Publications by authors named "Martin Golebiewski"

24 Publications

  • Page 1 of 1

Towards standardization guidelines for in silico approaches in personalized medicine.

J Integr Bioinform 2020 Jul 24;17(2-3). Epub 2020 Jul 24.

Medical Informatics Laboratory, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.

Despite the ever-progressing technological advances in producing data in health and clinical research, the generation of new knowledge for medical benefits through advanced analytics still lags behind its full potential. Reasons for this obstacle are the inherent heterogeneity of data sources and the lack of broadly accepted standards. Further hurdles are associated with legal and ethical issues surrounding the use of personal/patient data across disciplines and borders. Consequently, there is a need for broadly applicable standards compliant with legal and ethical regulations that allow interpretation of heterogeneous health data through in silico methodologies to advance personalized medicine. To tackle these standardization challenges, the Horizon2020 Coordinating and Support Action EU-STANDS4PM initiated an EU-wide mapping process to evaluate strategies for data integration and data-driven in silico modelling approaches to develop standards, recommendations and guidelines for personalized medicine. A first step towards this goal is a broad stakeholder consultation process initiated by an EU-STANDS4PM workshop at the annual COMBINE meeting (COMBINE 2019 workshop report in same issue). This forum analysed the status quo of data and model standards and reflected on possibilities as well as challenges for cross-domain data integration to facilitate in silico modelling approaches for personalized medicine.
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http://dx.doi.org/10.1515/jib-2020-0006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756614PMC
July 2020

Specifications of standards in systems and synthetic biology: status and developments in 2020.

J Integr Bioinform 2020 Jun 29;17(2-3). Epub 2020 Jun 29.

University Medicine Greifswald, Greifswald, Germany.

This special issue of the Journal of Integrative Bioinformatics presents papers related to the 10th COMBINE meeting together with the annual update of COMBINE standards in systems and synthetic biology.
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http://dx.doi.org/10.1515/jib-2020-0022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756620PMC
June 2020

The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE).

J Integr Bioinform 2020 Jun 29;17(2-3). Epub 2020 Jun 29.

Centre for Discovery Brain Sciences, The University of Edinburgh, Edinburgh, UK.

This paper presents a report on outcomes of the 10th Computational Modeling in Biology Network (COMBINE) meeting that was held in Heidelberg, Germany, in July of 2019. The annual event brings together researchers, biocurators and software engineers to present recent results and discuss future work in the area of standards for systems and synthetic biology. The COMBINE initiative coordinates the development of various community standards and formats for computational models in the life sciences. Over the past 10 years, COMBINE has brought together standard communities that have further developed and harmonized their standards for better interoperability of models and data. COMBINE 2019 was co-located with a stakeholder workshop of the European EU-STANDS4PM initiative that aims at harmonized data and model standardization for in silico models in the field of personalized medicine, as well as with the FAIRDOM PALs meeting to discuss findable, accessible, interoperable and reusable (FAIR) data sharing. This report briefly describes the work discussed in invited and contributed talks as well as during breakout sessions. It also highlights recent advancements in data, model, and annotation standardization efforts. Finally, this report concludes with some challenges and opportunities that this community will face during the next 10 years.
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http://dx.doi.org/10.1515/jib-2020-0005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756615PMC
June 2020

Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices.

Methods Mol Biol 2019 ;2049:285-314

School of Computer Science, University of Manchester, Manchester, UK.

Computational systems biology involves integrating heterogeneous datasets in order to generate models. These models can assist with understanding and prediction of biological phenomena. Generating datasets and integrating them into models involves a wide range of scientific expertise. As a result these datasets are often collected by one set of researchers, and exchanged with others researchers for constructing the models. For this process to run smoothly the data and models must be FAIR-findable, accessible, interoperable, and reusable. In order for data and models to be FAIR they must be structured in consistent and predictable ways, and described sufficiently for other researchers to understand them. Furthermore, these data and models must be shared with other researchers, with appropriately controlled sharing permissions, before and after publication. In this chapter we explore the different data and model standards that assist with structuring, describing, and sharing. We also highlight the popular standards and sharing databases within computational systems biology.
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http://dx.doi.org/10.1007/978-1-4939-9736-7_17DOI Listing
June 2020

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2019.

J Integr Bioinform 2019 Jul 13;16(2). Epub 2019 Jul 13.

University Medicine Greifswald, Greifswald, Germany.

This special issue of the Journal of Integrative Bioinformatics presents an overview of COMBINE standards and their latest specifications. The standards cover representation formats for computational modeling in synthetic and systems biology and include BioPAX, CellML, NeuroML, SBML, SBGN, SBOL and SED-ML. The articles in this issue contain updated specifications of SBGN Process Description Level 1 Version 2, SBML Level 3 Core Version 2 Release 2, SBOL Version 2.3.0, and SBOL Visual Version 2.1.
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http://dx.doi.org/10.1515/jib-2019-0035DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798822PMC
July 2019

Harmonizing semantic annotations for computational models in biology.

Brief Bioinform 2019 03;20(2):540-550

Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.
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http://dx.doi.org/10.1093/bib/bby087DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6433895PMC
March 2019

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2017.

J Integr Bioinform 2018 Mar 29;15(1). Epub 2018 Mar 29.

University of Rostock, Rostock, Germany.

Standards are essential to the advancement of Systems and Synthetic Biology. COMBINE provides a formal body and a centralised platform to help develop and disseminate relevant standards and related resources. The regular special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards by providing unified, easily citable access. This paper provides an overview of existing COMBINE standards and presents developments of the last year.
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http://dx.doi.org/10.1515/jib-2018-0013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167034PMC
March 2018

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2016.

J Integr Bioinform 2016 Dec 18;13(3):289. Epub 2016 Dec 18.

Standards are essential to the advancement of science and technology. In systems and synthetic biology, numerous standards and associated tools have been developed over the last 16 years. This special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards, as well as to provide centralised and easily citable access to them.
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http://dx.doi.org/10.2390/biecoll-jib-2016-289DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5431574PMC
December 2016

FAIRDOMHub: a repository and collaboration environment for sharing systems biology research.

Nucleic Acids Res 2017 01 28;45(D1):D404-D407. Epub 2016 Nov 28.

School of Computer Science, The University of Manchester, Kilburn Building, Oxford Road, Manchester, M13 9PL, UK

The FAIRDOMHub is a repository for publishing FAIR (Findable, Accessible, Interoperable and Reusable) Data, Operating procedures and Models (https://fairdomhub.org/) for the Systems Biology community. It is a web-accessible repository for storing and sharing systems biology research assets. It enables researchers to organize, share and publish data, models and protocols, interlink them in the context of the systems biology investigations that produced them, and to interrogate them via API interfaces. By using the FAIRDOMHub, researchers can achieve more effective exchange with geographically distributed collaborators during projects, ensure results are sustained and preserved and generate reproducible publications that adhere to the FAIR guiding principles of data stewardship.
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http://dx.doi.org/10.1093/nar/gkw1032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5210530PMC
January 2017

The Human Physiome: how standards, software and innovative service infrastructures are providing the building blocks to make it achievable.

Interface Focus 2016 Apr;6(2):20150103

Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand; Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.

Reconstructing and understanding the Human Physiome virtually is a complex mathematical problem, and a highly demanding computational challenge. Mathematical models spanning from the molecular level through to whole populations of individuals must be integrated, then personalized. This requires interoperability with multiple disparate and geographically separated data sources, and myriad computational software tools. Extracting and producing knowledge from such sources, even when the databases and software are readily available, is a challenging task. Despite the difficulties, researchers must frequently perform these tasks so that available knowledge can be continually integrated into the common framework required to realize the Human Physiome. Software and infrastructures that support the communities that generate these, together with their underlying standards to format, describe and interlink the corresponding data and computer models, are pivotal to the Human Physiome being realized. They provide the foundations for integrating, exchanging and re-using data and models efficiently, and correctly, while also supporting the dissemination of growing knowledge in these forms. In this paper, we explore the standards, software tooling, repositories and infrastructures that support this work, and detail what makes them vital to realizing the Human Physiome.
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http://dx.doi.org/10.1098/rsfs.2015.0103DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4759754PMC
April 2016

Specifications of Standards in Systems and Synthetic Biology.

J Integr Bioinform 2015 Sep 4;12(2):258. Epub 2015 Sep 4.

Standards shape our everyday life. From nuts and bolts to electronic devices and technological processes, standardised products and processes are all around us. Standards have technological and economic benefits, such as making information exchange, production, and services more efficient. However, novel, innovative areas often either lack proper standards, or documents about standards in these areas are not available from a centralised platform or formal body (such as the International Standardisation Organisation). Systems and synthetic biology is a relatively novel area, and it is only in the last decade that the standardisation of data, information, and models related to systems and synthetic biology has become a community-wide effort. Several open standards have been established and are under continuous development as a community initiative. COMBINE, the ‘COmputational Modeling in BIology’ NEtwork has been established as an umbrella initiative to coordinate and promote the development of the various community standards and formats for computational models. There are yearly two meeting, HARMONY (Hackathons on Resources for Modeling in Biology), Hackathon-type meetings with a focus on development of the support for standards, and COMBINE forums, workshop-style events with oral presentations, discussion, poster, and breakout sessions for further developing the standards. For more information see http://co.mbine.org/. So far the different standards were published and made accessible through the standards’ web- pages or preprint services. The aim of this special issue is to provide a single, easily accessible and citable platform for the publication of standards in systems and synthetic biology. This special issue is intended to serve as a central access point to standards and related initiatives in systems and synthetic biology, it will be published annually to provide an opportunity for standard development groups to communicate updated specifications.
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http://dx.doi.org/10.2390/biecoll-jib-2015-258DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5431569PMC
September 2015

SEEK: a systems biology data and model management platform.

BMC Syst Biol 2015 Jul 11;9:33. Epub 2015 Jul 11.

School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester, M13 9PL, UK.

Background: Systems biology research typically involves the integration and analysis of heterogeneous data types in order to model and predict biological processes. Researchers therefore require tools and resources to facilitate the sharing and integration of data, and for linking of data to systems biology models. There are a large number of public repositories for storing biological data of a particular type, for example transcriptomics or proteomics, and there are several model repositories. However, this silo-type storage of data and models is not conducive to systems biology investigations. Interdependencies between multiple omics datasets and between datasets and models are essential. Researchers require an environment that will allow the management and sharing of heterogeneous data and models in the context of the experiments which created them.

Results: The SEEK is a suite of tools to support the management, sharing and exploration of data and models in systems biology. The SEEK platform provides an access-controlled, web-based environment for scientists to share and exchange data and models for day-to-day collaboration and for public dissemination. A plug-in architecture allows the linking of experiments, their protocols, data, models and results in a configurable system that is available 'off the shelf'. Tools to run model simulations, plot experimental data and assist with data annotation and standardisation combine to produce a collection of resources that support analysis as well as sharing. Underlying semantic web resources additionally extract and serve SEEK metadata in RDF (Resource Description Format). SEEK RDF enables rich semantic queries, both within SEEK and between related resources in the web of Linked Open Data.

Conclusion: The SEEK platform has been adopted by many systems biology consortia across Europe. It is a data management environment that has a low barrier of uptake and provides rich resources for collaboration. This paper provides an update on the functions and features of the SEEK software, and describes the use of the SEEK in the SysMO consortium (Systems biology for Micro-organisms), and the VLN (virtual Liver Network), two large systems biology initiatives with different research aims and different scientific communities.
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http://dx.doi.org/10.1186/s12918-015-0174-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4702362PMC
July 2015

Promoting Coordinated Development of Community-Based Information Standards for Modeling in Biology: The COMBINE Initiative.

Front Bioeng Biotechnol 2015 24;3:19. Epub 2015 Feb 24.

Babraham Institute , Cambridge , UK ; European Molecular Biology Laboratory-European Bioinformatics Institute , Cambridge , UK.

The Computational Modeling in Biology Network (COMBINE) is a consortium of groups involved in the development of open community standards and formats used in computational modeling in biology. COMBINE's aim is to act as a coordinator, facilitator, and resource for different standardization efforts whose domains of use cover related areas of the computational biology space. In this perspective article, we summarize COMBINE, its general organization, and the community standards and other efforts involved in it. Our goals are to help guide readers toward standards that may be suitable for their research activities, as well as to direct interested readers to relevant communities where they can best expect to receive assistance in how to develop interoperable computational models.
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http://dx.doi.org/10.3389/fbioe.2015.00019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4338824PMC
March 2015

COMBINE archive and OMEX format: one file to share all information to reproduce a modeling project.

BMC Bioinformatics 2014 Dec 14;15:369. Epub 2014 Dec 14.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.

Background: With the ever increasing use of computational models in the biosciences, the need to share models and reproduce the results of published studies efficiently and easily is becoming more important. To this end, various standards have been proposed that can be used to describe models, simulations, data or other essential information in a consistent fashion. These constitute various separate components required to reproduce a given published scientific result.

Results: We describe the Open Modeling EXchange format (OMEX). Together with the use of other standard formats from the Computational Modeling in Biology Network (COMBINE), OMEX is the basis of the COMBINE Archive, a single file that supports the exchange of all the information necessary for a modeling and simulation experiment in biology. An OMEX file is a ZIP container that includes a manifest file, listing the content of the archive, an optional metadata file adding information about the archive and its content, and the files describing the model. The content of a COMBINE Archive consists of files encoded in COMBINE standards whenever possible, but may include additional files defined by an Internet Media Type. Several tools that support the COMBINE Archive are available, either as independent libraries or embedded in modeling software.

Conclusions: The COMBINE Archive facilitates the reproduction of modeling and simulation experiments in biology by embedding all the relevant information in one file. Having all the information stored and exchanged at once also helps in building activity logs and audit trails. We anticipate that the COMBINE Archive will become a significant help for modellers, as the domain moves to larger, more complex experiments such as multi-scale models of organs, digital organisms, and bioengineering.
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http://dx.doi.org/10.1186/s12859-014-0369-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4272562PMC
December 2014

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

BMC Syst Biol 2013 Nov 1;7:116. Epub 2013 Nov 1.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.

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.
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http://dx.doi.org/10.1186/1752-0509-7-116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4228421PMC
November 2013

Challenges for an enzymatic reaction kinetics database.

FEBS J 2014 Jan 25;281(2):572-82. Epub 2013 Oct 25.

Scientific Databases and Visualization Group, Heidelberg Institute for Theoretical Studies (HITS), Germany.

The scientific literature contains a tremendous amount of kinetic data describing the dynamic behaviour of biochemical reactions over time. These data are needed for computational modelling to create models of biochemical reaction networks and to obtain a better understanding of the processes in living cells. To extract the knowledge from the literature, biocurators are required to understand a paper and interpret the data. For modellers, as well as experimentalists, this process is very time consuming because the information is distributed across the publication and, in most cases, is insufficiently structured and often described without standard terminology. In recent years, biological databases for different data types have been developed. The advantages of these databases lie in their unified structure, searchability and the potential for augmented analysis by software, which supports the modelling process. We have developed the SABIO-RK database for biochemical reaction kinetics. In the present review, we describe the challenges for database developers and curators, beginning with an analysis of relevant publications up to the export of database information in a standardized format. The aim of the present review is to draw the experimentalist's attention to the problem (from a data integration point of view) of incompletely and imprecisely written publications. We describe how to lower the barrier to curators and improve this situation. At the same time, we are aware that curating experimental data takes time. There is a community concerned with making the task of publishing data with the proper structure and annotation to ontologies much easier. In this respect, we highlight some useful initiatives and tools.
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http://dx.doi.org/10.1111/febs.12562DOI Listing
January 2014

SABIO-RK--database for biochemical reaction kinetics.

Nucleic Acids Res 2012 Jan 18;40(Database issue):D790-6. Epub 2011 Nov 18.

Scientific Databases and Visualization Group, Heidelberg Institute for Theoretical Studies, gGmbH, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany.

SABIO-RK (http://sabio.h-its.org/) is a web-accessible database storing comprehensive information about biochemical reactions and their kinetic properties. SABIO-RK offers standardized data manually extracted from the literature and data directly submitted from lab experiments. The database content includes kinetic parameters in relation to biochemical reactions and their biological sources with no restriction on any particular set of organisms. Additionally, kinetic rate laws and corresponding equations as well as experimental conditions are represented. All the data are manually curated and annotated by biological experts, supported by automated consistency checks. SABIO-RK can be accessed via web-based user interfaces or automatically via web services that allow direct data access by other tools. Both interfaces support the export of the data together with its annotations in SBML (Systems Biology Markup Language), e.g. for import in modelling tools.
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http://dx.doi.org/10.1093/nar/gkr1046DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245076PMC
January 2012

Controlled vocabularies and semantics in systems biology.

Mol Syst Biol 2011 Oct 25;7:543. Epub 2011 Oct 25.

Terry Fox Laboratory, Vancouver, Canada.

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.
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http://dx.doi.org/10.1038/msb.2011.77DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3261705PMC
October 2011

Enzyme kinetics informatics: from instrument to browser.

FEBS J 2010 Sep 3;277(18):3769-79. Epub 2010 Aug 3.

Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.

A limited number of publicly available resources provide access to enzyme kinetic parameters. These have been compiled through manual data mining of published papers, not from the original, raw experimental data from which the parameters were calculated. This is largely due to the lack of software or standards to support the capture, analysis, storage and dissemination of such experimental data. Introduced here is an integrative system to manage experimental enzyme kinetics data from instrument to browser. The approach is based on two interrelated databases: the existing SABIO-RK database, containing kinetic data and corresponding metadata, and the newly introduced experimental raw data repository, MeMo-RK. Both systems are publicly available by web browser and web service interfaces and are configurable to ensure privacy of unpublished data. Users of this system are provided with the ability to view both kinetic parameters and the experimental raw data from which they are calculated, providing increased confidence in the data. A data analysis and submission tool, the kineticswizard, has been developed to allow the experimentalist to perform data collection, analysis and submission to both data resources. The system is designed to be extensible, allowing integration with other manufacturer instruments covering a range of analytical techniques.
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http://dx.doi.org/10.1111/j.1742-4658.2010.07778.xDOI Listing
September 2010

SBML2L(A)T(E)X: conversion of SBML files into human-readable reports.

Bioinformatics 2009 Jun 23;25(11):1455-6. Epub 2009 Mar 23.

Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Tübingen, Germany.

Summary: The XML-based Systems Biology Markup Language (SBML) has emerged as a standard for storage, communication and interchange of models in systems biology. As a machine-readable format XML is difficult for humans to read and understand. Many tools are available that visualize the reaction pathways stored in SBML files, but many components, e.g. unit declarations, complex kinetic equations or links to MIRIAM resources, are often not made visible in these diagrams. For a broader understanding of the models, support in scientific writing and error detection, a human-readable report of the complete model is needed. We present SBML2L(A)T(E)X, a Java-based stand-alone program to fill this gap. A convenient web service allows users to directly convert SBML to various formats, including DVI, L(A)T(E)X and PDF, and provides many settings for customization.

Availability: Source code, documentation and a web service are freely available at (http://www.ra.cs.uni-tuebingen.de/software/SBML2LaTeX).
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http://dx.doi.org/10.1093/bioinformatics/btp170DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2682517PMC
June 2009

Storing and annotating of kinetic data.

In Silico Biol 2007 ;7(2 Suppl):S37-44

Scientific Databases and Visualization Group, EML Research gGmbH, Heidelberg, Germany.

This paper briefly describes the SABIO-RK database model for the storage of reaction kinetics information and the guidelines followed within the SABIO-RK project to annotate the kinetic data. Such annotations support the definition of cross links to other related databases and augment the semantics of the data stored in the database.
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October 2007

The histone deacetylase inhibitor valproic acid selectively induces proteasomal degradation of HDAC2.

EMBO J 2003 Jul;22(13):3411-20

Georg-Speyer-Haus, Paul-Ehrlich-Strasse 42-44, D-60596 Frankfurt, Germany.

Histone-modifying enzymes play essential roles in physiological and aberrant gene regulation. Since histone deacetylases (HDACs) are promising targets of cancer therapy, it is important to understand the mechanisms of HDAC regulation. Selective modulators of HDAC isoenzymes could serve as efficient and well-tolerated drugs. We show that HDAC2 undergoes basal turnover by the ubiquitin-proteasome pathway. Valproic acid (VPA), in addition to selectively inhibiting the catalytic activity of class I HDACs, induces proteasomal degradation of HDAC2, in contrast to other inhibitors such as trichostatin A (TSA). Basal and VPA-induced HDAC2 turnover critically depend on the E2 ubiquitin conjugase Ubc8 and the E3 ubiquitin ligase RLIM. Ubc8 gene expression is induced by both VPA and TSA, whereas only TSA simultaneously reduces RLIM protein levels and therefore fails to induce HDAC2 degradation. Thus, poly-ubiquitination and proteasomal degradation provide an isoenzyme-selective mechanism for downregulation of HDAC2.
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http://dx.doi.org/10.1093/emboj/cdg315DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC165640PMC
July 2003