title: A consensus approach for estimating the predictive accuracy of dynamic models in biology.
abstract: Mathematical 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.
authors.affiliation: Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, 36208 Vigo, Spain. Electronic address: afvillaverde@iim.csic.es.
authors.forename: Alejandro F
authors.initials: AF
authors.lastname: Villaverde
authors.affiliation: Insilico Biotechnology AG, Meitnerstraße 8, 70563 Stuttgart, Germany. Electronic address: sophia.bongard@insilico-biotechnology.com.
authors.forename: Sophia
authors.initials: S
authors.lastname: Bongard
authors.affiliation: Insilico Biotechnology AG, Meitnerstraße 8, 70563 Stuttgart, Germany. Electronic address: klaus.mauch@insilico-biotechnology.com.
authors.forename: Klaus
authors.initials: K
authors.lastname: Mauch
authors.affiliation: Insilico Biotechnology AG, Meitnerstraße 8, 70563 Stuttgart, Germany. Electronic address: dirk.mueller@insilico-biotechnology.com.
authors.forename: Dirk
authors.initials: D
authors.lastname: Müller
authors.affiliation: Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, 36208 Vigo, Spain. Electronic address: ebalsa@iim.csic.es.
authors.forename: Eva
authors.initials: E
authors.lastname: Balsa-Canto
authors.affiliation: Insilico Biotechnology AG, Meitnerstraße 8, 70563 Stuttgart, Germany. Electronic address: joachim.schmid@insilico-biotechnology.com.
authors.forename: Joachim
authors.initials: J
authors.lastname: Schmid
authors.affiliation: Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, 36208 Vigo, Spain. Electronic address: julio@iim.csic.es.
authors.forename: Julio R
authors.initials: JR
authors.lastname: Banga
mesh_terms: D000818:Animals; D016466:CHO Cells; D019295:Computational Biology; D006224:Cricetinae; D003412:Cricetulus; D005285:Fermentation; D007700:Kinetics; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D011994:Recombinant Proteins / Q000096:biosynthesis; D015203:Reproducibility of Results; D049490:Systems Biology
chemical_list: D011994:Recombinant Proteins
keywords: Cell line engineering; Consensus; Dynamic modelling; Ensemble modelling; Metabolic engineering; Systems biology