MC3: a steady-state model and constraint consistency checker for biochemical networks

BACKGROUND: Stoichiometric models provide a structural framework for analyzing steady-state cellular behavior. Models are developed either through augmentations of existing models or more recently through automatic reconstruction tools. There is currently no standardized practice or method for validating the properties of a model before placing it in the public domain. Considerable effort is often required to understand a model’s inconsistencies before its reuse within new research efforts. RESULTS: We present a review of common issues in stoichiometric models typically uncovered during pathway analysis and constraint-based optimization, and we detail succinct and efficient ways to find them. We present MC(3), Model and Constraint Consistency Checker, a computational tool that can be used for two purposes: (a) identifying potential connectivity and topological issues for a given stoichiometric matrix, S, and (b) flagging issues that arise during constraint-based optimization. The MC(3) tool includes three distinct checking components. The first examines the results of computing the basis for the null space for Sv = 0; the second uses connectivity analysis; and the third utilizes Flux Variability Analysis. MC(3) takes as input a stoichiometric matrix and flux constraints, and generates a report summarizing issues. CONCLUSIONS: We report the results of applying MC(3) to published models for several systems including Escherichia coli, an adipocyte cell, a Chinese Hamster Ovary cell, and Leishmania major. Several issues with no prior documentation are identified. MC(3) provides a standalone MATLAB-based comprehensive tool for model validation, a task currently performed either ad hoc or implemented in part within other computational tools

Pathomx:an interactive workflow-based tool for the analysis of metabolomic data

BACKGROUND: Metabolomics is a systems approach to the analysis of cellular processes through small-molecule metabolite profiling. Standardisation of sample handling and acquisition approaches has contributed to reproducibility. However, the development of robust methods for the analysis of metabolomic data is a work-in-progress. The tools that do exist are often not well integrated, requiring manual data handling and custom scripting on a case-by-case basis. Furthermore, existing tools often require experience with programming environments such as MATLAB® or R to use, limiting accessibility. Here we present Pathomx, a workflow-based tool for the processing, analysis and visualisation of metabolomic and associated data in an intuitive and extensible environment. RESULTS: The core application provides a workflow editor, IPython kernel and a HumanCyc™-derived database of metabolites, proteins and genes. Toolkits provide reusable tools that may be linked together to create complex workflows. Pathomx is released with a base set of plugins for the import, processing and visualisation of data. The IPython backend provides integration with existing platforms including MATLAB® and R, allowing data to be seamlessly transferred. Pathomx is supplied with a series of demonstration workflows and datasets. To demonstrate the use of the software we here present an analysis of 1D and 2D (1)H NMR metabolomic data from a model system of mammalian cell growth under hypoxic conditions. CONCLUSIONS: Pathomx is a useful addition to the analysis toolbox. The intuitive interface lowers the barrier to entry for non-experts, while scriptable tools and integration with existing tools supports complex analysis. We welcome contributions from the community. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0396-9) contains supplementary material, which is available to authorized users

Path2Models: large-scale generation of computational models from biochemical pathway maps

Background: Systems biology projects and omics technologies have led to a growing number of biochemical pathway models and reconstructions. However, the majority of these models are still created de novo, based on literature mining and the manual processing of pathway data. Results: To increase the efficiency of model creation, the Path2Models project has automatically generated mathematical models from pathway representations using a suite of freely available software. Data sources include KEGG, BioCarta, MetaCyc and SABIO-RK. Depending on the source data, three types of models are provided: kinetic, logical and constraint-based. Models from over 2 600 organisms are encoded consistently in SBML, and are made freely available through BioModels Database at http://www.ebi.ac.uk/biomodels-main/path2models. Each model contains the list of participants, their interactions, the relevant mathematical constructs, and initial parameter values. Most models are also available as easy-to-understand graphical SBGN maps. Conclusions: To date, the project has resulted in more than 140 000 freely available models. Such a resource can tremendously accelerate the development of mathematical models by providing initial starting models for simulation and analysis, which can be subsequently curated and further parameterized

Consistency, inconsistency, and ambiguity of metabolite names in biochemical databases used for genome-scale metabolic modelling

Genome-scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community, but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in 11 biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. Finally, we discuss several possible approaches to facilitate (future) unambiguous mapping.</p

A Genome-Scale Metabolic Model for <i>Methylococcus capsulatus </i>(Bath) Suggests Reduced Efficiency Electron Transfer to the Particulate Methane Monooxygenase

Background: Genome-scale metabolic models allow researchers to calculate yields, to predict consumption and production rates, and to study the effect of genetic modifications in silico, without running resource-intensive experiments. While these models have become an invaluable tool for optimizing industrial production hosts like Escherichia coli and S. cerevisiae, few such models exist for one-carbon (C1) metabolizers.Results: Here, we present a genome-scale metabolic model for Methylococcus capsulatus (Bath), a well-studied obligate methanotroph, which has been used as a production strain of single cell protein (SCP). The model was manually curated, and spans a total of 879 metabolites connected via 913 reactions. The inclusion of 730 genes and comprehensive annotations, make this model not only a useful tool for modeling metabolic physiology, but also a centralized knowledge base for M. capsulatus (Bath). With it, we determined that oxidation of methane by the particulate methane monooxygenase could be driven both through direct coupling or uphill electron transfer, both operating at reduced efficiency, as either scenario matches well with experimental data and observations from literature.Conclusion: The metabolic model will serve the ongoing fundamental research of C1 metabolism, and pave the way for rational strain design strategies toward improved SCP production processes in M. capsulatus

Evolution of gene knockout strains of <i>E-coli</i> reveal regulatory architectures governed by metabolism

The function of metabolic genes in the context of regulatory networks is not well understood. Here, the authors investigate the adaptive responses of E. coli after knockout of metabolic genes and highlight the influence of metabolite levels in the evolution of regulatory function

\u3ci\u3eIn silico\u3c/i\u3e Driven Metabolic Engineering Towards Enhancing Biofuel and Biochemical Production

The development of a secure and sustainable energy economy is likely to require the production of fuels and commodity chemicals in a renewable manner. There has been renewed interest in biological commodity chemical production recently, in particular focusing on non-edible feedstocks. The fields of metabolic engineering and synthetic biology have arisen in the past 20 years to address the challenge of chemical production from biological feedstocks. Metabolic modeling is a powerful tool for studying the metabolism of an organism and predicting the effects of metabolic engineering strategies. Various techniques have been developed for modeling cellular metabolism, with the underlying principle of mass balance driving the analysis. In this dissertation, two industrially relevant organisms were examined for their potential to produce biofuels. First, Saccharomyces cerevisiae was used to create biodiesel in the form of fatty acid ethyl esters (FAEEs) through expression of a heterologous acyl-transferase enzyme. Several genetic manipulations of lipid metabolic and / or degradation pathways were rationally chosen to enhance FAEE production, and then culture conditions were modified to enhance FAEE production further. The results were used to identify the rate-limiting step in FAEE production, and provide insight to further optimization of FAEE production. Next, Clostridium thermocellum, a cellulolytic thermophile with great potential for consolidated bioprocessing but a weakly understood metabolism, was investigated for enhanced ethanol production. To accomplish the analysis, two models were created for C. thermocellum metabolism. The core metabolic model was used with extensive fermentation data to elucidate kinetic bottlenecks hindering ethanol production. The genome scale metabolic model was constructed and tuned using extensive fermentation data as well, and the refined model was used to investigate complex cellular phenotypes with Flux Balance Analysis. The work presented within provide a platform for continued study of S. cerevisiae and C. thermocellum for the production of valuable biofuels and biochemicals

Development of a data integration pipeline for human metabolic models and databases

Dissertação de mestrado em BioinformaticsSystems Biology aims to integrate experimental and computational approaches with the purpose of explaining and predicting the organisms' behavior. The development of mathematical models in silico gives us a better in-depth knowledge of their biological mechanism. Bioinformatics tools enabled the integration of a large amount of complex biological data into computer models, but also capable to perform computational simulations with these models, that can predict the organisms' phenotypic behavior in different conditions. Up to date, genome-scale metabolic models (GSMMs) include several metabolic components of an organism. These are related to the metabolic capabilities encoded in the genome. In recent years, multiple GSMMs have been built by several research groups. With the increase in number, of these models, important issues regarding the standardization have arisen, a common problem is the different nomenclatures used by each of the research groups. In this work, the major focus is to address these problems, specifically for the human GSSMs. Therefore, the two most recent human GSMMs were selected to go through a data integration process. Integration strategies of these models most important entities (metabolites and reactions), were defined based on an exhaustive analysis of the models. The broad knowledge of their attributes enabled the creation of effective and efficient integration methods, supported by a core database developed in the local research group. The final result of this work, is a unified repository of the human metabolism. It contains all the metabolites and reactions that were automatically integrated along with some manual curation.A Biologia de Sistemas pretende integrar abordagens experimentais e computacionais com o objetivo de explicar e prever o comportamento dos organismos. O desenvolvimento in silico de modelos matemáticos permite atingir um conhecimento mais aprofundado dos seus mecanismos biológicos. Através de ferramentas Bioinformáticas é possível integrar uma grande quantidade de dados complexos nestes modelos computadorizados, assim como, realizar simulações computacionais que permitem prever o comportamento fenotípico dos organismos em diferentes condições ambientais. Até à data, os Modelos Metabólicos à Escala Genómica (MMEGs) incluem muitos componentes metabólicos de um organismo, relacionando a codificação do seu genoma com as suas capacidades metabólicas. Nos últimos anos, têm sido construídos vários MMEGs, por diferentes grupos de investigação. Com o crescente surgimento destes, tem-se denotado grandes falhas ao nível da padronização, uma vez que são utilizadas diferentes nomenclaturas por cada grupo de investigação. Neste trabalho, pretende-se colmatar essas falhas especificamente para os MMEGs humanos. Deste modo, foram selecionados os dois MMEGs humanos mais recentes, para passarem por um processo de integração de dados. As estratégias de integração das entidades mais importantes destes modelos (os metabolitos e as reações) foram definidas com base numa análise exaustiva dos modelos. O conhecimento dos atributos destes permitiu construir métodos eficientes e eficazes, tendo como núcleo uma base de dados desenvolvida no grupo de acolhimento. O resultado final deste trabalho é um repositório unificado do metabolismo humano. Neste, estão contidos todos os metabolitos e reações que foram integrados automaticamente, com alguma verificação manual