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Related Experiment Video

Updated: Jun 27, 2025

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

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

Nilay Kumar¹, Mayesha Sahir Mim^1,2, Alexander Dowling¹

¹Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA.

NPJ Systems Biology and Applications

|May 7, 2024

Summary

This summary is machine-generated.

We developed a Bayesian optimization framework to predict organ shape by calibrating physics-based models. This method successfully infers cellular force distributions, aiding in understanding developmental processes and diseases.

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Area of Science:

Developmental Biology
Systems Biology
Biophysics
Computational Biology

Background:

Organ shape is determined by complex morphogenetic programs involving cell signaling and mechanical forces.
Predicting organ shape requires accurate physics-based models of subcellular force distribution, but parameter calibration from data is challenging.
Understanding these mechanisms is crucial for fields ranging from developmental biology to cancer research.

Purpose of the Study:

To develop a Bayesian optimization framework to solve the inverse problem of calibrating physics-based models for organ morphogenesis.
To determine optimal cellular force distributions that predict experimentally observed organ shapes.
To apply this framework to study epithelial processes in Drosophila wing imaginal discs.

Main Methods:

Utilized a Bayesian optimization framework integrated with Gaussian Process Regression (GPR) for machine learning.
Employed GPR to learn mapping functions between morphogenetic programs and final organ shape.
Calibrated and tested the framework on Drosophila wing imaginal disc data, including perturbations with collagenase.

Main Results:

The framework successfully inferred underlying parameter changes to match simulation and experimental imaging data.
Identified distinct parameter sets that mimic wild-type shapes and enable global sensitivity analysis.
Discovered that Piezo, a mechanosensitive ion channel, influences fold formation by regulating actomyosin contractility and extracellular matrix (ECM) elasticity.

Conclusions:

The developed computational pipeline effectively reverse-engineers morphogenetic mechanisms by inferring cellular force distributions.
Actomyosin contractility and basal ECM stiffness are key regulators of the curved shape in Drosophila wing imaginal discs.
This workflow is extensible for studying organ systems and for real-time control of multicellular systems.