UC Riverside

This thesis consists of two parts.

In the first part, we develop a model that will be used to investigate pavement cell morphogenesis. Pavement cells, the leaf epidermal cells in the Arabidopsis thaliana plant, have complex jigsaw puzzle piece shapes. The formation of these interlocking shapes relies on mechanical, chemical, and cell to cell signals at different scales. Because of this, pavement cells are an interesting model system used to study the mechanisms involved in cell morphogenesis in plant tissue. Utilizing the local level set method, biochemical dynamics on moving cell boundaries are captured. By incorporating cell-cell adhesion, the model is expanded to a multicellular framework that can be used to investigate the components involved in establishing these intricate cell shapes.

In the second part, we use a combination of experimental and modeling techniques to study new and existing regulations in the Dpp-Rho1-Cdc42 network in the Drosophila wing disc. During organogenesis in the wing disc, the regulation of epithelial cell height and curvature is crucial in developing correct tissue shapes. This requires the interplay between mechanical forces and morphogen-mechanogen pathways, at both the cell and tissue levels. Morphogens, such as Decapentaplegic (Dpp), regulate cell growth and division, as well as mechanogen activity. Mechanogens, such as RhoGTPases, are small diffusible molecules that regulate mechanical components, such as actin and myosin, to coordinate cell shape and tissue geometry. Even though the effect of morphogens in regulating mechanogens is critical for proper tissue formation, insufficient work has been done to understand this in the context of epithelial organogenesis. In this study, a combination of experimental and mathematical modeling approaches are used to study the linkage between Dpp, Rho1, and Cdc42 in the wing disc. By using experiments, new regulations between Dpp and Cdc42 have been identified, as well as the interaction between Cdc42 and Rho1. A mathematical model is developed by using a system of reaction-diffusion equations to model Dpp, Rho1, and Cdc42 dynamics, as well as the newly identified regulations. We utilize Bayesian optimization to explore this model and to investigate the robustness of the proposed networks.