Infectio: a Generic Framework for Computational Simulation of Virus Transmission between Cells

ABSTRACT Viruses spread between cells, tissues, and organisms by cell-free and cell-cell mechanisms, depending on the cell type, the nature of the virus, or the phase of the infection cycle. The mode of viral transmission has a large impact on disease development, the outcome of antiviral therapies...

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Autores principales: Artur Yakimovich, Yauhen Yakimovich, Michael Schmid, Jason Mercer, Ivo F. Sbalzarini, Urs F. Greber
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Publicado: American Society for Microbiology 2016
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Acceso en línea:https://doaj.org/article/aa29ccee14f4490e9ff61a192b92ce14
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spelling oai:doaj.org-article:aa29ccee14f4490e9ff61a192b92ce142021-11-15T15:21:38ZInfectio: a Generic Framework for Computational Simulation of Virus Transmission between Cells10.1128/mSphere.00078-152379-5042https://doaj.org/article/aa29ccee14f4490e9ff61a192b92ce142016-02-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSphere.00078-15https://doaj.org/toc/2379-5042ABSTRACT Viruses spread between cells, tissues, and organisms by cell-free and cell-cell mechanisms, depending on the cell type, the nature of the virus, or the phase of the infection cycle. The mode of viral transmission has a large impact on disease development, the outcome of antiviral therapies or the efficacy of gene therapy protocols. The transmission mode of viruses can be addressed in tissue culture systems using live-cell imaging. Yet even in relatively simple cell cultures, the mechanisms of viral transmission are difficult to distinguish. Here we present a cross-platform software framework called “Infectio,” which is capable of simulating transmission phenotypes in tissue culture of virtually any virus. Infectio can estimate interdependent biological parameters, for example for vaccinia virus infection, and differentiate between cell-cell and cell-free virus spreading. Infectio assists in elucidating virus transmission mechanisms, a feature useful for designing strategies of perturbing or enhancing viral transmission. The complexity of the Infectio software is low compared to that of other software commonly used to quantitate features of cell biological images, which yields stable and relatively error-free output from Infectio. The software is open source (GPLv3 license), and operates on the major platforms (Windows, Mac, and Linux). The complete source code can be downloaded from http://infectio.github.io/index.html . IMPORTANCE Infectio presents a generalized platform to analyze virus infection spread between cells. It allows the simulation of plaque phenotypes from image-based assays. Viral plaques are the result of virus spreading from primary infected cells to neighboring cells. This is a complex process and involves neighborhood effects at cell-cell contact sites or fluid dynamics in the extracellular medium. Infectio differentiates between two major modes of virus transmission between cells, allowing in silico testing of hypotheses about spreading mechanisms of any virus which can be grown in cell cultures, based on experimentally measured parameters, such as infection intensity or cell killing. The results of these tests can be compared with experimental data and allow interpretations with regard to biophysical mechanisms. Infectio also facilitates characterizations of the mode of action of therapeutic agents, such as oncolytic viruses or other infectious or cytotoxic agents.Artur YakimovichYauhen YakimovichMichael SchmidJason MercerIvo F. SbalzariniUrs F. GreberAmerican Society for Microbiologyarticleinfection spreadnumerical simulationhybrid modelingmultiscale modelingcellular automataparticle strength exchangeMicrobiologyQR1-502ENmSphere, Vol 1, Iss 1 (2016)
institution DOAJ
collection DOAJ
language EN
topic infection spread
numerical simulation
hybrid modeling
multiscale modeling
cellular automata
particle strength exchange
Microbiology
QR1-502
spellingShingle infection spread
numerical simulation
hybrid modeling
multiscale modeling
cellular automata
particle strength exchange
Microbiology
QR1-502
Artur Yakimovich
Yauhen Yakimovich
Michael Schmid
Jason Mercer
Ivo F. Sbalzarini
Urs F. Greber
Infectio: a Generic Framework for Computational Simulation of Virus Transmission between Cells
description ABSTRACT Viruses spread between cells, tissues, and organisms by cell-free and cell-cell mechanisms, depending on the cell type, the nature of the virus, or the phase of the infection cycle. The mode of viral transmission has a large impact on disease development, the outcome of antiviral therapies or the efficacy of gene therapy protocols. The transmission mode of viruses can be addressed in tissue culture systems using live-cell imaging. Yet even in relatively simple cell cultures, the mechanisms of viral transmission are difficult to distinguish. Here we present a cross-platform software framework called “Infectio,” which is capable of simulating transmission phenotypes in tissue culture of virtually any virus. Infectio can estimate interdependent biological parameters, for example for vaccinia virus infection, and differentiate between cell-cell and cell-free virus spreading. Infectio assists in elucidating virus transmission mechanisms, a feature useful for designing strategies of perturbing or enhancing viral transmission. The complexity of the Infectio software is low compared to that of other software commonly used to quantitate features of cell biological images, which yields stable and relatively error-free output from Infectio. The software is open source (GPLv3 license), and operates on the major platforms (Windows, Mac, and Linux). The complete source code can be downloaded from http://infectio.github.io/index.html . IMPORTANCE Infectio presents a generalized platform to analyze virus infection spread between cells. It allows the simulation of plaque phenotypes from image-based assays. Viral plaques are the result of virus spreading from primary infected cells to neighboring cells. This is a complex process and involves neighborhood effects at cell-cell contact sites or fluid dynamics in the extracellular medium. Infectio differentiates between two major modes of virus transmission between cells, allowing in silico testing of hypotheses about spreading mechanisms of any virus which can be grown in cell cultures, based on experimentally measured parameters, such as infection intensity or cell killing. The results of these tests can be compared with experimental data and allow interpretations with regard to biophysical mechanisms. Infectio also facilitates characterizations of the mode of action of therapeutic agents, such as oncolytic viruses or other infectious or cytotoxic agents.
format article
author Artur Yakimovich
Yauhen Yakimovich
Michael Schmid
Jason Mercer
Ivo F. Sbalzarini
Urs F. Greber
author_facet Artur Yakimovich
Yauhen Yakimovich
Michael Schmid
Jason Mercer
Ivo F. Sbalzarini
Urs F. Greber
author_sort Artur Yakimovich
title Infectio: a Generic Framework for Computational Simulation of Virus Transmission between Cells
title_short Infectio: a Generic Framework for Computational Simulation of Virus Transmission between Cells
title_full Infectio: a Generic Framework for Computational Simulation of Virus Transmission between Cells
title_fullStr Infectio: a Generic Framework for Computational Simulation of Virus Transmission between Cells
title_full_unstemmed Infectio: a Generic Framework for Computational Simulation of Virus Transmission between Cells
title_sort infectio: a generic framework for computational simulation of virus transmission between cells
publisher American Society for Microbiology
publishDate 2016
url https://doaj.org/article/aa29ccee14f4490e9ff61a192b92ce14
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AT jasonmercer infectioagenericframeworkforcomputationalsimulationofvirustransmissionbetweencells
AT ivofsbalzarini infectioagenericframeworkforcomputationalsimulationofvirustransmissionbetweencells
AT ursfgreber infectioagenericframeworkforcomputationalsimulationofvirustransmissionbetweencells
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