FluidEmbryo

During embryonic development, cells must perform large-scale coordinated motion to shape functional tissues and organs. The development of computational models for such tissue flows may radically change the way we understand and control tissue morphogenesis. However, we still lack fundamental insights into the underlying mechanics of these processes. At the tissue level, these flows result from a complex interplay between growth, active stresses and complex mechanical properties. The previously overlooked role of mechanical properties, also referred to as rheology, has recently received growing attention as rheology has been shown to play a key role in various morphogenetic events. Yet, accurate measurements of tissue rheology in conditions relevant to morphogenesis remain currently challenging. Moreover, the general role of tissue rheology and its coupling to growth and active stresses during morphogenesis remains unclear. In FluidEmbryo, we aim to leverage recent advances in machine learning and computational fluid dynamics (CFD) to determine how complex mechanical properties of embryonic tissues can drive tissue flows shaping organs, with two objectives: - Objective 1 aims to build effective mechanical models for embryonic tissues. We will infer rheological properties and active stresses in embryonic tissues from microfluidic experimental data using physics-informed neural networks (PINNs). - Objective 2 aims to determine how mechanical properties can sculpt tissues. We will use high performance CFD simulations coupling complex rheology, growth and active stresses, to (i) identify morphogenetic processes that can be driven by tissue rheology in generic configurations and (ii) determine the role of tissue rheology during the axis formation of embryonic organoids. These two objectives will be pursued in close collaboration with two experimental groups with internationally recognized expertise in tissue morphogenesis and embryonic organoids. As a continuation of this project, our mechanical models will later be coupled with data-driven biochemical models, opening the way to multi-physics computational simulations for tissue engineering.