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Assessing the impact of HIV treatment interruptions using stochastic cellular automata

Hillmann, Andreas orcid logoORCID: 0000-0002-6517-1615, Crane, Martin orcid logoORCID: 0000-0001-7598-3126 and Ruskin, Heather J. orcid logoORCID: 0000-0001-7101-2242 (2020) Assessing the impact of HIV treatment interruptions using stochastic cellular automata. Journal of Theoretical Biology, 502 . ISSN 0022-5193

Abstract
Chronic HIV infection causes a progressive decrease in the ability to maintain homeostasis resulting, after some time, in eventual break down of immune functions. Recent clinical research has shed light on a significant contribution of the lymphatic tissues, where HIV causes accumulation of collagen, (fibrosis). Specifically, where tissue is populated by certain types of functional stromal cells designated Fibroblastic Reticular Cells (FRCs), these have been found to play a crucial role in balancing out apoptosis and regeneration of naïve T-cells through 2-way cellular signaling. Tissue fibrosis not only impedes this signaling, effectively reducing T-cell levels through increased apoptosis of cells of both T- and FRC type but has been found to be irreversible by current HIV standard treatment (cART). While the therapy aims to block the viral lifecycle, cART-associated increase of T-cell levels in blood appears to conceal existing FRC impairment through fibrosis. This hidden impairment can lead to adverse consequences if treatment is interrupted, e.g. due to poor adherence (missing doses) or through periods recovering from drug toxicities. Formal clinical studies on treatment interruption have indicated possible adverse effects, but quantification of those effects in relation to interruption protocol and patient predisposition remains unclear. Accordingly, the impact of treatment interruption on lymphatic tissue structure and T-cell levels is explored here by means of computer simulation. A novel Stochastic Cellular Automata model is proposed, which utilizes all sources of clinical detail available to us (though sparse in part) for model parametrization. Sources are explicitly referenced and conflicting evidence from previous studies explored. The main focus is on (i) spatial aspects of collagen build up, together with (ii) collagen increase after repeated treatment interruptions to explore the dynamics of HIV-induced fibrosis and T-cell loss.
Metadata
Item Type:Article (Published)
Refereed:Yes
Additional Information:Article number: 110376
Uncontrolled Keywords:Cellular automata; Modeling; Tissue; Disease; Treatment interruption
Subjects:Medical Sciences > Biomechanics
Medical Sciences > Diseases
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > Scientific Computing and Complex Systems Modelling (Sci-Sym)
Publisher:Elsevier
Official URL:https://dx.doi.org/10.1016/j.jtbi.2020.110376
Copyright Information:© 2020 The Authors. Open Access (CC-BY 4.0)
ID Code:27486
Deposited On:04 Aug 2022 14:20 by Thomas Murtagh . Last Modified 04 Aug 2022 14:20
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