Thursday, November 21, 2024
11/21/2024
Abstract The molecular regulation of body shape and size during development and regeneration involves numerous pathways precisely integrated together with the biophysical properties of cellular and tissue dynamics, a complex process poorly understood at the level of whole animals. The overall goal of this project is to gain a mechanistic understanding of the genetic regulation and coordination of large-scale tissue growth by developing and applying a novel integrated systems biology approach. Combining in vivo experiments and their morphological formalization with machine learning of mathematical biophysical models, we will discern the molecular mechanisms that control growth, shape, and size regulation. We will leverage the robustness of the planarian worm to address the molecular and physical mechanisms regulating their extraordinary homeostatic and regenerative capacity to grow, degrow, and regenerate their whole-body shapes and organs from almost any amputation and across one order of magnitude in sizes. This work will develop novel computational systems biology methods and integrate them with whole-body gene expression imaging and surgical and genetic manipulations assays to elucidate the molecular regulators of body shape and size. Morphological, genetic, and surgical data will be formalized with novel mathematical ontologies, which will serve as input to new machine learning methods able to infer mechanistic gene regulatory networks. The regulatory networks will be quantitatively modeled with a novel mathematical continuous approach for whole-body biophysical simulation, including tissue growth, adhesion molecules, and gene regulation. This computational framework combining machine learning with biophysical modeling will be able to discover the mechanisms of growth and shape regulation from large formalized experimental datasets. Novel genetic interactions will be discovered by the machine learning methodology, which predictions in terms of morphological and gene expression outcomes resulting from genetic and surgical manipulations will be validated at the bench via RNAi and in situ hybridization assays. Integrating machine learning, biophysical mathematical modeling, ontological formalizations, and in vivo surgical and molecular assays represents a comprehensive systems biology approach for elucidating the regulation of shape and size. This work will provide a mechanistic understanding of the diverse genetic pathways that regulate tissue growth dynamics and how they interact precisely between them and with tissue biophysics to create and maintain whole-body scale targeted shapes and sizes. This work will pave the way for new applications and novel therapies in human developmental, regenerative, and cancer medicine.
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