Flows, Fates and Forces: A Biophysical Framework for Data-Driven Discovery in Development - Project Summary Understanding how single cells transform into formed, functional organisms or how stem cells differentiate is a grand challenge of modern biology, from human development to regenerative medicine. During embryogenesis, cells become coherently patterned by distinct profiles of gene expression as they flow and exert forces on one another to form the early embryo. Coherence in pattern entails the emergence of sharp and reproducible boundaries between domains of differential gene expression. Coherence in form entails the reproducible timing, shaping, and positioning of emerging morphological features. These two notions of coherence are related. My group develops innovative conceptual, theoretical, and computational tools to detect hidden coherent patterns in complex nonlinear systems and devise mechanistic biophysical models to predict and explain these patterns in continual feedback with experiments. Leveraging our distinct expertise in dynamical systems theory, fluid dynamics, and theoretical biophysics, we are uniquely positioned to disentangle the coupled emergence of pattern and form. The overarching aim of the proposed research is to close the circle from flows to fates to forces, addressing two distinct but related challenges: (1) From flows to fates, developing biophysical principles and data-driven methods to elucidate how signal propagation and integration by cells is affected by cell motion. (2) From fates to forces, learning the role of gene expression changes in the patterns of active forces and dynamic mechanical properties generating tissue flows. An outstanding challenge in the field is formalizing how coherent cell motion affects intercellular signaling and cell fates, with increasing evidence suggesting that cell fates depend on histories of signal exposure along cell trajectories. We develop dynamical systems techniques to reduce noisy cell trajectory data to sets of attractors and repellers that discover embryonic compartmentalization and cell-fate bifurcations for transcriptionally indistinguishable cells. The proposed research will yield a publicly available, practical framework applicable to any developmental stage or model organism to (i) recast molecular data in cells’ dynamic reference frames, (ii) characterize how cell motion modulates signal propagation through dynamic barriers and enhancers, and (iii) place quantitative constraints on signaling ranges and mechanisms in motile cell environments to guide data-driven discovery of their underlying mechanisms. We will test and calibrate this framework on unsolved problems in avian gastrulation and 2D and 3D gastruloids. Integrating what we learn with biophysical principles, we devise the first unified predictive model of morphogenesis and differentiation for avian gastrula- tion. This will (i) provide a powerful tool to rationalize rich experimental datasets, (ii) quantitatively test existing hypotheses, and (iii) generate new, tractable ones to guide future experiments. Our approach will provide insights to aid in detecting and preventing developmental abnormalities and biophysical principles to support stem cell technologies and engineering organoids for disease modeling and regenerative medicine.
