The goal of this project is to integrate novel multi-scale mathematical modeling approaches and specifically designed experiments for predicting how molecular signaling drives cell and tissue shape generation and maintenance during development. Experimentally validated multi-scale mathematical models of biological systems will be used to infer the basic principles and rules of epithelial morphogenesis. The interdisciplinary research program will be used to discover new roles of morphogens as regulators of cell mechanics and how cell mechanics provide indirect potential feedback into morphogen signaling during development of the fruit fly Drosophila wing imaginal disc, one of the main biological models for studying basic mechanisms of animal and human development. The study of perturbations to normal development will also provide general biological insights into the mechanistic basis for diseases from birth defects to cancers related to uncontrolled tissue growth. The project will also provide a basis for developing predictive design tools for synthetic multicellular systems, including engineering organoids or soft robotics. The project will provide training for students from underrepresented groups in mathematical and computational biology, quantitative biology and biological signaling pathways regulation. One-day workshops will be held by the UC Riverside Interdisciplinary Center for Quantitative Modeling in Biology to disseminate scientific knowledge and facilitate cross-fertilization between fields of mathematical and computational biology, applied mathematics, quantitative and experimental biology.
This research project will combine mathematical modeling and quantitative experiments to answer key questions regarding how morphogens such as the Bone Morphogenetic Protein and Wingless/WNT signaling pathways regulate the cytoskeletal proteins that control cell and tissue shape formation and maintenance during organ development. The research will focus on developing multi-scale models of epithelial morphogenesis in the fruit fly wing imaginal disc during larval development due to the wealth of genetic and imaging tools available to test specific hypotheses that connect morphogens to actomyosin contractility and the extracellular matrix. Models that incorporate biochemical signaling and cell mechanics in three dimensions will be developed and calibrated using experimental data. Breakthroughs in the theory of reaction-diffusion systems on deforming surfaces coupled with coarse graining approaches describing cell membrane and cytoskeleton will be utilized together with data driven surrogate models that derived from experimental images. Machine learning approaches will be used to extract quantitative information from biological data to facilitate a comparison between experimental outcomes and model predictions. Deep learning based super-resolution imaging approaches will be developed to determine subcellular properties of cells. Statistical methods and sensitivity analysis will be incorporated to prevent model overfitting. Combinatorial perturbation simulations and experiments will result in systematic model comparison. The project will also result in a general modeling platform for predicting morphogenesis outcomes. The project will advance goals for STEM recruitment and training students from underrepresented groups. Scientific workshops will be organized to facilitate cross-fertilization between fields of mathematical and computational biology and quantitative and experimental biology.
This award is being co-funded by the Division of Molecular and Cellular Biosciences (MCB) through the Systems and Synthetic Biology Program, and the MPS Division of Mathematical Sciences (DMS) through the Mathematical Biology Program.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
