MMT--a pathway modeling tool for data from rapid sampling experiments.

In Silico Biol

Institut fuer Biotechnologie 2, Forschungszentrum Juelich GmbH, 52425 Jülich, Germany.

Published: November 2003

AI Article Synopsis

  • The study of metabolic regulation is crucial in metabolic engineering, as it depends on understanding various intracellular compounds like enzymes and metabolites.
  • Rapid sampling techniques, along with tools like ESI-LC-MS and HPLC, have enabled the identification of multiple metabolites, aiding in the collection of quantitative data necessary for modeling.
  • The development of a Metabolic Modeling Tool (MMT) simplifies the analysis of complex metabolic pathways by integrating necessary algorithms, thus allowing for efficient modeling and simulation of metabolic behaviors in organisms like E. coli during experiments.

Article Abstract

The identification of metabolic regulation is a major concern in metabolic engineering. Metabolic regulation phenomena depend on intracellular compounds such as enzymes, metabolites and cofactors. A complete understanding of metabolic regulation requires quantitative information about these compounds under in vivo conditions. This quantitative knowledge in combination with the known network of metabolic pathways allows the construction of mathematical models that describe the dynamic changes in metabolite concentrations over time. Rapid sampling combined with pulse experiments is a useful tool for the identification of metabolic regulation owing to the transient data they provide. Enzymatic tests in combination with ESI-LC-MS (Electrospray Ionization Liquid Chromatographic Tandem Mass Spectrometry) and HPLC measurements have been used to identify up to 30 metabolites and nucleotides from rapid sampling experiments. A metabolic modeling tool (MMT) that is built on a relational database was developed specifically for analysis of rapid sampling experiments. The tool allows to construct complex pathway models with information stored in the relational database. Parameter fitting and simulation algorithms for the resulting system of Ordinary Differential Equations (ODEs) are part of MMT. Additionally explicit sensitivity functions are calculated. The integration of all necessary algorithms in one tool allows fast model analysis and comparison. Complex models have been developed to describe the central metabolic pathways of Escherichia coli during a glucose pulse experiment.

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