Adaptive non‐linear control for cancer therapy through a Fokker–Planck observer. Issue 2 (1st April 2018)

• Record Type: Journal Article
• Authors: Shakeri, Ehsan; Latif‐Shabgahi, Gholamreza; Esmaeili Abharian, Amir
• Is Part Of: IET systems biology. Volume 12:Issue 2(2018)
• Page Start: 73
• Page End: 82
• Publication Date: 2018-04-01
• Subjects: physiological models -- cancer -- patient treatment -- probability -- stochastic processes -- tumours -- Fokker‐Planck equation -- statistical analysis -- cellular biophysics; adaptive nonlinear control -- cancer therapy -- Fokker‐Planck observer -- tumour cell growth behavior -- mathematical modelling -- tumour‐cell population dynamics -- optimal control theory -- stochastic Gompertz model -- empirical data -- statistical methods -- logarithmic function -- probability density function -- nonlinear stochastic observer; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2017.0032
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : In recent years, many efforts have been made to present optimal strategies for cancer therapy through the mathematical modelling of tumour‐cell population dynamics and optimal control theory. In many cases, therapy effect is included in the drift term of the stochastic Gompertz model. By fitting the model with empirical data, the parameters of therapy function are estimated. The reported research works have not presented any algorithm to determine the optimal parameters of therapy function. In this study, a logarithmic therapy function is entered in the drift term of the Gompertz model. Using the proposed control algorithm, the therapy function parameters are predicted and adaptively adjusted. To control the growth of tumour‐cell population, its moments must be manipulated. This study employs the probability density function (PDF) control approach because of its ability to control all the process moments. A Fokker–Planck‐based non‐linear stochastic observer will be used to determine the PDF of the process. A cost function based on the difference between a predefined desired PDF and PDF of tumour‐cell population is defined. Using the proposed algorithm, the therapy function parameters are adjusted in such a manner that the cost function is minimised. The existence of an optimal therapy function is also proved. The numerical results are finally given to demonstrate the effectiveness of the proposed method.