231 research outputs found

    SABIO-RK: Curated Kinetic Data of Biochemical Reactions

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    SABIO-RK ("http://sabio.villa-bosch.de/SABIORK/":http://sabio.villa-bosch.de/SABIORK/) is a curated, web-accessible database for modellers and wet-lab scientists to get comprehensive information about biochemical reactions and their kinetic properties. It integrates data from different origin in order to facilitate the access to reaction kinetics data and corresponding information. Since most of the kinetic data is exclusively found in the literature SABIO-RK offers data manually extracted from the literature and related information obtained from other publicly available biological databases. For instance, the kinetic data are related to reactions, organisms, tissues and cellular locations. The type of the kinetic mechanism and corresponding rate equations are presented together with their parameters and experimental conditions. Additionally, SABIO-RK also includes data about the detailed mechanism for some of the reactions based on literature information. This not only includes the graphical representation of the mechanism but also the single reaction steps with their corresponding kinetic data.

The data in SABIO-RK are extracted manually from literature and the selection of articles is not restricted to any biological source (e.g. organisms or organism classifications). All the data are curated and annotated by biological experts using a web-based input interface. To support the curation process and data integration we have implemented different constraints in the input interface and offer several controlled vocabularies as lists of values, as well as additional semi-automatic consistency checks to avoid errors and inconsistencies in the database. Controlled vocabularies and annotations to external resources and ontologies were used to identify and relate the data to their biological context. All these efforts to unify and integrate the data augment the content and the semantics of the SABIO-RK database entries to enable a comprehensive understanding and comparison of the data for the user.

SABIO-RK can be accessed via a web-based user interface or via web-services. The user interface allows the definition of complex queries by specifying reactions and reaction participants, kinetic parameters, environmental conditions or literature sources. Links to other databases based on the annotations of the
data enable the user to gather further information for example for compounds, reactions or proteins. Selected data about reactions and their kinetics, together with their annotations, can be exported in SBML (Systems Biology Mark-up Language), a widely used standard exchange format in systems biology

    Exchanging Experimental Kinetic Data via SabioML

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    The simulation of quantitative biochemical models not only requires qualitative information about the stoichiometry of the described networks, but also kinetic data describing their dynamics. Such kinetic data have to be experimentally measured, collected, systematically structured and stored to finally make them accessible. However, the dataflow from the experiment to the model is still a bottleneck, calling for systems that capture the data directly from the instrument, process and normalize it to agreed standards and finally transfer the data to publicly available databases.

SABIO-RK (http://sabio.h-its.org) is a curated database system which we have developed for bundling data referring to biochemical reactions and their kinetics. It offers data for metabolic pathways and as a novelty also for signalling reactions. Until recently, the database solely has been compiled through manual data mining of published papers and merging the kinetic data excerpt with information collected from other databases. We have designed the novel XML-based schema SabioML for exchanging experimentally derived kinetic data and corresponding metadata between programs or databases. The schema is tailored to SABIO-RK, however also could serve for transferring data between other resources. It comprises the description of kinetic laws with their parameters and relevant metadata in a structured and standardised format applying controlled vocabulary, as well as the possibility to assign annotations complying with the MIRIAM standard (Minimum Information Required In the Annotation of Models). Based on this data description format we have developed a submission interface that allows transfer of reaction kinetics data directly from the experimental instrument to the SABIO-RK database. The data can be accessed by the submitting researcher and, after release by the submitter and curation to ensure completeness of the data, also by the public, either manually via a web-based user interface or automated via web-services, both supporting the export of the data together with its annotations in SBML (Systems Biology Mark-up Language).

The system introduced here considerably facilitates the exchange of kinetic data between experimentalists and modelers. We are convinced that in systems biology it will become quite useful for the integration of the results of high throughput assays into biochemical computer models for simulation.

References:

Swainston N, Golebiewski M, et al., FEBS Journal 277(18): 3769-3779 (September 2010)

Rojas I, Golebiewski M, et al., In Silico Biology 7(2 Suppl): S37-44 (2007)
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    Path2Models: large-scale generation of computational models from biochemical pathway maps

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    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

    Controlled vocabularies and semantics in systems biology

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    The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. Model structures, simulation descriptions and numerical results can be encoded in structured formats, but there is an increasing need to provide an additional semantic layer. Semantic information adds meaning to components of structured descriptions to help identify and interpret them unambiguously. Ontologies are one of the tools frequently used for this purpose. We describe here three ontologies created specifically to address the needs of the systems biology community. The Systems Biology Ontology (SBO) provides semantic information about the model components. The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. The Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior. The provision of semantic information extends a model's longevity and facilitates its reuse. It provides useful insight into the biology of modeled processes, and may be used to make informed decisions on subsequent simulation experiments

    Self-organization of signal transduction

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    We propose a model of parameter learning for signal transduction, where the objective function is defined by signal transmission efficiency. We apply this to learn kinetic rates as a form of evolutionary learning, and look for parameters which satisfy the objective. This is a novel approach compared to the usual technique of adjusting parameters only on the basis of experimental data. The resulting model is self-organizing, i.e. perturbations in protein concentrations or changes in extracellular signaling will automatically lead to adaptation. We systematically perturb protein concentrations and observe the response of the system. We find compensatory or co-regulation of protein expression levels. In a novel experiment, we alter the distribution of extracellular signaling, and observe adaptation based on optimizing signal transmission. We also discuss the relationship between signaling with and without transients. Signaling by transients may involve maximization of signal transmission efficiency for the peak response, but a minimization in steady-state responses. With an appropriate objective function, this can also be achieved by concentration adjustment. Self-organizing systems may be predictive of unwanted drug interference effects, since they aim to mimic complex cellular adaptation in a unified way.Comment: updated version, 13 pages, 4 figures, 3 Tables, supplemental tabl

    Software that goes with the flow in systems biology

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    A recent article in BMC Bioinformatics describes new advances in workflow systems for computational modeling in systems biology. Such systems can accelerate, and improve the consistency of, modeling through automation not only at the simulation and results-production stages, but also at the model-generation stage. Their work is a harbinger of the next generation of more powerful software for systems biologists

    Mathematical modeling of proteome constraints within metabolism

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    Genome-scale metabolic models (GEMs) are widely used to predict phenotypes with the aid of constraint-based modeling. In order to improve the predictive power of these models, there have been many efforts on imposing biological constraints, among which proteome constraints are of particular interest. Here we describe the concept of proteome constraints and review proteome-constrained GEMs, as well as their advantages and applications. In addition, we discuss a key issue in the field, i.e., low coverage of enzyme-specific turnover rates, and subsequently provide a few solutions to solve it. We end with a discussion on the trade-off between model complexity and capability

    Meta-All: a system for managing metabolic pathway information

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    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. RESULTS: We have designed META-ALL, an information system that allows the management of metabolic pathways, including reaction kinetics, detailed locations, environmental factors and taxonomic information. Data can be stored together with quality tags and in different parallel versions. META-ALL uses Oracle DBMS and Oracle Application Express. We provide the META-ALL information system for download and use. In this paper, we describe the database structure and give information about the tools for submitting and accessing the data. As a first application of META-ALL, we show how the information contained in a detailed kinetic model can be stored and accessed. CONCLUSION: META-ALL is a system for managing information about metabolic pathways. It facilitates the handling of pathway-related data and is designed to help biochemists and molecular biologists in their daily research. It is available on the Web at and can be downloaded free of charge and installed locally
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