Sentence examples for metabolic models using from inspiring English sources

In this contribution a computer framework is described which allows to build, analyze and visualize modular structured metabolic models using a abstract and general modeling methodology.

CytoSEED provides Model SEED users with a complementary means of visualizing their metabolic models using Cytoscape (Shannon et al., 2003).

In the process, we derived the strain-specific metabolic models using two commonly used algorithms of top-down metabolic reconstructions, including GIMME [ 21] and iMAT [ 22].

We have created Metingear, a desktop application to assist in the annotation of metabolic models using both manual and automated techniques.

Matthias Heinemann (ETH Zurich, Switzerland) presented a novel algorithm called Network Embedded Thermodynamic (NET) analysis [ 11] which systematically assigns reaction directionalities in genome-scale metabolic models using available thermodynamic information.

The analysis of the resulting genome-scale metabolic models using techniques such as flux balance analysis (FBA) [ 3, 4] can reveal important aspects of metabolism and regulation [ 5], help identify essential genes [ 6, 7] and potential drug targets [ 8], and suggest approaches to engineer new pathways to synthesize or degrade compounds of economic importance [ 9].

The metabolic models used in this study are included in additional file 1: Supplement.

The genome-scale metabolic models used in this text [ 10] are typical in that they are compartmentalized into standard intracellular compartments separated by membrane barriers, such as mitochondria.

Additional file 1: Table S1 lists the model attributes and perturbations associated with the three genome-scale metabolic models used in this study: E. coli, S. cerevisiae, and S. oneidensis.

Since the metabolic models used for this analysis can also be used in flux balance analysis (FBA), methods to combine computational flux estimates obtained from FBA with experimental data will be of particular interest in order to extend and refine our understanding of metabolic network function.

Under these premises, metabolic modelling using flux balance analysis (FBA)18 is the only approach that can predict the effect of genetic and environmental perturbations in the disruption of such metabolic phenotypes at the genome scale19,20, and applications of these models for studying cancer or metabolic diseases have been advocated21,22,23,24.