SrasV1, Main, Exploration, bibRecord, 001A08

A Simulation Framework to Investigate in vitro Viral Infection Dynamics.

Identifieur interne : 001A08 ( Main/Exploration ); précédent : 001A07; suivant : 001A09

A 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. Sloot

Source :

Journal of computational science [ 1877-7503 ] ; 2013.

RBID : pubmed:23682300

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

Affiliations:

Le document en format XML

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 Viral Infection Dynamics.</title>
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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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<country name="États-Unis"><region name="Oregon"><name sortKey="Bankhead, Armand" sort="Bankhead, Armand" uniqKey="Bankhead A" first="Armand" last="Bankhead">Armand Bankhead</name>
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A Simulation Framework to Investigate in vitro Viral Infection Dynamics.

A Simulation Framework to Investigate in vitro Viral Infection Dynamics.

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Abstract

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