Next Article in Journal
Quantitative Estimates of Nonlinear Flow Characteristics of Deformable Rough-Walled Rock Fractures with Various Lithologies
Next Article in Special Issue
Towards an Aspect-Oriented Design and Modelling Framework for Synthetic Biology
Previous Article in Journal
Diffusion in Nanoporous Materials: Novel Insights by Combining MAS and PFG NMR
Previous Article in Special Issue
Identifiability and Reconstruction of Biochemical Reaction Networks from Population Snapshot Data
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessFeature PaperArticle
Processes 2018, 6(9), 148; https://doi.org/10.3390/pr6090148

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

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
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.
*
Author to whom correspondence should be addressed.
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)
Full-Text   |   PDF [1974 KB, uploaded 1 September 2018]   |  

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Processes EISSN 2227-9717 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top