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Title

A consensus approach for estimating the predictive accuracy of dynamic models in biology

AuthorsVillaverde, A. F. ; Bongard, Sophia; Mauch, Klaus; Müller, Dirk; Balsa-Canto, Eva ; Schmid, Joachim; Banga, Julio R.
KeywordsSystems biology
Metabolic engineering
Cell line engineering
Dynamic modelling
Ensemble modelling
Consensus
Issue Date2015
PublisherElsevier
CitationComputer Methods and Programs in Biomedicine 119(1): 17-28 (2015)
AbstractMathematical models that predict the complex dynamic behaviour of cellular networks are fundamental in systems biology, and provide an important basis for biomedical and biotechnological applications. However, obtaining reliable predictions from large-scale dynamic models is commonly a challenging task due to lack of identifiability. The present work addresses this challenge by presenting a methodology for obtaining high-confidence predictions from dynamic models using time-series data. First, to preserve the complex behaviour of the network while reducing the number of estimated parameters, model parameters are combined in sets of meta-parameters, which are obtained from correlations between biochemical reaction rates and between concentrations of the chemical species. Next, an ensemble of models with different parameterizations is constructed and calibrated. Finally, the ensemble is used for assessing the reliability of model predictions by defining a measure of convergence of model outputs (consensus) that is used as an indicator of confidence. We report results of computational tests carried out on a metabolic model of Chinese Hamster Ovary (CHO) cells, which are used for recombinant protein production. Using noisy simulated data, we find that the aggregated ensemble predictions are on average more accurate than the predictions of individual ensemble models. Furthermore, ensemble predictions with high consensus are statistically more accurate than ensemble predictions with large variance. The procedure provides quantitative estimates of the confidence in model predictions and enables the analysis of sufficiently complex networks as required for practical applications
Description12 páginas, 8 figuras, 1 tabla
Publisher version (URL)http://dx.doi.org/10.1016/j.cmpb.2015.02.001
URIhttp://hdl.handle.net/10261/111818
DOIhttp://dx.doi.org/10.1016/j.cmpb.2015.02.001
ISSN0169-2607
E-ISSN1872-7565
Appears in Collections:(IIM) Artículos
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