Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.jocs.2011.08.007 | Journal of Computational Science |
Keywords | Field | DocType |
Cellular automata,Infection dynamics,SARS,Simulation | Cellular automaton,Population,Data interpretation,Computer science,Artificial intelligence,Viral dynamics,Bioinformatics,Computational biology,Machine learning,Latin hypercube sampling | Journal |
Volume | Issue | ISSN |
4 | 3 | 1877-7503 |
Citations | PageRank | References |
4 | 0.44 | 3 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Armand Bankhead | 1 | 10 | 1.24 |
| Emiliano Mancini | 2 | 6 | 2.28 |
| Amy C Sims | 3 | 6 | 1.20 |
| Ralph Baric | 4 | 13 | 1.96 |
| Shannon McWeeney | 5 | 19 | 2.90 |
| Peter M. A. Sloot | 6 | 3095 | 513.51 |