Contributors
Henrique de Assis, Laboratory for Systems Medicine, University of Florida. Title: Computational Modeling Reveals the Role of Macrophages in Respiratory A. fumigatus Infection in Immunocompromised Hosts.
T.J. Sego, Indiana University. Title: Generating Multicellular Spatiotemporal Models of Population Dynamics from Ordinary Differential Equations.
T.J. Sego, Indiana University. Title: Generating Multicellular Spatiotemporal Models of Population Dynamics from Ordinary Differential Equations.
Institution/ Affiliation
Henrique de Assis, Laboratory for Systems Medicine, University of Florida.
T.J. Sego, Indiana University.
T.J. Sego, Indiana University.
Presentation Details (date, conference, etc.)
August 12, 2021, IMAG/MSM WG on Multiscale Modeling and Viral Pandemics
- Henrique de Assis, Laboratory for Systems Medicine, University of Florida.Title: Computational Modeling Reveals the Role of Macrophages in Respiratory A. fumigatus Infection in Immunocompromised Hosts. Abstract: Fungal infections of the respiratory system are a life-threatening complication for immunocompromised patients. Invasive pulmonary aspergillosis, caused by the airborne mold Aspergillus fumigatus, has a mortality rate of up to 50% in this patient population. The lack of neutrophils, a common immunodeficiency caused by, e.g., chemotherapy, disables a mechanism of sequestering iron from the pathogen, an important virulence factor. This paper shows that a key reason why macrophages are unable to control the infection in the absence of neutrophils is the onset of hemorrhaging, as the fungus punctures the alveolar wall. The result is that the fungus gains access to heme-bound iron. At the same time, the macrophage response to the fungus is impaired. We show that these two phenomena together enable the infection to be successful. A key technology used in this work is a novel dynamic computational model used as a virtual laboratory to guide the discovery process. YouTube and Slides.
- T.J. Sego, Indiana University. Title: Generating Multicellular Spatiotemporal Models of Population Dynamics from Ordinary Differential Equations. Abstract: The biophysics of an organism span multiple scales from subcellular to organismal, and include processes characterized by spatial properties, such as the diffusion of molecules, cell migration, and flow of intravenous fluids. While non-spatial, ordinary differential equation (ODE) models are often used and readily calibrated to experimental data, they do not explicitly represent the spatial and stochastic features of a biological system, limiting their insights and applications. However, spatial models describing biological systems with spatial information are mathematically complex and computationally expensive, which limits the ability to calibrate and deploy them, and highlights the need for simpler methods able to model the spatial features of biological systems. This work develops a formal method for deriving cell-based, spatial, multicellular models from ODE models of population dynamics in biological systems, and vice-versa. The method is demonstrated by generating spatiotemporal, multicellular models from ODE models of viral infection and immune response. In these models the determinants of agreement of spatial and non-spatial models are the degree of spatial heterogeneity in viral production and rates of extracellular viral diffusion and decay. These generated spatial models show how ODE model parameters can implicitly represent spatial parameters, and cell-based spatial models can generate uncertain predictions through sensitivity to stochastic cellular events, which is not a feature of ODE models. Using the method, we can test ODE models in a multicellular, spatial context and translate information to and from non-spatial and spatial models, which help to employ spatiotemporal multicellular models using calibrated ODE model parameters. The method may be useful for generating new ODE model terms from spatiotemporal, multicellular models, recasting popular ODE models on a cellular basis, and generating better models for critical applications where spatial and stochastic features affect outcomes. YouTube and Slides.