Mathematical modeling of tumor-immune dynamics: stability, control, and synchronization via fractional calculus and numerical optimization

Aderyani S. R., Saadati R., Aderyani F. R., Tunç O.

Scientific Reports, cilt.15, sa.1, 2025 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 1
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1038/s41598-025-13683-z
  • Dergi Adı: Scientific Reports
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, Chemical Abstracts Core, MEDLINE, Veterinary Science Database, Directory of Open Access Journals
  • Anahtar Kelimeler: Adams-Bashforth method, Biological synchronization, Cancer dynamics, Discrete-time systems, Fractal-fractional derivatives, Fractional calculus, Immunotherapy optimization, Mathematical oncology, Numerical simulation, Optimal control theory, Stability analysis, Tumor-immune modeling
  • Van Yüzüncü Yıl Üniversitesi Adresli: Evet

Özet

This research introduces two distinct mathematical models to investigate the interactions between the tumor-immune system, both formulated within a random (stochastic) framework. The first model employs fractal-fractional derivatives, specifically the Atangana-Baleanu operator, to analyze tumor-immune dynamics from both qualitative and quantitative perspectives. We establish the well-posedness of this model by demonstrating the existence and uniqueness of solutions through fixed point theorems and examine stability via nonlinear analysis. Numerical simulations are performed using Lagrangian-piecewise interpolation across various fractional and fractal parameters, providing visual insights into the complex interplay between immune cells and cancer cells under different conditions. The second model consists of coupled nonlinear difference equations based on the Caputo fractional operator. Its solutions’ existence is guaranteed through classical fixed point theorems, and further properties such as stability, controllability, and synchronization are thoroughly explored to deepen understanding of the system’s behavior. Both models are thoroughly analyzed within a stochastic setting, which considers randomness inherent in biological systems, offering a more realistic depiction of tumor-immune interactions. Numerical simulations for specific scenarios reveal the dynamic characteristics and practical implications of the models, enhancing our insights into tumor-immune processes from a probabilistic perspective.