EAGER: Modular design of multiscale models, with an application to the innate immune response to fungal respiratory pathogens

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The wealth of biomedical data available today -- often with wide ranging size and time scales -- allows for the validation and calibration of complex computational models that integrate across levels from molecules to whole organisms. For those models to be usable by scientists and clinicians who are experts in the biomedical phenomenon being modeled, it is important that the model features and activity be presented visually to make them understandable. This project seeks to develop a novel, modular computational approach to this challenge. It will use modeling of the immune response to an important respiratory fungal infection as a test bed to demonstrate the feasibility and effectiveness of this approach. This project will advance knowledge in computational modeling of biomedical systems and in the pathophysiology of infection. This particular infection has become increasingly relevant as it occurs most frequently in immuno-compromised individuals, including cancer and transplant patients. The developed model will examine how an individual's immune system interacts with the fungal spores to better understand the progression of this infection. The eventual goal would be to take advantage of this improved understanding to spur the development of new treatments for this fungal infection.This complexity of multiscale models of biomedical processes poses multiple challenges related to mathematical modeling, software design, validation, reproducibility, and extensibility. The computational goal of this project is to develop a novel modular approach to model architecture, using a recently introduced technology of lightweight virtual machines and a user-friendly open-source platform for the construction and linking of these so-called "Docker containers" to create complex modular models in a transparent fashion. A key benefit of software containers is that they can encompass the entire computational environment of a model, enabling unprecedented reproducibility of computational results. For this project, this computational modeling will be focused on the development of a multiscale model capturing the early stages of invasive aspergillosis. Invasive aspergillosis is one of the most common fungal infections in immunocompromised hosts and carries a poor prognosis. The spores of the causative organism, Aspergillus fumigatus, are ubiquitously distributed in the environment. Healthy hosts clear the inhaled spores without developing disease, but individuals with impaired immunity are susceptible to a life-threatening respiratory infection that can then disseminate to other organs. The increasing use of immunosuppressive therapies in transplantation and cancer has dramatically increased suffering and death from this infection, and this trend is expected to continue. The biomedical focus of the proposed project is the battle over iron between the fungus and the host. The overarching biomedical goal is to develop a simulation tool to explore the role of iron in invasive aspergillosis across biochemical and biophysical conditions.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.