Use of Randomized Sampling for Analysis of Metabolic Networks

Genome-scale metabolic network reconstructions in microorganisms have been formulated and studied for about 8 years. The constraint-based approach has shown great promise in analyzing the systemic properties of these network reconstructions. Notably, constraint-based models have been used successful...

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Bibliographic Details
Published in:The Journal of biological chemistry 2009-02, Vol.284 (9), p.5457-5461
Main Authors: Schellenberger, Jan, Palsson, Bernhard Ø.
Format: Article
Language:English
Subjects:
Animals
Computer Simulation
Humans
Metabolic Networks and Pathways
Models, Biological
Monte Carlo Method
Signal Transduction
Systems Biology
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Summary:Genome-scale metabolic network reconstructions in microorganisms have been formulated and studied for about 8 years. The constraint-based approach has shown great promise in analyzing the systemic properties of these network reconstructions. Notably, constraint-based models have been used successfully to predict the phenotypic effects of knock-outs and for metabolic engineering. The inherent uncertainty in both parameters and variables of large-scale models is significant and is well suited to study by Monte Carlo sampling of the solution space. These techniques have been applied extensively to the reaction rate (flux) space of networks, with more recent work focusing on dynamic/kinetic properties. Monte Carlo sampling as an analysis tool has many advantages, including the ability to work with missing data, the ability to apply post-processing techniques, and the ability to quantify uncertainty and to optimize experiments to reduce uncertainty. We present an overview of this emerging area of research in systems biology.
ISSN:0021-9258
1083-351X
DOI:10.1074/jbc.R800048200