Date of Award:
Master of Science (MS)
Nicholas S. Flann
Nicholas S. Flann
Biological systems that contain multiple living cells exhibit complex self-organization during development of an embryo as individual cells coordinate their behaviors to form intricate patterns. Understanding the mechanisms that underlie this emergent behavior is within reach because of advances in cell imaging that can now track hundreds of cells’ states and positions in real time. However, computational methods are needed that can fit physical scientific models to these observations in space and time. This work introduces a new method to solve this problem that applies automatic differentiation and gradient descent, techniques that underlie deep-learning advances. The method fits a biomechanical model, represented as coupled ordinary differential equations, which describe forces among cells as they self-organize. A preliminary study using synthetic data generated by the model demonstrates the approach’s effectiveness in “reverse engineering” the model from observations of the cells moving. Even in the presence of noise, the method is able to determine the model parameters accurately. Two kinds of emergent behaviors were studied, where differential adhesion causes two distinct cell types to sort into clusters or to form regular mosaic patterns.
Raghuvanshi, Namita, "Fitting Cell-Based Biomechanical Models from Spatiotemporal Data: A Gradient Descent Approach" (2021). All Graduate Theses and Dissertations. 8153.
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