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<p>Oncolytic virotherapy (OV) is a promising cancer treatment that uses viruses to selectively eliminate cancer cells. However, studies have reported conflicting findings regarding the role of natural killer (NK) cells in this therapy. This study proposes a discrete fractional-order model to assess NK cells’ role in OV and identify conditions for their activation. The existence theory of the proposed model is explored using fixed-point theorems. Using the next-generation matrix method, the basic reproduction number (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="MediaObjects/11071_2025_12071_IEq1_HTML.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="120" Type="Linedraw" Width="25" /> </InlineMediaObject> </InlineEquation>) is computed, followed by a sensitivity analysis to determine the most influential model parameters. The stability of equilibrium points is also analyzed. Numerical simulations demonstrate how NK cell activation influences the interplay among cancer cells, infected cancer cells, and oncolytic viruses, highlighting the critical balance between viral bursting and NK cell response for effective virotherapy. Furthermore, an artificial neural network (ANN) is trained on numerically generated data to efficiently approximate the system dynamics. The ANN utilizes the Levenberg-Marquardt optimization method, with the dataset randomly divided into training, validation, and testing subsets. Mean squared error (MSE) is used as the performance metric. The ANN model accurately replicates the behavior of the discrete fractional-order system, offering a fast and reliable surrogate for real-time prediction and analysis in complex biological settings.</p>

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Modeling the Optimal Balance between Natural Killer Cells and Oncolytic Viruses: A Discrete Fractional-Order and Artificial Neural Network Approach

  • 期刊论文

  • 发表日期：

    2026年2月10日

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  • Zeeshan Ali,
  • Faranak Rabiei,
  • Ravi P Agarwal

摘要

Oncolytic virotherapy (OV) is a promising cancer treatment that uses viruses to selectively eliminate cancer cells. However, studies have reported conflicting findings regarding the role of natural killer (NK) cells in this therapy. This study proposes a discrete fractional-order model to assess NK cells’ role in OV and identify conditions for their activation. The existence theory of the proposed model is explored using fixed-point theorems. Using the next-generation matrix method, the basic reproduction number ( ) is computed, followed by a sensitivity analysis to determine the most influential model parameters. The stability of equilibrium points is also analyzed. Numerical simulations demonstrate how NK cell activation influences the interplay among cancer cells, infected cancer cells, and oncolytic viruses, highlighting the critical balance between viral bursting and NK cell response for effective virotherapy. Furthermore, an artificial neural network (ANN) is trained on numerically generated data to efficiently approximate the system dynamics. The ANN utilizes the Levenberg-Marquardt optimization method, with the dataset randomly divided into training, validation, and testing subsets. Mean squared error (MSE) is used as the performance metric. The ANN model accurately replicates the behavior of the discrete fractional-order system, offering a fast and reliable surrogate for real-time prediction and analysis in complex biological settings.