Mathematical modeling of interactions between colon cancer and immune system with a deep learning algorithm

被引:8
|
作者
Raeisi, Elham [1 ,6 ]
Yavuz, Mehmet [2 ,3 ]
Khosravifarsani, Mohammadreza [4 ]
Fadaei, Yasin [5 ]
机构
[1] Shahrekord Univ Med Sci, Basic Hlth Sci Inst, Clin Biochem Res Ctr, Shahrekord, Iran
[2] Necmettin Erbakan Univ, Fac Sci, Dept Math Comp Sci, TR-42090 Konya, Turkiye
[3] Univ Exeter, Fac Environm Sci & Econ, Ctr Environm Math, Cornwall TR10 9FE, England
[4] Shahrekord Univ Med Sci, Canc Res Ctr, Shahrekord, Iran
[5] Shahrekord Univ Med Sci, Modeling Hlth Res Ctr, Shahrekord, Iran
[6] Shahrekord Univ Med Sci, Sch Allied Med Sci, Dept Med Phys & Radiol Technol, Shahrekord, Iran
来源
EUROPEAN PHYSICAL JOURNAL PLUS | 2024年 / 139卷 / 04期
关键词
DENDRITIC CELLS; T-LYMPHOCYTES; TRANSMISSION; NETWORKS; GROWTH;
D O I
10.1140/epjp/s13360-024-05111-4
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Colon cancer is a complex disease with genetically unstable cell lines. In order to better understand the complexity of colon cancer cells and their metastatic mechanisms, we develop a mathematical model in this study. The model is based on a system of fractional-order differential equations and Fractional-Cancer-Informed Neural Networks (FCINN). The model captures a dynamic network of interactions between dendritic cells (DCs), cytotoxic T-cells (CD 8 + \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$8<^>+$$\end{document} ), helper T-cells (CD 4 + \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$4<^>+$$\end{document} ), and colon cancer cells through fractional differential equations. By varying the fractional order between 0 and 1, we can classify patients into different groups based on their immune patterns. The goal of this paper is to identify different immune patterns and cancer cell behaviors, as well as the parameters that play an important role in metastasis, control, or elimination of cancer cells in the model. However, several parameters in the model are difficult to estimate in a patient-specific manner. To address this challenge, we use FCINN as an effective deep-learning tool for parameter estimation and numerical simulation of the model. Our findings suggest that the most effective factors in controlling the progression and preventing metastasis of colon cancer are the initial number of cancer cells, the inhibiting rates of tumor cells by DCs, the source of DCs, and the activation of helper T-cells by DCs. These findings suggest that DCs can be used as an immunotherapy tool for the control and treatment of colon cancer.
引用
收藏
页数:16
相关论文
共 50 条
  • [31] Colon cancer diagnosis by means of explainable deep learning
    Di Giammarco, Marcello
    Martinelli, Fabio
    Santone, Antonella
    Cesarelli, Mario
    Mercaldo, Francesco
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [32] An Efficient Deep Learning Approach for Colon Cancer Detection
    Sakr, Ahmed S.
    Soliman, Naglaa F.
    Al-Gaashani, Mehdhar S.
    Plawiak, Pawel
    Ateya, Abdelhamied A.
    Hammad, Mohamed
    APPLIED SCIENCES-BASEL, 2022, 12 (17):
  • [33] Defining colon cancer biomarkers by using deep learning
    Specogna, Adrian V.
    Sinicrope, Frank A.
    LANCET, 2020, 395 (10221): : 314 - 316
  • [34] Deep learning for colon cancer histopathological images analysis
    Ben Hamida, A.
    Devanne, M.
    Weber, J.
    Truntzer, C.
    Derangere, V
    Ghiringhelli, F.
    Forestier, G.
    Wemmert, C.
    COMPUTERS IN BIOLOGY AND MEDICINE, 2021, 136
  • [35] A review of deep learning algorithms for modeling drug interactions
    Iqbal, Aga Basit
    Shah, Idris Afzal
    Injila
    Assad, Assif
    Ahmed, Mushtaq
    Shah, Syed Zubair
    MULTIMEDIA SYSTEMS, 2024, 30 (03)
  • [36] A continuous-time Markov chain modeling cancer-immune system interactions
    Burini, Diletta
    De Angelis, Elena
    Lachowicz, Miroslaw
    COMMUNICATIONS IN APPLIED AND INDUSTRIAL MATHEMATICS, 2018, 9 (02) : 106 - 118
  • [37] Interactions between the sympathetic nervous system and the immune system
    Schorr, EC
    Arnason, BGW
    BRAIN BEHAVIOR AND IMMUNITY, 1999, 13 (04) : 271 - 278
  • [38] MATHEMATICAL MODELING OF IN-VIVO TUMOR-IMMUNE INTERACTIONS FOR THE CANCER IMMUNOTHERAPY USING MATURED DENDRITIC CELLS
    Arabameri, Abazar
    Asemani, Davud
    Hajati, Jamshid
    JOURNAL OF BIOLOGICAL SYSTEMS, 2018, 26 (01) : 167 - 188
  • [39] Editorial: Mathematical Modeling of the Immune System in Homeostasis, Infection and Disease
    Bocharov, Gennady
    Volpert, Vitaly
    Ludewig, Burkhard
    Meyerhans, Andreas
    FRONTIERS IN IMMUNOLOGY, 2020, 10
  • [40] Mathematical modeling of the immune system recognition to mammary carcinoma antigen
    Carlo Bianca
    Ferdinando Chiacchio
    Francesco Pappalardo
    Marzio Pennisi
    BMC Bioinformatics, 13