Parameter estimation in models combining signal transduction and metabolic pathways: The dependent input approach

N. A.W. van Riel, E. D. Sontag

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


Biological complexity and limited quantitative measurements pose severe challenges to standard engineering methodologies for modelling and simulation of genes and gene products integrated in a functional network. In particular, parameter quantification is a bottleneck, and therefore parameter estimation, identifiability, and optimal experiment design are important research topics in systems biology. An approach is presented in which unmodelled dynamics are replaced by fictitious 'dependent inputs'. The dependent input approach is particularly useful in validation experiments, because it allows one to fit model parameters to experimental data generated by a reference cell type ('wild-type') and then test this model on data generated by a variation ('mutant'), so long as the mutations only affect the unmodelled dynamics that produce the dependent inputs. Another novel feature of the approach is in the inclusion of a priori information in a multi-objective identification criterion, making it possible to obtain estimates of parameter values and their variances from a relatively limited experimental data set. The pathways that control the nitrogen uptake fluxes in baker's yeast (Saccharomyces cerevisiae) have been studied. Well-defined perturbation experiments were performed on cells growing in steady-state. Time-series data of extracellular and intracellular metabolites were obtained, as well as mRNA levels. A nonlinear model was proposed and was shown to be structurally identifiable given data of its inputs and outputs. The identified model is a reliable representation of the metabolic system, as it could correctly describe the responses of mutant cells and different perturbations. 2006

Original languageEnglish (US)
Pages (from-to)263-274
Number of pages12
JournalIEE Proceedings: Systems Biology
Issue number4
StatePublished - Jul 2006

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Molecular Medicine
  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Cell Biology


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