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/.

We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a “cycle of knowledge” strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought.

BackgroundTo date, several genome-scale network reconstructions have been used to describe the metabolism of the yeast Saccharomyces cerevisiae, each differing in scope and content. The recent community-driven reconstruction, while rigorously evidenced and well annotated, under-represented metabolite transport, lipid metabolism and other pathways, and was not amenable to constraint-based analyses because of lack of pathway connectivity.ResultsWe have expanded the yeast network reconstruction to incorporate many new reactions from the literature and represented these in a well-annotated and standards-compliant manner. The new reconstruction comprises 1102 unique metabolic reactions involving 924 unique metabolites - significantly larger in scope than any previous reconstruction. The representation of lipid metabolism in particular has improved, with 234 out of 268 enzymes linked to lipid metabolism now present in at least one reaction. Connectivity is emphatically improved, with more than 90% of metabolites now reachable from the growth medium constituents. The present updates allow constraint-based analyses to be performed; viability predictions of single knockouts are comparable to results from in vivo experiments and to those of previous reconstructions.ConclusionsWe report the development of the most complete reconstruction of yeast metabolism to date that is based upon reliable literature evidence and richly annotated according to MIRIAM standards. The reconstruction is available in the Systems Biology Markup Language (SBML) and via a publicly accessible database http://www.comp-sys-bio.org/yeastnet/.

Evolution of mitochondrial genes is far from clock-like. The substitution rate varies considerably between species, and there are many species that have a significantly increased rate with respect to their close relatives. There is also considerable variation among species in the rate of gene order rearrangement. Using a set of 55 complete arthropod mitochondrial genomes, we estimate the evolutionary distance from the common ancestor to each species using protein sequences, tRNA sequences, and breakpoint distances (a measure of the degree of genome rearrangement). All these distance measures are correlated. We use relative rate tests to compare pairs of related species in several animal phyla. In the majority of cases, the species with the more highly rearranged genome also has a significantly higher rate of sequence evolution. Species with higher amino acid substitution rates in mitochondria also have more variable amino acid composition in response to mutation pressure. We discuss the possible causes of variation in rates of sequence evolution and gene rearrangement among species and the possible reasons for the observed correlation between the two rates.

Organellar Genome Retrieval (OGRe) is a relational database of complete mitochondrial genome sequences for over 250 Metazoan species. OGRe provides a resource for the comparative analysis of mitochondrial genomes at several levels. At the sequence level, OGRe allows the retrieval of any selected set of mitochondrial genes from any selected set of species. Species are classified using a taxonomic system that allows easy selection of related groups of species. Sequence alignments are also available for some species. At the level of individual nucleotides, the system contains information on base frequencies and codon usage frequencies that can be compared between organisms. At the level of whole genomes, OGRe provides several ways of visualizing information on gene order. Diagrams illustrating the genome arrangement can be generated for any selected set of species automatically from the information in the database. Searches can be done based on gene arrangement to find sets of species that have the same order as one another. Diagrams for pairwise comparison of species can be produced that show the positions of break-points in the gene order and use colour to highlight the sections of the genome that have moved. OGRe is available from http://www.bioinf.man.ac.uk/ogre.

Animal mitochondrial genomes usually have two transfer RNAs for leucine: one, with anticodon UAG, translates the four-codon family CUN, while the other, with anticodon UAA, translates the two-codon family UUR. These two genes must differ at the third anticodon position, but in some species the genes differ at many additional sites, indicating that these genes have been independent for a long time. Duplication and deletion of genes in mitochondrial genomes occur frequently during the evolution of the Metazoa. If a tRNA-Leu gene were duplicated and a substitution occurred in the anticodon, this would effectively turn one type of tRNA into the other. The original copy of the second tRNA type might then be lost by a deletion elsewhere in the genome. There are several groups of species in which the two tRNA-Leu genes occur next to one another (or very close) on the genome, which suggests that tandem duplication has occurred. Here we use RNA-specific phylogenetic methods to determine evolutionary trees for both genes. We present evidence that the process of duplication, anticodon mutation, and deletion of tRNA-Leu genes has occurred at least five times during the evolution of the metazoa-once in the common ancestor of all protostomes, once in the common ancestor of echinoderms and hemichordates, once in the hermit crab, and twice independently in mollusks.

BackgroundThe behaviour of biological systems can be deduced from their mathematical models. However, multiple sources of data in diverse forms are required in the construction of a model in order to define its components and their biochemical reactions, and corresponding parameters. Automating the assembly and use of systems biology models is dependent upon data integration processes involving the interoperation of data and analytical resources.ResultsTaverna workflows have been developed for the automated assembly of quantitative parameterised metabolic networks in the Systems Biology Markup Language (SBML). A SBML model is built in a systematic fashion by the workflows which starts with the construction of a qualitative network using data from a MIRIAM-compliant genome-scale model of yeast metabolism. This is followed by parameterisation of the SBML model with experimental data from two repositories, the SABIO-RK enzyme kinetics database and a database of quantitative experimental results. The models are then calibrated and simulated in workflows that call out to COPASIWS, the web service interface to the COPASI software application for analysing biochemical networks. These systems biology workflows were evaluated for their ability to construct a parameterised model of yeast glycolysis.ConclusionsDistributed information about metabolic reactions that have been described to MIRIAM standards enables the automated assembly of quantitative systems biology models of metabolic networks based on user-defined criteria. Such data integration processes can be implemented as Taverna workflows to provide a rapid overview of the components and their relationships within a biochemical system.

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