Development of Computational Models and Platforms for Multicellular Development and Preclinical Therapeutic Screening

With the emanation of big data, there is an ever-increasing need for advanced quantitative, computational, and statistical approaches to comprehensively study biology. From dynamical models, data processing, high-throughput screening, to high-dimensional data analysis, computational tools and pipelines are revolutionizing the landscape of biological research and medicine. However, there are still many challenges associated with harnessing the data revolution in computational biology. More specifically, efforts to develop computational, simulation-based models of multicellular development and high-throughput preclinical therapeutic screening assays are broadly needed to provide insight into novel treatment approaches in modern medicine.

The work herein describes the development of experimental platforms, modeling tools, and statistical approaches to expand upon and drive novel discoveries in multicellular models of organ formation and preclinical therapeutic development. This dissertation builds upon the existing resources available to study crosstalk in developmental biology, identify therapeutic targets of interest, and evaluate efficacy of novel small molecule therapeutics. In this dissertation, Drosophila melanogaster is used as a model organism to develop computational and high-throughput screening platforms to advance the state-of-the-art in each field