Volume 15, Issue 2 (Journal of Control, V.15, N.2 Summer 2021)                   JoC 2021, 15(2): 159-175 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

kalhor E, noori A, Tavakol Afshari J, Saboori Rad S. Modeling for the body defense mechanisms in stage I melanoma patient and sensitivity analysis by using Partial Rank Correlation Coefficient (PRCC) method. JoC. 2021; 15 (2) :159-175
URL: http://joc.kntu.ac.ir/article-1-729-en.html
1- Faculty of Electrical and Biomedical Engineering, Sadjad University of Technology
2- 3Department of Immunology, Faculty of Medicine,Mashhad University of Medical Sciences
3- Department of Dermatology, School of Medicine, Mashhad University of Medical Sciences
Abstract:   (7301 Views)
The scientific modeling of cellular growth and proliferation can significantly help to physicians to offer an appropriate treatment. In this paper the modeling of the body's immune system function for a patient with melanoma cancer has been studied. The main advantage of this model is considering more variables to describe dynamics of the melanoma patient’s body, which will make our model more realistic. For estimating the coefficients of the mathematical model, a multi-objective optimization method, "Non-Dominated Sorting Genetic Algorithm", is used. One of the major advantages of such method is considering multiple objectives and simultaneously constraining them. Simulation results reveal that our mathematical model can successfully simulate the function of the body defense system of the melanoma patient. In order to analyze the sensitivity and determine the correlation of the mathematical model output to changing in some parameters, Partial Rank Correlation Coefficient (PRCC) method is employed. Finally, it is demonstrated that Interleukin-2 (IL-2) generation rate change has the highest PRCC value and changing this parameter will have a high impact on the model outputs. 
Full-Text [PDF 991 kb]   (133 Downloads)    
Type of Article: Research paper | Subject: Special
Received: 2020/01/24 | Accepted: 2020/07/10 | ePublished ahead of print: 2020/08/24 | Published: 2021/07/11

References
1. [1] Suryapraba, M., Rajanarayanee, G. and Kumari, P. (2015). Analysis of Skin Cancer Classification Using GLCM Based On Feature Extraction in Artificial Neural Network, International Journal of Emerging Technology in Computer Science & Electronics.
2. [2] Siavash, M. (2015). Modeling the Effect of Chemotherapy on Melanoma B16F10 in Mice Using Cellular Automata and Genetic Algorithm in Tapered Dosage of FBS and Cisplatin. Frontiers in Biomedical Technologies 2.2, 103-108.
3. [3] Pennisi, M. (2012). A mathematical model of immune-system-melanoma competition. Computational and mathematical methods in medicine. [DOI:10.1155/2012/850754]
4. [4] Eikenberry, S., Craig, T. and Yang, K. (2009). Tumor-immune interaction, surgical treatment, and cancer recurrence in a mathematical model of melanoma. PLoSComputBiol. [DOI:10.1371/journal.pcbi.1000362]
5. [5] Kogan, Y., Zvia, A. and Moran, E. (2013). A mathematical model for the immunotherapeutic control of the Th1/Th2 imbalance in melanoma. Discrete and Continuous Dynamical Systems Series B. [DOI:10.3934/dcdsb.2013.18.1017]
6. [6] Sun, X., Bao, J. and Shao, Y. (2016). Mathematical modeling of therapy-induced cancer drug resistance: connecting cancer mechanisms to population survival rates. Scientific reports, 6, 22498. [DOI:10.1038/srep22498]
7. [7] Castiglione, F. and Piccoli, B. (2006). Optimal Control in a Model of Dendritic Cell Transfection Cancer Immunotherapy. Bulletin of Mathematical Biology, Vol.68, 255-274. [DOI:10.1007/s11538-005-9014-3]
8. [8] ] Castiglione, F. and Piccoli, B. (2007). Cancer Immunotherapy, Mathematical Modeling and Optimal Control. Journal of Theoretical Biology, Vol.247, 723-732. [DOI:10.1016/j.jtbi.2007.04.003]
9. [9] DePillisZ, L. G. and Radunskaya, A. (2013). A model of dendritic cell therapy for melanoma. Frontiers in oncology 3. [DOI:10.3389/fonc.2013.00056]
10. [10] Aherne, N. J., Dhawan, A., Scott, J. G., & Enderling, H. (2020). Mathematical oncology and it's application in non melanoma skin cancer-A primer for radiation oncology professionals. Oral Oncology, 103, 104473. [DOI:10.1016/j.oraloncology.2019.104473]
11. [11] Nikolov, S., & Nenov, M. (2019). Modelling vaccine quantity in mathematical models of melanoma treatment. Series on Biomechanics.
12. [12] Diabate, M., Coquille, L., & Samson, A. (2018). Parameter estimation and treatment optimization in a stochastic model for immunotherapy of cancer. arXiv preprint arXiv:1806.01915.
13. [13] Quinonez, J. A. (2011). A MATHEMATICAL INVESTIGATION OF THE INNATE AND ADAPTIVE IMMUNE SURVEILLANCE OF TUMOR GROWTH. A Senior Honors Thesis Submitted to the Faculty of the University of Utah.
14. [14] Dawkins, B. A. (2016). Mathematical models of the adaptive immune response in a novel cancer immunotherapy. University of Central Oklahoma.
15. [15] Qomlaqi, M., Bahrami, F., Ajami, M. and Hajati, J. (2017). An extended mathematical model of tumor growth and its interaction with the immune system, to be used for developing an optimized immunotherapy treatment protocol. Mathematical biosciences, 292, 1-9. [DOI:10.1016/j.mbs.2017.07.006]
16. [16] Makhlouf, A. M., El-Shennawy, L., & Elkaranshawy, H. A. (2020). Mathematical Modelling for the Role of CD4+ T Cells in Tumor-Immune Interactions. Computational and Mathematical Methods in Medicine, 2020. [DOI:10.1155/2020/7187602]
17. [17] Jenner, A. L., Yun, C. O., Kim, P. S., & Coster, A. C. (2018). Mathematical modelling of the interaction between cancer cells and an oncolytic virus: insights into the effects of treatment protocols. Bulletin of mathematical biology, 80(6), 1615-1629. [DOI:10.1007/s11538-018-0424-4]
18. [18] Mao, Y., Yin, S., Zhang, J., Hu, Y., Huang, B., Cui, L. and He, W. (2016). A new effect of IL-4 on human γδ T cells: promoting regulatory Vδ1 T cells via IL-10 production and inhibiting function of Vδ2 T cells. Cellular & molecular immunology, 13(2), 217-228. [DOI:10.1038/cmi.2015.07]
19. [19] Wuest, S. C., Edwan, J. H., Martin, J. F., Han, S., Perry, J. S., Cartagena, C. M. and Bielekova, B. (2011). A role for interleukin-2 trans-presentation in dendritic cell-mediated T cell activation in humans, as revealed by daclizumab therapy. Nature medicine, 17(5), 604. [DOI:10.1038/nm.2365]
20. [20] Carmenate, T., Ortíz, Y., Enamorado, M., García-Martínez, K., Avellanet, J., Moreno, E. and León, K. (2018). Blocking IL-2 Signal In Vivo with an IL-2 Antagonist Reduces Tumor Growth through the Control of Regulatory T Cells. The Journal of Immunology, ji1700433. [DOI:10.4049/jimmunol.1700433]
21. [21] Mariani, L., Schulz, E. G., Lexberg, M. H., Helmstetter, C., Radbruch, A., Löhning, M. and Höfer, T. (2010). Short‐term memory in gene induction reveals the regulatory principle behind stochastic IL‐4 expression. Molecular systems biology, 6(1), 359. [DOI:10.1038/msb.2010.13]
22. [22] Joly, M. and Odloak, D. (2013). Modeling interleukin-2-based immunotherapy in AIDS pathogenesis. Journal of theoretical biology, 335, 57-78. [DOI:10.1016/j.jtbi.2013.06.019]
23. [23] Carter, P., Smith, L. and Ryan, M. (2004). Identification and validation of cell surface antigens for antibody targeting in oncology. Endocrine-related cancer, 11(4), 659-687. [DOI:10.1677/erc.1.00766]
24. [24] García-Martínez, K. and León, K. (2010). Modeling the role of IL-2 in the interplay between CD4+ helper and regulatory T cells: assessing general dynamical properties. Journal of theoretical biology, 262(4), 720-732. [DOI:10.1016/j.jtbi.2009.10.025]
25. [25] Challita-Eid, P. M., Penichet, M. L., Shin, S. U., Poles T., Mosammaparast, N., Mahmood, K. and Rosenblatt, J. D. (2018).A B7. 1-antibody fusion protein retains antibody specificity and ability to activate via the T cell costimulatory pathway. The Journal of Immunology, 160(7), 3419-3426.
26. [26] Lindgren, H., Axcrona, K., and Leanderson, T. (2001). Regulation of transcriptional activity of the murine CD40 ligand promoter in response to signals through TCR and the costimulatory molecules CD28 and CD2. The Journal of Immunology, 166(7), 4578-4585. [DOI:10.4049/jimmunol.166.7.4578]
27. [27] Kim, Y., Lee, S., Kim, Y. S., Lawler, S., Gho, Y. S., Kim, Y. K., and Hwang, H. J. (2013).Regulation of Th1/Th2 cells in asthma development: a mathematical model. Mathematical Biosciences & Engineering, 10(4), 1095-1133. [DOI:10.3934/mbe.2013.10.1095]
28. [28] Andre, N., Barbolosi, D., Billy, F., Chapuisat, G., Hubert, F., Grenier, E., and Rovini, A. (2013). Mathematical model of cancer growth controled by metronomic chemotherapies. In ESAIM: Proceedings,Vol. 41, pp. 77-94. [DOI:10.1051/proc/201341004]
29. [29] Bocharov, G., Ford, N. J., and Ludewig, B. (2005). A mathematical approach for optimizing dendritic cell-based immunotherapy. In Adoptive Immunotherapy: Methods and Protocols. 19-33.
30. [30] de Pillis, L. G., Fister, K. R., Gu, W., Head, T., Head, K. x, Neal, T. and Kozai, K. (2008). Optimal control of mixed immunotherapy and chemotherapy of tumors. Journal of Biological systems, 16(01), 51-80. [DOI:10.1142/S0218339008002435]
31. [31] Pappalardo, F., Forero, I. M., Pennisi, M., Palazon, A., Melero, I. and Motta, S. (2011). SimB16: modeling induced immune system response against B16-melanoma. PloS one, 6(10), e26523. [DOI:10.1371/journal.pone.0026523]
32. [32] de Pillis, L. G., Radunskaya, A. E. and Wiseman, C. L. (2005). A validated mathematical model of cell-mediated immune response to tumor growth. Cancer research, 65(17), 7950-7958. [DOI:10.1158/0008-5472.CAN-05-0564]
33. [33] Karev, G. P., Novozhilov, A. S. and Koonin, E. V. (2006). Mathematical modeling of tumor therapy with oncolytic viruses: effects of parametric heterogeneity on cell dynamics. Biology direct, 1(1). [DOI:10.1186/1745-6150-1-30]
34. [34] Wei, H. C. (2018). A mathematical model of tumour growth with Beddington-DeAngelis functional response: a case of cancer without disease. Journal of biological dynamics, 12(1), 194-210. [DOI:10.1080/17513758.2017.1418028]
35. [35] Fasano, A., Bertuzzi, A. and Gandolfi, A. (2006). Mathematical modelling of tumour growth and treatment. In Complex systems in biomedicine, 71-108. [DOI:10.1007/88-470-0396-2_3]
36. [36] Lorz, A., Botesteanu, D. A. and Levy, D. (2017). Modeling cancer cell growth dynamics in vitro in response to antimitotic drug treatment. Frontiers in Oncology, 7, 189. [DOI:10.3389/fonc.2017.00189]
37. [37] Ghaffari, A. and Nasserifar, N. (2009). Mathematical modeling and lyapunov-based drug administration in cancer chemotherapy. Iranian Journal of Electrical and Electronic Engineering, 5(3), 151-158.
38. [38] de Pillis, L. G., Gu, W. and Radunskaya, A. E. (2006). Mixed immunotherapy and chemotherapy of tumors: modeling, applications and biological interpretations. Journal of theoretical biology, 238(4), 841-862. [DOI:10.1016/j.jtbi.2005.06.037]
39. [39] d'Onofrio, A. (2005). A general framework for modeling tumor-immune system competition and immunotherapy: Mathematical analysis and biomedical inferences. Physica D: Nonlinear Phenomena, 208(3-4), 220-235. [DOI:10.1016/j.physd.2005.06.032]
40. [40] Jackson, R. C., Di Veroli, G. Y., Koh, S. B., Goldlust, I., Richards, F. M. and Jodrell, D. I. (2017). Modelling of the cancer cell cycle as a tool for rational drug development: A systems pharmacology approach to cyclotherapy. PLoS computational biology, 13(5), e1005529. [DOI:10.1371/journal.pcbi.1005529]
41. [41] Wilson, S. and Levy, D. (2012). A mathematical model of the enhancement of tumor vaccine efficacy by immunotherapy. Bulletin of mathematical biology, 74(7), 1485-1500. [DOI:10.1007/s11538-012-9722-4]
42. [42] Kuznetsov, V. A. and Knott, G. D. (2001). Modeling tumor regrowth and immunotherapy. Mathematical and Computer Modelling, 33(12-13), 1275-1288. [DOI:10.1016/S0895-7177(00)00314-9]
43. [43] Gustavsson, P. and Syberfeldt, A. (2018), A new algorithm using the non-dominated tree to improve non-dominated sorting. Evolutionary computation, 26(1), 89-116. [DOI:10.1162/evco_a_00204]
44. [44] Marino, S., Hogue, I. B., Ray C. J. and Kirschner, D. E. (2008). A methodology for performing global uncertainty and sensitivity analysis in systems biology. Journal of theoretical biology, 254(1), 178-196. [DOI:10.1016/j.jtbi.2008.04.011]
45. [45] Hajiabadi, R. and Zarghami, M. (2014). Multi-objective reservoir operation with sediment flushing; case study of Sefidrud reservoir. Water resources management, 28(15), 5357-5376. [DOI:10.1007/s11269-014-0806-9]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2022 CC BY-NC 4.0 | Journal of Control

Designed & Developed by : Yektaweb