A Simulation Framework to Investigate in vitro Viral Infection Dynamics.
Identifieur interne : 002675 ( Ncbi/Curation ); précédent : 002674; suivant : 002676A Simulation Framework to Investigate in vitro Viral Infection Dynamics.
Auteurs : Armand Bankhead [États-Unis] ; Emiliano Mancini ; Amy C. Sims ; Ralph S. Baric ; Shannon Mcweeney ; Peter M A. SlootSource :
- Journal of computational science [ 1877-7503 ] ; 2013.
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 hours 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.
DOI: 10.1016/j.jocs.2011.08.007
PubMed: 23682300
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<front><div type="abstract" xml:lang="en">Virus infection is a complex biological phenomenon for which <i>in vitro</i>
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 <i>in vitro</i>
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 hours 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.</div>
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