Mathematical Modeling of Multienzyme Biosensor System

Abstract

A mathematical model of hybrid inhibitor biosensor system is discussed. This model consists of five nonlinear partial differential equations for bisubstrate sensitive amperometric system. Simple and closed form of analytical expressions for concentration of glucose-6-phosphate (substrate), potassium dihydrogen phosphate (inhibitor), oxygen (co-substrate), glucose (product 1), and hydrogen peroxide (product 3) is obtained in terms of rate constant using modified Adomian decomposition method (MADM). In this study, behavior of biokinetic parameters is analyzed using this theoretical result. The obtained analytical results (concentrations) are compared with the numerical results and are found to be in satisfactory agreement.

Investigation of biokinetic effects on hybrid multienzyme biosensor system

“…Parametric range selected in this paper is owing to biosensor descending function output, since conversion of enzymes consumes oxygen. This is in tandem with (Ganesan et al , 2014). Effects of various biokinetic parameters are investigated on reagents concentration such as substrate concentration, inhibition concentration, cosubstrate concentration, product 1 and product 3.…”

“…The assumption that diffusion is in one dimension in space is followed, which is described using Fick's second law. Concentration profiles for multienzyme biosensor system for the five reagents are described by second-order ordinarily nonlinear equations expressed as (Ganesan et al , 2014):where D S , D C , DP1 and DP3 are diffusion coefficients for substrate, cosubstrate, product 1 and product 3. K i connotes the reaction constant for concentration profiles ( i = S , I , C , P i ), respectively.…”

“…The use of FIA-based bacteria biosensors was presented by (Lanyon et al , 2005) to analyze benzene in workplace air. The enzyme inhibition biosensors were developed for agricultural and environmental purpose due to its response to molecular detection (Sandstrom et al , 2000; Nok and Hasirci, 2004; Amine et al , 2006; De Castro and Herrera, 2003; Dzyadevych et al , 2003; Sadana and Sadana, 2010; Ganesan et al , 2014). Rahbari et al (2017) presented the effect of nanoparticles on blood flow through arterial vessels.…”

“…However, owing to the problem of weak nonlinearities and small parameters, which are sometimes artificial, it makes it necessary to develop other analytical methods of solutions to overcome these limits (Filobello-Niño et al , 2013; Lim and Wu, 2002; Cheung et al , 1991). Consequently, the use of other analytical schemes such as differential transform method (DTM), homotopy analysis method (HAM), Adomian decomposition method (ADM), optimal homotopy asymptotic method (OHAM) and some other approximation methods has been developed (Lim and Wu, 2002; Cheung et al , 1991; Fakour et al , 2015; Hatami, 2017; Tang and Jing, 2017; Pourmehran et al , 2015, 2018; Ghasemi et al , 2014; Hatami and Ganji, 2014; Hatami et al , 2014a, b; Ganesan et al , 2014; Rahimi-Gorji et al , 2016; Biglarian et al , 2017; Mosayebidorcheh et al , 2017; Akinshilo, 2019, 2017; Sobamowo and Akinshilo (2017); Akinshilo and Ilegbusi, 2019; Akinshilo and Olofinkua, 2018; Dogonchi and Ganji, 2016a, b, c; Dogonchi et al , 2016, 2017; Ramsey and Tipton, 2017; Mehmood et al (2018); Pourmehran et al (2020)). Methods such as DTM, HAM and ADM, however, require the need to find an initial condition that will satisfy the boundary condition, for which theories have not been rigorously proven for all cases.…”