RUI: Mathematical Modeling of Immune Response to SARS-CoV-2

This research investigates the human immune response to SARS-CoV-2 virus to elucidate the key mechanisms responsible for disease severity exhibited by some COVID-19 patients. Despite a significant volume of clinical and experimental studies for the detailed mechanisms of SARS-CoV-2 virus, there is a lack of understanding about the host immune response to the virus, which is largely responsible for the variability in disease severity. To accelerate and supplement our understanding of key target pathways in the immune response, this project will develop and analyze a fundamental, comprehensive model for the host immune dynamics of SARS-CoV-2. Given we continue as a nation under pandemic conditions with new variants emerging and vaccination rollouts, the theoretical explorations through mathematical modeling will serve as a complement to lab-based and data-based approaches. Other features of this work include student involvement in this research, development of a network of collaborators across three institutions, curricula development, recruitment of students from underrepresented groups, and efforts to bring a broad community of researchers studying the host immune dynamics of COVID-19 together to advance our understanding of the interactions between the immune system and SARS-CoV-2.

This project aims to accomplish two specific research goals: (i) development of a mathematical model of the host immune dynamics of COVID-19; and (ii) exploration of the model to address important COVID-19 treatment-related questions. For the first goal, the PI will develop and analyze a mathematical model that explicitly represents the virus, immune cells, cytokines, and their interactions, formulated in a system of coupled ordinary and delay differential equations. The main objective is to obtain a better understanding of key aspects of immune response to SARS-CoV-2, specifically its sensitive pathways. For the second goal, the PI will investigate the importance of timing of specific immune responses in disease severity and divergent outcomes, and the emergence of the so-called cytokine storm, excessive production of proinflammatory cytokines in the immune system. The aim is to identify the key mechanisms responsible for disease severity, which could help to identify other pathways to target therapeutically. The primary tools to be used for this project are model parameterization using a series of clinical and experimental data, sensitivity analysis, and numerical simulations. The primary mathematical contribution is the development of computational techniques to analyze high-dimensional nonlinear dynamical systems. In addition, the results from this study on the mechanisms involved in COVID-19 pathology and identification of several therapeutic targets would provide hypotheses to be clinically tested, thus, serving as a foundation for the development of evidence-based treatment protocols to address the global challenge.

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.