The Edinburgh human metabolic network reconstruction and its functional analysis

A better understanding of human metabolism and its relationship with diseases is an important task in human systems biology studies. In this paper, we present a high-quality human metabolic network manually reconstructed by integrating genome annotation information from different databases and metabolic reaction information from literature. The network contains nearly 3000 metabolic reactions, which were reorganized into about 70 human-specific metabolic pathways according to their functional relationships. By analysis of the functional connectivity of the metabolites in the network, the bow-tie structure, which was found previously by structure analysis, is reconfirmed. Furthermore, the distribution of the disease related genes in the network suggests that the IN (substrates) subset of the bow-tie structure has more flexibility than other parts

Reconstruction of Metabolic Networks Using Incomplete Information

This paper describes an approach that uses methods for automated sequence analysis (Gaasterland et al. August 1994) and multiple databases accessed through an object+attribute view of the data (Baehr et al. 1992), together with metabolic pathways, reaction equations, and compounds parsed into a logical representation from the Enzyme and Metabolic Pathway Database (Selkov, Yunus, & et.al. 1994), as the sources of data for automatically reconstructing a weighted partial metabolic network for a prokaryotic organism. Additional information can be provided interactively by the expert user to guide reconstruction. Introduction As available genome sequence data for microbial organisms increases both in the amount of data for individual organisms and in the number of organisms with data, we ask: how much of an organism's metabolic structure can be pieced together using sequence evidence, knowledge about metabolism, and encoded metabolic pathways ? How much of this process can be automated? H..

Terry Gaasterland

This paper describes an approach that uses methods for automated sequence analysis [GLMC94] and multiple databases accessed through an object+attribute view of the data [BDG + 92], together with metabolic pathways, reaction equations, and compounds parsed into a logical representation from the Enzyme and Metabolic Pathway Database [SYet.al.94], as the sources of data for automatically reconstructing a weighted partial metabolic network for a prokaryotic organism. Additional information can be provided interactively by the expert user to guide reconstruction. 1 Introduction As available genome sequence data for microbial organisms increases both in the amount of data for individual organisms and in the number of organisms with data, we ask: how much of an organism's metabolic structure can be pieced together using sequence evidence, knowledge about metabolism, and encoded metabolic pathways? How much of this process can be automated? How can the resulting tool be used to help people ..

A community-driven global reconstruction of human metabolism

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2× more reactions and ~1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/