Mechanistic models of cell-fate transitions from single-cell data

Our knowledge of how individual cells self-organize to form complex multicellular systems is being revolutionized by a data outburst, coming from high-throughput experimental breakthroughs such as single-cell RNA sequencing and spatially resolved single-molecule FISH. This information starting be leveraged machine-learning approaches that are helping us establish census timeline cell types in developing organisms, shedding light on biochemistry regulates cell-fate decisions. In parallel, imaging tools light-sheet microscopy revealing self-assemble space time the organism forms, thereby elucidating role mechanics development. Here we argue mathematical modeling can bring together these two perspectives, enabling test hypotheses about specific mechanisms, which further validated experimentally. We review recent literature this subject, focusing representative examples use better understand behavior shapes organisms.