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

by Yakimovich, Artur; Yakimovich, Yauhen; Schmid, Michael; Mercer, Jason; Sbalzarini, Ivo F. and Greber, Urs F.
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 textquotedblleftInfectio,textquotedblright 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.
Reference:
Infectio: a Generic Framework for Computational Simulation of Virus Transmission between Cells (Yakimovich, Artur; Yakimovich, Yauhen; Schmid, Michael; Mercer, Jason; Sbalzarini, Ivo F. and Greber, Urs F.), In mSphere (Imperiale, Michael J., ed.), American Society for Microbiology Journals, volume 1, 2016.
Bibtex Entry:
@Article{Yakimovich2016,
  Title                    = {Infectio: a Generic Framework for Computational Simulation of Virus Transmission between Cells},
  Author                   = {Yakimovich, Artur and Yakimovich, Yauhen and Schmid, Michael and Mercer, Jason and Sbalzarini, Ivo F. and Greber, Urs F.},
  Journal                  = {mSphere},
  Year                     = {2016},
  Number                   = {1},
  Volume                   = {1},

  __markedentry            = {[michael:6]},
  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 {textquotedblleft}Infectio,{textquotedblright} 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.},
  Doi                      = {10.1128/mSphere.00078-15},
  Editor                   = {Imperiale, Michael J.},
  Eprint                   = {https://msphere.asm.org/content/1/1/e00078-15.full.pdf},
  Owner                    = {michael},
  Publisher                = {American Society for Microbiology Journals},
  Timestamp                = {2018.08.04},
  Url                      = {https://msphere.asm.org/content/1/1/e00078-15}
}