(337ab) Immunoengineering in Diseases and Treatments

My goal is to become a leader in developing novel process systems engineering approaches for translational biomedical and clinical applications using immunoengineering approaches to investigate disease progression by external particulates and to identify targets for modulating these mechanisms. I will combine my expertise in chemical engineering and systems biology to connect chemical, physical, and biological processes at multiple lengths and time scales and develop mechanistic mathematical models to predict immune regulation during disease progression and treatments.

In my postdoctoral training, I have experience in computational modeling of biological systems to predict local and systemic immune response during tissue damage. My first project focused on the local and systemic immune regulation in the gut-bone axis in response to probiotic stimulus in the gut. We connected multicompartment physiology-based pharmacokinetics (PK)-pharmacodynamics (PD) and mechanistic modeling approaches to quantify the effect of butyrate treatment on regulatory T cells (Tregs) in the gut, blood, and bone (gut-bone axis) and estimate the direct and indirect immune-mediated impacts of butyrate on bone metabolism. The project gave me experience in computational modeling of PKPD aspects of a drug candidate, local and systemic immune response, and tissue damage. In an extension, we explored the detailed bone tissue mechanisms and investigated the effect of butyrate on bone formation. At the beginning of the COVID-19 pandemic, we formed a coalition with expert investigators in virology, immunology, mathematical biology, quantitative systems physiology, and pharmacology to develop a SARS-CoV-2 lung tissue simulator. We use the tissue simulator to identify the mechanisms of SARS-CoV-2-mediated fibrosis in the lung tissue of COVID-19 patients. We also extended the model to investigate and quantify patient-specific premorbidity, age, and gender differences in COVID-19 lung fibrosis outcomes. The ABM tissue simulator was integrated into a renin-angiotensin system (RAS) and a fibrosis model to account for systemic alterations in RAS and contribution to lung fibrosis due to patient heterogeneity.

During my graduate training, I had experience in both computational modeling and experiments. We developed an ABM to systematically transform a single-cell biochemical network into a cell population model. The goal was to predict the effect of the stochastic nature of a single-cell biochemical network in the population scale coordinated behavior. Using the model, we investigated how bacterial cell coordinated their behavior despite their individual stochastic nature. We developed another multiscale ABM model by connecting the length and time scale features of drug-delivery nanoparticles in tissue and applied that to study the particle size effect on tissue penetration efficacy. I also designed and performed experiments to investigate the motility and physiological properties of bacterial cells using microscopy and flow cytometry and the distribution of nanoparticles of different sizes in tumor tissue using microfluidic devices.