Inferring the molecular mechanisms that guide developmental bifurcations

Embryonic and tissue development is often viewed as a sequence of discrete state transitions through a low-dimensional cell-fate landscape, but it is unclear how this view relates to the high-dimensional molecular data collected in experiments. We have recently shown that cell-fate transitions which stem from their underlying dynamical systems bifurcating can be pin-pointed directly from transcriptomic trajectories. Moreover, the direction of the bifurcation, corresponding to the soft mode of the dynamical system’s Jacobian, is analytically extractable, providing critical clues toward the correlative mechanisms that drive the transition. Here, we investigate how the dynamics of the Jacobian’s soft mode can be used to infer asymmetric gene relationships for the design of predictive molecular network models. We demonstrate our analysis on in-silico gene-regulatory networks, and use it to elucidate transcriptomic trajectories in the mouse endoderm and neural tube. We also examine live-imaging data from the formation of the compound fly-eye, and show how bifurcations can elucidate the mechanical connectivity that enables morphogenesis. Our work demonstrates how dynamical systems theory enables inferring molecular-scale mechanisms from cell-scale state changes.