly accumulating quantitative data and for guiding future studies of embryogenesis.