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ORIGINAL ARTICLE
Year : 2022  |  Volume : 12  |  Issue : 3  |  Page : 202-218

Predicting efficacy of 5-fluorouracil therapy via a mathematical model with fuzzy uncertain parameters


1 Department of Biomedical Engineering, Research Center for Biomedical Technologies and Robotics, School of Medicine; Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
2 Departments of Biomedical Engineering, Research Center for Biomedical Technologies and Robotics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
3 Department of Medical Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
4 Department of Biomedical Engineering, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
5 Department of Medical Biotechnology, School of Paramedical Sciences; Cellular and Molecular Research Center, Research Institute for Prevention of Noncommunicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran

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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jmss.jmss_92_21

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Background: Due to imprecise/missing data used for parameterization of ordinary differential equations (ODEs), model parameters are uncertain. Uncertainty of parameters has hindered the application of ODEs that require accurate parameters. Methods: We extended an available ODE model of tumor-immune system interactions via fuzzy logic to illustrate the fuzzification procedure of an ODE model. The fuzzy ODE (FODE) model assigns a fuzzy number to the parameters, to capture parametric uncertainty. We used the FODE model to predict tumor and immune cell dynamics and to assess the efficacy of 5-fluorouracil (5-FU) chemotherapy. Result: FODE model investigates how parametric uncertainty affects the uncertainty band of cell dynamics in the presence and absence of 5-FU treatment. In silico experiments revealed that the frequent 5-FU injection created a beneficial tumor microenvironment that exerted detrimental effects on tumor cells by enhancing the infiltration of CD8+ T cells, and natural killer cells, and decreasing that of myeloid-derived suppressor cells. The global sensitivity analysis was proved model robustness against random perturbation to parameters. Conclusion: ODE models with fuzzy uncertain kinetic parameters cope with insufficient/imprecise experimental data in the field of mathematical oncology and can predict cell dynamics uncertainty band.


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