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
  • [1] Mathematical modeling of tumor-immune system interactions: the effect of rituximab on breast cancer immune response
    Bitsouni, Vasiliki
    Tsilidis, Vasilis
    JOURNAL OF THEORETICAL BIOLOGY, 2022, 539
  • [2] Mathematical modeling of radiotherapy and its impact on tumor interactions with the immune system
    Bekker, Rebecca Anne
    Kim, Sungjune
    Pilon-Thomas, Shari
    Enderling, Heiko
    NEOPLASIA, 2022, 28
  • [3] Interactions Between the Immune System and Cancer: A Brief Review of Non-spatial Mathematical Models
    Eftimie, Raluca
    Bramson, Jonathan L.
    Earn, David J. D.
    BULLETIN OF MATHEMATICAL BIOLOGY, 2011, 73 (01) : 2 - 32
  • [4] Interactions Between the Immune System and Cancer: A Brief Review of Non-spatial Mathematical Models
    Raluca Eftimie
    Jonathan L. Bramson
    David J. D. Earn
    Bulletin of Mathematical Biology, 2011, 73 : 2 - 32
  • [5] Mathematical modeling of cancer–immune system, considering the role of antibodies
    Sumana Ghosh
    Sandip Banerjee
    Theory in Biosciences, 2018, 137 : 67 - 78
  • [6] Mathematical modeling of cancer-immune system, considering the role of antibodies
    Ghosh, Sumana
    Banerjee, Sandip
    THEORY IN BIOSCIENCES, 2018, 137 (01) : 67 - 78
  • [7] Leveraging Marine Predators Algorithm with Deep Learning for Lung and Colon Cancer Diagnosis
    Mengash, Hanan Abdullah
    Alamgeer, Mohammad
    Maashi, Mashael
    Othman, Mahmoud
    Hamza, Manar Ahmed
    Ibrahim, Sara Saadeldeen
    Zamani, Abu Sarwar
    Yaseen, Ishfaq
    CANCERS, 2023, 15 (05)
  • [8] The Colloquy between Microbiota and the Immune System in Colon Cancer: Repercussions on the Cancer Therapy
    Pal, Soumya
    Saini, Adesh K. K.
    Kaushal, Ankur
    Gupta, Shagun
    Gaur, Naseem A. A.
    Chhillar, Anil K. K.
    Sharma, Anil K. K.
    Gupta, Vijai K. K.
    Saini, Reena V. V.
    CURRENT PHARMACEUTICAL DESIGN, 2022, 28 (43) : 3478 - 3485
  • [9] Mathematical Modeling the Time-Delay Interactions between Tumor Viruses and the Immune System with the Effects of Chemotherapy and Autoimmune Diseases
    Pham, Hoang
    MATHEMATICS, 2022, 10 (05)
  • [10] Mathematical modeling of tumor-immune cell interactions
    Mahlbacher, Grace E.
    Reihmer, Kara C.
    Frieboes, Hermann B.
    JOURNAL OF THEORETICAL BIOLOGY, 2019, 469 : 47 - 60