CAREER: Learning and teaching how to program morphogenesis with synthetic genetic circuits, a case study for elongation of mammalian cell spheroids

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Growing organs and multicellular machines in the lab is at the leading edge of regenerative medicine. However, controlling the shape of lab-grown materials is elusive. Creating a computational framework that can help design control networks to drive groups of cells to self-assemble into elongated structures is the primary objective of this project. Supporting curriculum and teacher development is another objective. Presenting workshops and training teachers in synthetic biology will help cultivate interest in STEM fields among under-represented high school students from local neighborhoods.The primary objective is to rationally design and autonomously manufacture axially elongated, three-dimensional tissues, organs and biobots. The central hypothesis is that artificial genetic circuits, based on synthetic Notch (synNotch) contact-dependent cell-cell signaling, could be designed and optimized in silico to control axial elongation of in vitro grown multicellular structures, when combined with control of adhesion and proliferation. A validated platform for computational modeling based on an augmented Cellular Potts model will be used to develop genetic programs. These programs will be implemented in fibroblasts. Quantitative fluorescent imaging will indicate their capacity to sustain elongation. The validated, predictive computational platform developed during this project will be available for use on a broader range of shapes and structures. Identification of genetic drivers of axial elongation will increase our understanding of normal developmental processes and provide insights into network malfunctions that may play a role in the pathophysiology of certain birth defects as well as advancing control of in vitro-grown tissues for applications in regenerative medicine and soft robotics.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.