1. Angela Reynolds, Virginia Commonwealth University, Title: Review of Mathematical Modeling of the Inflammatory Response in Lung Infections and Injuries

Abstract: Lung inflammation may occur due to viral and bacterial infections, structural damage, or inhalation of dangerous particles. These injuries may be quickly resolved by the immune system, treated effectively through various interventions, become chronic problems, or lead to death. Mathematical modeling has been used to understand immune system dynamics during a number of pulmonary infections and injuries, identify key mechanisms, and provide important insights into new treatments. In this review, we present long-accepted modeling techniques and novel strategies for simulating various lung injuries to highlight the usefulness of mathematical modeling in addressing these life-threatening conditions. Advances in computational power have allowed for a diverse collection of models ranging from those using only Boolean operators to complex hybrid multi-scale models, each specifically designed to address relevant biological questions. To illustrate the findings from these mathematical approaches, we present detailed examples, summarize results, and consider future directions from modeling influenza, pneumonia, COVID-19, tuberculosis, anthrax, and other non-infectious injuries. YouTube and Slides.

2. Reinhard Laubenbacher, Dean’s Professor of Systems Medicine, Director, Laboratory for Systems Medicine, Department of Medicine, University of Florida. Title: A modular computational framework for medical digital twins

Abstract: This paper presents a modular software design for the construction of computational modeling technology that will help implement precision medicine. In analogy to a common industrial strategy used for preventive maintenance of engineered products, medical digital twins are computational models of disease processes calibrated to individual patients using multiple heterogeneous data streams. They have the potential to help improve diagnosis, prognosis, and personalized treatment for a wide range of medical conditions. Their large-scale development relies on both mechanistic and data driven techniques and requires the integration and ongoing update of multiple component models developed across many different laboratories. Distributed model building and integration requires an open-source modular software platform for the integration and simulation of models that is scalable and supports a decentralized, community-based model building process. This paper presents such a platform, including a case study in an animal model of a respiratory fungal infection. YouTube and Slides.