Poster P14 - Reverse engineering morphogenesis through Bayesian optimization of physics-based models

Introduction: Morphogenetic programs coordinate cell signaling and mechanical interactions to shape organs. In systems and synthetic biology, a key challenge is determining optimal cellular interactions for predicting organ shape, size, and function. Physics-based models defining the subcellular force distribution facilitate this, but it is challenging to calibrate parameters in these models from data.

Materials and

Methods: To solve this inverse problem, we created a Bayesian optimization framework to determine the optimal cellular force distribution such that the predicted organ shapes match the experimentally observed organ shapes. This integrative framework employs Gaussian Process Regression, a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that maintain the final organ shape.

Results, Conclusions, and Discussions: We calibrated and tested the method on Drosophila wing imaginal discs to study mechanisms that regulate epithelial processes ranging from development to cancer. The parameter estimation framework successfully infers the underlying changes in core parameters needed to match simulation data with imaging data of wing discs perturbed with collagenase. The computational pipeline identifies distinct parameter sets mimicking wild-type shapes. It enables a global sensitivity analysis to support the regulation of actomyosin contractility and basal ECM stiffness to generate and maintain the curved shape of the wing imaginal disc. The optimization framework, combined with experimental imaging, identified that Piezo, a mechanosensitive ion channel, impacts fold formation by regulating the apical-basal balance of actomyosin contractility and elasticity of ECM. This workflow is extensible toward reverse-engineering morphogenesis across organ systems and for real-time control of complex multicellular systems.

Acknowledgements (Optional): N.K., M.S.M., and J.J.Z. were supported in part by the NIH grant R35GM124935. N.K., A.D., and J.J.Z. were supported by NSF-Simons Center for Quantitative Biology Pilot program and NSF award 2029814. J.J.Z. and M.S.M. were supported in part by EMBRIO Institute, contract #2120200, a National Science Foundation (NSF) Biology Integration Institute, and NSF CBET-2225601 (RECODE). A.D. received partial support from NSF Award CBET-1941596. J.J.Z. and A.D. received partial support from NSF Award 2225601. We thank Dr. Sara Cole from Notre Dame Integrated Imaging Facility Optical Microscopy Core for the assistance with confocal imaging. The authors thank P. Brodskiy, A. Madhwan, M. Unger, V. Velagala, and members of the Multicellular Systems Engineering Lab for their helpful comments and suggestions.