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

Morphogenetic programs direct the cell signaling and nonlinear mechanical interactions between multiple cell types and tissue layers to define organ shape and size. A key challenge for systems and synthetic biology is determining optimal combinations of intra- and inter-cellular interactions to predict an organ's shape, size, and function. Physics-based mechanistic models that define the subcellular force distribution facilitate this, but it is extremely challenging to calibrate parameters in these models from data. 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 desired organ shapes observed within the experimental imaging data. This integrative framework employs Gaussian Process Regression (GPR), a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that generate and maintain the final organ shape. We calibrated and tested the method on cross-sections of