A simulation framework to investigate in vitro viral infection dynamics

Armand Bankhead; Emiliano Mancini; Amy C. Sims; Ralph S. Baric; Shannon Mcweeney; Peter M.A. Sloot

doi:10.1016/j.procs.2011.04.195

A simulation framework to investigate in vitro viral infection dynamics

Armand Bankhead, Emiliano Mancini, Amy C. Sims, Ralph S. Baric, Shannon Mcweeney, Peter M.A. Sloot

Research output: Contribution to journal › Conference article › peer-review

4 Scopus citations

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. We 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.

Original language	English (US)
Pages (from-to)	1798-1807
Number of pages	10
Journal	Procedia Computer Science
Volume	4
DOIs	https://doi.org/10.1016/j.procs.2011.04.195
State	Published - 2011
Event	11th International Conference on Computational Science, ICCS 2011 - Singapore, Singapore Duration: Jun 1 2011 → Jun 3 2011

Keywords

Cellular automata
Infection dynamics
SARS
Simulation

ASJC Scopus subject areas

General Computer Science

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10.1016/j.procs.2011.04.195

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title = "A simulation framework to investigate in vitro viral infection dynamics",

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. We 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.",

keywords = "Cellular automata, Infection dynamics, SARS, Simulation",

author = "Armand Bankhead and Emiliano Mancini and Sims, {Amy C.} and Baric, {Ralph S.} and Shannon Mcweeney and Sloot, {Peter M.A.}",

note = "Funding Information: This work was made possible by funding from the National Institute of Allergy and Infectious Diseases, NIH, Department of Health and Human Services contract # HHSN272200800060C Thanks to Casey Long for his work in plating Calu-3 cells for SARS-CoV infection experiments. Also thanks to Dr.Andrea Cavazzoni of the Unit of Experimental Oncology (University of Parma) for the useful discussions.; 11th International Conference on Computational Science, ICCS 2011 ; Conference date: 01-06-2011 Through 03-06-2011",

year = "2011",

doi = "10.1016/j.procs.2011.04.195",

language = "English (US)",

volume = "4",

pages = "1798--1807",

journal = "Procedia Computer Science",

issn = "1877-0509",

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T1 - A simulation framework to investigate in vitro viral infection dynamics

AU - Bankhead, Armand

AU - Mancini, Emiliano

AU - Sims, Amy C.

AU - Baric, Ralph S.

AU - Mcweeney, Shannon

AU - Sloot, Peter M.A.

N1 - Funding Information: This work was made possible by funding from the National Institute of Allergy and Infectious Diseases, NIH, Department of Health and Human Services contract # HHSN272200800060C Thanks to Casey Long for his work in plating Calu-3 cells for SARS-CoV infection experiments. Also thanks to Dr.Andrea Cavazzoni of the Unit of Experimental Oncology (University of Parma) for the useful discussions.

PY - 2011

Y1 - 2011

N2 - 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. We 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.

AB - 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. We 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.

KW - Cellular automata

KW - Infection dynamics

KW - SARS

KW - Simulation

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AN - SCOPUS:79958284999

SN - 1877-0509

VL - 4

SP - 1798

EP - 1807

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 11th International Conference on Computational Science, ICCS 2011

Y2 - 1 June 2011 through 3 June 2011

ER -

A simulation framework to investigate in vitro viral infection dynamics

Abstract

Keywords

ASJC Scopus subject areas

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