CANCER GROWTH TREATMENT USING IMMUNE LINEAR QUADRATIC REGULATOR BASED ON CROW SEARCH OPTIMIZATION ALGORITHM

Mohammed A. Hussein

mohammediyad95@gmail.com
Mustansiriyah University, College of Engineering, Computer Engineering Department, Baghdad (Iraq)

Ekhlas H. Karam


Mustansiriyah University, College of Engineering, Computer Engineering Department, Baghdad (Iraq)

Rokaia S. Habeeb


Mustansiriyah University, College of Engineering, Computer Engineering Department, Baghdad (Iraq)

Abstract

The rapid and uncontrollable cell division that spreads to surrounding tissues medically termed as malignant neoplasm, cancer is one of the most common diseases worldwide. The need for effective cancer treatment arises due to the increase in the number of cases and the anticipation of higher levels in the coming years. Oncolytic virotherapy is a promising technique that has shown encouraging results in several cases. Mathematical models of virotherapy have been widely developed, and one such model is the interaction between tumor cells and oncolytic virus. In this paper an artificially optimized Immune- Linear Quadratic Regulator (LQR) is introduced to improve the outcome of oncolytic virotherapy. The control strategy has been evaluated in silico on number of subjects. The crow search algorithm is used to tune immune and LQR parameters. The study is conducted on two subjects, S1 and S3, with LQR and Immune-LQR. The experimental results reveal a decrease in the number of tumor cells and remain in the treatment area from day ten onwards, this indicates the robustness of treatment strategies that can achieve tumor reduction regardless of the uncertainty in the biological parameters.


Keywords:

oncolytic virotherapy, feedback mechanism, crow search algorithm, Immune-LQR

Anelone, A.J.N., Villa-Tamayo, M.F., & Rivadeneira, P.S. (2020). Oncolytic virus therapy benefits from control theory. Royal Society Open Science, 7(7), 200473. https://doi.org/10.1098/rsos.200473
DOI: https://doi.org/10.1098/rsos.200473   Google Scholar

Arum, A.K., Handayani, D., & Saragih, R. (2019). Robust control design for virotherapy model using successive method. Journal of Physics: Conference Series, 1245(1), 12054. https://doi.org/10.1088/1742-6596/1245/1/012054
DOI: https://doi.org/10.1088/1742-6596/1245/1/012054   Google Scholar

Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers & Structures, 169, 1–12. https://doi.org/10.1016/j.compstruc.2016.03.001
DOI: https://doi.org/10.1016/j.compstruc.2016.03.001   Google Scholar

Cancer Research UK. (2016). Worldwide cancer statistics. Cancer Research UK. Cancer Research UK (pp. 1–5). https://www.cancerresearchuk.org/health-professional/cancer-statistics/worldwide-cancer
  Google Scholar

Crivelli, J.J., Földes, J., Kim, P.S., & Wares, J.R. (2012). A mathematical model for cell cycle-specific cancer virotherapy. Journal of Biological Dynamics, 6(sup1), 104–120. https://doi.org/10.1080/17513758.2011.613486
DOI: https://doi.org/10.1080/17513758.2011.613486   Google Scholar

Ding, Y., Chen, L., & Hao, K. (2018). Bio-Inspired Collaborative Intelligent Control and Optimization. Springer.
DOI: https://doi.org/10.1007/978-981-10-6689-4   Google Scholar

Jenner, A.L. (2020). Applications of mathematical modelling in oncolytic virotherapy and immunotherapy. Bulletin of the Australian Mathematical Society, 101(3), 522–524. https://doi.org/10.1017/S0004972720000283
DOI: https://doi.org/10.1017/S0004972720000283   Google Scholar

Jenner, A.L., Yun, C.-O., Kim, P.S., & Coster, A.C.F. (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. https://doi.org/10.1007/s11538-018-0424-4
DOI: https://doi.org/10.1007/s11538-018-0424-4   Google Scholar

Kim, P.-H., Sohn, J.-H., Choi, J.-W., Jung, Y., Kim, S.W., Haam, S., & Yun, C.-O. (2011). Active targeting and safety profile of PEG-modified adenovirus conjugated with herceptin. Biomaterials, 32(9), 2314–2326. https://doi.org/10.1016/j.biomaterials.2010.10.031
DOI: https://doi.org/10.1016/j.biomaterials.2010.10.031   Google Scholar

NIH. (2016). Cancer Statistics – National Cancer Institute. NIH. https://www.cancer.gov/about-cancer/understanding/statistics
  Google Scholar

Priya, P., & Reyes, V.M. (2015). A Cancer Biotherapy Resource. ArXiv Preprint ArXiv:1602.08111. https://arxiv.org/abs/1602.08111
  Google Scholar

Purnawan, H., & Purwanto, E.B. (2017). Design of linear quadratic regulator (LQR) control system for flight stability of LSU-05. Journal of Physics: Conference Series, 890(1), 12056.
DOI: https://doi.org/10.1088/1742-6596/890/1/012056   Google Scholar

Rochdi, B. (2014). Design and application of fuzzy immune PID control based on genetic optimization. International Workshop on Advanced Control IWAC (pp. 10–14).
  Google Scholar

Saputra, J., Saragih, R., & Handayani, D. (2019). Robust H∞ controller for bilinear system to minimize HIV concentration in blood plasma. Journal of Physics: Conference Series, 1245(1), 12055.
DOI: https://doi.org/10.1088/1742-6596/1245/1/012055   Google Scholar

Takahashi, K., & Yamada, T. (1998). Application of an immune feedback mechanism to control systems. JSME International Journal Series C Mechanical Systems, Machine Elements and Manufacturing, 41(2), 184–191. https://doi.org/10.1299/jsmec.41.184
DOI: https://doi.org/10.1299/jsmec.41.184   Google Scholar

Yang, X.-S. (2020). Nature-inspired optimization algorithms. Academic Press.
DOI: https://doi.org/10.1016/B978-0-12-821986-7.00018-4   Google Scholar

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Published
2021-06-30

Cited by

Hussein, M. A. ., Karam, E. H., & Habeeb, R. S. (2021). CANCER GROWTH TREATMENT USING IMMUNE LINEAR QUADRATIC REGULATOR BASED ON CROW SEARCH OPTIMIZATION ALGORITHM. Applied Computer Science, 17(2), 56–69. https://doi.org/10.23743/acs-2021-13

Authors

Mohammed A. Hussein 
mohammediyad95@gmail.com
Mustansiriyah University, College of Engineering, Computer Engineering Department, Baghdad Iraq

Authors

Ekhlas H. Karam 

Mustansiriyah University, College of Engineering, Computer Engineering Department, Baghdad Iraq

Authors

Rokaia S. Habeeb 

Mustansiriyah University, College of Engineering, Computer Engineering Department, Baghdad Iraq

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