Date of Award

8-4-2023

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Physics

Advisor(s)

Lisa Manning

Keywords

Biophysics;Computational;Modeling

Subject Categories

Biochemistry, Biophysics, and Structural Biology | Biophysics | Life Sciences

Abstract

Collective cell behavior such as the formation of boundaries and collective cell motion is crucial for numerous biological functions including development, wound healing, and homeostasis. In this thesis, I investigate how changes to heterotypic behavior can drive collective behavior in models for confluent tissue, tissue with no gaps between cells. First, I examine how cell collectives can integrate signals from their environment to climb biochemical gradients when individual cells cannot. We identify two possible mechanisms that could drive this collective climbing behavior and develop an open-source framework that can be used to couple a biochemical gradient to any intercellular interaction. I also show that the advection of this gradient by cells has a minor impact in physically relevant regimes. Next, I construct a graph neural network to make predictions about the fluidity of cell tissue based on the tissue structure. Using this framework the neural network accurately predicts shear modulus and edge tensions in a spring vertex model. Next, we analyze the differences between the 3D vertex and Voronoi models. The systems share the same energy and many of the same geometric properties of cell tissue on heterotypic interfaces. However, we discover that there are differences in cell orientation on the interface boundary between cell types driven by a difference in discontinuous restoring force for cells to exit this boundary. Then, we examine the stratified epithelium as a model system with many layers of heterotypic cell interfaces. We identify changes to heterotypic interfacial tension as one possible mechanism for cells to migrate through tissue boundaries. We also create a toy model to accurately represent the integrin-based adhesions between cells and extracellular matrix in real tissue and use this model as a way to inform a similar addition to the 3D vertex model. Finally, we create a model for hair follicle development in the stratified epithelium. In conjunction with our experimental collaborators, we identify a dominant mechanism for the cell shape and tissue morphology changes seen during development. The model predicts a difference in tissue flow between the mechanisms investigated that is confirmed by experiments. All of the work I have done demonstrates how changes to individual cells, especially changes to heterotypic interactions, can drive large-scale changes in tissue behavior.

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Open Access

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