Methodology for inference of intercellular gene interactions, Saurabh Modi (Poster)
From Lauren Mosesso on April 14th, 2021
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Proper tissue and organ function are dictated, in part, by the correct spatial organization and interaction of multiple cell populations. During embryonic development, this coordinated spatial and temporal organization is frequently achieved by having sender-receiver type signaling systems between different cell populations. In these systems, specific morphogens are secreted by sender cells and receiver cells then sense the morphogen gradient using intracellular genes. This overall network of genes interacting with each other across different cell types is called the intercellular gene regulatory network (GRN). Dysregulation of these GRNs is related to developmental diseases in humans. Hence there is a need to pinpoint and fix bottlenecks in the process of development. Existing techniques focus on inference of intracellular gene interactions. Methods to infer the intercellular GRN operate under the assumption that the intercellular signals are quantifiable in situ. This is possible if the molecular make-up of the morphogens/gene products are known and sensors to measure their absolute concentration are available. However, in general this is not true; my dissertation aims to develop a combined experimental and computational tool to infer the minimal structure and parameters of GRN using reporter genes as an indirect measurement of these signals. Reporter genes produce fluorescent proteins together with a gene of interest, and the fluorescent intensity becomes a measure of the target gene concentration. To validate the computational tool, we use a bacterial system with a sender cell population that secretes a factor that diffuses through the medium into a receiver cell population to activate gene expression in receiver cells.