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Abstract

The central question in developmental biology is how an embryo self-organizes from a ball of cells into a structured animal. We use experimental and theoretical approaches to study how the activity of a small number of signaling molecules is spatiotemporally controlled to allow for embryonic self-construction. Here, we propose how machine learning could be harnessed to gain a new understanding of the mechanisms by which interactions between signaling pathways control robust embryogenesis and morphology changes in evolution.