A FRACTIONAL STOCHASTIC DIFFERENTIAL MODELING OF CANCER CELLS WITH AN APPLICATION TO IMMUNE RESPONSE OF TUMOR DYNAMICS

Articles in Press

Document Type : Research Paper

Authors

1 Department of Mathematics, Faculty of Mathematics, Statistics and Computer Science, Semnan University, Semnan, Iran.

2 Department of Financial Mathematics, Faculty of Finance Sciences, Kharazmi University, Tehran, Iran.

Abstract

In this paper, we present a fractional stochastic model that examines the response of cancer cells to
the immune system. The model combines the long-term memory dependence of fractional derivatives with the stochastic nature of cancer cell growth. The geometric Brownian motion is used to present the stochastic nature of this model. By applying the global derivative from different versions of Caputo, Riemann-Liouville, Caputo-Fabrizio, and Atangana-Baleanu fractional derivatives, and converting them into the fractional integral version, we demonstrate the memory property of the model by maintaining the initial conditions. We also prove the stability of the model analytically in the two states of the ordinary differential equation and the fractional differential equation by obtaining the equilibrium points of the model in the disease-free state and the disease state. Additionally, we use the numerical method based on Lagrange polynomials, and Newton’s polynomials, to examine and compare the approximate solution of the model in two different states of disease-free state and disease state. Finally, using numerical simulation, we examine the stability of the model in the fractional-random state. We show that using Newton’s polynomial will preserve the stability condition better than Lagrange’s polynomial. Further, we analyze that the solutions of the stochastic fractional model are positive and bounded, and we also prove their uniqueness and existence.

Keywords

Main Subjects

Computational Methods for Differential Equations

Articles in Press, Accepted Manuscript
Available Online from 17 July 2025

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History

  • Receive Date: 20 September 2024
  • Revise Date: 27 June 2025
  • Accept Date: 02 July 2025

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