dc.contributor.authorBankhead, Armand
dc.contributor.authorMancini, Emiliano
dc.contributor.authorSims, Amy C.
dc.contributor.authorBaric, Ralph S.
dc.contributor.authorMcWeeney, Shannon
dc.contributor.authorSloot, Peter M. A.
dc.identifier.citationBankhead, A., Mancini, E., Sims, A. C., Baric, R. S., McWeeney, S., & Sloot, P. M. A. (2013). A Simulation Framework to Investigate in vitro Viral Infection Dynamics. Journal of Computational Science, 4(3), 127-134.en_US
dc.description.abstractVirus 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.en_US
dc.relation.ispartofseriesJournal of computational scienceen_US
dc.rights© 2011 Elsevier B.V.en_US
dc.subjectDRNTU::Engineering::Computer science and engineering
dc.titleA simulation framework to investigate in vitro viral infection dynamicsen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Engineeringen_US

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