Towards a Multiscale Model of Acute HIV Infection

Bouchnita, Anass; Bocharov, Gennady; Meyerhans, Andreas; Volpert, Vitaly

doi:10.3390/computation5010006

Cited by 17 publications

(8 citation statements)

References 61 publications

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“…The formulated model and its reduced versions can be used as building blocks for multiscale hybrid models of HIV-1 infection [48,49]. The current version of our model has the limitation that is does not consider the discrete nature and stochasticity of the biochemistry of the infection.…”

Section: Discussionmentioning

confidence: 99%

Modeling of the HIV-1 Life Cycle in Productively Infected Cells to Predict Novel Therapeutic Targets

et al. 2020

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There are many studies that model the within-host population dynamics of Human Immunodeficiency Virus Type 1 (HIV-1) infection. However, the within-infected-cell replication of HIV-1 remains to be not comprehensively addressed. There exist rather few quantitative models describing the regulation of the HIV-1 life cycle at the intracellular level. In treatment of HIV-1 infection, there remain issues related to side-effects and drug-resistance that require further search “...for new and better drugs, ideally targeting multiple independent steps in the HIV-1 replication cycle” (as highlighted recently by Tedbury & Freed, The Future of HIV-1 Therapeutics, 2015). High-resolution mathematical models of HIV-1 growth in infected cells provide an additional analytical tool in identifying novel drug targets. We formulate a high-dimensional model describing the biochemical reactions underlying the replication of HIV-1 in target cells. The model considers a nonlinear regulation of the transcription of HIV-1 mediated by Tat and the Rev-dependent transport of fully spliced and singly spliced transcripts from the nucleus to the cytoplasm. The model is calibrated using available information on the kinetics of various stages of HIV-1 replication. The sensitivity analysis of the model is performed to rank the biochemical processes of HIV-1 replication with respect to their impact on the net production of virions by one actively infected cell. The ranking of the sensitivity factors provides a quantitative basis for identifying novel targets for antiviral therapy. Our analysis suggests that HIV-1 assembly depending on Gag and Tat-Rev regulation of transcription and mRNA distribution present two most critical stages in HIV-1 replication that can be targeted to effectively control virus production. These processes are not covered by current antiretroviral treatments.

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Section: Discussionmentioning

confidence: 99%

Modeling of the HIV-1 Life Cycle in Productively Infected Cells to Predict Novel Therapeutic Targets

et al. 2020

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“…The dynamics of infectious diseases in humans is subject to many random effects resulting from stochastic fluctuations in biochemical and cellular processes underlying the infection of target cells, within-thecell replication and effect of the immune responses. The potential of the stochastic models has been appreciated in recent studies based on single scale models formulated with SDEs [21], discrete stochastic models [24,27], genetic algorithms [4] as well as multi-scale hybrid models [6,7,23,30].…”

Section: Resultsmentioning

confidence: 99%

Modelling Stochastic and Deterministic Behaviours in Virus Infection Dynamics

Sazonov

Grebennikov

Kelbert

et al. 2017

Math. Model. Nat. Phenom.

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“…Here, we formulate a discrete-continuous multiscale model to describe the interaction between cancer cells and immune cells in the lymph node. We have previously used hybrid models to study various physiological systems such as erythropoiesis (Eymard et al, 2015; Bouchnita et al, 2016b), multiple myeloma (Bouchnita et al, 2016a, 2017a), the immune response (Bouchnita et al, 2017b), and HIV infection (Bouchnita et al, 2017c). The hybrid model is based on some hypotheses.…”

Section: Mathematical Modeling Of the Tumor-immune Interaction In mentioning

confidence: 99%

“…We adapt the previously developed model of adaptive immune response (Bouchnita et al, 2017b,c) to the specific case of tumor growth in the lymph node. The model includes different types of immune cells such as APCs, naive and mature T lymphocytes.…”

Section: Mathematical Modeling Of the Tumor-immune Interaction In mentioning

confidence: 99%

Mathematical Modeling Reveals That the Administration of EGF Can Promote the Elimination of Lymph Node Metastases by PD-1/PD-L1 Blockade

Benchaib

Bouchnita

Volpert

et al. 2019

Front. Bioeng. Biotechnol.

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In the advanced stages of cancers like melanoma, some of the malignant cells leave the primary tumor and infiltrate the neighboring lymph nodes (LNs). The interaction between secondary cancer and the immune response in the lymph node represents a complex process that needs to be fully understood in order to develop more effective immunotherapeutic strategies. In this process, antigen-presenting cells (APCs) approach the tumor and initiate the adaptive immune response for the corresponding antigen. They stimulate the naive CD4 + and CD8 + T lymphocytes which subsequently generate a population of helper and effector cells. On one hand, immune cells can eliminate tumor cells using cell-cell contact and by secreting apoptosis inducing cytokines. They are also able to induce their dormancy. On the other hand, the tumor cells are able to escape the immune surveillance using their immunosuppressive abilities. To study the interplay between tumor progression and the immune response, we develop two new models describing the interaction between cancer and immune cells in the lymph node. The first model consists of partial differential equations (PDEs) describing the populations of the different types of cells. The second one is a hybrid discrete-continuous model integrating the mechanical and biochemical mechanisms that define the tumor-immune interplay in the lymph node. We use the continuous model to determine the conditions of the regimes of tumor-immune interaction in the lymph node. While we use the hybrid model to elucidate the mechanisms that contribute to the development of each regime at the cellular and tissue levels. We study the dynamics of tumor growth in the absence of immune cells. Then, we consider the immune response and we quantify the effects of immunosuppression and local EGF concentration on the fate of the tumor. Numerical simulations of the two models show the existence of three possible outcomes of the tumor-immune interactions in the lymph node that coincide with the main phases of the immunoediting process: tumor elimination, equilibrium, and tumor evasion. Both models predict that the administration of EGF can promote the elimination of the secondary tumor by PD-1/PD-L1 blockade.

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Towards a Multiscale Model of Acute HIV Infection

Cited by 17 publications

References 61 publications

Modeling of the HIV-1 Life Cycle in Productively Infected Cells to Predict Novel Therapeutic Targets

Modeling of the HIV-1 Life Cycle in Productively Infected Cells to Predict Novel Therapeutic Targets

Modelling Stochastic and Deterministic Behaviours in Virus Infection Dynamics

Mathematical Modeling Reveals That the Administration of EGF Can Promote the Elimination of Lymph Node Metastases by PD-1/PD-L1 Blockade

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