Publications by authors named "Tobias Czauderna"

27 Publications

  • Page 1 of 1

Visual Exploration of Large Metabolic Models.

Bioinformatics 2021 May 10. Epub 2021 May 10.

Department of Computer and Information Science, University of Konstanz, Konstanz, Germany.

Motivation: Large metabolic models, including genome-scale metabolic models (GSMMs), are nowadays common in systems biology, biotechnology and pharmacology. They typically contain thousands of metabolites and reactions and therefore methods for their automatic visualisation and interactive exploration can facilitate a better understanding of these models.

Results: We developed a novel method for the visual exploration of large metabolic models and implemented it in LMME (Large Metabolic Model Explorer), an add-on for the biological network analysis tool VANTED. The underlying idea of our method is to analyse a large model as follows. Starting from a decomposition into several subsystems, relationships between these subsystems are identified and an overview is computed and visualised. From this overview, detailed subviews may be constructed and visualised in order to explore subsystems and relationships in greater detail. Decompositions may either be predefined or computed, using built-in or self-implemented methods. Realised as add-on for VANTED, LMME is embedded in a domain-specific environment, allowing for further related analysis at any stage during the exploration. We describe the method, provide a use case, and discuss the strengths and weaknesses of different decomposition methods.

Availability: The methods and algorithms presented here are implemented in LMME, an open-source add-on for VANTED. LMME can be downloaded from www.cls.uni-konstanz.de/software/lmme and VANTED can be downloaded from www.vanted.org. The source code of LMME is available from GitHub, at https://github.com/LSI-UniKonstanz/lmme.
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http://dx.doi.org/10.1093/bioinformatics/btab335DOI Listing
May 2021

Streamline pair selection for comparative flow field visualization.

Vis Comput Ind Biomed Art 2020 Aug 27;3(1):20. Epub 2020 Aug 27.

Monash University, Wellington Road, Clayton, Victoria, Australia.

Fluid dynamics simulation is often repeated under varying conditions. This leads to a generation of large amounts of results, which are difficult to compare. To compare results under different conditions, it is effective to overlap the streamlines generated from each condition in a single three-dimensional space. Streamline is a curved line, which represents a wind flow. This paper presents a technique to automatically select and visualize important streamlines that are suitable for the comparison of the simulation results. Additionally, we present an implementation to observe the flow fields in virtual reality spaces.
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http://dx.doi.org/10.1186/s42492-020-00056-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7450024PMC
August 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

Systems biology graphical notation markup language (SBGNML) version 0.3.

J Integr Bioinform 2020 Jun 22;17(2-3). Epub 2020 Jun 22.

cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.

This document defines Version 0.3 Markup Language (ML) support for the Systems Biology Graphical Notation (SBGN), a set of three complementary visual languages developed for biochemists, modelers, and computer scientists. SBGN aims at representing networks of biochemical interactions in a standard, unambiguous way to foster efficient and accurate representation, visualization, storage, exchange, and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling. SBGN is defined neutrally to programming languages and software encoding; however, it is oriented primarily towards allowing models to be encoded using XML, the eXtensible Markup Language. The notable changes from the previous version include the addition of attributes for better specify metadata about maps, as well as support for multiple maps, sub-maps, colors, and annotations. These changes enable a more efficient exchange of data to other commonly used systems biology formats (e. g., BioPAX and SBML) and between tools supporting SBGN (e. g., CellDesigner, Newt, Krayon, SBGN-ED, STON, cd2sbgnml, and MINERVA). More details on SBGN and related software are available at http://sbgn.org. With this effort, we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner.
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http://dx.doi.org/10.1515/jib-2020-0016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756621PMC
June 2020

OntoPlot: A Novel Visualisation for Non-hierarchical Associations in Large Ontologies.

IEEE Trans Vis Comput Graph 2020 01 22;26(1):1140-1150. Epub 2019 Aug 22.

Ontologies are formal representations of concepts and complex relationships among them. They have been widely used to capture comprehensive domain knowledge in areas such as biology and medicine, where large and complex ontologies can contain hundreds of thousands of concepts. Especially due to the large size of ontologies, visualisation is useful for authoring, exploring and understanding their underlying data. Existing ontology visualisation tools generally focus on the hierarchical structure, giving much less emphasis to non-hierarchical associations. In this paper we present OntoPlot, a novel visualisation specifically designed to facilitate the exploration of all concept associations whilst still showing an ontology's large hierarchical structure. This hybrid visualisation combines icicle plots, visual compression techniques and interactivity, improving space-efficiency and reducing visual structural complexity. We conducted a user study with domain experts to evaluate the usability of OntoPlot, comparing it with the de facto ontology editor Protégé. The results confirm that OntoPlot attains our design goals for association-related tasks and is strongly favoured by domain experts.
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http://dx.doi.org/10.1109/TVCG.2019.2934557DOI Listing
January 2020

Systems Biology Graphical Notation: Process Description language Level 1 Version 2.0.

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

cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA.

The Systems Biology Graphical Notation (SBGN) is an international community effort that aims to standardise the visualisation of pathways and networks for readers with diverse scientific backgrounds as well as to support an efficient and accurate exchange of biological knowledge between disparate research communities, industry, and other players in systems biology. SBGN comprises the three languages Entity Relationship, Activity Flow, and Process Description (PD) to cover biological and biochemical systems at distinct levels of detail. PD is closest to metabolic and regulatory pathways found in biological literature and textbooks. Its well-defined semantics offer a superior precision in expressing biological knowledge. PD represents mechanistic and temporal dependencies of biological interactions and transformations as a graph. Its different types of nodes include entity pools (e.g. metabolites, proteins, genes and complexes) and processes (e.g. reactions, associations and influences). The edges describe relationships between the nodes (e.g. consumption, production, stimulation and inhibition). This document details Level 1 Version 2.0 of the PD specification, including several improvements, in particular: 1) the addition of the equivalence operator, subunit, and annotation glyphs, 2) modification to the usage of submaps, and 3) updates to clarify the use of various glyphs (i.e. multimer, empty set, and state variable).
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http://dx.doi.org/10.1515/jib-2019-0022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798820PMC
June 2019

Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa.

Gigascience 2018 04;7(4)

Monash Biomedicine Discovery Institute, Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne 3800, Australia.

Background: Pseudomonas aeruginosa often causes multidrug-resistant infections in immunocompromised patients, and polymyxins are often used as the last-line therapy. Alarmingly, resistance to polymyxins has been increasingly reported worldwide recently. To rescue this last-resort class of antibiotics, it is necessary to systematically understand how P. aeruginosa alters its metabolism in response to polymyxin treatment, thereby facilitating the development of effective therapies. To this end, a genome-scale metabolic model (GSMM) was used to analyze bacterial metabolic changes at the systems level.

Findings: A high-quality GSMM iPAO1 was constructed for P. aeruginosa PAO1 for antimicrobial pharmacological research. Model iPAO1 encompasses an additional periplasmic compartment and contains 3022 metabolites, 4265 reactions, and 1458 genes in total. Growth prediction on 190 carbon and 95 nitrogen sources achieved an accuracy of 89.1%, outperforming all reported P. aeruginosa models. Notably, prediction of the essential genes for growth achieved a high accuracy of 87.9%. Metabolic simulation showed that lipid A modifications associated with polymyxin resistance exert a limited impact on bacterial growth and metabolism but remarkably change the physiochemical properties of the outer membrane. Modeling with transcriptomics constraints revealed a broad range of metabolic responses to polymyxin treatment, including reduced biomass synthesis, upregulated amino acid catabolism, induced flux through the tricarboxylic acid cycle, and increased redox turnover.

Conclusions: Overall, iPAO1 represents the most comprehensive GSMM constructed to date for Pseudomonas. It provides a powerful systems pharmacology platform for the elucidation of complex killing mechanisms of antibiotics.
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http://dx.doi.org/10.1093/gigascience/giy021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6333913PMC
April 2018

Information Visualization for Biological Data.

Methods Mol Biol 2017 ;1526:403-415

Faculty of Information Technology, Monash University, Clayton, VIC, Australia.

Visualization is a powerful method to present and explore a large amount of data. It is increasingly important in the life sciences and is used for analyzing different types of biological data, such as structural information, high-throughput data, and biochemical networks. This chapter gives a brief introduction to visualization methods for bioinformatics, presents two commonly used techniques in detail, and discusses a graphical standard for biological networks and cellular processes.
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http://dx.doi.org/10.1007/978-1-4939-6613-4_21DOI Listing
January 2018

Toward Community Standards and Software for Whole-Cell Modeling.

IEEE Trans Biomed Eng 2016 10 10;63(10):2007-14. Epub 2016 Jun 10.

Objective: Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate comprehensive models of complex cells.

Methods: We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in the Systems Biology Markup Language.

Results: Our analysis revealed several challenges to representing WC models using the current standards.

Conclusion: We, therefore, propose several new WC modeling standards, software, and databases.

Significance: We anticipate that these new standards and software will enable more comprehensive models.
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http://dx.doi.org/10.1109/TBME.2016.2560762DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5451320PMC
October 2016

Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants.

Front Bioeng Biotechnol 2015 21;3:167. Epub 2015 Oct 21.

Faculty of Information Technology, Monash University , Clayton, VIC , Australia ; Institute of Computer Science, Martin Luther University Halle-Wittenberg , Halle , Germany.

Life on earth depends on dynamic chemical transformations that enable cellular functions, including electron transfer reactions, as well as synthesis and degradation of biomolecules. Biochemical reactions are coordinated in metabolic pathways that interact in a complex way to allow adequate regulation. Biotechnology, food, biofuel, agricultural, and pharmaceutical industries are highly interested in metabolic engineering as an enabling technology of synthetic biology to exploit cells for the controlled production of metabolites of interest. These approaches have only recently been extended to plants due to their greater metabolic complexity (such as primary and secondary metabolism) and highly compartmentalized cellular structures and functions (including plant-specific organelles) compared with bacteria and other microorganisms. Technological advances in analytical instrumentation in combination with advances in data analysis and modeling have opened up new approaches to engineer plant metabolic pathways and allow the impact of modifications to be predicted more accurately. In this article, we review challenges in the integration and analysis of large-scale metabolic data, present an overview of current bioinformatics methods for the modeling and visualization of metabolic networks, and discuss approaches for interfacing bioinformatics approaches with metabolic models of cellular processes and flux distributions in order to predict phenotypes derived from specific genetic modifications or subjected to different environmental conditions.
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http://dx.doi.org/10.3389/fbioe.2015.00167DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4617106PMC
November 2015

Systems Biology Graphical Notation: Activity Flow language Level 1 Version 1.2.

J Integr Bioinform 2015 Sep 4;12(2):265. Epub 2015 Sep 4.

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail. The SBGN Activity Flow language represents the influences of activities among various entities within a network. Unlike SBGN PD and ER that focus on the entities and their relationships with others, SBGN AF puts the emphasis on the functions (or activities) performed by the entities, and their effects to the functions of the same or other entities. The nodes (elements) describe the biological activities of the entities, such as protein kinase activity, binding activity or receptor activity, which can be easily mapped to Gene Ontology molecular function terms. The edges (connections) provide descriptions of relationships (or influences) between the activities, e.g., positive influence and negative influence. Among all three languages of SBGN, AF is the closest to signaling pathways in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.
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http://dx.doi.org/10.2390/biecoll-jib-2015-265DOI Listing
September 2015

Systems Biology Graphical Notation: Entity Relationship language Level 1 Version 2.

J Integr Bioinform 2015 Sep 4;12(2):264. Epub 2015 Sep 4.

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail. The SBGN Entity Relationship language (ER) represents biological entities and their interactions and relationships within a network. SBGN ER focuses on all potential relationships between entities without considering temporal aspects. The nodes (elements) describe biological entities, such as proteins and complexes. The edges (connections) provide descriptions of interactions and relationships (or influences), e.g., complex formation, stimulation and inhibition. Among all three languages of SBGN, ER is the closest to protein interaction networks in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.
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http://dx.doi.org/10.2390/biecoll-jib-2015-264DOI Listing
September 2015

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

Translation of SBGN maps: Process Description to Activity Flow.

BMC Syst Biol 2013 Oct 31;7:115. Epub 2013 Oct 31.

Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstrasse 3, 06466 Stadt Seeland OT Gatersleben, Germany.

Background: The Systems Biology Graphical Notation (SBGN) provides standard graphical languages for representing cellular processes, interactions, and biological networks. SBGN consists of three languages: Process Descriptions (PD), Entity Relationships (ER), and Activity Flows (AF). Maps in SBGN PD are often large, detailed, and complex, therefore there is a need for a simplified illustration.

Results: To solve this problem we define translations of SBGN PD maps into the more abstract SBGN AF maps. We present a template-based translation which allows the user to focus on different aspects of the underlying biological system. We also discuss aspects of laying out the AF map and of interactive navigation between both the PD and the AF map. The methods developed here have been implemented as part of SBGN-ED ( http://www.sbgn-ed.org).

Conclusions: SBGN PD maps become much smaller and more manageable when translated into SBGN AF. The flexible translation of PD into AF and related interaction methods are an initial step in translating the different SBGN languages and open the path to future research for translation methods between other SBGN languages.
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http://dx.doi.org/10.1186/1752-0509-7-115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4228393PMC
October 2013

Conversion of KEGG metabolic pathways to SBGN maps including automatic layout.

BMC Bioinformatics 2013 Aug 16;14:250. Epub 2013 Aug 16.

Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.

Background: Biologists make frequent use of databases containing large and complex biological networks. One popular database is the Kyoto Encyclopedia of Genes and Genomes (KEGG) which uses its own graphical representation and manual layout for pathways. While some general drawing conventions exist for biological networks, arbitrary graphical representations are very common. Recently, a new standard has been established for displaying biological processes, the Systems Biology Graphical Notation (SBGN), which aims to unify the look of such maps. Ideally, online repositories such as KEGG would automatically provide networks in a variety of notations including SBGN. Unfortunately, this is non-trivial, since converting between notations may add, remove or otherwise alter map elements so that the existing layout cannot be simply reused.

Results: Here we describe a methodology for automatic translation of KEGG metabolic pathways into the SBGN format. We infer important properties of the KEGG layout and treat these as layout constraints that are maintained during the conversion to SBGN maps.

Conclusions: This allows for the drawing and layout conventions of SBGN to be followed while creating maps that are still recognizably the original KEGG pathways. This article details the steps in this process and provides examples of the final result.
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http://dx.doi.org/10.1186/1471-2105-14-250DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3765910PMC
August 2013

OPTIMAS-DW: a comprehensive transcriptomics, metabolomics, ionomics, proteomics and phenomics data resource for maize.

BMC Plant Biol 2012 Dec 29;12:245. Epub 2012 Dec 29.

Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Corrensstr. 3, 06466 Stadt Seeland, OT Gatersleben, Germany.

Background: Maize is a major crop plant, grown for human and animal nutrition, as well as a renewable resource for bioenergy. When looking at the problems of limited fossil fuels, the growth of the world's population or the world's climate change, it is important to find ways to increase the yield and biomass of maize and to study how it reacts to specific abiotic and biotic stress situations. Within the OPTIMAS systems biology project maize plants were grown under a large set of controlled stress conditions, phenotypically characterised and plant material was harvested to analyse the effect of specific environmental conditions or developmental stages. Transcriptomic, metabolomic, ionomic and proteomic parameters were measured from the same plant material allowing the comparison of results across different omics domains. A data warehouse was developed to store experimental data as well as analysis results of the performed experiments.

Description: The OPTIMAS Data Warehouse (OPTIMAS-DW) is a comprehensive data collection for maize and integrates data from different data domains such as transcriptomics, metabolomics, ionomics, proteomics and phenomics. Within the OPTIMAS project, a 44K oligo chip was designed and annotated to describe the functions of the selected unigenes. Several treatment- and plant growth stage experiments were performed and measured data were filled into data templates and imported into the data warehouse by a Java based import tool. A web interface allows users to browse through all stored experiment data in OPTIMAS-DW including all data domains. Furthermore, the user can filter the data to extract information of particular interest. All data can be exported into different file formats for further data analysis and visualisation. The data analysis integrates data from different data domains and enables the user to find answers to different systems biology questions. Finally, maize specific pathway information is provided.

Conclusions: With OPTIMAS-DW a data warehouse for maize was established, which is able to handle different data domains, comprises several analysis results that will support researchers within their work and supports systems biological research in particular. The system is available at http://www.optimas-bioenergy.org/optimas_dw.
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http://dx.doi.org/10.1186/1471-2229-12-245DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3577462PMC
December 2012

VANTED v2: a framework for systems biology applications.

BMC Syst Biol 2012 Nov 10;6:139. Epub 2012 Nov 10.

Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstr. 3, 06466 Gatersleben, Germany.

Background: Experimental datasets are becoming larger and increasingly complex, spanning different data domains, thereby expanding the requirements for respective tool support for their analysis. Networks provide a basis for the integration, analysis and visualization of multi-omics experimental datasets.

Results: Here we present VANTED (version 2), a framework for systems biology applications, which comprises a comprehensive set of seven main tasks. These range from network reconstruction, data visualization, integration of various data types, network simulation to data exploration combined with a manifold support of systems biology standards for visualization and data exchange. The offered set of functionalities is instantiated by combining several tasks in order to enable users to view and explore a comprehensive dataset from different perspectives. We describe the system as well as an exemplary workflow.

Conclusions: VANTED is a stand-alone framework which supports scientists during the data analysis and interpretation phase. It is available as a Java open source tool from http://www.vanted.org.
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http://dx.doi.org/10.1186/1752-0509-6-139DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3610154PMC
November 2012

Software support for SBGN maps: SBGN-ML and LibSBGN.

Bioinformatics 2012 Aug 10;28(15):2016-21. Epub 2012 May 10.

EMBL European Bioinformatics Institute, Hinxton, UK.

Motivation: LibSBGN is a software library for reading, writing and manipulating Systems Biology Graphical Notation (SBGN) maps stored using the recently developed SBGN-ML file format. The library (available in C++ and Java) makes it easy for developers to add SBGN support to their tools, whereas the file format facilitates the exchange of maps between compatible software applications. The library also supports validation of maps, which simplifies the task of ensuring compliance with the detailed SBGN specifications. With this effort we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner.

Availability And Implementation: Milestone 2 was released in December 2011. Source code, example files and binaries are freely available under the terms of either the LGPL v2.1+ or Apache v2.0 open source licenses from http://libsbgn.sourceforge.net.

Contact: sbgn-libsbgn@lists.sourceforge.net.
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http://dx.doi.org/10.1093/bioinformatics/bts270DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3400951PMC
August 2012

Creating interactive, web-based and data-enriched maps with the Systems Biology Graphical Notation.

Nat Protoc 2012 Mar 1;7(3):579-93. Epub 2012 Mar 1.

Leibniz Institute of Plant Genetics and Crop Plant Research Gatersleben (IPK), Gatersleben, Germany.

The Systems Biology Graphical Notation (SBGN) is an emerging standard for the uniform representation of biological processes and networks. By using examples from gene regulation and metabolism, this protocol shows the construction of SBGN maps by either manual drawing or automatic translation using the tool SBGN-ED. In addition, it discusses the enrichment of SBGN maps with different kinds of -omics data to bring numerical data into the context of these networks in order to facilitate the interpretation of experimental data. Finally, the export of such maps to public websites, including clickable images, supports the communication of results within the scientific community. With regard to the described functionalities, other tools partially overlap with SBGN-ED. However, currently, SBGN-ED is the only tool that combines all of these functions, including the representation in SBGN, data mapping and website export. This protocol aims to assist scientists with the step-by-step procedure, which altogether takes ∼90 min.
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http://dx.doi.org/10.1038/nprot.2012.002DOI Listing
March 2012

MetaCrop 2.0: managing and exploring information about crop plant metabolism.

Nucleic Acids Res 2012 Jan 15;40(Database issue):D1173-7. Epub 2011 Nov 15.

Leibniz Institute of Plant Genetics and Crop Plant Research IPK, Corrensstrasse 3, 06466 Gatersleben, Germany.

MetaCrop is a manually curated repository of high-quality data about plant metabolism, providing different levels of detail from overview maps of primary metabolism to kinetic data of enzymes. It contains information about seven major crop plants with high agronomical importance and two model plants. MetaCrop is intended to support research aimed at the improvement of crops for both nutrition and industrial use. It can be accessed via web, web services and an add-on to the Vanted software. Here, we present several novel developments of the MetaCrop system and the extended database content. MetaCrop is now available in version 2.0 at http://metacrop.ipk-gatersleben.de.
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http://dx.doi.org/10.1093/nar/gkr1004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245004PMC
January 2012

HTPheno: an image analysis pipeline for high-throughput plant phenotyping.

BMC Bioinformatics 2011 May 12;12:148. Epub 2011 May 12.

Leibniz Institute of Plant Genetics and Crop Plant Research, Corrensstrasse 3, 06466 Gatersleben, Germany.

Background: In the last few years high-throughput analysis methods have become state-of-the-art in the life sciences. One of the latest developments is automated greenhouse systems for high-throughput plant phenotyping. Such systems allow the non-destructive screening of plants over a period of time by means of image acquisition techniques. During such screening different images of each plant are recorded and must be analysed by applying sophisticated image analysis algorithms.

Results: This paper presents an image analysis pipeline (HTPheno) for high-throughput plant phenotyping. HTPheno is implemented as a plugin for ImageJ, an open source image processing software. It provides the possibility to analyse colour images of plants which are taken in two different views (top view and side view) during a screening. Within the analysis different phenotypical parameters for each plant such as height, width and projected shoot area of the plants are calculated for the duration of the screening. HTPheno is applied to analyse two barley cultivars.

Conclusions: HTPheno, an open source image analysis pipeline, supplies a flexible and adaptable ImageJ plugin which can be used for automated image analysis in high-throughput plant phenotyping and therefore to derive new biological insights, such as determination of fitness.
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http://dx.doi.org/10.1186/1471-2105-12-148DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3113939PMC
May 2011

Editing, validating and translating of SBGN maps.

Bioinformatics 2010 Sep 13;26(18):2340-1. Epub 2010 Jul 13.

Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Martin Luther University of Halle-Wittenberg, Halle, Germany.

Motivation: The recently proposed Systems Biology Graphical Notation (SBGN) provides a standard for the visual representation of biochemical and cellular processes. It aims to support more efficient and accurate communication of biological knowledge between different research communities in the life sciences. However, to increase the use of SBGN, tools for editing, validating and translating SBGN maps are desirable.

Results: We present SBGN-ED, a tool which allows the creation of all three types of SBGN maps from scratch or the editing of existing maps, the validation of these maps for syntactical and semantical correctness, the translation of networks from the KEGG and MetaCrop databases into SBGN and the export of SBGN maps into several file and image formats.

Availability: SBGN-ED is freely available from http://vanted.ipk-gatersleben.de/addons/sbgn-ed. The web site contains also tutorials and example files.
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http://dx.doi.org/10.1093/bioinformatics/btq407DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2935428PMC
September 2010

Novel developments of the MetaCrop information system for facilitating systems biological approaches.

J Integr Bioinform 2010 Mar 25;7(3). Epub 2010 Mar 25.

Leibniz Institute of Plant Genetics and Crop Plant Research, Corrensstr 3, 06466 Gatersleben, Germany.

Crop plants play a major role in human and animal nutrition and increasingly contribute to chemical or pharmaceutical industry and renewable resources. In order to achieve important goals, such as the improvement of growth or yield, it is indispensable to understand biological processes on a detailed level. Therefore, the well-structured management of fine-grained information about metabolic pathways is of high interest. Thus, we developed the MetaCrop information system, a manually curated repository of high quality information concerning the metabolism of crop plants. However, the data access to and flexible export of information of MetaCrop in standard exchange formats had to be improved. To automate and accelerate the data access we designed a set of web services to be integrated into external software. These web services have already been used by an add-on for the visualisation toolkit VANTED. Furthermore, we developed an export feature for the MetaCrop web interface, thus enabling the user to compose individual metabolic models using SBML.
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http://dx.doi.org/10.2390/biecoll-jib-2010-125DOI Listing
March 2010

Intraspecific hybrids of Arabidopsis thaliana revealed no gross alterations in endopolyploidy, DNA methylation, histone modifications and transcript levels.

Theor Appl Genet 2010 Jan;120(2):215-26

Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstr 3, 06466 Gatersleben, Germany.

Arabidopsis accessions Col-0 and C24 and their reciprocal hybrids were employed as a model system to investigate the potential relationship between changes in DNA methylation, chromatin structure, endopolyploidization and gene expression in heterotic genotypes. Nucleolus size, endopolyploidization level and distribution of DNA and histone H3 methylation at the microscopic level does not differ between parents and their hybrids. Methylation sensitive amplified polymorphism revealed a largely constant pattern of DNA methylation (97% of signals analyzed) after intraspecific crosses. The parental expression profile of selected genes was maintained in hybrid offspring. No correlation was found between expression pattern and DNA methylation levels at restriction sites within 5' regulatory regions. Thus, the results revealed only minor changes of chromatin properties and other nuclear features in response to intraspecific hybridization in Arabidopsis thaliana.
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http://dx.doi.org/10.1007/s00122-009-1127-xDOI Listing
January 2010

Different hormonal regulation of cellular differentiation and function in nucellar projection and endosperm transfer cells: a microdissection-based transcriptome study of young barley grains.

Plant Physiol 2008 Nov 10;148(3):1436-52. Epub 2008 Sep 10.

Leibniz-Institut für Pflanzengenetik und Kulturpflanzenforschung, D-06466 Gatersleben, Germany.

Nucellar projection (NP) and endosperm transfer cells (ETC) are essential tissues in growing barley (Hordeum vulgare) grains, responsible for nutrient transfer from maternal to filial tissues, endosperm/embryo nutrition, and grain development. A laser microdissection pressure catapulting-based transcriptome analysis was established to study NP and ETC separately using a barley 12K macroarray. A major challenge was to isolate high-quality mRNA from preembedded, fixed tissue while maintaining tissue integrity. We show that probes generated from fixed and embedded tissue sections represent largely the transcriptome (>70%) of nonchemically treated and nonamplified references. In NP, the top-down gradient of cellular differentiation is reflected by the expression of C3HC4-type ubiquitin ligases and different histone genes, cell wall biosynthesis and expansin/extensin genes, as well as genes involved in programmed cell death-related proteolysis coupled to nitrogen remobilization, indicating distinct areas simultaneously undergoing mitosis, cell elongation, and disintegration. Activated gene expression related to gibberellin synthesis and function suggests a regulatory role for gibberellins in establishment of the differentiation gradient. Up-regulation of plasmalemma-intrinsic protein and tonoplast-intrinsic protein genes indicates involvement in nutrient transfer and/or unloading. In ETC, AP2/EREBP-like transcription factors and ethylene functions are transcriptionally activated, a response possibly coupled to activated defense mechanisms. Transcriptional activation of nucleotide sugar metabolism may be attributed to ascorbate synthesis and/or cell wall biosynthesis. These processes are potentially controlled by trehalose-6-P synthase/phosphatase, as suggested by expression of their respective genes. Up-regulation of amino acid permeases in ETC indicates important roles in active nutrient uptake from the apoplastic space into the endosperm.
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http://dx.doi.org/10.1104/pp.108.127001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2577268PMC
November 2008