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Digital
Library of the European Council for Modelling and Simulation |
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 23rd
– May 26th, 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. |
Full
text: |