Effect of spatial distribution of T-Cells and HIV load on HIV progression

MOTIVATION We present a spatial-temporal (ST) human immunodeficiency virus (HIV) 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 sub-regional (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 10 weeks (throughout a 10-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.

[1]  John von Neumann,et al.  Theory Of Self Reproducing Automata , 1967 .

[2]  Bryan Chan,et al.  Human immunodeficiency virus reverse transcriptase and protease sequence database , 2003, Nucleic Acids Res..

[3]  Felipe Pereira,et al.  A locally conservative Eulerian–Lagrangian numerical method and its application to nonlinear transport in porous media , 2000 .

[4]  Tzi-Kang Chen,et al.  A coupled finite element and meshless local Petrov–Galerkin method for two-dimensional potential problems , 2003 .

[5]  A. Perelson,et al.  Modeling Plasma Virus Concentration and CD4+ T Cell Kinetics during Primary HIV Infection , 1999 .

[6]  J. Dolezal,et al.  A mathematical model and CD4+ lymphocyte dynamics in HIV infection. , 1996, Emerging infectious diseases.

[7]  Tshilidzi Marwala,et al.  Agent-based modelling: a case study in HIV epidemic , 2004, Fourth International Conference on Hybrid Intelligent Systems (HIS'04).

[8]  D. Kirschner,et al.  Dynamics of co-infection with M. Tuberculosis and HIV-1. , 1999, Theoretical population biology.

[9]  Alan S. Perelson Virus Dynamics: Mathematical Principles of Immunology and Virology, Martin A. Nowak, Robert M. May. Oxford University Press, Oxford, 2000. $34.95 (paper), $70 (hardcover) , 2001 .

[10]  A. F. Marée,et al.  Release of Virus from Lymphoid Tissue Affects Human Immunodeficiency Virus Type 1 and Hepatitis C Virus Kinetics in the Blood , 2001, Journal of Virology.

[11]  Mark A Fleming,et al.  Smoothing and accelerated computations in the element free Galerkin method , 1996 .

[12]  G. Drusano,et al.  Mathematical Modeling of the Interrelationship of CD4 Lymphocyte Count and Viral Load Changes Induced by the Protease Inhibitor Indinavir , 1998, Antimicrobial Agents and Chemotherapy.

[13]  Ishwar K. Sethi,et al.  Mining HIV Dynamics Using Independent Component Analysis , 2003, Bioinform..

[14]  L. White,et al.  A lattice Boltzmann model for the simulation of fluid flow , 1992 .

[15]  Francis E. Tocher Some modifications of a point-counting computer program for fabric analysis of axial orientations , 1978 .

[16]  Simon Wain-Hobson Virus Dynamics: Mathematical Principles of Immunology and Virology , 2001, Nature Medicine.