Infectio: A generic framework for computational simulation of virus transmission between cells

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

  • Artur Yakimovich - , University of Zurich (Autor:in)
  • Yauhen Yakimovich - , University of Zurich (Autor:in)
  • Michael Schmid - , University of Zurich (Autor:in)
  • Jason Mercer - , University College London (Autor:in)
  • Ivo F. Sbalzarini - , Professur für Wissenschaftliches Rechnen für Systembiologie, Zentrum für Systembiologie Dresden (CSBD), Max Planck Institute of Molecular Cell Biology and Genetics (Autor:in)
  • Urs F. Greber - , University of Zurich (Autor:in)

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 crossplatform 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.

Details

OriginalspracheEnglisch
Aufsatznummere00078-15
FachzeitschriftmSphere
Jahrgang1
Ausgabenummer1
PublikationsstatusVeröffentlicht - 1 Jan. 2016
Peer-Review-StatusJa

Externe IDs

ORCID /0000-0003-4414-4340/work/142252144

Schlagworte

ASJC Scopus Sachgebiete

Schlagwörter

  • Advection, Cell population, Cellular automata, Convection, Diffusion, Fluorescence microscopy, Hybrid modeling, Infection spread, Multiscale modeling, Numerical simulation, Particle strength exchange, Phenotypic properties