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dc.contributor.authorМарценюк, Василь Петрович
dc.contributor.authorСверстюк, Андрій Степанович
dc.contributor.authorДзядевич, Сергій
dc.contributor.authorMartsenyuk, Vasyl
dc.contributor.authorSverstiuk, Andrii
dc.contributor.authorDzyadevych, Sergei
dc.date.accessioned2020-05-21T04:05:41Z-
dc.date.available2020-05-21T04:05:41Z-
dc.date.created2020-01-28
dc.date.issued2020-01-28
dc.date.submitted2019-12-20
dc.identifier.citationMartsenyuk V. Identification of parameters and investigation of stability of the mathematical model biosensor formeasuring α-chaconine / Vasyl Martsenyuk, Andrii Sverstiuk, Sergei Dzyadevych // Scientific Journal of TNTU. — Ternopil : TNTU, 2019. — Vol 96. — No 4. — P. 101–111.
dc.identifier.issn2522-4433
dc.identifier.urihttp://elartu.tntu.edu.ua/handle/lib/31716-
dc.description.abstractПрисвячено проблемі вдосконалення існуючих математичних і обчислювальних засобів для отримання та аналізу результатів чисельного моделювання при проектуванні біосенсорів. Ідентифіковано параметри, досліджено стійкість та проведено верифікацію математичної моделі потенціометричного біосенсору на основі зворотного інгібування бутирихолінестерази для визначення α-чаконіну. Математична модель досліджуваного біосенсору представлена системою семи лінійних диференціальних рівнянь, які описують динаміку біохімічних реакцій під час повного циклу вимірювання концентрації α-чаконіну. При цьому кожне із диференціальних рівнянь описує концентрації ферменту, субстрату, інгібітора, продукту, фермент-субстратного, фермент-інгібіторного, фермент-субстрат-інгібіторного комплексів залежно від часу. Математична модель біосенсора для визначення α-чаконіну розв’язана чисельно за допомогою пакета R. Вхідними параметрами системи є початкові концентрації ферменту, субстрату та інгібітора (5,8×10-4 М бутирихолінестерази, 1×10-3 М бутирихолін хлориду та 1×10−6; 2×10−6; 5×10−6; 10×10−6 М α-чаконіну відповідно), які експериментально розраховані. Для верифікації моделі та порівняння з експериментальним відгуком використано існуючий потенціометричний біосенсор на основі іммобілізованої бутирихолінестерази. Прямі та зворотні константи швидкостей ферментативних реакцій підібрані таким чином, щоб результат чисельного моделювання максимально відповідав експериментальному відгуку досліджуваного біосенсора. За результатами порівняльного аналізу встановлено залежність відхилення змодельованого та експериментального відгуків біосенсора для визначення α-чаконіну. Встановлено, що абсолютна похибка не перевищує 0,045 ум.од. На основі отриманих результатів чисельного моделювання зроблено висновок, що розроблена кінетична модель потенціометричного біосенсора дає змогу адекватно визначати усі основні складові компартментних компонент біохімічних реакцій при вимірюванні концентрації α-чаконіну
dc.description.abstractThe article is devoted to the problem of improving the existing mathematical and computational tools for obtaining and analyzing the results of numerical modeling in the design of biosensors. Parameters are identified in the work, stability is investigated and mathematical model is verified of a potentiometric biosensor based on the inverse inhibition of butyricolinesterase to determine α-chaconin is substantiated. The mathematical model of the biosensor under study is represented by a system of seven linear differential equations that describe the dynamics of biochemical reactions during a complete cycle of measurement of α-chaconine concentration. In this case, each of the differential equations describes the concentration of enzyme, substrate, inhibitor, product, enzyme-substrate, enzyme-inhibitory, enzyme-substrate-inhibitory complexes depending on time. A mathematical model of the biosensor for the determination of α-chaconine is numerically solved using Wolfram Mathematica software. The initial parameters of the system are the initial concentrations of the enzyme, substrate and inhibitor (5.8×10-4 M butyricholinesterase, 1×10-3 M butyrylcholine chloride and 1×10-6; 2×10-6; 5×10-6; 10×10-6 M α-chaconine, respectively), which are experimentally calculated. An existing potentiometric biosensor based on immobilized butyrylcholinesterase was used to verify the model and compare it with the experimental response. The forward and reverse rate constants of the enzymatic reactions are chosen so that the result of the numerical simulation is as consistent as possible with the experimental response of the biosensor under study. According to the results of the comparative analysis, the dependence of the deviation of the simulated and experimental responses of the biosensor to determine α-chaconine is established. It is found that the absolute error does not exceed 0.045 conventional units. Based on the results of numerical simulation, it is concluded that the developed kinetic model of the potentiometric biosensor allows to adequately determine all the main components of the compartment components of biochemical reactions when measuring the concentration of α-chaconine
dc.format.extent101-111
dc.language.isoen
dc.publisherТНТУ
dc.publisherTNTU
dc.relation.ispartofВісник Тернопільського національного технічного університету, 4 (96), 2019
dc.relation.ispartofScientific Journal of the Ternopil National Technical University, 4 (96), 2019
dc.relation.urihttps://doi.org/10.3390/foods3030491
dc.relation.urihttps://doi.org/10.14232/ejqtde.2018.1.27
dc.relation.urihttps://doi.org/10.1615/JAutomatInfScien.v50.i6.50
dc.relation.urihttps://doi.org/10.1615/JAutomatInfScien.v51.i2.70
dc.relation.urihttps://doi.org/10.1007/s10559-019-00171-2
dc.relation.urihttps://doi.org/10.1021/ac60352a006
dc.relation.urihttps://doi.org/10.1021/ac9806355
dc.relation.urihttps://doi.org/10.1016/j.snb.2011.12.079
dc.relation.urihttps://doi.org/10.1016/j.electacta.2010.04.050
dc.relation.urihttps://doi.org/10.1016/j.memsci.2011.02.033
dc.relation.urihttps://doi.org/10.1016/j.jelechem.2010.03.027
dc.relation.urihttps://doi.org/10.1016/j.electacta.2014.08.125
dc.relation.urihttps://doi.org/10.1016/j.jelechem.2012.06.025
dc.relation.urihttps://doi.org/10.1007/10_2013_224
dc.relation.urihttps://doi.org/10.1080/00032719.2012.713069
dc.relation.urihttps://doi.org/10.1016/j.aca.2014.11.027
dc.relation.urihttps://doi.org/10.1155/2013/731501
dc.relation.urihttps://doi.org/10.1016/j.snb.2014.10.033
dc.relation.urihttps://doi.org/10.1016/S0956-5663(02)00222-1
dc.relation.urihttps://doi.org/10.1016/j.snb.2004.04.070
dc.subjectматематична модель
dc.subjectбіосенсор
dc.subjectдослідження стійкості α-чаконін
dc.subjectчисельне моделювання
dc.subjectmathematical model
dc.subjectbiosensor
dc.subjectinvestigation of stability
dc.subjectα-chaconine
dc.subjectnumerical modeling
dc.titleIdentification of parameters and investigation of stability of the mathematical model biosensor formeasuring α-chaconine
dc.title.alternativeІдентифікація параметрів та дослідження стійкості математичної моделі біосенсору для визначення
dc.typeArticle
dc.rights.holder© Тернопільський національний технічний університет імені Івана Пулюя, 2019
dc.coverage.placenameТернопіль
dc.coverage.placenameTernopil
dc.format.pages11
dc.subject.udc004
dc.subject.udc94
dc.subject.udc53
dc.subject.udc616-073
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dc.relation.referencesen1. Mosinska L., Fabisiak K., Paprocki K., Kowalska M., Popielarski P., Szybowicz M., Stasiak A. Diamond as a transducer material for the production of biosensors. Przemysl Chemiczny. 2013. Vol. 92. No. 6.Р. 919–923.
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dc.relation.referencesen3. Martsenyuk V. P., Klos-Witkowska A., Sverstiuk A. S. Study of classification of immunosensors from viewpoint of medical tasks. Medical informatics and engineering. 2018. № 1 (41). Р. 13–19.
dc.relation.referencesen4. Martsenyuk V. P., Klos-Witkowska A., Sverstiuk A. S., Bihunyak T. V. On principles, methods and areas of medical and biological application of optical immunosensors. Medical informatics and engineering. 2018. № 2 (42). Р. 28–36.
dc.relation.referencesen5. Martsenyuk V., Klos–Witkowska A., Sverstiuk A. Stability, bifurcation and transition to chaos in a model of immunosensor based on lattice differential equations with delay. Electronic Journal of Qualitative Theory of Differential Equations. 2018. No. 27. Р. 1–31. https://doi.org/10.14232/ejqtde.2018.1.27
dc.relation.referencesen6. Martsenyuk V. P., Andrushchak I. Ye., Zinko P. M., Sverstiuk A. S. On Application of Latticed Differential Equations with a Delay for Immunosensor Modeling. Journal of Automation and Information Sciences. 2018. Vol. 50 (6). P. 55–65. https://doi.org/10.1615/JAutomatInfScien.v50.i6.50
dc.relation.referencesen7. Martsenyuk V. P., Sverstiuk A. S., Andrushchak I. Ye. Approach to the Study of Global Asymptotic Stability of Lattice Differential Equations with Delay for Modeling of Immunosensors. Journal of Automation and Information Sciences. 2019. Vol. 48 (8). P. 58–71. https://doi.org/10.1615/JAutomatInfScien.v51.i2.70
dc.relation.referencesen8. Martsenyuk V., Sverstiuk А., Gvozdetska I. Using Differential Equations with Time Delay on a Hexagonal Lattice for Modeling Immunosensors. Cybernetics and Systems Analysis. 2019. Vol. 55 (4).P. 625–636. https://doi.org/10.1007/s10559-019-00171-2
dc.relation.referencesen9. Martsenyuk V. P., Klos-Witkowska A., Sverstiuk A. S. Stability, bifurcation and transition to chaos in a model of immunosensor based on lattice differential equations with delay. Electronic Journal of Qualitative Theory of Differential Equations. 2018. No. 27. P. 1–31. https://doi.org/10.14232/ejqtde.2018.1.27
dc.relation.referencesen10. Martsenyuk V. P., Andrushchak I. Ye., Zinko P. M., Sverstiuk A. S. On Application of Latticed Differential Equations with a Delay for Immunosensor Modeling. Journal of Automation and Information Sciences. Vol. 50 (6). 2018. P. 55–65. https://doi.org/10.1615/JAutomatInfScien.v50.i6.50
dc.relation.referencesen11. Mell L. D., Maloy J. T. A model for the amperometric enzyme electrode obtained through digital simulation and applied to the immobilized glucose oxidase system. Anal. Chem. 1975. Vol. 47. No. 2.P. 299–307. https://doi.org/10.1021/ac60352a006
dc.relation.referencesen12. Gajovic N., Warsinke A., Huang T., Schulmeister T., Scheller F. W. Characterization and Mathematical Modeling of a Bienzyme Electrode for l-Malate with Cofactor Recycling. Analytical Chemistry. 1999.Vol. 71. No. 20. P. 4657–4662. https://doi.org/10.1021/ac9806355
dc.relation.referencesen13. Romero M. R., Baruzzi A. M., Garay F. Mathematical modeling and experimental results of a sandwich-type amperometric biosensor. Sensors Actuators, B Chemistry. 2012. Vol. 162. No. 1. P. 284–291. https://doi.org/10.1016/j.snb.2011.12.079
dc.relation.referencesen14. Loghambal S., Rajendran L. Mathematical modeling of diffusion and kinetics in amperometric immobilized enzyme electrodes. Electrochimica Acta. 2010. Vol. 55. No. 18. P. 5230–5238. https://doi.org/10.1016/j.electacta.2010.04.050
dc.relation.referencesen15. Loghambal S., Rajendran L. Mathematical modeling in amperometric oxidase enzyme-membrane electrodes. Journal of Membrane Science. Vol. 373. No. 1–2. 2011. P. 20–28. https://doi.org/10.1016/j.memsci.2011.02.033
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dc.relation.referencesen17. Ašeris V., Gaidamauskaitė E., Kulys J., Baronas R. Modelling glucose dehydrogenase-based amperometric biosensor utilizing synergistic substrates conversion. Electrochimica Acta. 2014. Vol. 146.P. 752–758. https://doi.org/10.1016/j.electacta.2014.08.125
dc.relation.referencesen18. Ašeris V., Baronas R., Kulys J. Modelling the biosensor utilising parallel substrates conversion. Journal of Electroanalytical Chemistry. 2012. Vol. 685. P. 63–71. https://doi.org/10.1016/j.jelechem.2012.06.025
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dc.relation.referencesen20. Upadhyay L. S., Verma N. Enzyme Inhibition Based Biosensors: A Review. Analytical Letters. 2012.Vol. 46. P. 225–241. https://doi.org/10.1080/00032719.2012.713069
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dc.relation.referencesen22. Dhull V., Gahlaut A., Dilbaghi N., Hooda V. Acetylcholinesterase biosensors for electrochemical detection of organophosphorus compounds: A review. Biochemistry Research International.2013. P. 1–18. https://doi.org/10.1155/2013/731501
dc.relation.referencesen23. Achi F., Bourouina-Bacha S., Bourouina M., Amine A. Mathematical model and numerical simulation of inhibition based biosensor for the detection of Hg(II). Sensors & Actuators, B: Chemical. 2015. Vol. 207.P. 413–423. https://doi.org/10.1016/j.snb.2014.10.033
dc.relation.referencesen24. Arkhypova V. N, Dzyadevych S. V., Soldatkin A. P., El’skaya A. V., Martelet C., Jaffrezic-Renault N. Development and optimisation of biosensors based on pH-sensitive field effect transistor and cholinesterase for sensitive detection of solanaceous glycoalkaloids. Biosensors & Bioelectronics. 2003.Vol. 18. P. 1047–1053. https://doi.org/10.1016/S0956-5663(02)00222-1
dc.relation.referencesen25. Arkhypova V. N., Dzyadevych S. V., Soldatkin A. P., Korpan Y. I., El’skaya A. V., Gravoueille J.-M., Martelet C., Jaffrezic-Renault N. Application of enzyme field effect transistors for fast detection of total glycoalkaloids content in potatoes. Sensors and Actuators B. 2004. Vol. 103. P. 416–422. https://doi.org/10.1016/j.snb.2004.04.070
dc.relation.referencesen26. Arrowsmith D. K., Place C. M. The Linearization Theorem. Dynamical Systems: Differential Equations, Maps, and Chaotic Behaviour. London: Chapman & Hall. 1992. P. 77–81.
dc.identifier.citationenMartsenyuk V., Sverstiuk A., Dzyadevych S. (2019) Identification of parameters and investigation of stability of the mathematical model biosensor formeasuring α-chaconine. Scientific Journal of TNTU (Ternopil), vol. 96, no 4, pp. 101-111.
dc.identifier.doihttps://doi.org/10.33108/visnyk_tntu2019.04.101
dc.contributor.affiliationУніверситет в Бєльсько-Бялій, Бєльсько-Бяла, Польща
dc.contributor.affiliationТернопільський національний медичний університет імені І. Я. Горбачевського, Тернопіль, Україна
dc.contributor.affiliationІнститут молекулярної біології та генетики НАН України, Київ, Україна
dc.contributor.affiliationUniversity of Bielsko-Biala, Bielsko-Biala, Poland
dc.contributor.affiliationTernopil National Medical University, Ternopil, Ukraine
dc.contributor.affiliationDepartment of of Biomolecular Electronics, Institute of Molecular Biology and Genetics, NAS of Ukraine, Kyiv, Ukraine
dc.citation.journalTitleВісник Тернопільського національного технічного університету
dc.citation.volume96
dc.citation.issue4
dc.citation.spage101
dc.citation.epage111
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