Please use this identifier to cite or link to this item:
Title: A simulation framework to investigate in vitro viral infection dynamics
Authors: Bankhead, Armand
Mancini, Emiliano
McWeeney, Shannon
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.
Rights: © 2011 Elsevier B.V.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

Google ScholarTM



Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.