title: Identifying optimal models to represent biochemical systems.
abstract: Biochemical systems involving a high number of components with intricate interactions often lead to complex models containing a large number of parameters. Although a large model could describe in detail the mechanisms that underlie the system, its very large size may hinder us in understanding the key elements of the system. Also in terms of parameter identification, large models are often problematic. Therefore, a reduced model may be preferred to represent the system. Yet, in order to efficaciously replace the large model, the reduced model should have the same ability as the large model to produce reliable predictions for a broad set of testable experimental conditions. We present a novel method to extract an "optimal" reduced model from a large model to represent biochemical systems by combining a reduction method and a model discrimination method. The former assures that the reduced model contains only those components that are important to produce the dynamics observed in given experiments, whereas the latter ensures that the reduced model gives a good prediction for any feasible experimental conditions that are relevant to answer questions at hand. These two techniques are applied iteratively. The method reveals the biological core of a model mathematically, indicating the processes that are likely to be responsible for certain behavior. We demonstrate the algorithm on two realistic model examples. We show that in both cases the core is substantially smaller than the full model.
authors.affiliation: Biometris, Wageningen University, Wageningen, The Netherlands ; Netherlands Consortium for Systems Biology, Amsterdam, The Netherlands ; Industrial and Financial Mathematics Group, Bandung Institute of Technology, Bandung, Indonesia.
authors.forename: Mochamad
authors.initials: M
authors.lastname: Apri
authors.affiliation: Biometris, Wageningen University, Wageningen, The Netherlands ; Netherlands Consortium for Systems Biology, Amsterdam, The Netherlands.
authors.forename: Maarten
authors.initials: M
authors.lastname: de Gee
authors.affiliation: Biometris, Wageningen University, Wageningen, The Netherlands ; Netherlands Consortium for Systems Biology, Amsterdam, The Netherlands.
authors.forename: Simon
authors.initials: S
authors.lastname: van Mourik
authors.affiliation: Biometris, Wageningen University, Wageningen, The Netherlands ; Netherlands Consortium for Systems Biology, Amsterdam, The Netherlands.
authors.forename: Jaap
authors.initials: J
authors.lastname: Molenaar
mesh_terms: D017360:Arabidopsis / Q000235:genetics / Q000502:physiology; D066246:ErbB Receptors / Q000378:metabolism; D035264:Flowers / Q000235:genetics / Q000502:physiology; D053263:Gene Regulatory Networks; D017343:Genes, Plant; D008954:Models, Biological; D015203:Reproducibility of Results; D049490:Systems Biology
chemical_list: D066246:ErbB Receptors