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Article ; Online: Multicellular spatial model of RNA virus replication and interferon responses reveals factors controlling plaque growth dynamics.

Aponte-Serrano, Josua O / Weaver, Jordan J A / Sego, T J / Glazier, James A / Shoemaker, Jason E

2021 Volume 17, Issue 10, Page(s) e1008874

Abstract: Respiratory viruses present major public health challenges, as evidenced by the 1918 Spanish Flu, the 1957 H2N2, 1968 H3N2, and 2009 H1N1 influenza pandemics, and the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Severe ... ...

Abstract	Respiratory viruses present major public health challenges, as evidenced by the 1918 Spanish Flu, the 1957 H2N2, 1968 H3N2, and 2009 H1N1 influenza pandemics, and the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Severe RNA virus respiratory infections often correlate with high viral load and excessive inflammation. Understanding the dynamics of the innate immune response and its manifestations at the cell and tissue levels is vital to understanding the mechanisms of immunopathology and to developing strain-independent treatments. Here, we present a novel spatialized multicellular computational model of RNA virus infection and the type-I interferon-mediated antiviral response that it induces within lung epithelial cells. The model is built using the CompuCell3D multicellular simulation environment and is parameterized using data from influenza virus-infected cell cultures. Consistent with experimental observations, it exhibits either linear radial growth of viral plaques or arrested plaque growth depending on the local concentration of type I interferons. The model suggests that modifying the activity of signaling molecules in the JAK/STAT pathway or altering the ratio of the diffusion lengths of interferon and virus in the cell culture could lead to plaque growth arrest. The dependence of plaque growth arrest on diffusion lengths highlights the importance of developing validated spatial models of cytokine signaling and the need for in vitro measurement of these diffusion coefficients. Sensitivity analyses under conditions leading to continuous or arrested plaque growth found that plaque growth is more sensitive to variations of most parameters and more likely to have identifiable model parameters when conditions lead to plaque arrest. This result suggests that cytokine assay measurements may be most informative under conditions leading to arrested plaque growth. The model is easy to extend to include SARS-CoV-2-specific mechanisms or to use as a component in models linking epithelial cell signaling to systemic immune models.
MeSH term(s)	Cells, Cultured ; Computational Biology ; Epithelial Cells/immunology ; Host-Pathogen Interactions/immunology ; Humans ; Immunity, Innate/immunology ; Interferons/immunology ; Interferons/metabolism ; Lung/cytology ; Lung/immunology ; Models, Biological ; RNA Virus Infections/immunology ; RNA Virus Infections/virology ; RNA Viruses/immunology ; RNA Viruses/physiology ; Virus Replication/immunology ; Virus Replication/physiology
Chemical Substances	Interferons (9008-11-1)
Language	English
Publishing date	2021-10-25
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1008874
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Generation of multicellular spatiotemporal models of population dynamics from ordinary differential equations, with applications in viral infection.

Sego, T J / Aponte-Serrano, Josua O / Gianlupi, Juliano F / Glazier, James A

BMC biology

2021 Volume 19, Issue 1, Page(s) 196

Abstract: Background: 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. Mathematical ... ...

Abstract	Background: 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. Mathematical biology seeks to explain biophysical processes in mathematical terms at, and across, all relevant spatial and temporal scales, through the generation of representative models. 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. Results: In this work, we develop a formal method for deriving cell-based, spatial, multicellular models from ODE models of population dynamics in biological systems, and vice versa. We provide examples of 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. We 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 our 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. We additionally investigate objects and processes implicitly represented by ODE model terms and parameters and improve the reproducibility of spatial, stochastic models. Conclusion: We developed and demonstrate a method for generating spatiotemporal, multicellular models from non-spatial population dynamics models of multicellular systems. We envision employing our method to generate new ODE model terms from spatiotemporal and multicellular models, recast popular ODE models on a cellular basis, and generate better models for critical applications where spatial and stochastic features affect outcomes.
MeSH term(s)	Computer Simulation ; Humans ; Models, Biological ; Population Dynamics ; Reproducibility of Results ; Virus Diseases
Language	English
Publishing date	2021-09-08
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2133020-7
ISSN	1741-7007 ; 1741-7007
ISSN (online)	1741-7007
ISSN	1741-7007
DOI	10.1186/s12915-021-01115-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Generation of multicellular spatiotemporal models of population dynamics from ordinary differential equations, with applications in viral infection

T. J. Sego / Josua O. Aponte-Serrano / Juliano F. Gianlupi / James A. Glazier

BMC Biology, Vol 19, Iss 1, Pp 1-

2021 Volume 24

Abstract: Abstract Background 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. ... ...

Abstract	Abstract Background 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. Mathematical biology seeks to explain biophysical processes in mathematical terms at, and across, all relevant spatial and temporal scales, through the generation of representative models. 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. Results In this work, we develop a formal method for deriving cell-based, spatial, multicellular models from ODE models of population dynamics in biological systems, and vice versa. We provide examples of 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. We 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 our 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. We additionally investigate objects and processes implicitly represented by ODE ...
Keywords	Agent-based modeling ; Multicellular systems ; Multiscale modeling ; Biology (General) ; QH301-705.5
Subject code	910
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Multicellular spatial model of RNA virus replication and interferon responses reveals factors controlling plaque growth dynamics.

Josua O Aponte-Serrano / Jordan J A Weaver / T J Sego / James A Glazier / Jason E Shoemaker

PLoS Computational Biology, Vol 17, Iss 10, p e

2021 Volume 1008874

Abstract	Respiratory viruses present major public health challenges, as evidenced by the 1918 Spanish Flu, the 1957 H2N2, 1968 H3N2, and 2009 H1N1 influenza pandemics, and the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Severe RNA virus respiratory infections often correlate with high viral load and excessive inflammation. Understanding the dynamics of the innate immune response and its manifestations at the cell and tissue levels is vital to understanding the mechanisms of immunopathology and to developing strain-independent treatments. Here, we present a novel spatialized multicellular computational model of RNA virus infection and the type-I interferon-mediated antiviral response that it induces within lung epithelial cells. The model is built using the CompuCell3D multicellular simulation environment and is parameterized using data from influenza virus-infected cell cultures. Consistent with experimental observations, it exhibits either linear radial growth of viral plaques or arrested plaque growth depending on the local concentration of type I interferons. The model suggests that modifying the activity of signaling molecules in the JAK/STAT pathway or altering the ratio of the diffusion lengths of interferon and virus in the cell culture could lead to plaque growth arrest. The dependence of plaque growth arrest on diffusion lengths highlights the importance of developing validated spatial models of cytokine signaling and the need for in vitro measurement of these diffusion coefficients. Sensitivity analyses under conditions leading to continuous or arrested plaque growth found that plaque growth is more sensitive to variations of most parameters and more likely to have identifiable model parameters when conditions lead to plaque arrest. This result suggests that cytokine assay measurements may be most informative under conditions leading to arrested plaque growth. The model is easy to extend to include SARS-CoV-2-specific mechanisms or to use as a component in models linking ...
Keywords	Biology (General) ; QH301-705.5
Subject code	612
Language	English
Publishing date	2021-10-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Modular Framework for Multiscale Spatial Modeling of Viral Infection and Immune Response in Epithelial Tissue

T.J. Sego / Josua O. Aponte-Serrano / Juliano Ferrari-Gianlupi / Samuel Heaps / Ellen M. Quardokus / James A. Glazier

Abstract: The COVID-19 crisis has shown that classic sequential models for scientific research are too slow and do not easily encourage multidisciplinary scientific collaboration. The need to rapidly understand the causes of differing infection outcomes and ... ...

Abstract	The COVID-19 crisis has shown that classic sequential models for scientific research are too slow and do not easily encourage multidisciplinary scientific collaboration. The need to rapidly understand the causes of differing infection outcomes and vulnerabilities, to provide mechanistic frameworks for the interpretation of experimental and clinical data and to suggest drug and therapeutic targets and to design optimized personalized interventions all require the development of detailed predictive quantitative models of all aspects of COVID-19. Many of these models will require the use of common submodels describing specific aspects of infection (e.g., viral replication) but combine them in novel configurations. As a contribution to this development and as a proof-of-concept for some components of these models, we present a multi-layered 2D multiscale, multi-cell model and associated computer simulations of the infection of epithelial tissue by a virus, the proliferation and spread of the virus, the cellular immune response and tissue damage. Our initial, proof-of-concept model is built of modular components to allow it to be easily extended and adapted to describe specific viral infections, tissue types and immune responses. Immediately after a cell becomes infected, the virus replicates inside the cell. After an eclipse period, the infected cells start shedding diffusing infectious virus, infecting nearby cells, and secretes a short-diffusing cytokine signal. Neighboring cells can take up the diffusing extracellular virus and become infected. The cytokine signal calls for immune cells from a simple model of the systemic immune response. These immune cells chemotax and activate within the tissue in response to the cytokine profile. Activated immune cells can kill underlying epithelial cells directly or by secreting a short-diffusible toxic chemical. Infected cells can also die by apoptosis due to the stress of viral replication. We do not include direct cytokine mediated protective factors in the tissue or distinguish the complexity of the immune response in this simple model. Despite unrealistically fast viral production and immune response, the current base model allows us to define three parameter regimes, where the immune system rapidly controls the virus, where it controls the virus after extensive tissue damage, and where the virus escapes control and infects and kills all cells. We can simulate a number of drug therapy concepts, like delayed rate of production of viral RNAs, reduced viral entry, and higher and lower levels of immune response, which we demonstrate with simulation results of parameter sweeps of select model parameters. From results of these sweeps, we found that successful containment of infection in simulation directly relates to inhibited viral internalization and rapid immune cell recruitment, while spread of infection occurs in simulations with fast viral internalization and slower immune response. In contrast to other simulations of viral infection, our simulated tissue demonstrates spatial and cellular events of viral infection as resulting from subcellular, cellular, and systemic mechanisms. To support rapid development of current and new submodels, we are developing a shared, publicly available environment to support collaborative development of this framework and its components. We warmly invite interested members of the biological, medical, mathematical and computational communities to contribute to improving and extending the framework.
Keywords	covid19
Publisher	biorxiv
Document type	Article ; Online
DOI	10.1101/2020.04.27.064139
Database	COVID19

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Article: 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.

Sego, T J / Aponte-Serrano, Josua O / Gianlupi, Juliano Ferrari / Heaps, Samuel R / Breithaupt, Kira / Brusch, Lutz / Crawshaw, Jessica / Osborne, James M / Quardokus, Ellen M / Plemper, Richard K / Glazier, James A

bioRxiv : the preprint server for biology

2020

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 ... ...

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 (
Keywords	covid19
Language	English
Publishing date	2020-09-26
Publishing country	United States
Document type	Preprint
DOI	10.1101/2020.04.27.064139
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: 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.

Sego, T J / Aponte-Serrano, Josua O / Ferrari Gianlupi, Juliano / Heaps, Samuel R / Breithaupt, Kira / Brusch, Lutz / Crawshaw, Jessica / Osborne, James M / Quardokus, Ellen M / Plemper, Richard K / Glazier, James A

PLoS computational biology

2020 Volume 16, Issue 12, Page(s) e1008451

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.
MeSH term(s)	Antiviral Agents/therapeutic use ; COVID-19/immunology ; Computational Biology/methods ; Computer Simulation ; Epithelium/immunology ; Epithelium/virology ; Hepacivirus/immunology ; Hepatitis C/drug therapy ; Hepatitis C/immunology ; Humans ; Models, Immunological ; SARS-CoV-2/immunology ; Virus Diseases/drug therapy ; Virus Diseases/immunology
Chemical Substances	Antiviral Agents
Language	English
Publishing date	2020-12-21
Publishing country	United States
Document type	Journal Article
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1008451
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Modular Framework for Multiscale Spatial Modeling of Viral Infection and Immune Response in Epithelial Tissue

Sego, T.J. / Aponte-Serrano, Josua O. / Ferrari-Gianlupi, Juliano / Heaps, Samuel / Quardokus, Ellen M. / Glazier, James A.

bioRxiv

Abstract	The COVID-19 crisis has shown that classic sequential models for scientific research are too slow and do not easily encourage multidisciplinary scientific collaboration. The need to rapidly understand the causes of differing infection outcomes and vulnerabilities, to provide mechanistic frameworks for the interpretation of experimental and clinical data and to suggest drug and therapeutic targets and to design optimized personalized interventions all require the development of detailed predictive quantitative models of all aspects of COVID-19. Many of these models will require the use of common submodels describing specific aspects of infection (e.g., viral replication) but combine them in novel configurations. As a contribution to this development and as a proof-of-concept for some components of these models, we present a multi-layered 2D multiscale, multi-cell model and associated computer simulations of the infection of epithelial tissue by a virus, the proliferation and spread of the virus, the cellular immune response and tissue damage. Our initial, proof-of-concept model is built of modular components to allow it to be easily extended and adapted to describe specific viral infections, tissue types and immune responses. Immediately after a cell becomes infected, the virus replicates inside the cell. After an eclipse period, the infected cells start shedding diffusing infectious virus, infecting nearby cells, and secretes a short-diffusing cytokine signal. Neighboring cells can take up the diffusing extracellular virus and become infected. The cytokine signal calls for immune cells from a simple model of the systemic immune response. These immune cells chemotax and activate within the tissue in response to the cytokine profile. Activated immune cells can kill underlying epithelial cells directly or by secreting a short-diffusible toxic chemical. Infected cells can also die by apoptosis due to the stress of viral replication. We do not include direct cytokine mediated protective factors in the tissue or distinguish the complexity of the immune response in this simple model. Despite unrealistically fast viral production and immune response, the current base model allows us to define three parameter regimes, where the immune system rapidly controls the virus, where it controls the virus after extensive tissue damage, and where the virus escapes control and infects and kills all cells. We can simulate a number of drug therapy concepts, like delayed rate of production of viral RNAs, reduced viral entry, and higher and lower levels of immune response, which we demonstrate with simulation results of parameter sweeps of select model parameters. From results of these sweeps, we found that successful containment of infection in simulation directly relates to inhibited viral internalization and rapid immune cell recruitment, while spread of infection occurs in simulations with fast viral internalization and slower immune response. In contrast to other simulations of viral infection, our simulated tissue demonstrates spatial and cellular events of viral infection as resulting from subcellular, cellular, and systemic mechanisms. To support rapid development of current and new submodels, we are developing a shared, publicly available environment to support collaborative development of this framework and its components. We warmly invite interested members of the biological, medical, mathematical and computational communities to contribute to improving and extending the framework.
Keywords	covid19
Language	English
Publishing date	2020-04-28
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2020.04.27.064139
Database	COVID19

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Article ; Online: 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.

T J Sego / Josua O Aponte-Serrano / Juliano Ferrari Gianlupi / Samuel R Heaps / Kira Breithaupt / Lutz Brusch / Jessica Crawshaw / James M Osborne / Ellen M Quardokus / Richard K Plemper / James A Glazier

PLoS Computational Biology, Vol 16, Iss 12, p e

2020 Volume 1008451

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.
Keywords	Biology (General) ; QH301-705.5
Subject code	570
Language	English
Publishing date	2020-12-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Multicellular spatial model of RNA virus replication and interferon responses reveals factors controlling plaque growth dynamics.

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Article ; Online: Generation of multicellular spatiotemporal models of population dynamics from ordinary differential equations, with applications in viral infection.

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Article ; Online: Generation of multicellular spatiotemporal models of population dynamics from ordinary differential equations, with applications in viral infection

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Article ; Online: Multicellular spatial model of RNA virus replication and interferon responses reveals factors controlling plaque growth dynamics.

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Article ; Online: A Modular Framework for Multiscale Spatial Modeling of Viral Infection and Immune Response in Epithelial Tissue

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Article: 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.

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Article ; Online: 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.

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Article ; Online: A Modular Framework for Multiscale Spatial Modeling of Viral Infection and Immune Response in Epithelial Tissue

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Article ; Online: 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.

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