Use of Randomized Sampling for Analysis of Metabolic Networks Export

@article{citeulike:4022637, abstract = {10.1074/jbc.R800048200 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.}, author = {Schellenberger, Jan and Palsson, Bernhard}, citeulike-article-id = {4022637}, citeulike-linkout-0 = {http://dx.doi.org/10.1074/jbc.R800048200}, citeulike-linkout-1 = {http://www.jbc.org/content/284/9/5457.abstract}, citeulike-linkout-2 = {http://www.jbc.org/content/284/9/5457.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/18940807}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=18940807}, day = {27}, doi = {10.1074/jbc.R800048200}, journal = {Journal of Biological Chemistry}, keywords = {fluxomics, metabolomics}, month = {February}, number = {9}, pages = {5457--5461}, posted-at = {2009-11-06 02:51:35}, priority = {2}, title = {Use of Randomized Sampling for Analysis of Metabolic Networks}, url = {http://dx.doi.org/10.1074/jbc.R800048200}, volume = {284}, year = {2009} }

TY - JOUR ID - citeulike:4022637 L3 - citeulike-article-id:4022637 N2 - 10.1074/jbc.R800048200 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. IS - 9 JF - Journal of Biological Chemistry EP - 5461 TI - Use of Randomized Sampling for Analysis of Metabolic Networks VL - 284 SP - 5457 KW - fluxomics KW - metabolomics AU - Schellenberger, Jan AU - Palsson, Bernhard PY - 2009/02/27/ UR - http://dx.doi.org/10.1074/jbc.R800048200 ER -