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

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