Understanding the biochemical and mechanical cues that cause epithelial sheets to reorganize themselves into three-dimensional organs remains a central challenge in biology. During Drosophila wing-disc eversion, a pseudostratified pouch folds outward and fuses into a bilayer that goes on to form the adult wing. This project unites quantitative experiments with a 3D multiscale mechano-chemical (M3D) model to decode the late-stage morphogenesis of the Drosophila wing imaginal disc during eversion. The work couples: (1) a GPU-accelerated particle-based model that resolves apical, basal and extracellular-matrix mechanics on a deforming epithelial surface; (2) reaction-diffusion models for hormone (ecdysone) and morphogen signaling (Dpp, Wg) extended down to intracellular Rho1/Cdc42 dynamics; and (3) machine-learning pipelines—Gaussian-process surrogate modeling, Bayesian optimization and neural-network solvers—to calibrate and accelerate simulations against time-lapse light-sheet imaging, biomechanical perturbations and quantitative immunostaining. Iterative experimentation will map how spatially patterned actomyosin contractility, cell-ECM adhesion and ECM stiffness drive coordinated cell reshaping, layer coupling and tissue folding. The resulting framework will yield predictive, systems-level insights into how hormonal timing interfaces with morphogen gradients to orchestrate organ-scale shape changes.
Navaira Sherwani (Sun,) studied this question.
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