1
|
Soares Dos Santos MP, Bernardo RMC, Vidal J, Moreira A, Torres DFM, Herdeiro CAR, Santos HA, Gonçalves G. Next-generation chemotherapy treatments based on black hole algorithms: From cancer remission to chronic disease management. Comput Biol Med 2024; 180:108961. [PMID: 39106673 DOI: 10.1016/j.compbiomed.2024.108961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 07/10/2024] [Accepted: 07/26/2024] [Indexed: 08/09/2024]
Abstract
PROBLEM Therapeutic planning strategies have been developed to enhance the effectiveness of cancer drugs. Nevertheless, their performance is highly limited by the inefficient biological representativeness of predictive tumor growth models, which hinders their translation to clinical practice. OBJECTIVE This study proposes a disruptive approach to oncology based on nature-inspired control using realistic Black Hole physical laws, in which tumor masses are trapped to experience attraction dynamics on their path to complete remission or to become a chronic disease. This control method is designed to operate independently of individual patient idiosyncrasies, including high tumor heterogeneities and highly uncertain tumor dynamics, making it a promising avenue for advancing beyond the limitations of the traditional survival probabilistic paradigm. DESIGN Here, we provide a multifaceted study of chemotherapy therapeutic planning that includes: (1) the design of a pioneering controller algorithm based on physical laws found in the Black Holes; (2) investigation of the ability of this controller algorithm to ensure stable equilibrium treatments; and (3) simulation tests concerning tumor volume dynamics using drugs with significantly different pharmacokinetics (Cyclophosphamide and Atezolizumab), tumor volumes (200 mm3 and 12 732 mm3) and modeling characterizations (Gompertzian and Logistic tumor growth models). RESULTS Our results highlight the ability of this new astrophysical-inspired control algorithm to perform effective chemotherapy treatments for multiple tumor-treatment scenarios, including tumor resistance to chemotherapy, clinical scenarios modelled by time-dependent parameters, and highly uncertain tumor dynamics. CONCLUSIONS Our findings provide strong evidence that cancer therapy inspired by phenomena found in black holes can emerge as a disruptive paradigm. This opens new high-impacting research directions, exploring synergies between astrophysical-inspired control algorithms and Artificial Intelligence applied to advanced personalized cancer therapeutics.
Collapse
Affiliation(s)
- Marco P Soares Dos Santos
- Center for Mechanical Technology & Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal; Intelligent Systems Associate Laboratory (LASI), Guimarães, Portugal.
| | - Rodrigo M C Bernardo
- Center for Mechanical Technology & Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal
| | - JoãoV Vidal
- Department of Physics and Aveiro Institute of Materials (CICECO), University of Aveiro, Aveiro, Portugal; Department of Physics and Institute for Nanostructures, Nanomodelling and Nanofabrication (I3N), University of Aveiro, Aveiro, Portugal
| | - Ana Moreira
- Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, 3810-193, Aveiro, Portugal
| | - Delfim F M Torres
- Center for Research and Development in Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, Aveiro, Portugal
| | - Carlos A R Herdeiro
- Center for Research and Development in Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, Aveiro, Portugal
| | - Hélder A Santos
- Department of Biomaterials and Biomedical Technology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands; Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
| | - Gil Gonçalves
- Center for Mechanical Technology & Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal; Intelligent Systems Associate Laboratory (LASI), Guimarães, Portugal
| |
Collapse
|
2
|
Rsetam K, Al-Rawi M, Cao Z, Alsadoon A, Wang L. Model based smooth super-twisting control of cancer chemotherapy treatment. Comput Biol Med 2024; 169:107957. [PMID: 38190767 DOI: 10.1016/j.compbiomed.2024.107957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 12/11/2023] [Accepted: 01/01/2024] [Indexed: 01/10/2024]
Abstract
Chemotherapy is one of the most efficient methods for treating cancer patients. Chemotherapy aims to eliminate cancer cells as thoroughly as possible. Delivering medications to patients' bodies through various methods, either oral or intravenous is part of the chemotherapy process. Different cell-kill hypotheses take into account the interactions of the expansion of the tumor volume, external drugs, and the rate of their eradication. For the control of drug usage and tumor volume, a model based smooth super-twisting control (MBSSTC) is proposed in this paper. Firstly, three nonlinear cell-kill mathematical models are considered in this work, including the log-kill, Norton-Simon, and Emax hypotheses subject to parametric uncertainties and exogenous perturbations. In accordance with clinical recommendations, the tumor volume follows a predefined trajectory after chemotherapy. Secondly, the MBSSTC is applied for the three cell-kill models to attain accurate trajectory tracking even in the presence of uncertainties and disturbances. Compared to conventional super-twisting control (STC), the non-smooth term is introduced in the proposed control to enhance the anti-disturbance capability. Finally, simulation comparisons are performed across the proposed MBSSTC, conventional STC, and proportional-integral (PI) control methods to show the effectiveness and merits of our designed control method.
Collapse
Affiliation(s)
- Kamal Rsetam
- Al Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq; School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, 3122, Australia.
| | - Mohammad Al-Rawi
- Faculty of Engineering, Chemical and Materials Engineering, The University of Auckland, Auckland, New Zealand; Centre for Engineering and Industrial Design, Waikato Institute of Technology, Hamilton, New Zealand.
| | - Zhenwei Cao
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, 3122, Australia.
| | - Abeer Alsadoon
- School of Computing Mathematics and Engineering, Charles Sturt University (CSU), Wagga Wagga, Australia; Asia Pacific International College (APIC), Sydney, Australia.
| | - Lulu Wang
- Biomedical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China.
| |
Collapse
|
3
|
Pang N. Nonlinear neural networks adaptive control for a class of fractional-order tuberculosis model. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:10464-10478. [PMID: 37322943 DOI: 10.3934/mbe.2023461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The problem of nonlinear adaptive control for a class of fractional-order tuberculosis (TB) model is studied in this paper. By analyzing the transmission mechanism of TB and the characteristics of fractional calculus, a fractional-order TB dynamical model is established with media coverage and treatment as control variables. With the help of universal approximation principle of radial basis function neural networks and the positive invariant set of established TB model, the expressions of control variables are designed and the stability of error model is analyzed. Thus, the adaptive control method can guarantee that the number of susceptible and infected individuals can be kept close to the corresponding control targets. Finally, the designed control variables are illustrated by numerical examples. The results indicate that the proposed adaptive controllers can effectively control the established TB model and ensure the stability of controlled model, and two control measures can protect more people from tuberculosis infection.
Collapse
Affiliation(s)
- Na Pang
- Department of Information and Computing Sciences, Xinhua College of Ningxia University, Yinchuan 750021, China
| |
Collapse
|
4
|
Khalili P, Vatankhah R. Optimal control design for drug delivery of immunotherapy in chemoimmunotherapy treatment. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107248. [PMID: 36463673 DOI: 10.1016/j.cmpb.2022.107248] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 11/04/2022] [Accepted: 11/15/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE There are various approaches to control a mathematical dynamic of cancer, each of which is suitable for a special goal. Optimal control is considered as an applicable method to calculate the minimum necessary drug delivery in such systems. METHODS In this paper, a mathematical dynamic of cancer is proposed considering tumor cells, natural killer cells, CD8+T cells, circulating lymphocytes, IL-2 cytokine and Regulatory T cells as the system states, and chemotherapy, IL-2 and activated CD8+T cells injection rate as the control signals. After verifying the proposed mathematical model, the importance of the drug delivery timing and the effect of cancer cells initial condition are discussed. Afterwards, an optimal control is designed by defining a proper cost function with the goal of minimizing the number of tumor cells, and two immunotherapy drug amounts during treatment CONCLUSIONS: Results show that inappropriate injection of immunotherapy time schedule and the number of initial conditions of cancer cells might result in chemoimmunotherapy failure and auxiliary treatment must be prescribed to decrease tumor size before any treatment takes place. The obtained optimal control signals show that with lower amount of drug delivery and a suitable drug injection time schedule, tumor cells can be eliminated while a fixed immunotherapy time schedule protocol fails with larger amount of drug injection. This conclusion can be utilized with the aim of personalizing drug delivery and designing more accurate clinical trials based on the improved model simulations in order to save cost and time.
Collapse
Affiliation(s)
- Pariya Khalili
- PhD Candidate, School of Mechanical Engineering, Shiraz University, Shiraz, Iran
| | - Ramin Vatankhah
- Associated Professor, School of Mechanical Engineering, Shiraz University, Shiraz, Iran.
| |
Collapse
|
5
|
Javadi A, Keighobadi F, Nekoukar V, Ebrahimi M. Finite-Set Model Predictive Control of Melanoma Cancer Treatment Using Signaling Pathway Inhibitor of Cancer Stem Cell. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1504-1511. [PMID: 31514151 DOI: 10.1109/tcbb.2019.2940658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Drug delivery is one of the most important issues in the treatment of cancer and surviving the patient. Recently, with a combination of mathematical models of the tumor growth and control theory, optimal drug delivery can be planned, individually. The goal is reducing the tumor volume with minimum side effects on the patient. One of the most important challenges of the modeling is considering the drug resistance, which may lead to failure of the treatment. In this paper, a mathematical model is proposed for describing the growth dynamics of the melanoma tumor cells. It is assumed that the melanoma cancer is treated with Notch signaling pathway inhibitors of the cancer stem cells. The model parameters are identified based on experimental data obtained from 13 male nude mice with an induced melanoma cancer involved in a dual antiplatelet therapy (DAPT) program. The mathematical model is used to determine if DAPT can reduce the growth rate of the tumor. Then an optimal drug delivery plan for the treatment of every animal model is presented, individually using finite-set model predictive control method. The results show that the proposed model can estimate the drug's effect on the treatment of melanoma cancer.
Collapse
|
6
|
Rajaei A, Raeiszadeh M, Azimi V, Sharifi M. State estimation-based control of COVID-19 epidemic before and after vaccine development. JOURNAL OF PROCESS CONTROL 2021; 102:1-14. [PMID: 33867698 PMCID: PMC8041156 DOI: 10.1016/j.jprocont.2021.03.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 02/06/2021] [Accepted: 03/30/2021] [Indexed: 05/09/2023]
Abstract
In this study, a nonlinear robust control policy is designed together with a state observer in order to manage the novel coronavirus disease (COVID-19) outbreak having an uncertain epidemiological model with unmeasurable variables. This nonlinear model for the COVID-19 epidemic includes eight state variables (susceptible, exposed, infected, quarantined, hospitalized, recovered, deceased, and insusceptible populations). Two plausible scenarios are put forward in this article to control this epidemic before and after its vaccine invention. In the first scenario, the social distancing and hospitalization rates are employed as two applicable control inputs to diminish the exposed and infected groups. However, in the second scenario after the vaccine development, the vaccination rate is taken into account as the third control input to reduce the susceptible populations, in addition to the two objectives of the first scenario. The proposed feedback control measures are defined in terms of the hospitalized and deceased populations due to the available statistical data, while other unmeasurable compartmental variables are estimated by an extended Kalman filter (EKF). In other words, the susceptible, exposed, infected, quarantined, recovered, and insusceptible individuals cannot be identified precisely because of the asymptomatic infection of COVID-19 in some cases, its incubation period, and the lack of an adequate community screening. Utilizing the Lyapunov theorem, the stability and bounded tracking convergence of the closed-loop epidemiological system are investigated in the presence of modeling uncertainties. Finally, a comprehensive simulation study is conducted based on Canada's reported cases for two defined timing plans (with different treatment rates). Obtained results demonstrate that the developed EKF-based control scheme can achieve desired epidemic goals (exponential decrease of infected, exposed, and susceptible people).
Collapse
Affiliation(s)
- Arman Rajaei
- Department of Mechanical Engineering, School of Engineering, Shiraz University, Shiraz, Iran
| | - Mahsa Raeiszadeh
- Department of Computer Science & Engineering and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Vahid Azimi
- Department of Energy Resources Engineering, Stanford University, Stanford, CA, USA
| | - Mojtaba Sharifi
- Department of Medicine and Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| |
Collapse
|
7
|
Adaptive fuzzy controller design of drug dosage using optimal trajectories in a chemoimmunotherapy cancer treatment model. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
|
8
|
Heiran F, Khodaei-Mehr J, Vatankhah R, Sharifi M. Nonlinear adaptive control of immune response of renal transplant recipients in the presence of uncertainties. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102163] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
9
|
A new approach in cancer treatment regimen using adaptive fuzzy back-stepping sliding mode control and tumor-immunity fractional order model. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.09.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
10
|
Khorashadizadeh S, Akbarzadeh Kalat A. Adaptive back-stepping cancer control using Legendre polynomials. IET Syst Biol 2020; 14:8-15. [PMID: 31931476 DOI: 10.1049/iet-syb.2019.0038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Here, a model-free controller for cancer treatment is presented. The treatment objective is to find a proper drug dosage that can reduce the population of tumour cells. Recently, some solutions have been proposed according to the control theory. In these approaches, based on the mathematical description of the number of effector cells, tumour cells, and concentration of the interleukin-2 (IL-2), a non-linear controller is designed. Here, based on the back-stepping design procedure and function approximation property of Legendre polynomials, a novel controller for MIMO cancer immunotherapy is presented. In fact, Legendre polynomials play the role of uncertainty estimation and compensation. In comparison with other uncertainty estimators such as neural networks, Legendre polynomials have simpler structure. Thus, the contribution of this study is simplifying the design procedure and reducing the controller computational load in comparison with Neuro-Fuzzy controllers. The resulting closed-loop system is capable of overcoming various uncertainties. Simulation results verify the efficiency of the proposed method in the fast reduction of tumour cells. Moreover, a comparison between the performance of Legendre polynomials and a radial basis functions neural network (RBFN) is presented.
Collapse
Affiliation(s)
- Saeed Khorashadizadeh
- Faculty of Electrical and Computer Engineering, University of Birjand, 97175/615 Birjand, Iran.
| | - Ali Akbarzadeh Kalat
- Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, 361995161 Shahrood, Iran
| |
Collapse
|
11
|
Nematollahi MH, Vatankhah R, Sharifi M. Nonlinear adaptive control of tuberculosis with consideration of the risk of endogenous reactivation and exogenous reinfection. J Theor Biol 2019; 486:110081. [PMID: 31730772 DOI: 10.1016/j.jtbi.2019.110081] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 07/18/2019] [Accepted: 11/11/2019] [Indexed: 11/16/2022]
Abstract
Tuberculosis is one of deadly diseases in many countries that attacks to the human body and causes damage to the lung, causing bloody coughing and if left untreated, it will kill half of the affected people. Tuberculosis bacteria can stay latent and reactivate after passing appropriate conditions. For this reason, control of this disease and treatment of infected people has a significant importance, and observing health issues can prevent the spread of it. In this paper, a nonlinear adaptive control method is proposed for the first time in order to control and treat tuberculosis outbreak subjected to the modeling uncertainty. To design a control system being robust against uncertainties, an adaptation law is defined to update values of estimated parameters and ensures the whole system stability. The treatment achievement and stability of the closed-loop system is proved by the Lyapunov theorem and confirmed by some simulations. The proposed strategy has the capability to control the tuberculosis outbreak by reducing the numbers of active infectious and persistent latent individuals based on their desired values in the society.
Collapse
Affiliation(s)
| | - Ramin Vatankhah
- School of Mechanical Engineering, Shiraz University, Shiraz, Iran.
| | - Mojtaba Sharifi
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| |
Collapse
|
12
|
Mehra AHA, Zamani I, Abbasi Z, Ibeas A. Observer-based adaptive PI sliding mode control of developed uncertain SEIAR influenza epidemic model considering dynamic population. J Theor Biol 2019; 482:109984. [PMID: 31449819 DOI: 10.1016/j.jtbi.2019.08.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Revised: 08/16/2019] [Accepted: 08/21/2019] [Indexed: 12/13/2022]
Abstract
This paper presents a new Susceptible, Exposed, Infected, Asymptomatic, and Recovered individuals (SEIAR) model for influenza considering a dynamic population. In the given model, the possibility of transmission of asymptomatic individuals (infectious with no visible symptoms) to infected individuals (infectious exhibiting symptoms) is considered. The basic reproduction number and the equilibrium points of the new model are given while the stability of the equilibrium points is analyzed by using the Jacobian matrix. Then a multi-controller scheme consisting of a parallel controller defined by two control inputs (vaccination and antiviral treatment) is given where both of them are based on Proportional-Integral (PI) and sliding mode controllers, which are parameterized adaptively to guarantee the convergence of trajectories to the sliding surface with minimum amount of chattering. The proposed control scheme is able to asymptotically stabilize the SEIAR model in the sense of eradication of the infected and susceptible individuals. Moreover, a (reduced-order) observer is designed to estimate the actual state variables that are used in the implementation of the control signals. By using MATLAB® software, a comprehensive simulation and evaluation of treatment and performance are carried out to support the presented theoretical results.
Collapse
Affiliation(s)
| | - Iman Zamani
- Electrical and Electronic Engineering Department, Shahed University, Tehran, Iran.
| | - Zohreh Abbasi
- Electrical and Computer Engineering Department, Qom University of Technology, Qom, Iran.
| | - Asier Ibeas
- Departament de Telecomunicació i Enginyeria de Sistemes, Escolad'Enginyeria. UniversitatAutònoma de Barcelona, Barcelona, Spain.
| |
Collapse
|
13
|
Khalili P, Vatankhah R. Derivation of an optimal trajectory and nonlinear adaptive controller design for drug delivery in cancerous tumor chemotherapy. Comput Biol Med 2019; 109:195-206. [DOI: 10.1016/j.compbiomed.2019.04.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 04/15/2019] [Accepted: 04/15/2019] [Indexed: 10/27/2022]
|
14
|
Nonlinear composite adaptive control of cancer chemotherapy with online identification of uncertain parameters. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.07.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
15
|
Aghajanzadeh O, Sharifi M, Tashakori S, Zohoor H. Robust adaptive Lyapunov-based control of hepatitis B infection. IET Syst Biol 2019. [PMID: 29533219 PMCID: PMC8687267 DOI: 10.1049/iet-syb.2017.0057] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
A new robust adaptive controller is developed for the control of the hepatitis B virus (HBV) infection inside the body. The non-linear HBV model has three state variables: uninfected cells, infected cells and free viruses. A control law is designed for the antiviral therapy such that the volume of infected cells and the volume of free viruses are decreased to their desired values which are zero. One control input represents the efficiency of drug therapy in inhibiting viral production and the other control input represents the efficiency of drug therapy in blocking new infection. The proposed controller ensures the stability and robust performance in the presence of parametric and non-parametric uncertainties (and/or bounded disturbances). The global stability and tracking convergence of the process are investigated by employing the Lyapunov theorem. The performance of the proposed controller is evaluated using simulations by considering different levels of uncertainties. Based on the obtained results, the proposed strategy can achieve its desired objectives with different cases of uncertainties.
Collapse
Affiliation(s)
- Omid Aghajanzadeh
- Department of Mechanical Engineering, Sharif University of Technology, Tehran 11155-9567, Iran
| | - Mojtaba Sharifi
- Department of Mechanical Engineering, Shiraz University, Shiraz 71936, Iran.
| | - Shabnam Tashakori
- Department of Mechanical Engineering, Sharif University of Technology, Tehran 11155-9567, Iran
| | - Hassan Zohoor
- Department of Mechanical Engineering, Sharif University of Technology, Tehran 11155-9567, Iran
| |
Collapse
|
16
|
Khalili P, Vatankhah R, Taghvaei S. Optimal sliding mode control of drug delivery in cancerous tumour chemotherapy considering the obesity effects. IET Syst Biol 2018; 12:185-189. [DOI: 10.1049/iet-syb.2017.0094] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 03/18/2018] [Accepted: 04/04/2018] [Indexed: 11/20/2022] Open
Affiliation(s)
- Pariya Khalili
- School of Mechanical Engineering, Shiraz UniversityMolla Sadra St., 7193616548ShirazIran
| | - Ramin Vatankhah
- School of Mechanical Engineering, Shiraz UniversityMolla Sadra St., 7193616548ShirazIran
| | - Sajjad Taghvaei
- School of Mechanical Engineering, Shiraz UniversityMolla Sadra St., 7193616548ShirazIran
| |
Collapse
|
17
|
Nasiri H, Akbarzadeh Kalat A. Adaptive fuzzy back-stepping control of drug dosage regimen in cancer treatment. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.02.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
18
|
Sharifi M, Jamshidi A, Sarvestani NN. An Adaptive Robust Control Strategy in a Cancer Tumor-Immune System under Uncertainties. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 16:865-873. [PMID: 29994095 DOI: 10.1109/tcbb.2018.2803175] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
We propose an adaptive robust control for a second order nonlinear model of the interaction between cancer and immune cells of the body to control the growth of cancer and maintain the number of immune cells in an appropriate level. Most of the control approaches are based on minimizing the drug dosage based on an optimal control structure. However, in many cases, measuring the exact quantity of the model parameters is not possible. This is due to limitation in measuring devices, variational and undetermined characteristics of micro-environmental factors. It is of great importance to present a control strategy that can deal with these unknown factors in a nonlinear model.
Collapse
|
19
|
Aghajanzadeh O, Sharifi M, Tashakori S, Zohoor H. Nonlinear adaptive control method for treatment of uncertain hepatitis B virus infection. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2017.06.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
|
20
|
Rokhforoz P, Jamshidi AA, Sarvestani NN. Adaptive robust control of cancer chemotherapy with extended Kalman filter observer. INFORMATICS IN MEDICINE UNLOCKED 2017. [DOI: 10.1016/j.imu.2017.03.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
|