期刊
INTERFACE FOCUS
卷 3, 期 4, 页码 -出版社
ROYAL SOC
DOI: 10.1098/rsfs.2013.0008
关键词
systems biology; ordinary differential equation modelling; parameter uncertainty; Fisher information; optimal experimental design
类别
资金
- U.S. National Science Foundation [0821391]
- U.S. National Institutes of Health [CA112967]
- MIT Portugal Programme
- Singapore MIT Alliance for Research and Technology
- Direct For Biological Sciences
- Div Of Biological Infrastructure [0821391] Funding Source: National Science Foundation
Typically, biological models fitted to experimental data suffer from significant parameter uncertainty, which can lead to inaccurate or uncertain predictions. One school of thought holds that accurate estimation of the true parameters of a biological system is inherently problematic. Recent work, however, suggests that optimal experimental design techniques can select sets of experiments whose members probe complementary aspects of a biochemical network that together can account for its full behaviour. Here, we implemented an experimental design approach for selecting sets of experiments that constrain parameter uncertainty. We demonstrated with a model of the epidermal growth factor-nerve growth factor pathway that, after synthetically performing a handful of optimal experiments, the uncertainty in all 48 parameters converged below 10 per cent. Furthermore, the fitted parameters converged to their true values with a small error consistent with the residual uncertainty. When untested experimental conditions were simulated with the fitted models, the predicted species concentrations converged to their true values with errors that were consistent with the residual uncertainty. This paper suggests that accurate parameter estimation is achievable with complementary experiments specifically designed for the task, and that the resulting parametrized models are capable of accurate predictions.
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