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Mathematical models of complex biological systems are typically represented in the form of nonlinear ordinary differential equations. This raises three important problems for optimal experimental design based on the Fisher information matrix (FIM) (Optimal Experiment Design, Fisher Information). The first is that, due to the linear nature of the FIM, it may be a poor measure of the size of the uncertainty region. The second is that this matrix depends on the value assumed for the parameters in the case of nonlinear models. And the third, that applies to any optimal experimental design, either for accurate estimation of the parameters (Designing Experiments for Sound Statistical Inference), model discrimination (Optimal Experimental Design, Model Discrimination), or maximization of a model response, is that the resultant nonlinear optimization problem is generally nonconvex. Therefore, optimal experimental design for nonlinear models...
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Rodriguez-Fernandez, M., Doyle, F.J. (2013). Optimal Experiment Design, Multimodality. 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_1224
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DOI: https://doi.org/10.1007/978-1-4419-9863-7_1224
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-9862-0
Online ISBN: 978-1-4419-9863-7
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