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May 17, 2026Systems Science & Control Engineering0 citationsOpen Access

Mathematical simulation and network pharmacology approach to predict and analyze non-small cell lung cancer (NSCLC) behavior using fractional derivatives

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FSFaiqa SulemanUniversity of SargodhaASAamir ShahzadBaba Guru Nanak UniversityTNTasadduq NiazUniversity of Sargodha

Key Points

  • The aim is to develop a mathematical model using fractional derivatives to analyze NSCLC progression.
  • Introduced fractional-order dynamics via Caputo’s definition.
  • Ensured solution properties using fixed-point theorem.
  • Performed sensitivity analyses and numerical simulations using Adams-Bashforth method.
  • Demonstrated robustness through Routh-Hurwitz technique and Lyapunov-based function.
  • Achieved improved prediction accuracy via numerical simulations.
  • Showed enhanced insights into the effects of fractional order on NSCLC behavior.

Abstract

Non-small cell lung cancer(NSCLC) progresses through distinct clinical stages, from localized growth to metastatic spread. This study introduces a mathematical model that incorporates fractional-order dynamics using Caputo’s definition.Fractional models capture the memory effects present in biological processes.The existence, uniqueness, positivity and boundedness of solutions for fractional model are ensured by using fixed-point theorem. The reproduction number Formula: see text was determined using the next-generation method, and outcomes of the sensitivity analysis for Formula: see text were derived by various parameters. The local and global robustness are demonstrated by Routh-Hurwitz technique and the Lyapunov-based function. The robustness of the fractional model was examined using Ulam-Hyers (UH) stability and generalized UH stability criteria. The Adams-Bashforth predictor-corrector method demonstrates well compared to RK4 approach. Using Adams-Bashforth predictor-corrector approach, we performed numerical simulations to investigate the impact of the fractional order (x). Graphical representations show the impact of various modeling and results show a clear improvement in prediction accuracy.

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Cite This Study

Suleman et al. (2026) studied this question.

synapsesocial.com/papers/6a095a427880e6d24efe05a2https://doi.org/10.1080/21642583.2026.2671516
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