Journal article Open Access

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

Bandiera, Lucia; Hou, Zhaozheng; Kothamachu, Varun B.; Balsa-Canto, Eva; Swain, Peter S.; Menolascina, Filippo


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    <subfield code="a">Model-based optimal experimental design</subfield>
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    <subfield code="a">Syntethic biology</subfield>
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    <subfield code="a">Model calibration</subfield>
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    <subfield code="a">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.</subfield>
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    <subfield code="u">Synthsys—Centre for Synthetic and Systems Biology, The University of Edinburgh, Edinburgh EH9 3BF, UK</subfield>
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    <subfield code="u">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</subfield>
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    <subfield code="u">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</subfield>
    <subfield code="a">Bandiera, Lucia</subfield>
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    <subfield code="a">On-Line Optimal Input Design Increases the Efficiency and Accuracy of the Modelling of an Inducible Synthetic Promoter</subfield>
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    <subfield code="a">Control Engineering of Biological Systems for Reliable Synthetic Biology Applications</subfield>
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    <subfield code="a">&lt;p&gt;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;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&amp;nbsp;poorly informative&amp;nbsp;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&amp;nbsp;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 &amp;sim;60%. Results further improve up to&amp;nbsp;84%&amp;nbsp;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.&lt;/p&gt;</subfield>
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