Optimal experimental design aims to determine experimental conditions which will result in a rich data set for parameter estimation. Optimal experimental design is especially valuable for analysis of complex systems where experiments are expensive and time-consuming. Experimental conditions, which can be adjusted in the design, include initial conditions, input profiles, sampling time points, sensor locations, etc.
Optimal experimental design is performed by optimizing a criterion function which measures the information content of the data generated by the designed experiment. A set of design criteria has been developed and these are named alphabetically (Atkinson et al. 2007). Each criterion is a real function of the Fisher information matrix as the inverse of the Fisher information matrix provides a lower bound for the covariance matrix of the estimated parameters.
The main challenge for experiment design...
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Atkinson AC, Donev AN, Tobias RD (2007) Optimum experimental designs, with SAS. Oxford University Press, Oxford
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Chu, Y., Hahn, J. (2013). Optimal Experiment Design. In: Dubitzky, W., Wolkenhauer, O., Cho, KH., Yokota, H. (eds) Encyclopedia of Systems Biology. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9863-7_1284
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-9862-0
Online ISBN: 978-1-4419-9863-7