METANNOGEN: compiling features of biochemical reactions needed for the reconstruction of metabolic networks

Background  

One central goal of computational systems biology is the mathematical modelling of complex metabolic reaction networks. The first and most time-consuming step in the development of such models consists in the stoichiometric reconstruction of the network, i. e. compilation of all metabolites, reactions and transport processes relevant to the considered network and their assignment to the various cellular compartments. Therefore an information system is required to collect and manage data from different databases and scientific literature in order to generate a metabolic network of biochemical reactions that can be subjected to further computational analyses.     

Meta-All: a system for managing metabolic pathway information

Background  

Many attempts are being made to understand biological subjects at a systems level. A major resource for these approaches are biological databases, storing manifold information about DNA, RNA and protein sequences including their functional and structural motifs, molecular markers, mRNA expression levels, metabolite concentrations, protein-protein interactions, phenotypic traits or taxonomic relationships. The use of these databases is often hampered by the fact that they are designed for special application areas and thus lack universality. Databases on metabolic pathways, which provide an increasingly important foundation for many analyses of biochemical processes at a systems level, are no exception from the rule. Data stored in central databases such as KEGG, BRENDA or SABIO-RK is often limited to read-only access. If experimentalists want to store their own data, possibly still under investigation, there are two possibilities. They can either develop their own information system for managing that own data, which is very time-consuming and costly, or they can try to store their data in existing systems, which is often restricted. Hence, an out-of-the-box information system for managing metabolic pathway data is needed.     

Correcting ligands, metabolites, and pathways

Background  

A wide range of research areas in bioinformatics, molecular biology and medicinal chemistry require precise chemical structure information about molecules and reactions, e.g. drug design, ligand docking, metabolic network reconstruction, and systems biology. Most available databases, however, treat chemical structures more as illustrations than as a datafield in its own right. Lack of chemical accuracy impedes progress in the areas mentioned above. We present a database of metabolites called BioMeta that augments the existing pathway databases by explicitly assessing the validity, correctness, and completeness of chemical structure and reaction information.     

Visualizing regulatory interactions in metabolic networks

Background  

Direct visualization of data sets in the context of biochemical network drawings is one of the most appealing approaches in the field of data evaluation within systems biology. One important type of information that is very helpful in interpreting and understanding metabolic networks has been overlooked so far. Here we focus on the representation of this type of information given by the strength of regulatory interactions between metabolite pools and reaction steps.     

A transcriptional analysis of carotenoid, chlorophyll and plastidial isoprenoid biosynthesis genes during development and osmotic stress responses in Arabidopsis thaliana

Background

Multiple pathway databases are available that describe the human metabolic network and have proven their usefulness in many applications, ranging from the analysis and interpretation of high-throughput data to their use as a reference repository. However, so far the various human metabolic networks described by these databases have not been systematically compared and contrasted, nor has the extent to which they differ been quantified. For a researcher using these databases for particular analyses of human metabolism, it is crucial to know the extent of the differences in content and their underlying causes. Moreover, the outcomes of such a comparison are important for ongoing integration efforts.

Results

We compared the genes, EC numbers and reactions of five frequently used human metabolic pathway databases. The overlap is surprisingly low, especially on reaction level, where the databases agree on 3% of the 6968 reactions they have combined. Even for the well-established tricarboxylic acid cycle the databases agree on only 5 out of the 30 reactions in total. We identified the main causes for the lack of overlap. Importantly, the databases are partly complementary. Other explanations include the number of steps a conversion is described in and the number of possible alternative substrates listed. Missing metabolite identifiers and ambiguous names for metabolites also affect the comparison.

Conclusions

Our results show that each of the five networks compared provides us with a valuable piece of the puzzle of the complete reconstruction of the human metabolic network. To enable integration of the networks, next to a need for standardizing the metabolite names and identifiers, the conceptual differences between the databases should be resolved. Considerable manual intervention is required to reach the ultimate goal of a unified and biologically accurate model for studying the systems biology of human metabolism. Our comparison provides a stepping stone for such an endeavor.     

A new dynamical layout algorithm for complex biochemical reaction networks

Background  

To study complex biochemical reaction networks in living cells researchers more and more rely on databases and computational methods. In order to facilitate computational approaches, visualisation techniques are highly important. Biochemical reaction networks, e.g. metabolic pathways are often depicted as graphs and these graphs should be drawn dynamically to provide flexibility in the context of different data. Conventional layout algorithms are not sufficient for every kind of pathway in biochemical research. This is mainly due to certain conventions to which biochemists/biologists are used to and which are not in accordance to conventional layout algorithms. A number of approaches has been developed to improve this situation. Some of these are used in the context of biochemical databases and make more or less use of the information in these databases to aid the layout process. However, visualisation becomes also more and more important in modelling and simulation tools which mostly do not offer additional connections to databases. Therefore, layout algorithms used in these tools have to work independently of any databases. In addition, all of the existing algorithms face some limitations with respect to the number of edge crossings when it comes to larger biochemical systems due to the interconnectivity of these. Last but not least, in some cases, biochemical conventions are not met properly.     

Improving the <Emphasis Type="Italic">i</Emphasis>MM904 <Emphasis Type="Italic">S. cerevisiae</Emphasis> metabolic model using essentiality and synthetic lethality data

Background  

Saccharomyces cerevisiae is the first eukaryotic organism for which a multi-compartment genome-scale metabolic model was constructed. Since then a sequence of improved metabolic reconstructions for yeast has been introduced. These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions. They have also served as a starting point and a benchmark for the reconstruction of genome-scale metabolic models for other eukaryotic organisms. In spite of the successive improvements in the details of the described metabolic processes, even the recent yeast model (i.e., i MM904) remains significantly less predictive than the latest E. coli model (i.e., i AF1260). This is manifested by its significantly lower specificity in predicting the outcome of grow/no grow experiments in comparison to the E. coli model.     

Flux-sum analysis: a metabolite-centric approach for understanding the metabolic network

Background  

Constraint-based flux analysis of metabolic network model quantifies the reaction flux distribution to characterize the state of cellular metabolism. However, metabolites are key players in the metabolic network and the current reaction-centric approach may not account for the effect of metabolite perturbation on the cellular physiology due to the inherent limitation in model formulation. Thus, it would be practical to incorporate the metabolite states into the model for the analysis of the network.     

FunctSNP: an R package to link SNPs to functional knowledge and dbAutoMaker: a suite of Perl scripts to build SNP databases

Background  

Whole genome association studies using highly dense single nucleotide polymorphisms (SNPs) are a set of methods to identify DNA markers associated with variation in a particular complex trait of interest. One of the main outcomes from these studies is a subset of statistically significant SNPs. Finding the potential biological functions of such SNPs can be an important step towards further use in human and agricultural populations (e.g., for identifying genes related to susceptibility to complex diseases or genes playing key roles in development or performance). The current challenge is that the information holding the clues to SNP functions is distributed across many different databases. Efficient bioinformatics tools are therefore needed to seamlessly integrate up-to-date functional information on SNPs. Many web services have arisen to meet the challenge but most work only within the framework of human medical research. Although we acknowledge the importance of human research, we identify there is a need for SNP annotation tools for other organisms.     

Compartmentalization of the Edinburgh Human Metabolic Network

Background  

Direct in vivo investigation of human metabolism is complicated by the distinct metabolic functions of various sub-cellular organelles. Diverse micro-environments in different organelles may lead to distinct functions of the same protein and the use of different enzymes for the same metabolic reaction. To better understand the complexity in the human metabolism, a compartmentalized human metabolic network with integrated sub-cellular location information is required.     

FiatFlux – a software for metabolic flux analysis from <Superscript>13</Superscript>C-glucose experiments

Background  

Quantitative knowledge of intracellular fluxes is important for a comprehensive characterization of metabolic networks and their functional operation. In contrast to direct assessment of metabolite concentrations, in vivo metabolite fluxes must be inferred indirectly from measurable quantities in ¹³C experiments. The required experience, the complicated network models, large and heterogeneous data sets, and the time-consuming set-up of highly controlled experimental conditions largely restricted metabolic flux analysis to few expert groups. A conceptual simplification of flux analysis is the analytical determination of metabolic flux ratios exclusively from MS data, which can then be used in a second step to estimate absolute in vivo fluxes.     

Optimization based automated curation of metabolic reconstructions

Background  

Currently, there exists tens of different microbial and eukaryotic metabolic reconstructions (e.g., Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis) with many more under development. All of these reconstructions are inherently incomplete with some functionalities missing due to the lack of experimental and/or homology information. A key challenge in the automated generation of genome-scale reconstructions is the elucidation of these gaps and the subsequent generation of hypotheses to bridge them.     

PathwayBooster: a tool to support the curation of metabolic pathways

Background

Despite several recent advances in the automated generation of draft metabolic reconstructions, the manual curation of these networks to produce high quality genome-scale metabolic models remains a labour-intensive and challenging task.

Results

We present PathwayBooster, an open-source software tool to support the manual comparison and curation of metabolic models. It combines gene annotations from GenBank files and other sources with information retrieved from the metabolic databases BRENDA and KEGG to produce a set of pathway diagrams and reports summarising the evidence for the presence of a reaction in a given organism’s metabolic network. By comparing multiple sources of evidence within a common framework, PathwayBooster assists the curator in the identification of likely false positive (misannotated enzyme) and false negative (pathway hole) reactions. Reaction evidence may be taken from alternative annotations of the same genome and/or a set of closely related organisms.

Conclusions

By integrating and visualising evidence from multiple sources, PathwayBooster reduces the manual effort required in the curation of a metabolic model. The software is available online at http://www.theosysbio.bio.ic.ac.uk/resources/pathwaybooster/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0447-2) contains supplementary material, which is available to authorized users.     

Dynamics and Control of the Central Carbon Metabolism in Hepatoma Cells

Background  

The liver plays a major role in metabolism and performs a number of vital functions in the body. Therefore, the determination of hepatic metabolite dynamics and the analysis of the control of the respective biochemical pathways are of great pharmacological and medical importance. Extra- and intracellular time-series data from stimulus-response experiments are gaining in importance in the identification of in vivo metabolite dynamics, while dynamic network models are excellent tools for analyzing complex metabolic control patterns. This is the first study that has been undertaken on the data-driven identification of a dynamic liver central carbon metabolism model and its application in the analysis of the distribution of metabolic control in hepatoma cells.     

Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'

Background  

Metabolomics experiments using Mass Spectrometry (MS) technology measure the mass to charge ratio (m/z) and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensional metabolite 'fingerprint' or metabolite 'profile' data. High resolution MS instruments perform routinely with a mass accuracy of < 5 ppm (parts per million) thus providing potentially a direct method for signal putative annotation using databases containing metabolite mass information. Most database interfaces support only simple queries with the default assumption that molecules either gain or lose a single proton when ionised. In reality the annotation process is confounded by the fact that many ionisation products will be not only molecular isotopes but also salt/solvent adducts and neutral loss fragments of original metabolites. This report describes an annotation strategy that will allow searching based on all potential ionisation products predicted to form during electrospray ionisation (ESI).     

Gene and protein nomenclature in public databases

Background  

Frequently, several alternative names are in use for biological objects such as genes and proteins. Applications like manual literature search, automated text-mining, named entity identification, gene/protein annotation, and linking of knowledge from different information sources require the knowledge of all used names referring to a given gene or protein. Various organism-specific or general public databases aim at organizing knowledge about genes and proteins. These databases can be used for deriving gene and protein name dictionaries. So far, little is known about the differences between databases in terms of size, ambiguities and overlap.     

Robust detection and verification of linear relationships to generate metabolic networks using estimates of technical errors

Background  

The size and magnitude of the metabolome, the ratio between individual metabolites and the response of metabolic networks is controlled by multiple cellular factors. A tight control over metabolite ratios will be reflected by a linear relationship of pairs of metabolite due to the flexibility of metabolic pathways. Hence, unbiased detection and validation of linear metabolic variance can be interpreted in terms of biological control. For robust analyses, criteria for rejecting or accepting linearities need to be developed despite technical measurement errors. The entirety of all pair wise linear metabolic relationships then yields insights into the network of cellular regulation.     

Domain fusion analysis by applying relational algebra to protein sequence and domain databases

Background  

Domain fusion analysis is a useful method to predict functionally linked proteins that may be involved in direct protein-protein interactions or in the same metabolic or signaling pathway. As separate domain databases like BLOCKS, PROSITE, Pfam, SMART, PRINTS-S, ProDom, TIGRFAMs, and amalgamated domain databases like InterPro continue to grow in size and quality, a computational method to perform domain fusion analysis that leverages on these efforts will become increasingly powerful.     

Of Monkeys and Men: A Metabolomic Analysis of Static and Dynamic Urinary Metabolic Phenotypes in Two Species

Background

Metabolomics has attracted the interest of the medical community for its potential in predicting early derangements from a healthy to a diseased metabolic phenotype. One key issue is the diversity observed in metabolic profiles of different healthy individuals, commonly attributed to the variation of intrinsic (such as (epi)genetic variation, gut microbiota, etc.) and extrinsic factors (such as dietary habits, life-style and environmental conditions). Understanding the relative contributions of these factors is essential to establish the robustness of the healthy individual metabolic phenotype.

Methods

To assess the relative contribution of intrinsic and extrinsic factors we compared multilevel analysis results obtained from subjects of Homo sapiens and Macaca mulatta, the latter kept in a controlled environment with a standardized diet by making use of previously published data and results.

Results

We observed similarities for the two species and found the diversity of urinary metabolic phenotypes as identified by nuclear magnetic resonance (NMR) spectroscopy could be ascribed to the complex interplay of intrinsic factors and, to a lesser extent, of extrinsic factors in particular minimizing the role played by diet in shaping the metabolic phenotype. Moreover, we show that despite the standardization of diet as the most relevant extrinsic factor, a clear individual and discriminative metabolic fingerprint also exists for monkeys. We investigate the metabolic phenotype both at the static (i.e., at the level of the average metabolite concentration) and at the dynamic level (i.e., concerning their variation over time), and we show that these two components sum up to the overall phenotype with different relative contributions of about 1/4 and 3/4, respectively, for both species. Finally, we show that the great degree diversity observed in the urinary metabolic phenotype of both species can be attributed to differences in both the static and dynamic part of their phenotype.     

OpenFLUX: efficient modelling software for 13C-based metabolic flux analysis

Background  

The quantitative analysis of metabolic fluxes, i.e., in vivo activities of intracellular enzymes and pathways, provides key information on biological systems in systems biology and metabolic engineering. It is based on a comprehensive approach combining (i) tracer cultivation on ¹³C substrates, (ii) ¹³C labelling analysis by mass spectrometry and (iii) mathematical modelling for experimental design, data processing, flux calculation and statistics. Whereas the cultivation and the analytical part is fairly advanced, a lack of appropriate modelling software solutions for all modelling aspects in flux studies is limiting the application of metabolic flux analysis.