Abstract
Dynamic modeling in systems and synthetic biology is still quite a challenge—the complex nature of the interactions results in nonlinear models, which include unknown parameters (or functions). Ideally, time-series data support the estimation of model unknowns through data fitting. Goodness-of-fit measures would lead to the best model among a set of candidates. However, even when state-of-the-art measuring techniques allow for an unprecedented amount of data, not all data suit dynamic modeling.
Model-based optimal experimental design (OED) is intended to improve model predictive capabilities. OED can be used to define the set of experiments that would (a) identify the best model or (b) improve the identifiability of unknown parameters. In this chapter, we present a detailed practical procedure to compute optimal experiments using the AMIGO2 toolbox.
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Acknowledgments
The authors acknowledge financial support from the Spanish Ministry of Science, Innovation and Universities and the European Union FEDER (project grant RTI2018-093744-B-C33). This work was also supported by a Royal Society of Edinburgh-MoST grant, EPSRC grant EP/R035350/1 and EP/S001921/1 to Dr. Menolascina, and the EPSRC grant EP/P017134/1-CONDSYC to Dr. Bandiera.
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Balsa-Canto, E., Bandiera, L., Menolascina, F. (2021). Optimal Experimental Design for Systems and Synthetic Biology Using AMIGO2. In: Menolascina, F. (eds) Synthetic Gene Circuits . Methods in Molecular Biology, vol 2229. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1032-9_11
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DOI: https://doi.org/10.1007/978-1-0716-1032-9_11
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