Global reconstruction of the human metabolic network based on genomic and bibliomic data

By Natalie C Duarte, Scott A. Becker, N. Jamshidi, Ines Thiele, Monica L. Mo, Thuy D. Vo, R. Srivas, Bernhard O. Palsson. Proceedings of the National Academy of Sciences of the United States of America, 2007

1,461 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a15390c697fe38d4e6aa64127b7082027b732072

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Citations in the corpus

Applications of genome-scale metabolic reconstructions (2009, methodology): “The networks were derived from the global human metabolic network reconstruction (Duarte et al, 2007), and 13C-flux, literature-derived phenotypic data, and in silico computed flux states were used to discern differences between the normal and diseased states.”
The implications of human metabolic network topology for disease comorbidity (2008, methodology): “As a starting point of our analysis, we used the Kyoto Encyclopedia of Genes and Genomes (KEGG) Ligand database (15) and a database of biochemically, genetically and genomically structured genome-scale metabolic network reconstructions (BiGG) (16), each representing a manually curated list of metabo”
NCBI GEO: archive for functional genomics data sets—10 years on (2010, background): “Others have attempted to define systems-wide relationships and maps between GEO expression data and other data types such as gene pathways (14,15) and ontologies (16).”
Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT (2012, methodology): “Database construction The existing methods for the inference of tissue specific active metabolic networks have only used Recon1 as a scaffold.”
Constraint-based models predict metabolic and associated cellular functions (2014, background): “63) — is incomplete owing to gaps in our knowledge of human metabolism.”
An atlas of human metabolism (2020, methodology): “This effort began in earnest with the development of Recon1 (10) and the Edinburgh Human Metabolic Network (EHMN) (11), which served as the starting point for two parallel model series: the Recon series (Recon1, 2, and 3D) (10, 12, 13) and the Human Metabolic Reaction series (HMR1 and 2) (14, 15).”
BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions (2016, background): “In previous studies, particular attention has been paid to a variety of molecular interaction networks and their potential roles in disease mechanism and drug development [1, 4–7], including transcriptional and post-transcriptional regulatory networks [8–10], functional RNA networks [11–13], protein”
Flux balance analysis of biological systems: applications and challenges (2009, background): “Reconstructions of human metabolism have also been reported [43, 44].”
Structure-based Systems Biology for Analyzing Off-target Binding (2011, background): “Recent advances in the proteome-wide reconstruction of human metabolic networks [115] and algorithms for context-specific modeling [116,117] make it possible to simulate drug induced perturbations in human.”
Computationally efficient flux variability analysis (2010, neutral): “Human [16] 3820 2785”