@article{10.11648/j.sdmath.20260101.14,
  author = {Tewele Assefa Welu and Subrata Kumar Sahu and Ataklti Araya and Habtu Alemayehu and Yohannes Yirga},
  title = {Mathematical Modeling of Tumor-Immune Interaction with Optimal Control in the Human System},
  journal = {Science Discovery Mathematics},
  volume = {1},
  number = {1},
  pages = {34-42},
  doi = {10.11648/j.sdmath.20260101.14},
  url = {https://doi.org/10.11648/j.sdmath.20260101.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sdmath.20260101.14},
  abstract = {This paper presents a mathematical analysis of the immune response to tumor growth, conceptualized through the lens of predator-prey interactions. We investigate a three-dimensional mathematical model that captures the complex dynamics between tumor cells (the prey), hunting immune cells (active predators), and resting immune cells (reservoir population). The model extends classical ecological frameworks to immunology, recognizing that tumors and immune cells engage in a continuous battle for survival within the human body much like species competing in an ecosystem. We first establish the biological validity of our model by proving that all solutions remain positive, exist uniquely, and stay bounded over time essential properties when modeling living systems where negative populations would make no sense. Our analysis reveals that this seemingly simple system harbors surprisingly rich dynamical behavior. Unlike earlier models based on mass-action kinetics, our formulation shows the existence of multiple equilibrium points, representing different disease outcomes: tumor elimination, uncontrolled growth, or chronic persistence. Most notably, we identify conditions under which Hopf bifurcations occur, giving rise to limit cycles periodic oscillations in tumor and immune cell populations that mirror clinical observations of cancer remission and relapse cycles. Recognizing that clinical reality demands more than just understanding these dynamics, we implement a feedback control strategy designed to stabilize tumor populations at clinically manageable levels. Rather than aiming for complete eradication (which may not always be achievable), this approach seeks to transform aggressive growth into a controlled, chronic condition. Numerical simulations demonstrate that our control mechanism can successfully stabilize otherwise unstable equilibria, effectively "taming" the tumor's behavior. Parameter sensitivity analysis reveals that the half-saturation constants play particularly crucial roles in determining system outcomes. These constants, which govern how quickly immune responses saturate with increasing tumor burden, emerge as potential therapeutic targets. The findings suggest that treatments modifying these immunological thresholds might be as important as those directly killing tumor cells a perspective that could inform future immunotherapy strategies. This work bridges ecological modeling, dynamical systems theory, and clinical oncology, offering both theoretical insights into tumor-immune interactions and practical tools for treatment optimization.},
 year = {2026}
}

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TY  - JOUR
T1  - Mathematical Modeling of Tumor-Immune Interaction with Optimal Control in the Human System
AU  - Tewele Assefa Welu
AU  - Subrata Kumar Sahu
AU  - Ataklti Araya
AU  - Habtu Alemayehu
AU  - Yohannes Yirga
Y1  - 2026/03/12
PY  - 2026
N1  - https://doi.org/10.11648/j.sdmath.20260101.14
DO  - 10.11648/j.sdmath.20260101.14
T2  - Science Discovery Mathematics
JF  - Science Discovery Mathematics
JO  - Science Discovery Mathematics
SP  - 34
EP  - 42
PB  - Science Publishing Group
UR  - https://doi.org/10.11648/j.sdmath.20260101.14
AB  - This paper presents a mathematical analysis of the immune response to tumor growth, conceptualized through the lens of predator-prey interactions. We investigate a three-dimensional mathematical model that captures the complex dynamics between tumor cells (the prey), hunting immune cells (active predators), and resting immune cells (reservoir population). The model extends classical ecological frameworks to immunology, recognizing that tumors and immune cells engage in a continuous battle for survival within the human body much like species competing in an ecosystem. We first establish the biological validity of our model by proving that all solutions remain positive, exist uniquely, and stay bounded over time essential properties when modeling living systems where negative populations would make no sense. Our analysis reveals that this seemingly simple system harbors surprisingly rich dynamical behavior. Unlike earlier models based on mass-action kinetics, our formulation shows the existence of multiple equilibrium points, representing different disease outcomes: tumor elimination, uncontrolled growth, or chronic persistence. Most notably, we identify conditions under which Hopf bifurcations occur, giving rise to limit cycles periodic oscillations in tumor and immune cell populations that mirror clinical observations of cancer remission and relapse cycles. Recognizing that clinical reality demands more than just understanding these dynamics, we implement a feedback control strategy designed to stabilize tumor populations at clinically manageable levels. Rather than aiming for complete eradication (which may not always be achievable), this approach seeks to transform aggressive growth into a controlled, chronic condition. Numerical simulations demonstrate that our control mechanism can successfully stabilize otherwise unstable equilibria, effectively "taming" the tumor's behavior. Parameter sensitivity analysis reveals that the half-saturation constants play particularly crucial roles in determining system outcomes. These constants, which govern how quickly immune responses saturate with increasing tumor burden, emerge as potential therapeutic targets. The findings suggest that treatments modifying these immunological thresholds might be as important as those directly killing tumor cells a perspective that could inform future immunotherapy strategies. This work bridges ecological modeling, dynamical systems theory, and clinical oncology, offering both theoretical insights into tumor-immune interactions and practical tools for treatment optimization.
VL  - 1
IS  - 1
ER  - 

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