Experiment design for parameter estimation in models combining signal transduction and metabolic pathways: control of nitrogen uptake in yeast

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Abstract

Biological complexity and limited quantitative measurements impose severe challenges to standard engineering methodologies for modelling and simulation of genes and gene products integrated in a functional network. Especially parameter quantification is a bottleneck and therefore parameter estimation, identifiability and optimal experiment design are important research topics in systems biology.As a case study, the pathways that control the nitrogen uptake fluxes in baker's yeast (Saccharomyces cerevisiae) have been studied. The regulation mechanism known as Nitrogen Catabolic Repression (NCR) enables the microorganism to optimally respond to changes in nitrogen availability. An ongoing discussion between cell biologists is whether only a signal derived from intracellular glutamine triggers NCR or that other signals might be involved. Well-defined perturbation experiments and a nonlinear state-space model were applied to investigate the dynamics of the underlying metabolic and genetic control.The 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. To prevent that the entire central metabolic pathways had to be modeled, a modular approach was adopted. Unmodeled dynamics have been replaced by a ‘dependent input’, which is the measured intracellular glutamine profile. 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 testing this model on data generated by a variation (mutant), so long as the mutations only affect the unmodeled dynamics that produce the dependent inputs. It was previously shown that the developed model is structurally identifiable given data of its inputs and outputs (Van Riel and Sontag, IEE Syst. Biol., 2006, in press). Here the possibility to identify the model parameters given the actual data from the perturbation experiments has been investigated (practical identifiability). It is shown how the inclusion of a priori information in a multi-objective identification criterion, makes it possible to obtain unique estimates of the parameter values from a relatively limited experimental dataset. The identified model was validated with data from 2 knock-out cell lines, mutated in one of the nitrogen catabolic enzymes. Finally, based on an analysis of the numerical optimization problem involved in estimating the model parameters new perturbation experiments were designed and carried out that yield a more optimal excitation of the system dynamics, increasing the accuracy of the estimated parameters.
Original languageEnglish
Title of host publicationSeventh International Conference on Systems Biology
Place of PublicationJapan, Yokohama
PagesCD-rom
Publication statusPublished - 2006

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