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Processes
Volume 6
Issue 9
10.3390/pr6090148
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Article

On-Line Optimal Input Design Increases the Efficiency and Accuracy of the Modelling of an Inducible Synthetic Promoter †

by
Lucia Bandiera
1,2,
Zhaozheng Hou
1,
Varun B. Kothamachu
1,
Eva Balsa-Canto
3,
Peter S. Swain
2 and
Filippo Menolascina
1,2,*
1
School of Engineering, Institute for Bioengineering, The University of Edinburgh, Edinburgh EG9 3DW, UK
2
Synthsys—Centre for Synthetic and Systems Biology, The University of Edinburgh, Edinburgh EH9 3BF, UK
3
(Bio)Process Engineering Group, IIM-CSIC Spanish Reasearch Council, 36208 Vigo, Spain
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in 57th IEEE Conference on Decision and Control, Miami Beach, FL, USA, 17–19 December 2018.
Processes 2018, 6(9), 148; https://doi.org/10.3390/pr6090148
Received: 29 June 2018 / Revised: 24 August 2018 / Accepted: 27 August 2018 / Published: 1 September 2018
(This article belongs to the Special Issue Computational Synthetic Biology)
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Abstract

Synthetic biology seeks to design biological parts and circuits that implement new functions in cells. Major accomplishments have been reported in this field, yet predicting a priori the in vivo behaviour of synthetic gene circuits is major a challenge. Mathematical models offer a means to address this bottleneck. However, in biology, modelling is perceived as an expensive, time-consuming task. Indeed, the quality of predictions depends on the accuracy of parameters, which are traditionally inferred from poorly informative data. How much can parameter accuracy be improved by using model-based optimal experimental design (MBOED)? To tackle this question, we considered an inducible promoter in the yeast S. cerevisiae. Using in vivo data, we re-fit a dynamic model for this component and then compared the performance of standard (e.g., step inputs) and optimally designed experiments for parameter inference. We found that MBOED improves the quality of model calibration by ∼60%. Results further improve up to 84 % when considering on-line optimal experimental design (OED). Our in silico results suggest that MBOED provides a significant advantage in the identification of models of biological parts and should thus be integrated into their characterisation. View Full-Text
Keywords: model-based optimal experimental design; synthetic biology; model calibration; optimal inputs; system identification model-based optimal experimental design; synthetic biology; model calibration; optimal inputs; system identification
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MDPI and ACS Style

Bandiera, L.; Hou, Z.; Kothamachu, V.B.; Balsa-Canto, E.; Swain, P.S.; Menolascina, F. On-Line Optimal Input Design Increases the Efficiency and Accuracy of the Modelling of an Inducible Synthetic Promoter. Processes 2018, 6, 148. https://doi.org/10.3390/pr6090148

AMA Style

Bandiera L, Hou Z, Kothamachu VB, Balsa-Canto E, Swain PS, Menolascina F. On-Line Optimal Input Design Increases the Efficiency and Accuracy of the Modelling of an Inducible Synthetic Promoter. Processes. 2018; 6(9):148. https://doi.org/10.3390/pr6090148

Chicago/Turabian Style

Bandiera, Lucia, Zhaozheng Hou, Varun B. Kothamachu, Eva Balsa-Canto, Peter S. Swain, and Filippo Menolascina. 2018. "On-Line Optimal Input Design Increases the Efficiency and Accuracy of the Modelling of an Inducible Synthetic Promoter" Processes 6, no. 9: 148. https://doi.org/10.3390/pr6090148

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