Mathematical modeling for pattern design in networks of mammalian cells
Briers, Demarcus Irvin
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During the early stages of embryonic development, mammalian cells communicate, undergo morphological changes, and self-assemble into highly organized tissues, and eventually organ systems. Recently, there have been several efforts to engineer the multicellular patterning in mammalian cells to better understand early development and create organoid systems to better understand human disease and drug interactions. However, existing approaches to engineer large scale multicellular patterning in mammalian cells are limited to reproducing well known behaviors or trail-and-error based design. In this thesis, I developed mathematical models to predictively design and quantitatively validate de novo multicellular patterning in mammalian cells. First, I have developed a computational to automate self-organized multicellular organization in human pluripotent stem cells that quantitatively matches the in vitro velocity distribution, temporal dynamics of CRISPR induced perturbations to protein expression, and the resulting changes in spatial organization in human pluripotent stem cell colonies. I have also developed a mathematical model to predict the programmable self-assembly from a single cell into 3D shapes. Overall, this work offers insights into how mathematical modeling can be integrated with pattern recognition and optimization algorithms to efficiently discover and direct self-organized multicellular patterning in cell aggregates and tissues.
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