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|Title:||A simulation framework to investigate in vitro viral infection dynamics||Authors:||Bankhead, Armand
Sims, Amy C.
Baric, Ralph S.
Sloot, Peter M. A.
|Issue Date:||2011||Source:||Bankhead, A., Mancini, E., Sims, A. C., Baric, R. S., McWeeney, S., & Sloot, P. M. A. (2011). A Simulation Framework to Investigate in vitro Viral Infection Dynamics. Proceedings of the International Conference on Computational Science, 4(1), 1798-1807.||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.||URI:||https://hdl.handle.net/10356/96899
|DOI:||http://dx.doi.org/10.1016/j.procs.2011.04.195||Rights:||© 2011 Elsevier B.V.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Conference Papers|
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