publication . Article . Other literature type . 2018

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

Lucia Bandiera; Zhaozheng Hou; Varun B. Kothamachu; Eva Balsa-Canto; Peter S. Swain; Filippo Menolascina;
Open Access
  • Published: 01 Sep 2018 Journal: Processes, volume 6, page 148 (eissn: 2227-9717, Copyright policy)
  • Publisher: MDPI AG
Abstract
Peer reviewed
Subjects
free text keywords: model-based optimal experimental design, synthetic biology, model calibration, optimal inputs, system identification, Model-based optimal experimental design, Syntethic biology, Model calibration, Optimal inputs, System identification, Synthetic biology, /dk/atira/pure/subjectarea/asjc/1500/1502, Bioengineering, /dk/atira/pure/subjectarea/asjc/1500/1501, Chemical Engineering (miscellaneous), /dk/atira/pure/subjectarea/asjc/1500/1508, Process Chemistry and Technology, Mathematical model, Inference, Synthetic biology, Input design, Machine learning, computer.software_genre, computer, In silico, A priori and a posteriori, Artificial intelligence, business.industry, business, Bottleneck, System identification, lcsh:Chemical technology, lcsh:TP1-1185, lcsh:Chemistry, lcsh:QD1-999
Funded by
EC| COSY-BIO
Project
COSY-BIO
Control Engineering of Biological Systems for Reliable Synthetic Biology Applications
  • Funder: European Commission (EC)
  • Project Code: 766840
  • Funding stream: H2020 | RIA
Validated by funder
Communities
FET H2020FET OPEN: FET-Open research and innovation actions
FET H2020FET OPEN: Control Engineering of Biological Systems for Reliable Synthetic Biology Applications
20 references, page 1 of 2

1. Cameron, D.E.; Bashor, C.J.; Collins, J.J. A brief history of synthetic biology. Nat. Rev. Microbiol. 2014, 12, 381-390. [CrossRef] [PubMed]

2. Ellis, T.; Wang, X.; Collins, J.J. Diversity-based, model-guided construction of synthetic gene networks with predicted functions. Nat. Biotechnol. 2009, 27, 465-471. [CrossRef] [PubMed] [OpenAIRE]

3. Nielsen, A.A.; Der, B.S.; Shin, J.; Vaidyanathan, P.; Paralanov, V.; Strychalski, E.A.; Ross, D.; Densmore, D.; Voigt, C.A. Genetic circuit design automation. Science 2016, 352, aac7341. [CrossRef] [PubMed] [OpenAIRE]

4. Salis, H.M. The ribosome binding site calculator. In Methods in Enzymology; Elsevier: Amsterdam, The Netherlands, 2011; Volume 498, pp. 19-42. [OpenAIRE]

5. Borkowski, O.; Ceroni, F.; Stan, G.B.; Ellis, T. Overloaded and stressed: Whole-cell considerations for bacterial synthetic biology. Curr. Opin. Microbiol. 2016, 33, 123-130. [CrossRef] [PubMed]

6. Ljung, L. System Identification: Theory for the User, Ptr Prentice Hall Information and System Sciences Series; Prentice Hall: Upper Saddle River, NJ, USA, 1999.

7. Bandara, S.; Schlöder, J.P.; Eils, R.; Bock, H.G.; Meyer, T. Optimal experimental design for parameter estimation of a cell signaling model. PLoS Comput. Biol. 2009, 5, e1000558. [CrossRef] [PubMed]

8. Ruess, J.; Parise, F.; Milias-Argeitis, A.; Khammash, M.; Lygeros, J. Iterative experiment design guides the characterization of a light-inducible gene expression circuit. Proc. Natl. Acad. Sci. USA 2015, 112, 8148-8153. [CrossRef] [PubMed]

9. Balsa-Canto, E.; Henriques, D.; Gábor, A.; Banga, J.R. AMIGO2, a toolbox for dynamic modeling, optimization and control in systems biology. Bioinformatics 2016, 32, 3357-3359. [CrossRef] [PubMed] [OpenAIRE]

10. Gnugge, R.; Dharmarajan, L.; Lang, M.; Stelling, J. An Orthogonal Permease-Inducer-Repressor Feedback Loop Shows Bistability. ACS Synth. Biol. 2016, 5, 1098-1107. [CrossRef] [PubMed]

11. Egea, J.A.; Balsa-Canto, E.; García, M.S.G.; Banga, J.R. Dynamic Optimization of Nonlinear Processes with an Enhanced Scatter Search Method. Ind. Eng. Chem. Res. 2009, 48, 4388-4401. [CrossRef]

12. Balsa-Canto, E.; Alonso, A.A.; Banga, J.R. Computational procedures for optimal experimental design in biological systems. IET Syst. Biol. 2008, 2, 163-172. [CrossRef] [PubMed]

13. Walter, E.; Pronzato, L. Identification of Parametric Models from Experimental Data; Springer: Berlin, Germany, 1997.

14. Kohonen, T.; Maps, S.O. Springer series in information sciences. Self-Organ. Maps 1995, 30, 105-176.

15. Vesanto, J.; Himberg, J.; Alhoniemi, E.; Parhankangas, J.; Parhankangas, J. Self-organizing map in Matlab: The SOM Toolbox. In Proceedings of the Matlab DSP Conference, Espoo, Finland, 16-17 November 1999; Volume 99, pp. 35-40.

20 references, page 1 of 2
Abstract
Peer reviewed
Subjects
free text keywords: model-based optimal experimental design, synthetic biology, model calibration, optimal inputs, system identification, Model-based optimal experimental design, Syntethic biology, Model calibration, Optimal inputs, System identification, Synthetic biology, /dk/atira/pure/subjectarea/asjc/1500/1502, Bioengineering, /dk/atira/pure/subjectarea/asjc/1500/1501, Chemical Engineering (miscellaneous), /dk/atira/pure/subjectarea/asjc/1500/1508, Process Chemistry and Technology, Mathematical model, Inference, Synthetic biology, Input design, Machine learning, computer.software_genre, computer, In silico, A priori and a posteriori, Artificial intelligence, business.industry, business, Bottleneck, System identification, lcsh:Chemical technology, lcsh:TP1-1185, lcsh:Chemistry, lcsh:QD1-999
Funded by
EC| COSY-BIO
Project
COSY-BIO
Control Engineering of Biological Systems for Reliable Synthetic Biology Applications
  • Funder: European Commission (EC)
  • Project Code: 766840
  • Funding stream: H2020 | RIA
Validated by funder
Communities
FET H2020FET OPEN: FET-Open research and innovation actions
FET H2020FET OPEN: Control Engineering of Biological Systems for Reliable Synthetic Biology Applications
20 references, page 1 of 2

1. Cameron, D.E.; Bashor, C.J.; Collins, J.J. A brief history of synthetic biology. Nat. Rev. Microbiol. 2014, 12, 381-390. [CrossRef] [PubMed]

2. Ellis, T.; Wang, X.; Collins, J.J. Diversity-based, model-guided construction of synthetic gene networks with predicted functions. Nat. Biotechnol. 2009, 27, 465-471. [CrossRef] [PubMed] [OpenAIRE]

3. Nielsen, A.A.; Der, B.S.; Shin, J.; Vaidyanathan, P.; Paralanov, V.; Strychalski, E.A.; Ross, D.; Densmore, D.; Voigt, C.A. Genetic circuit design automation. Science 2016, 352, aac7341. [CrossRef] [PubMed] [OpenAIRE]

4. Salis, H.M. The ribosome binding site calculator. In Methods in Enzymology; Elsevier: Amsterdam, The Netherlands, 2011; Volume 498, pp. 19-42. [OpenAIRE]

5. Borkowski, O.; Ceroni, F.; Stan, G.B.; Ellis, T. Overloaded and stressed: Whole-cell considerations for bacterial synthetic biology. Curr. Opin. Microbiol. 2016, 33, 123-130. [CrossRef] [PubMed]

6. Ljung, L. System Identification: Theory for the User, Ptr Prentice Hall Information and System Sciences Series; Prentice Hall: Upper Saddle River, NJ, USA, 1999.

7. Bandara, S.; Schlöder, J.P.; Eils, R.; Bock, H.G.; Meyer, T. Optimal experimental design for parameter estimation of a cell signaling model. PLoS Comput. Biol. 2009, 5, e1000558. [CrossRef] [PubMed]

8. Ruess, J.; Parise, F.; Milias-Argeitis, A.; Khammash, M.; Lygeros, J. Iterative experiment design guides the characterization of a light-inducible gene expression circuit. Proc. Natl. Acad. Sci. USA 2015, 112, 8148-8153. [CrossRef] [PubMed]

9. Balsa-Canto, E.; Henriques, D.; Gábor, A.; Banga, J.R. AMIGO2, a toolbox for dynamic modeling, optimization and control in systems biology. Bioinformatics 2016, 32, 3357-3359. [CrossRef] [PubMed] [OpenAIRE]

10. Gnugge, R.; Dharmarajan, L.; Lang, M.; Stelling, J. An Orthogonal Permease-Inducer-Repressor Feedback Loop Shows Bistability. ACS Synth. Biol. 2016, 5, 1098-1107. [CrossRef] [PubMed]

11. Egea, J.A.; Balsa-Canto, E.; García, M.S.G.; Banga, J.R. Dynamic Optimization of Nonlinear Processes with an Enhanced Scatter Search Method. Ind. Eng. Chem. Res. 2009, 48, 4388-4401. [CrossRef]

12. Balsa-Canto, E.; Alonso, A.A.; Banga, J.R. Computational procedures for optimal experimental design in biological systems. IET Syst. Biol. 2008, 2, 163-172. [CrossRef] [PubMed]

13. Walter, E.; Pronzato, L. Identification of Parametric Models from Experimental Data; Springer: Berlin, Germany, 1997.

14. Kohonen, T.; Maps, S.O. Springer series in information sciences. Self-Organ. Maps 1995, 30, 105-176.

15. Vesanto, J.; Himberg, J.; Alhoniemi, E.; Parhankangas, J.; Parhankangas, J. Self-organizing map in Matlab: The SOM Toolbox. In Proceedings of the Matlab DSP Conference, Espoo, Finland, 16-17 November 1999; Volume 99, pp. 35-40.

20 references, page 1 of 2
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