Skip to main content

Advertisement

Log in

Mathematical modelling of HIV-1 transcription inhibition: a comparative study between optimal control and impulsive approach

  • Published:
Computational and Applied Mathematics Aims and scope Submit manuscript

Abstract

Through the utilization of a proactive approach, interaction with human immunodeficiency virus type I (HIV-1) is facilitated, enabling the sequential stages of its fusion mechanism to be navigated successfully. As a result, the efficient infiltration of a target \(CD4^+T\) helper cell within the host organism by the virus is achieved. The onset of the virus’s replication cycle is initiated through this infiltration. As a retrovirus, the orchestration of the conversion of its single-stranded viral RNA genome into a more stable double-stranded DNA structure by HIV-1 is observed. Integration of this newly formed DNA with the host cell’s genetic material occurs. This pivotal transformation of the integrated pro-viral DNA into fully functional messenger RNA (mRNA) is facilitated by the host enzyme RNA polymerase II (Pol II). The central focus of the present ongoing research involves the construction of a meticulous mathematical framework consisting of a system of nonlinear differential equations. The investigation of the impact of a Tat inhibitor on the suppression of the transcriptional activity of HIV-1 is the aim of this inquiry. The perspective of an optimal control problem is assumed for this investigation. Furthermore, the assessment of the efficacy of the Tat inhibitor as a potential therapeutic intervention for HIV-1 infection is undertaken. Integration of a one-dimensional impulsive differential equation model, which determines a mathematically derived maximum concentration of the elongating complex (\(P_2\)), is employed for this assessment. The crucial aspect of this investigation is the consideration of the optimal timing between successive dosages. A comparative analysis is conducted to evaluate the distinct effects of continuous dosing versus impulse dosing of the Tat inhibitor. Numerical analysis is employed to contrast the outcomes of these dosing strategies. The present findings highlight that impulsive dosing demonstrates superior effectiveness compared to continuous dosing in the inhibition of HIV-1 transcription. Ultimately, the model’s parameter sensitivities are visualized through graphical representations. These visualizations serve to enhance the understanding of the underlying physiological and biochemical processes within this intricate system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availibility

The authors confirm that the data supporting the findings of this study are available within the article.

References

  • Arlen PA, Brooks DG, Gao LY, Vatakis D, Brown HJ, Zack JA (2006) Rapid expression of human immunodeficiency virus following activation of latently infected cells. J Virol 80(3):1599–1603

    Article  Google Scholar 

  • Bainov DD, Simeonov P (1995) Impulsive differential equations: asymptotic properties of the solutions (Vol. 28). World Scientific

  • Bainov D, Dishliev A, Hfustova S (1995) Lyapunov’s functions and boundedness of the solutions of impulsive differential equations. Appl Anal 59(1–4):257–269

    Article  MathSciNet  MATH  Google Scholar 

  • Bonnans JF, Hermant A (2009) Revisiting the analysis of optimal control problems with several state constraints. Control Cybern 38(4A):1021–1052

    MathSciNet  MATH  Google Scholar 

  • Briggs GE, Haldane JBS (1925) A note on the kinetics of enzyme action. Biochemistry 19(2):338–339

    Google Scholar 

  • Carlotti F, Dower SK, Qwarnstrom EE (2000) Dynamic shuttling of nuclear factor kappa B between the nucleus and cytoplasm as a consequence of inhibitor dissociation. J Biol Chem 275(52):41028–41034

    Article  Google Scholar 

  • Cecchetti V, Parolin C, Moro S, Pecere T, Filipponi E, Calistri A, Tabarrini O, Gatto B, Palumbo M, Fravolini A, Giorgio P (2000) 6-Aminoquinolones as new potential anti-HIV agents. J Med Chem 43(20):3799–3802

    Article  Google Scholar 

  • Covert D, Kirschner DE (2000) Revisiting early models of the host-pathogen interactions in HIV infection

  • Das K, Arnold E (2013) HIV-1 reverse transcriptase and antiviral drug resistance. Part 1. Curr Opin Virol 3(2):111–118

    Article  Google Scholar 

  • Engelman A, Mizuuchi K, Craigie R (1991) HIV-1 DNA integration: mechanism of viral DNA cleavage and DNA strand transfer. Cell 67(6):1211–1221

    Article  Google Scholar 

  • Fleming WH, Rishel RW (1975) Deterministic and Stochastic Optimal Control Springer Verlag Berlin. DANIEL AKUME, BERND LUDERER AND RALF WUNDERLICH, pp 156–167

  • Friedrich BM, Dziuba N, Li G, Endsley MA, Murray JL, Ferguson MR (2011) Host factors mediating HIV-1 replication. Virus Res 161(2):101–114

    Article  Google Scholar 

  • Gonze D, Kaufman M (2016) Chemical and enzyme kinetics (Doctoral dissertation, Master’s thesis)

  • Grigorieva EV, Khailov EN, Korobeinikov A (2013) An optimal control problem in HIV treatment. Discrete Contin. Dyn. Syst. - S

  • Grigorieva EV, Khailov EN, Bondarenko NV, Korobeinikov A (2014) Modeling and optimal control for antiretroviral therapy. J Biol Syst 22(02):199–217

    Article  MathSciNet  MATH  Google Scholar 

  • Kim H, Yin J (2005) Robust growth of human immunodeficiency virus type 1 (HIV-1). Biophys J 89(4):2210–2221

    Article  Google Scholar 

  • Kirk DE (2004) Optimal control theory: an introduction. Courier Corporation

  • Kirschner D, Lenhart S, Serbin S (1997) Optimal control of the chemotherapy of HIV. J Math Biol 35(7):775–792

    Article  MathSciNet  MATH  Google Scholar 

  • Lakshmikantham V, Simeonov PS (1989) Theory of impulsive differential equations (Vol. 6). World Scientific

  • Lee EB, Markus L (1967) Foundation of optimal control theory. Wiley, New York

    Google Scholar 

  • Lenhart S, Workman JT (2007) Optimal control applied to biological models. CRC Press

    Book  MATH  Google Scholar 

  • Likhoshvai VA, Khlebodarova TM, Bazhan SI, Gainova IA, Chereshnev VA, Bocharov GA (2014) Mathematical model of the Tat-Rev regulation of HIV-1 replication in an activated cell predicts the existence of oscillatory dynamics in the synthesis of viral components. BMC Genom 15(12):1–18

    Article  Google Scholar 

  • Lou J, Smith RJ (2011) Modelling the effects of adherence to the HIV fusion inhibitor enfuvirtide. J Theor Biol 268(1):1–13

    Article  MathSciNet  MATH  Google Scholar 

  • Milev NV, Bainov DD (1990) Stability of linear impulsive differential equations. Int J Syst Sci 21(11):2217–2224

    Article  MathSciNet  MATH  Google Scholar 

  • Mohammadi P, Desfarges S, Bartha I, Joos B, Zangger N, Munoz M, Günthard HF, Beerenwinkel N, Telenti A, Ciuffi A (2013) 24 hours in the life of HIV-1 in a T cell line. PLoS Pathog 9(1):e1003161

    Article  Google Scholar 

  • Mousseau G, Clementz MA, Bakeman WN, Nagarsheth N, Cameron M, Shi J, Baran P, Fromentin R, Chomont N, Valente ST (2012) An analog of the natural steroidal alkaloid cortistatin A potently suppresses Tat-dependent HIV transcription. Cell Host Microbe 12(1):97–108

    Article  Google Scholar 

  • Mousseau G, Mediouni S, Valente ST (2015) Targeting HIV transcription: the quest for a functional cure. The Future of HIV-1 Therapeutics, 121–145

  • Nowak M, May RM (2000) Virus dynamics: mathematical principles of immunology and virology: mathematical principles of immunology and virology. Oxford University Press

    Book  MATH  Google Scholar 

  • Ott M, Geyer M, Zhou Q (2011) The control of HIV transcription: keeping RNA polymerase II on track. Cell Host Microbe 10(5):426–435

    Article  Google Scholar 

  • Perelson AS (2002) Modelling viral and immune system dynamics. Nat Rev Immunol 2(1):28–36

    Article  Google Scholar 

  • Pontryagin LS (1987) Mathematical theory of optimal processes. CRC Press

    Google Scholar 

  • Reddy B, Yin J (1999) Quantitative intracellular kinetics of HIV type 1. AIDS Res Hum Retrovir 15(3):273–283

    Article  Google Scholar 

  • Robert-Guroff M, Popovic M, Gartner S, Markham P, Gallo RC, Reitz MS (1990) Structure and expression of tat-, rev-, and nef-specific transcripts of human immunodeficiency virus type 1 in infected lymphocytes and macrophages. J Virol 64(7):3391–3398

    Article  Google Scholar 

  • Sarafianos SG, Marchand B, Das K, Himmel DM, Parniak MA, Hughes SH, Arnold E (2009) Structure and function of HIV-1 reverse transcriptase: molecular mechanisms of polymerization and inhibition. J Mol Biol 385(3):693–713

    Article  Google Scholar 

  • Segel IH (1975) Enzyme kinetics: behavior and analysis of rapid equilibrium and steady state enzyme systems, vol 115. Wiley, New York

    Google Scholar 

  • Shcherbatova O, Grebennikov D, Sazonov I, Meyerhans A, Bocharov G (2020) Modeling of the HIV-1 life cycle in productively infected cells to predict novel therapeutic targets. Pathogens 9(4):255

    Article  Google Scholar 

  • Slice LW, Codner E, Antelman D, Holly M, Wegrzynski B, Wang J, Toome V, Hsu MC, Nalin CM (1992) Characterization of recombinant HIV-1 Tat and its interaction with TAR RNA. Biochemistry 31(48):12062–12068

    Article  Google Scholar 

  • Smith RJ, Cloutier P, Harrison J, Desforges A (2012) A mathematical model for the eradication of Guinea worm disease. Understanding the dynamics of emerging and re-emerging infectious diseases using mathematical models 37(661)

  • Smith RJ (2006) Adherence to antiretroviral HIV drugs: how many doses can you miss before resistance emerges? Proc R Soc B Biol Sci 273(1586):617–624

    Article  Google Scholar 

  • Wahl LM, Nowak MA (2000) Adherence and drug resistance: predictions for therapy outcome. Proc R Soc B Biol Sci 267(1445):835–843

    Article  Google Scholar 

  • Wan Z, Chen X (2014) Triptolide inhibits human immunodeficiency virus type 1 replication by promoting proteasomal degradation of Tat protein. Retrovirology 11(1):1–13

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the unanimous reviewers for their fruitful comments and suggestions. Srijita Mondal was the recipient of the “Innovation in Science Pursuit for Inspired Research” (INSPIRE) Program Fellowship (Grant no. IF170692), Department of Science and Technology, Government of India.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Srijita Mondal.

Ethics declarations

Conflict of interest

There is no potential conflict of interest as reported by the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mondal, S., Murmu, T., Chakravarty, K. et al. Mathematical modelling of HIV-1 transcription inhibition: a comparative study between optimal control and impulsive approach. Comp. Appl. Math. 42, 340 (2023). https://doi.org/10.1007/s40314-023-02473-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40314-023-02473-w

Keywords

Mathematics Subject Classification

Navigation