Dynamical Analysis of Generalized Tumor Model with Caputo Fractional-Order Derivative

Author:

Padder Ausif1ORCID,Almutairi Laila2,Qureshi Sania345ORCID,Soomro Amanullah3,Afroz Afroz6,Hincal Evren4,Tassaddiq Asifa7ORCID

Affiliation:

1. Department of Applied Sciences, University Institute of Engineering and Technology, Guru Nanak University, Hyderabad 501506, India

2. Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia

3. Department of Basic Sciences and Related Studies, Mehran University of Engineering & Technology, Jamshoro 76062, Pakistan

4. Department of Mathematics, Near East University, Mersin 99138, Turkey

5. Department of Computer Science and Mathematics, Lebanese American University, Beirut 1102, Lebanon

6. Department of Mathematics, Maulana Azad National Urdu University, Hyderabad 500032, India

7. Department of Basic Sciences and Humanities, College of Computer and Information Sciences, Majmaah University, Al-Majmaah 11952, Saudi Arabia

Abstract

In this study, we perform a dynamical analysis of a generalized tumor model using the Caputo fractional-order derivative. Tumor growth models are widely used in biomedical research to understand the dynamics of tumor development and to evaluate potential treatments. The Caputo fractional-order derivative is a mathematical tool that is recently being applied to model biological systems, including tumor growth. We present a detailed mathematical analysis of the generalized tumor model with the Caputo fractional-order derivative and examine its dynamical behavior. Our results show that the Caputo fractional-order derivative provides a more accurate description of the tumor growth dynamics compared to classical integer-order derivatives. We also provide a comprehensive stability analysis of the tumor model and show that the fractional-order derivative allows for a more nuanced understanding of the stability of the system. The least-square curve fitting method fits several biological parameters, including the fractional-order parameter α. In conclusion, our study provides new insights into the dynamics of tumor growth and highlights the potential of the Caputo fractional-order derivative as a valuable tool in biomedical research. The results of this study shell have significant implications for the development of more effective treatments for tumor growth and the design of more accurate mathematical models of tumor development.

Publisher

MDPI AG

Subject

Statistics and Probability,Statistical and Nonlinear Physics,Analysis

Reference49 articles.

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