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Issue 10, 2010
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Sloppy models, parameter uncertainty, and the role of experimental design

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Computational models are increasingly used to understand and predict complex biological phenomena. These models contain many unknown parameters, at least some of which are difficult to measure directly, and instead are estimated by fitting to time-course data. Previous work has suggested that even with precise data sets, many parameters are unknowable by trajectory measurements. We examined this question in the context of a pathway model of epidermal growth factor (EGF) and neuronal growth factor (NGF) signaling. Computationally, we examined a palette of experimental perturbations that included different doses of EGF and NGF as well as single and multiple gene knockdowns and overexpressions. While no single experiment could accurately estimate all of the parameters, experimental design methodology identified a set of five complementary experiments that could. These results suggest optimism for the prospects for calibrating even large models, that the success of parameter estimation is intimately linked to the experimental perturbations used, and that experimental design methodology is important for parameter fitting of biological models and likely for the accuracy that can be expected from them.

Graphical abstract: Sloppy models, parameter uncertainty, and the role of experimental design

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Publication details

The article was received on 03 Sep 2009, accepted on 25 May 2010 and first published on 17 Jun 2010

Article type: Paper
DOI: 10.1039/B918098B
Citation: Mol. BioSyst., 2010,6, 1890-1900
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    Sloppy models, parameter uncertainty, and the role of experimental design

    J. F. Apgar, D. K. Witmer, F. M. White and B. Tidor, Mol. BioSyst., 2010, 6, 1890
    DOI: 10.1039/B918098B

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