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Reverse engineering morphogenesis through Bayesian optimization of physics-based models.

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    We developed a Bayesian optimization framework using Gaussian Process Regression (GPR) to predict organ shape by inferring cellular force distributions from imaging data. This method successfully reverse-engineers morphogenesis and identifies key mechanical factors influencing organ development.

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    Area of Science:

    • Developmental Biology
    • Systems Biology
    • Computational Biology

    Background:

    • Organ shape and size are governed by complex cell signaling and mechanical interactions.
    • Predicting organogenesis requires understanding intra- and inter-cellular forces, but calibrating physics-based models is challenging.
    • Systems and synthetic biology aim to determine optimal interactions for organ development.

    Approach:

    • Developed a Bayesian optimization framework to solve the inverse problem of inferring cellular force distributions.
    • Employed Gaussian Process Regression (GPR) to learn mapping functions between morphogenetic programs and organ shape.
    • Validated the framework on *Drosophila* wing imaginal discs, using time-series imaging data.

    Key Points:

    • Successfully inferred parameters matching simulation data to experimental imaging of perturbed wing discs.
    • Identified multiple distinct parameter sets yielding wild-type organ shapes, revealing flexibility in developmental mechanisms.
    • Enabled global sensitivity analysis, highlighting the importance of actomyosin contractility and ECM stiffness for wing disc shape.
    • Discovered Piezo channel's role in regulating actomyosin contractility and ECM elasticity during fold formation.

    Conclusions:

    • The framework accurately reverse-engineers organ morphogenesis from imaging data.
    • It provides insights into the mechanical underpinnings of epithelial development and disease.
    • The approach is extensible to any organ system and applicable to real-time control of multicellular systems.