(2fg) Immunoengineering in Gut-Lung Axis

The central aim of my proposed research lab is to understand the gut-lung axis mechanisms using immunoengineering approaches to investigate disease progression by external particulates and to identify targets for modulating these mechanisms. My research program will combine my expertise in chemical engineering and systems biology to connect chemical, physical, and biological processes of the gut-lung axis at multiple lengths and time scales and develop mechanistic mathematical models to predict immune regulation during disease progression and treatments.

The crosstalk between the gut and lung via the blood and lymphatic system (gut-lung axis) has gained significant attention as local changes in one organ have causes and consequences in another in various diseases and treatments. In healthy states, gut and lung microbiota modulate the gut-lung axis crosstalk through immune cells, cytokines, and gut-derived metabolites. Exposure to external particulates, such as viruses, foodborne nanoparticles, air pollutants, and toxic substances, negatively modulates beneficial microbiota leading to gastrointestinal, respiratory, and inflammatory diseases. Current treatment strategies targeting the gut-lung axis mechanisms include gut-derived probiotics, metabolites, and dietary supplements for positive modulation of the in-host bacterial population and local and systemic immune response. However, a gap still exists in connecting the local and systemic mechanisms of the gut-lung axis for therapeutic interventions. I will use computational modeling techniques to identify detailed gut-lung axis mechanisms and translate the quantitative descriptions of positive and negative modulators to an environment similar to the human gut and lung for translational biomedical and clinical applications.

The long-term goal is to investigate the gut-lung axis mechanisms and quantify negative and positive modulators’ effects on diseases and treatments, respectively, for respiratory and gastrointestinal systems. The preliminary models will correspond to the immune response in humans, in vivo, and in vitro experiments where gut-lung axis positive modulators (probiotic treatment in the gut) or negative modulators (viral infection in the lung) were applied. Specifically, positive modulators Lactobacillus and Bifidobacterium and metabolite butyrate in the gut will be selected to understand their gut-lung axis adaptive immune CD8+ T cells and Tregs modulation in response to negative modulator influenza in the lung epithelial cells. I have formulated following aims to understand and connect intracellular, cellular, tissue, and gut-lung axis immune mechanisms.

Aim 1 will develop computational models to predict the changes in adaptive immune cell phenotypes in the gut in response to positive modulators. The phenotypic variance of immune cells depends on the response of a cell to external stimuli. The relative changes in protein and gene expression in presence of external stimulation will be quantified using logic-based normalized-Hill differential equations. The detailed intracellular network for T cell phenotypes (e.g., Th2, Th17, and Tregs) will be connected and validated utilizing literature and existing database to determine cell fate in the presence of multiple external stimuli and cytokines (e.g., IL-6, IL-4, IFN-g, and TGF-β). In future, a similar modeling approach will be used for the lung to predict the changes in adaptive immune cells due to negative modulator influenza. Other techniques to connect intracellular networks, including biochemical modeling, boolean modeling, and bifurcation analysis, will be tested in the case of a mismatch of predicted phenotypes to experimental observations.

Aim 2 will build computational models to identify the mechanisms of influenza-induced lung tissue damage and quantify the effect of immune recruitment from probiotics in viral clearance, inflammation, and tissue damage. Agent-based modeling (ABM) will be used to define the functionality of cells and extracellular matrix, and partial differential equations (PDEs) to simulate the dynamics of pro- and anti-inflammatory cytokines. The disease progression in the lung tissue will be modeled without and with the estimated immune cell recruitment from probiotics as input. The therapeutic efficacy of probiotics in attenuating inflammation and lung tissue damage will be determined using metrics, such as clearance of infected cells and viruses, the temporal and spatial profile of viruses, lung cells, immune cells, pro- and anti-inflammatory cytokines, and collagen. In future, intracellular models from aim 1 will be connected to account for immune cells phenotypic variations. Similar tissue scale modeling approaches will also be developed for gastrointestinal diseases and treatments.

Aim 3 will use multicompartment physiology-based pharmacokinetics (PK)-pharmacodynamics (PD) and mechanistic models to connect gut, blood, and lung as 3 compartments. Using the model, I will test the hypothesis that probiotic strains from Lactobacillus and Bifidobacterium and their metabolite butyrate have therapeutic dosages to activate CD8+ T cells and Tregs functionality in the clearance of influenza infection and balancing cytokine profile to restore healthy lung. The model output will predict the effect of probiotic treatments in the clearance of influenza infection, and corresponding in vivo and human data will be used for parameter estimation and model validation. In the initial model, ordinary differential equations (ODEs) will describe the changes in cell population and cytokine profile in the gut-lung axis. The antiviral efficacy of probiotics will be quantified using metrics, such as initial concentration and dosage of probiotic strains/ probiotic consortia, composition of probiotics in probiotic consortia, local and systemic change in the activity and populations of immune cells in the gut-lung axis, and clearance of infected cells and viruses in the lung. In future, molecular and tissue scale mechanisms will be connected utilizing models from aim 1 and 2 to account for intracellular and tissue scale stochastic response.

Upon successful completion, the project will develop novel process systems engineering approaches targeting the gut-lung axis mechanisms to predict, test, and translate the quantitative descriptions of gut-lung axis modulators for translational biomedical and clinical applications. The proposed research will have future applications in gastrointestinal infections, inflammatory and pulmonary diseases, including cancer, and health consequences of air pollution, targeting gut-lung axis modulators as potential therapeutic strategies. I will actively seek out collaborations with mutual interests to expand the scope of my research aims.

In my postdoctoral work with Prof. Ashlee N. Ford Versypt, 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 PKPD 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 with Prof. Dipak Barua, 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.

Teaching Interests

My teaching philosophy focuses on creating an environment to guide students from all backgrounds through intellectual development and promote their curiosity for lifelong learning. I am committed to inspiring my students with the motivation to become critical thinkers in the scientific process and endure the hard work to follow. My teaching strategy involves students understanding the fundamental concepts and helping them develop a rational set of rules with critical thinking to solve problems. I will create a learning environment to engage students in course materials, provide conceptual and technical training with tangible applications, and prepare them for future academic and career goals. I am interested in teaching core Chemical and Biomedical engineering courses, including Transport, Systems Biology, and Numerical and Statistical Methods. I envision developing elective courses, particularly on biological transport, quantitative biology, quantitative computational science, and advanced numerical methods. My research interests, background, expertise, and collaborations with computer science, chemistry, biology, and nutritional science make me suited to develop these interdisciplinary, cutting-edge courses.