Respiratory virus infections are a constant threat to public health. Influenza infection results in up to 700,000 hospitalizations and 56,000 deaths each year in the US. Extreme lung inflammation due to an excessive immune response is a major factor of severe disease outcomes in influenza virus and coronavirus infection (including SARS, MERS, and in emerging evidence, the COVID-19 virus.) The immune system is a complex, interactive, dynamic system that must optimally clear the virus infection while minimizing collateral damage to the lungs and other organs initiated by immune-regulated processes. Engineering-based mathematical modeling approaches are ideally suited to create computational simulations of the immune system that can be used to estimate the system’s response to virus infection. The overall goal of this research project is to construct realistic, predictive, physiologically accurate computational models of the natural immune response during influenza virus infection. The computational models will form the foundation for simulation-based research to identify the immunologic conditions that allow for excessive immune responses to occur. Simulations will also identify the best way to suppress immune activity, using chemical inhibition to reduce tissue inflammation while ensuring rapid clearance of the infection. As excessive inflammation is a common feature of many respiratory virus infections, the insights on immune regulation generated are expected to impact a variety of respiratory virus diseases. In parallel with this research effort, the research team will develop virtual reality (VR) games to better engage the public on matters associated with respiratory infection and immune responses. The VR games will be distributed to user smartphones for free to ensure strong public engagement. The VR games will also be used during workshops at local high schools to promote student engagement in engineering and immunology.
The Investigator’s long-term CAREER vision is to engineer computational modeling-based solutions to regulate and improve immune system responses. Toward this vision, the objectives of this CAREER project are to identify the molecular/cellular mechanisms that drive lung inflammation during influenza infection and to evaluate immunomodulatory treatment in silico using multiscale computational modeling. Studies show that selectively inhibiting the immune system can significantly improve infection outcomes by reducing inflammation without compromising virus clearance. Immunomodulation has also been shown to offer greater protection than administration of antiviral medicines (e.g. oseltamivir), but no comprehensive guidelines for administering immunomodulatory treatments, such as anti-inflammatory corticosteroids, exist. Such immunomodulatory strategies are inherently an engineering optimization challenge: modifying immune responses to limit inflammation while ensuring virus clearance. The predictive models designed can be used to evaluate specific hypotheses on the mechanisms regulating inflammation during influenza virus infection. The research program is organized under three synergistic objectives. The FIRST Objective is to construct and use an ODE (Ordinary Differential Equations) model to identify the molecular drivers of inflammation in influenza-infected human lung epithelial cells. A model of epithelial intracellular signaling will be developed and used to uncover the molecular drivers of inflammatory protein production, evaluate trade-offs between inflammation and suppressing virus replication, and to identify possible virus-specific inflammatory regulation when comparing highly pathogenic (H5N1) and milder virus infections. Completion of this objective will provide evidence of the molecular mechanisms regulating lung epithelial inflammation in general and in specific virus infections. The SECOND Objective is to construct and use an ODE model to identify the immunologic conditions that drive enhanced inflammation in influenza-infected mouse lungs. The model will link lung epithelial signaling with immune cell infiltration. The model will be used to identify the mechanisms that drive enhanced inflammation at the tissue level and explore treatment options. Comparisons between infection with different viruses and between male (less severe) vs female (more severe) infection may reveal virus and cohort-specific inflammation regulation. Completion of this objective will identify the key molecular and cellular drivers of influenza-induced inflammation, provide a novel computational model of the lung immune system that enables comparisons between important infection cohorts, and potentially identify virus-specific or sex-specific immune regulation that is driving differential inflammation. The THIRD Objective is to construct and use an ABM (Agent Based Model) of the lung immune system to quantify the impact of cell heterogeneity on tissue-level inflammation. Interferon production is stochastic and may factor into variable infection outcomes. Using customized code, an ABM in which ODEs define how agents (epithelial cells) interact with their environment will be constructed and used to interrogate how epithelial cellular heterogeneity impacts tissue-level inflammation during influenza infection. Completion of this objective will identify the components of the immune system responsible for maintaining a tightly regulated inflammatory response, provide knowledge of how intra-subject inflammation may vary due to differences in immune regulation, produce new code for the simulation community, and produce a novel ABM of the lung immune system during influenza infection.
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.
