Stem Cell Based Fractional-Order Dynamical Model of Psoriasis: A Mathematical Study

Subhankar Kushary, Tushar Ghosh, Oluwole Daniel Makinde, Xue-Zhi Li, Priti Kumar Roy

Abstract


Psoriasis is a chronic  autoimmune skin  disorder driven by  dysregulated immune responses,  where  abnormal interactions between  T  cells  and  dendritic cells  lead to  excessive   inflammatory  cytokine  production.    This triggers the hyper-proliferation of  epidermal keratinocytes while  depleting mesenchymal stem  cells  (MSCs), which play  a crucial role in immune modulation. The progression behavior of psoriasis is not only  influenced by their present  state but also by  the historical evolution of underlying  cellular interactions. Memory stages  and complex interplay  among   immune  components at  different   temporal   scales  significantly  modulate disease expression. Motivated by this, we proposed a mathematical model  of psoriasis to a fractional-order framework in  order  to  incorporate  memory-dependent  effects  and  non-local  characteristics.   This   article  deals  with   a four-dimensional  model  of  psoriasis involving  concentrations of  T  cells,  dendritic cells,  keratinocytes, and mesenchymal stem  cells  (MSCs) in  order  to predict  the  temporal   evolution in  the  considered cell  densities during the  disease  dissemination process.     Using  Caputo, Caputo-Fabrizio, and  Atangana-Baleanu-Caputo operators,   we  analyze  how   memory  influences disease   dynamics.    In-depth  mathematical analysis  of  the solution of  the  fractionalized  model   has  been  thoroughly  investigated.   The  stability of  the  model   is  also examined using generalized Ulam–Hyers stability criteria.  The considered population densities  are numerically evaluated using  various  fractional orders  with   considered  fractional  operators  to  capture  non-local effects. Optimal control  is  implemented on  the  fractionalized system  using the  Forward-Backward Sweep  Method (FBSM), emphasizing the impacts  of two biologics, namely TNF-α inhibitors and IL-23 blockers,  via  considered operators.  Numerical simulations are performed in support of the theoretical analyses, accompanied by detailed discussions from  both mathematical and  biological viewpoints.  Results based on optimal control  effectiveness analysis indicate  that a combined control  strategy,  particularly under  the Caputo-Fabrizio operator,  optimally reduces  keratinocyte density.  Which offers  deeper  insights into  disease  progression and  effective  therapeutic approaches.

Keywords


Psoriasis; Fractional order differential equations; Optimal control; Forward-Backward sweep method

Full Text:

PDF

References


P. Łakuta et al., “How does stigma affect people with psoriasis?” Advances in Dermatology and Allergology/Poste˛py Dermatologii i Alergologii, vol. 34, no. 1, pp. 36–41, 2017. DOI:10.5114/pdia.2016.62286

A. Menter et al., “Guidelines of care for the management of psoriasis and psoriatic arthritis: Section 1. overview of psoriasis and guidelines of care for the treatment of psoriasis with biologics,” Journal of the American Academy of Dermatology, vol. 58, no. 5, pp. 826–850, 2008. DOI:10.1016/j.jaad.2008.02.039

C. Albanesi et al., “The interplay between keratinocytes and immune cells in the pathogenesis of psoriasis,” Frontiers in Immunology, vol. 9, p. 1549, 2018. DOI:10.3389/fimmu.2018.01549

P. Blanco et al., “Dendritic cells and cytokines in human inflammatory and autoimmune diseases,” Cytokine & growth factor reviews, vol. 19, no. 1, pp. 41–52, 2008. DOI:10.1016/j.cytogfr.2007.10.004

Y. Deng, C. Chang, and Q. Lu, “The inflammatory response in psoriasis: a comprehensive review,” Clinical reviews in allergy & immunology, vol. 50, no. 3, pp. 377–389, 2016. DOI:10.1007/s12016-016-8535-x

Y. Zheng et al., “Interleukin-22, a th17 cytokine, mediates il-23-induced dermal inflammation and acanthosis,” Nature, vol. 445, no. 7128, pp. 648–651, 2007. DOI:10.1038/nature05505

A. Datta and P. K. Roy, “T-cell proliferation on immunopathogenic mechanism of psoriasis: a control based theoretical approach,” Control and Cybernetics, vol. 42, no. 2, pp. 365–386, 2013.

I. Ullah, R. B. Subbarao, and G. J. Rho, “Human mesenchymal stem cells-current trends and future prospective,” Bioscience reports, vol. 35, no. 2, p. e00191, 2015. DOI:10.1042/BSR20150025

L. Cheng et al., “Human umbilical cord mesenchymal stem cells for psoriasis: a phase 1/2a, single-arm study,” Signal Transduction and Targeted Therapy, vol. 7, no. 1, p. 263, 2022. DOI:10.1038/s41392-022-01059-y

N. J. Savill, R. Weller, and J. A. Sherratt, “Mathematical modelling of nitric oxide regulation of rete peg formation in psoriasis,” Journal of theoretical biology, vol. 214, no. 1, pp. 1–16, 2002. DOI:10.1006/jtbi.2001.2400

H. B. Oza et al., “Modelling and finite-time stability analysis of psoriasis pathogenesis,” International Journal of Control, vol. 90, no. 8, pp. 1664–1677, 2017. DOI:10.1080/00207179.2016.1217566

H. Zhang et al., “Modelling epidermis homoeostasis and psoriasis pathogenesis,” Journal of The Royal Society Interface, vol. 12, no. 103, p. 20141071, 2015. DOI:10.1098/rsif.2014.1071

A. K. Roy, M. Nelson, and P. K. Roy, “A control-based mathematical study on psoriasis dynamics with special emphasis on il- 21 and ifn- γ interaction network,” Mathematical Methods in the Applied Sciences, vol. 44, no. 17, pp. 13403–13420, 2021. DOI:10.1002/mma.7635

S. Kushary, P. K. Roy, and X. Cao, “Introducing msc therapy to inhibit Th1 and TH17 mediated cytokines: A mathematical study to regulate psoriasis,” in International Conference on Mathematical Analysis and Application in Modeling, pp. 87–101, 2023. DOI:10.1007/978-981-97-9194-1_7

A. A. Nelson et al., “Cost-effectiveness of biologic treatments for psoriasis based on subjective and objective efficacy measures assessed over a 12-week treatment period,” Journal of the American Academy of Dermatology, vol. 58, no. 1, pp. 125–135, 2008. DOI:10.1016/j.jaad.2007.09.018

M. Lebwohl et al., “Combination therapy to treat moderate to severe psoriasis,” Journal of the American Academy of Dermatology, vol. 50, no. 3, pp. 416–430, 2004. DOI:10.1016/j.jaad.2002.12.002

A. K. Roy, F. Al Basir, and P. K. Roy, “A vivid cytokines interaction model on psoriasis with the effect of impulse biologic (TNF-α inhibitor) therapy,” Journal of Theoretical Biology, vol. 474, pp. 63–77, 2019. DOI:10.1016/j.jtbi.2019.04.007

A. K. Roy et al., “A model analysis to measure the adherence of etanercept and fezakinumab therapy for the treatment of psoriasis,” Nonlinear Analysis: Modelling and Control, vol. 27, no. 3, pp. 1–21, 2022. DOI:10.15388/namc.2022.27.26483

R. Agarwal and C. Midha, “Study and mathematical analysis of the novel fractional bone mineralization model.” Journal of Computational Analysis & Applications, vol. 33, no. 1, 2024.

R. Agarwal, P. Airan, and C. Midha, “Mathematical analysis of the non-linear dynamics of bone mineralization,” in Mathematical Methods in Medical and Biological Sciences, pp. 207–225, 2025. DOI:10.1016/B978-0-44-328814-2.00017-5

R. R. Musafir et al., “Comparison of fractional-order monkeypox model with singular and non-singular kernels,” Jambura Journal of Biomathematics (JJBM), vol. 5, no. 1, pp. 1–9, 2024. DOI:10.37905/jjbm.v5i1.24920

K. Das et al., “A qualitative analysis of leukemia fractional order sicw model,” Jambura Journal of Biomathematics (JJBM), vol. 5, no. 1, pp. 46–53, 2024. DOI:10.37905/jjbm.v5i1.24961

A. Pandey and S. Ghosh, “Numerical study of childhood disease model with lyapunov stability analysis,” Indian Journal of Physics, vol. 99, pp. 3393–3408, 2025. DOI:10.1007/s12648-024-03537-1

S. Kushary et al., “A mathematical insight to control the disease psoriasis using mesenchymal stem cell transplantation with a biologic inhibitor,” Scientific Reports, vol. 14, no. 1, p. 21897, 2024. DOI:10.1038/s41598-024-71251-3

M. Caputo, Elasticit‘a e dissipazione. Zanichelli, 1965.

M. Caputo and M. Fabrizio, “A new definition of fractional derivative without singular kernel,” Progress in Fractional Differentiation & Applications, vol. 1, no. 2, pp. 73–85, 2015.

A. Atangana and D. Baleanu, “New fractional derivatives with nonlocal and non-singular kernel: theory and application to heat transfer model,” arXiv preprint arXiv:1602.03408, 2016.

I. Ameen, D. Baleanu, and H. M. Ali, “An efficient algorithm for solving the fractional optimal control of sirv epidemic model with a combination of vaccination and treatment,” Chaos, Solitons & Fractals, vol. 137, p. 109892, 2020. DOI:10.1016/j.chaos.2020.109892

M. A. Krasnosel’skii, “Two remarks on the method of successive approximations,” Uspekhi matematicheskikh nauk, vol. 10, no. 1, pp. 123–127, 1955.

A. Atangana and S. Qureshi, “Modeling attractors of chaotic dynamical systems with fractal–fractional operators,” Chaos, solitons & fractals, vol. 123, pp. 320–337, 2019. DOI:10.1016/j.chaos.2019.04.020

B. Ghosh, “Fractional order modeling of ecological and epidemiological systems: ambiguities and challenges,” The Journal of Analysis, vol. 33, pp. 341–366, 2025. DOI:10.1007/s41478-024-00836-y

D. Roy and B. Ghosh, “Dimensionally homogeneous fractional order rosenzweig–macarthur model: a new perspective of paradox of enrichment and harvesting,” Nonlinear Dynamics, vol. 112, no. 20, pp. 18137–18161, 2024. DOI:10.1007/s11071-024-09959-0

M. Nagumo, “Über die lage der integralkurven gewöhnlicher differentialgleichungen,” Proceedings of the Physico-Mathematical Society of Japan. 3rd Series, vol. 24, pp. 551–559, 1942.

S. Ulam, Problems in modern mathematics. New York: Dover Publications, Inc., 2004.

A. Kilbas, H. Srivastava, and J. Trujillo, Theory and applications of fractional differential equations. New York: Elsevier, 2006.

W. H. Fleming and R. W. Rishel, Deterministic and stochastic optimal control. New York: Springer, 2012. DOI:10.1007/978-1-4612-6380-7

H. Schättler and U. Ledzewicz, Optimal control for mathematical models of cancer therapies. New York: Springer, 2015. DOI:10.1007/978-1-4939-2972-6

I. L. Correa-Escudero et al., “Correcting dimensional mismatch in fractional models with power, exponential and proportional kernel: Application to electrical systems,” Results in Physics, vol. 40, p. 105867, 2022. DOI:10.1016/j.rinp.2022.105867




DOI: https://doi.org/10.37905/jjbm.v6i3.33134

Copyright (c) 2025 Subhankar Kushary, Tushar Ghosh, Oluwole Daniel Makinde, Xue-Zhi Li, Priti Kumar Roy

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


Jambura Journal of Biomathematics (JJBM) has been indexed by:


EDITORIAL OFFICE OF JAMBURA JOURNAL OF BIOMATHEMATICS

 Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Negeri Gorontalo
Jl. Prof. Dr. Ing. B. J. Habibie, Moutong, Tilongkabila, Kabupaten Bone Bolango 96554, Gorontalo, Indonesia
 Email: [email protected]
 Jambura Journal of Biomathematics (JJBM) by Department of Mathematics Universitas Negeri Gorontalo is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Powered by Public Knowledge Project OJS.