DMS/NIGMS 1: Developing a mathematical framework to understand viral-cellular transduction for cellular reprogramming

Genetically engineered cell therapies are transforming how doctors treat challenging diseases like cancer, sickle cell disease, HIV, and autoimmune conditions. These treatments work by giving a patient’s own cells a new ability to find and fight disease more effectively. A crucial step in making these cell therapies accessible is by swiftly delivering new genetic material into cells using viral vectors in a liquid medium. Porous materials that guide the flow of cells and viruses dramatically improve delivery, but we do not yet fully understand how they work. Understanding how porous materials enhance cell therapy production will help make cell therapies more reliable and affordable for patients in dire need of treatment. This project will use artificial intelligence, computer modeling and experiments to determine how flow inside porous materials efficiently produces genetically modified cells. In answering this important scientific question, this project will engage students from the high school to graduate levels and help train future scientists and engineers. By integrating research outcomes in classrooms and through partnerships with industries and foundations, this work will also help spread awareness of advanced engineered cell therapies and how they can improve health outcomes. This research will develop a mechanistic understanding of how liquid flow in microfluidic devices and porous scaffolds enhances the efficiency of viral transduction in engineered cell therapies. The project will integrate experimental studies with detailed mathematical modeling to quantify cell-virus collision frequencies and the biological pathways that lead to successful gene delivery. A combination of computational fluid dynamics, discrete particle simulations, and electrostatic interaction models will be used to capture the complex flow physics at the microscale level. Novel neural network approaches, including generative adversarial models and neural net optimization algorithms, will design porous biomaterials and predict the interactions involved in viral binding, internalization, and gene integration in cells. By rigorously varying flow conditions, the research will uncover how convective and diffusive transport mechanisms impact cell-virus encounters and transduction success rates. The resulting models will provide design principles for improving gene delivery platforms and could also shed light on related biological processes, such as immune cell infiltration in tumors. The project outcomes will advance both the theory and application of cellular transduction for next-generation gene and cell therapies. 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.