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Bioinformatics Advance Access published online on January 10, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn008
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Effect of Spatial Distribution of T-Cells and HIV Load on HIV Progression

Frank M. Graziano 1, Samira Y. Kettoola 2, Judy M. Munshower 3, Jack T. Stapleton 4 and George J. Towfic 2,*

1University of Wisconsin Hospital and Clinics, Wisconsin-Madison, 2Department of Computer Science, Clarke College, Dubuque, IA, 3Department of Mathematics, Clarke College, Dubuque, IA, 4Department of Internal Medicine, University of Iowa

*To whom correspondence should be addressed. Dr George J. Towfic, E-mail: george.towfic{at}clarke.edu


  Abstract

Motivation: We present a Spatial-Temporal (ST) Human Immunodeficiency Virus simulation model to investigate the spatial distribution of viral load and T-cells during HIV progression. The proposed model uses the Finite Element (FE) method to divide a considered infected region into interconnected subregions each containing viral population and T-cells. HIV T-cells and viral load are traced and counted within and between subregions to estimate their effect upon neighboring regions. The objective is to estimate overall ST changes of HIV progression and to study the ST therapeutic effect upon HIV dynamics in spatial and temporal domains. We introduce subregional (spatial) parameters of T-cells and viral load production and elimination to estimate the spatial propagation and interaction of HIV dynamics under the influence of a 3TC D4T Reverse Transcriptase Inhibitors (RTI) drug regimen.

Results: In terms of percentage change standard deviation, we show that the average rate per ten weeks (throughout a ten year clinical trial) of the ST CD4+ change is 5.35% 1.3 different than that of the CD4+ rate of change in laboratory datasets, and the average rate of change of the ST CD8+ is 4.98% 1.93 different than that of the CD8+ rate of change. The half-life of the ST CD4+ count is 1.68% 3.381 different than the actual half-life of the CD4+ count obtained from laboratory datasets. The distribution of the viral load and T-cells in a considered region tends to cluster during the HIV progression once a threshold of 86-89% viral accumulation is reached.

Availability: Executable code and data libraries are available by contacting the corresponding author.

Implementation: C++ and Java have been used to simulate the suggested model. A Pentium III or higher platform is recommended.

Contact: george.towfic{at}clarke.edu

Associate Editor: Prof. Thomas Lengauer

Received on March 7, 2007; revised on December 25, 2007; accepted on January 6, 2008

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