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Sego TJ, Aponte-Serrano JO, Ferrari Gianlupi J, Heaps SR, Breithaupt K, Brusch L, Crawshaw J, Osborne JM, Quardokus EM, Plemper RK, Glazier JA. A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness. PLoS Comput Biol 2020; 16:e1008451. [PMID: 33347439 PMCID: PMC7785254 DOI: 10.1371/journal.pcbi.1008451] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 01/05/2021] [Accepted: 10/20/2020] [Indexed: 12/23/2022] Open
Abstract
Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.
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Affiliation(s)
- T. J. Sego
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Josua O. Aponte-Serrano
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Juliano Ferrari Gianlupi
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Samuel R. Heaps
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
| | - Kira Breithaupt
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Cognitive Science Program, Indiana University, Bloomington, Indiana, United States of America
| | - Lutz Brusch
- Center for Information Services and High Performance Computing (ZIH), Technische Universität, Dresden, Germany
| | - Jessica Crawshaw
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - James M. Osborne
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Ellen M. Quardokus
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
| | - Richard K. Plemper
- Institute for Biomedical Sciences, Georgia State University, Atlanta, Georgia, United States of America
| | - James A. Glazier
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
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Sego TJ, Aponte-Serrano JO, Gianlupi JF, Heaps SR, Breithaupt K, Brusch L, Crawshaw J, Osborne JM, Quardokus EM, Plemper RK, Glazier JA. A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness: A multiscale model of viral infection in epithelial tissues. bioRxiv 2020:2020.04.27.064139. [PMID: 32511367 PMCID: PMC7263495 DOI: 10.1101/2020.04.27.064139] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.
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Affiliation(s)
- T J Sego
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - Josua O Aponte-Serrano
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - Juliano Ferrari Gianlupi
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - Samuel R Heaps
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Kira Breithaupt
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
| | - Lutz Brusch
- Center for Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Germany
| | - Jessica Crawshaw
- School of Mathematics and Statistics, University of Melbourne, Melbourne, 3010, Australia
| | - James M Osborne
- School of Mathematics and Statistics, University of Melbourne, Melbourne, 3010, Australia
| | - Ellen M Quardokus
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Richard K Plemper
- Institute for Biomedical Sciences, Georgia State University, Atlanta, GA, USA
| | - James A Glazier
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
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