Andrew Jin
BME PhD Proposal Presentation
Date: 2024-06-26
Time: 12:00 PM - 2:00 PM
Location / Meeting Link: EBB 4029 / https://gatech.zoom.us/j/91555328317?pwd=CYk7UBDqN67kNHR32pxeDAxqq80u0b.1
Committee Members:
Melissa Kemp, PhD (Advisor); Shuichi Takayama, PhD; Denis Tsygankov, PhD; Eberhard Voit, PhD; Ron Weiss , PhD
Title: Investigating multicellular pattern formation in organoid systems through agent-based modeling
Abstract:
Organoids are 3D assembled tissues derived from cultured cells that have the same structural characteristics and basic physiological functions as the respective organ. They are advantageous for drug screening over traditional 2D cell culture models, which do not recapitulate the complex spatial organization derived from 3D cell-cell and cell-environment interactions that are essential for the intricate patterning found in each organ. However, current organoids have limited spatial organization that results in limited functionality and organoid lifespan, features essential for translational applications and key developmental studies. Computational models are a powerful tool for studying multicellular pattern formation. Agent-based models (ABMs) represent cells as autonomous agents capable of making decisions dependent on their position and state. Simulations of agents can be integrated with mathematical description of gene regulatory networks (such as differential equations), providing a flexible, multiscale approach for simulating spatiotemporal dynamics. These qualities make ABMs a useful tool to understand the emergent complex behavior associated with pattern formation arising from simple cues and responses made by individual cells. The overall objectives of this project are to develop computational agent-based models that are used to better understand biological mechanisms that influence pattern formation and improve organoid design. In Aim 1, I develop a model of cadherin mediated pattern formation after cell lineage commitment in a model system designed to recapitulate stochastic symmetry breaking events. This aim provides a computational platform for future integrated models of organoid assembly mediated by differential cadherin expression. In Aim 2, I investigate the noise driven gene expression dynamics that may result in symmetry breaking events observed in Aim 1. This knowledge will allow us to optimize cell type composition of organoids through titration of initial stem cell ratios of heterogeneous potential and to better understand gene expression variability as a driver of symmetry breaking and subsequent patterning in organoid formation. Finally in aim 3, we investigate cell behavior driving interactions at the tissue level through multi-organoid models of organoid assembly.