Journal Article

System estimation from metabolic time-series data

Gautam Goel, I-Chun Chou and Eberhard O. Voit

in Bioinformatics

Volume 24, issue 21, pages 2505-2511
Published in print November 2008 | ISSN: 1367-4803
Published online September 2008 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btn470
System estimation from metabolic time-series data

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Motivation: At the center of computational systems biology are mathematical models that capture the dynamics of biological systems and offer novel insights. The bottleneck in the construction of these models is presently the identification of model parameters that make the model consistent with observed data. Dynamic flux estimation (DFE) is a novel methodological framework for estimating parameters for models of metabolic systems from time-series data. DFE consists of two distinct phases, an entirely model-free and assumption-free data analysis and a model-based mathematical characterization of process representations. The model-free phase reveals inconsistencies within the data, and between data and the alleged system topology, while the model-based phase allows quantitative diagnostics of whether—or to what degree—the assumed mathematical formulations are appropriate or in need of improvement. Hallmarks of DFE are the facility to: diagnose data and model consistency; circumvent undue compensation of errors; determine functional representations of fluxes uncontaminated by errors in other fluxes and pinpoint sources of remaining errors. Our results suggest that the proposed approach is more effective and robust than presently available methods for deriving metabolic models from time-series data. Its avoidance of error compensation among process descriptions promises significantly improved extrapolability toward new data or experimental conditions.

Contact: eberhard.voit@bme.gatech.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5407 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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