Comment on "Sloppy models, parameter uncertainty, and the role of experimental design".
نویسندگان
چکیده
We explain that part of the reduction in the parameter uncertainties in the computations of Apgar et al. (Mol. Biosyst. 2010, 6, 1890-900) is due to a greatly increased number of effective data points.
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David R. Hagena,b,†, Joshua F. Apgara,b,†,‡, David K. Witmerb,c,†, Forest M. Whitea,d, and Bruce Tidora,b,c,* aDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA bComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA cDepartment of Electrical Engineering an...
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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 thi...
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Multi-parameter models in systems biology are typically 'sloppy': some parameters or combinations of parameters may be hard to estimate from data, whereas others are not. One might expect that parameter uncertainty automatically leads to uncertain predictions, but this is not the case. We illustrate this by showing that the prediction uncertainty of each of six sloppy models varies enormously a...
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عنوان ژورنال:
- Molecular bioSystems
دوره 7 8 شماره
صفحات -
تاریخ انتشار 2011