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Title:	*Application Of Two Phase Multi-Objective Optimization To Design Of Biosensors Utilizing Cyclic Substrate Conversion*
Authors:	Linas Litvinas, Romas Baronas, Antanas Zilinskas
Published in:	(2017).ECMS 2017 Proceedings Edited by: Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics European Council for Modeling and Simulation. doi:10.7148/2017 ISBN: 978-0-9932440-4-9/ ISBN: 978-0-9932440-5-6 (CD) 31st European Conference on Modelling and Simulation, Budapest, Hungary, May 23^rd – May 26^th, 2017
Citation format:	Linas Litvinas, Romas Baronas, Antanas Zilinskas (2017). Application Of Two Phase Multi-Objective Optimization To Design Of Biosensors Utilizing Cyclic Substrate Conversion, ECMS 2017 Proceedings Edited by: Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics European Council for Modeling and Simulation. doi: 10.7148/2017-0469
DOI:	https://doi.org/10.7148/2017-0469
Abstract:	A method for the optimal design of amperometric biosensors with cyclic substrate conversion is proposed. The design is multi-objective since biosensors must meet numerous, frequently conflicting, requirements of users and manufacturers. Moreover, they should be technologically and economically competitive. To apply a multi-optimization technique, a mathematical model should be developed where the most important characteristics of the biosensor are defined as objectives, and the other characteristics and requirements are defined as constrains. For the considered biosensors the following characteristics are taken as objectives: the output current, the enzyme amount, and the biosensor sensitivity. The proposed method consists of two phases. At the first phase an approximated Pareto front is constructed, and a preliminary solution is selected. The second phase is aimed at specification of the Pareto front around the preliminary solution, and at making the final decision. A numerical example is presented using a computational model of an industrially relevant biosensor.
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