A simulation framework to investigate in vitro viral infection dynamics

Armand Bankhead, Emiliano Mancini, Amy C. Sims, Ralph S. Baric, Shannon McWeeney, Peter M.A. Sloot

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Virus infection is a complex biological phenomenon for which in vitro experiments provide a uniquely concise view where data is often obtained from a single population of cells, under controlled environmental conditions. Nonetheless, data interpretation and real understanding of viral dynamics is still hampered by the sheer complexity of the various intertwined spatio-temporal processes. In this paper we present a tool to address these issues: a cellular automata model describing critical aspects of in vitro viral infections taking into account spatial characteristics of virus spreading within a culture well. The aim of the model is to understand the key mechanisms of SARS-CoV infection dynamics during the first 24. h post infection. Using a simulated annealing algorithm we tune free parameters with data from SARS-CoV infection of cultured lung epithelial cells. We also interrogate the model using a Latin Hypercube sensitivity analysis to identify which mechanisms are critical to the observed infection of host cells and the release of measured virus particles.

Original languageEnglish (US)
Pages (from-to)127-134
Number of pages8
JournalJournal of Computational Science
Volume4
Issue number3
DOIs
StatePublished - Jan 1 2013

Keywords

  • Cellular automata
  • Infection dynamics
  • SARS
  • Simulation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)
  • Modeling and Simulation

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