Modeling the interplay between albumin-globulin metabolism and HIV infection

Vivek Sreejithkumar; Kia Ghods; Tharusha Bandara; Maia Martcheva; Necibe Tuncer; Vivek Sreejithkumar; Kia Ghods; Tharusha Bandara; Maia Martcheva; Necibe Tuncer

doi:10.3934/mbe.2023865

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 11: 19527-19552. doi: 10.3934/mbe.2023865

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Modeling the interplay between albumin-globulin metabolism and HIV infection

1.
Florida Atlantic University, Department of Mathematical Sciences, Boca Raton, FL 33431, USA
2.
University of Florida, Department of Mathematics, Gainesville, FL 32611, USA

Received: 14 June 2023 Revised: 27 September 2023 Accepted: 06 October 2023 Published: 25 October 2023

Human immunodeficiency virus (HIV) infection is a major public health concern with 1.2 million people living with HIV in the United States. The role of nutrition in general, and albumin/globulin in particular in HIV progression has long been recognized. However, no mathematical models exist to describe the interplay between HIV and albumin/globulin. In this paper, we present a family of models of HIV and the two protein components albumin and globulin. We use albumin, globulin, viral load and target cell data from simian immunodeficiency virus (SIV)-infected monkeys to perform model selection on the family of models. We discover that the simplest model accurately and uniquely describes the data. The selection of the simplest model leads to the observation that albumin and globulin do not impact the infection rate of target cells by the virus and the clearance of the infected target cells by the immune system. Moreover, the recruitment of target cells and immune cells are modeled independently of globulin in the selected model. Mathematical analysis of the selected model reveals that the model has an infection-free equilibrium and a unique infected equilibrium when the immunological reproduction number is above one. The infection-free equilibrium is locally stable when the immunological reproduction number is below one, and unstable when the immunological reproduction number is greater than one. The infection equilibrium is locally stable whenever it exists. To determine the parameters of the best fitted model we perform structural and practical identifiability analysis. The structural identifiability analysis reveals that the model is identifiable when the immune cell infection rate is fixed at a value obtained from the literature. Practical identifiability reveals that only seven of the sixteen parameters are practically identifiable with the given data. Practical identifiability of parameters performed with synthetic data sampled a lot more frequently reveals that only two parameters are practically unidentifiable. We conclude that experiments that will improve the quality of the data can help improve the parameter estimates and lead to better understanding of the interplay of HIV and albumin-globulin metabolism.
- HIV,
- albumin and globulin,
- within-host HIV model,
- model selection,
- AIC,
- structural identifiability,
- practical identifiability,
- Monte Carlo simulations
Citation: Vivek Sreejithkumar, Kia Ghods, Tharusha Bandara, Maia Martcheva, Necibe Tuncer. Modeling the interplay between albumin-globulin metabolism and HIV infection[J]. Mathematical Biosciences and Engineering, 2023, 20(11): 19527-19552. doi: 10.3934/mbe.2023865

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Abstract

Human immunodeficiency virus (HIV) infection is a major public health concern with 1.2 million people living with HIV in the United States. The role of nutrition in general, and albumin/globulin in particular in HIV progression has long been recognized. However, no mathematical models exist to describe the interplay between HIV and albumin/globulin. In this paper, we present a family of models of HIV and the two protein components albumin and globulin. We use albumin, globulin, viral load and target cell data from simian immunodeficiency virus (SIV)-infected monkeys to perform model selection on the family of models. We discover that the simplest model accurately and uniquely describes the data. The selection of the simplest model leads to the observation that albumin and globulin do not impact the infection rate of target cells by the virus and the clearance of the infected target cells by the immune system. Moreover, the recruitment of target cells and immune cells are modeled independently of globulin in the selected model. Mathematical analysis of the selected model reveals that the model has an infection-free equilibrium and a unique infected equilibrium when the immunological reproduction number is above one. The infection-free equilibrium is locally stable when the immunological reproduction number is below one, and unstable when the immunological reproduction number is greater than one. The infection equilibrium is locally stable whenever it exists. To determine the parameters of the best fitted model we perform structural and practical identifiability analysis. The structural identifiability analysis reveals that the model is identifiable when the immune cell infection rate is fixed at a value obtained from the literature. Practical identifiability reveals that only seven of the sixteen parameters are practically identifiable with the given data. Practical identifiability of parameters performed with synthetic data sampled a lot more frequently reveals that only two parameters are practically unidentifiable. We conclude that experiments that will improve the quality of the data can help improve the parameter estimates and lead to better understanding of the interplay of HIV and albumin-globulin metabolism.

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