Stability and Hopf bifurcation analysis of immune response delayed HIV type 1 infection model with two target cells

Balasubramaniam, P.; Mani, Prakash; Tamilalagan, P.

doi:10.1002/mma.3306

Cited by 4 publications

(2 citation statements)

References 22 publications

(33 reference statements)

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“…To understand how models are currently used to represent the progression of this disease, we considered 40 recent models of within-host dynamics, all of which are expressed as nonlinear systems of differential equations. Of these models, one used stochastic differential equations Wang, Liu, Xu & Zhang (2015) and eight used delayed differential equations (Huang et al (2016) , Alshorman et al (2016), Pitchaimani & Monica (2015), Sahani (2016), Balasubramaniam et al (2015), Elaiw & Almuallem (2016)). In addition, the focus of several of these papers was somewhat different from ours.…”

Section: Of 32mentioning

confidence: 99%

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A mathematical model of HIV dynamics treated with a population of gene-edited haematopoietic progenitor cells exhibiting threshold phenomenon

Ratti

Nanda

Eszterhas

et al. 2019

Mathematical Medicine and Biology: A Journal of the IMA

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The use of gene-editing technology has the potential to excise the CCR5 gene from haematopoietic progenitor cells, rendering their differentiated CD4-positive (CD4+) T cell descendants HIV resistant. In this manuscript, we describe the development of a mathematical model to mimic the therapeutic potential of gene editing of haematopoietic progenitor cells to produce a class of HIV-resistant CD4+ T cells. We define the requirements for the permanent suppression of viral infection using gene editing as a novel therapeutic approach. We develop non-linear ordinary differential equation models to replicate HIV production in an infected host, incorporating the most appropriate aspects found in the many existing clinical models of HIV infection, and extend this model to include compartments representing HIV-resistant immune cells. Through an analysis of model equilibria and stability and computation of $R_0$ for both treated and untreated infections, we show that the proposed therapy has the potential to suppress HIV infection indefinitely and return CD4+ T cell counts to normal levels. A computational study for this treatment shows the potential for a successful ‘functional cure’ of HIV. A sensitivity analysis illustrates the consistency of numerical results with theoretical results and highlights the parameters requiring better biological justification. Simulations of varying level production of HIV-resistant CD4+ T cells and varying immune enhancements as the result of these indicate a clear threshold response of the model and a range of treatment parameters resulting in a return to normal CD4+ T cell counts.

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Section: Of 32mentioning

confidence: 99%

“…One includes free virus but not infected T cells (Joly et al (2016)). Two include infected T cells but not free virus (Balasubramaniam et al (2015), Rana et al (2015)). The rest include both.…”

Section: Of 32mentioning

confidence: 99%