Ralf Hofestädt

BioDWH: a data warehouse kit for life science data integration.

This paper presents a novel bioinformatics data warehouse software kit that integrates biological information from multiple public life science data sources into a local database management system. It stands out from other approaches by providing up-to-date integrated knowledge, platform and database independence as well as high usability and customization. This open source software can be used as a general infrastructure for integrative bioinformatics research and development. The advantages of the approach are realized by using a Java-based system architecture and object-relational mapping (ORM) technology. Finally, a practical application of the system is presented within the emerging area of medical bioinformatics to show the usefulness of the approach. The BioDWH data warehouse software is available for the scientific community at http://sourceforge.net/projects/biodwh/.

PathAligner: Metabolic Pathway Retrieval and Alignment

MOTIVATION Analysis of metabolic pathways is a central topic in understanding the relationship between genotype and phenotype. The rapid accumulation of biological data provides the possibility of studying metabolic pathways at both the genomic and the metabolic levels. Retrieving metabolic pathways from current biological data sources, reconstructing metabolic pathways from rudimentary pathway components, and aligning metabolic pathways with each other are major tasks. Our motivation was to develop a conceptual framework and computational system that allows the retrieval of metabolic pathway information and the processing of alignments to reveal the similarities between metabolic pathways. RESULTS PathAligner extracts metabolic information from biological databases via the Internet and builds metabolic pathways with data sources of genes, sequences, enzymes, metabolites etc. It provides an easy-to-use interface to retrieve, display and manipulate metabolic information. PathAligner also provides an alignment method to compare the similarity between metabolic pathways. AVAILABILITY PathAligner is available at http://bibiserv.techfak.uni-bielefeld.de/pathaligner.

Interactive modelling and simulation of biochemical networks

The analysis of biochemical processes can be supported using methods of modelling and simulation. New methods of computer science are discussed in this field of research. This paper presents a new method which allows the modelling and analysis of complex metabolic networks. Moreover, our simulation shell is based on this formalization and represents the first tool for the interactive simulation of metabolic processes.

Logical and semantic database integration

Two fundamental approaches for database integration exist: the data warehouse approach attempts to physically merge data sets from several source databases, whereas database federations simultaneously query source databases online. In this paper, a database federation approach based on two components is introduced. MARGBench is a system which, among other features, enables the querying of several databases in SQL by translating SQL queries into a source database-specific interface. In this system, SQL queries use the database field labels of the original database, i.e. fields that contain the same kind of information are labelled differently in different databases. In order to solve this problem, a second system, based on ontologies, is currently being developed. This ontology system not only includes information about the semantics of database fields but also contains information about the databases themselves. Thus, it facilitates both database binding and intelligent database querying. The main concepts and ideas of these two systems are explained. By using an imaginary database query, it is demonstrated how the two systems (the ontology system and MARGBench) work together in order to enable the querying of several databases simultaneously.

Modeling of cell-to-cell communication processes with Petri nets using the example of quorum sensing.

The understanding of the molecular mechanism of cell-to-cell communication is fundamental for system biology. Up to now, the main objectives of bioinformatics have been reconstruction, modeling and analysis of metabolic, regulatory and signaling processes, based on data generated from high-throughput technologies. Cell-to-cell communication or quorum sensing (QS), the use of small molecule signals to coordinate complex patterns of behavior in bacteria, has been the focus of many reports over the past decade. Based on the quorum sensing process of the organism Aliivibrio salmonicida, we aim at developing a functional Petri net, which will allow modeling and simulating cell-to-cell communication processes. Using a new editor-controlled information system called VANESA (http://vanesa.sf.net), we present how to combine different fields of studies such as life-science, database consulting, modeling, visualization and simulation for a semi-automatic reconstruction of the complex signaling quorum sensing network. We show how cell-to-cell communication processes and information-flow within a cell and across cell colonies can be modeled using VANESA and how those models can be simulated with Petri net network structures in a sophisticated way.

Identification of Novel Cholesteatoma-Related Gene Expression Signatures Using Full-Genome Microarrays

Background Cholesteatoma is a gradually expanding destructive epithelial lesion within the middle ear. It can cause extensive local tissue destruction in the temporal bone and can initially lead to the development of conductive hearing loss via ossicular erosion. As the disease progresses, sensorineural hearing loss, vertigo or facial palsy may occur. Cholesteatoma may promote the spread of infection through the tegmen of the middle ear and cause meningitis or intracranial infections with abscess formation. It must, therefore, be considered as a potentially life-threatening middle ear disease. Methods and Findings In this study, we investigated differentially expressed genes in human cholesteatomas in comparison to regular auditory canal skin using Whole Human Genome Microarrays containing 19,596 human genes. In addition to already described up-regulated mRNAs in cholesteatoma, such as MMP9, DEFB2 and KRT19, we identified 3558 new cholesteatoma-related transcripts. 811 genes appear to be significantly differentially up-regulated in cholesteatoma. 334 genes were down-regulated more than 2-fold. Significantly regulated genes with protein metabolism activity include matrix metalloproteinases as well as PI3, SERPINB3 and SERPINB4. Genes like SPP1, KRT6B, PRPH, SPRR1B and LAMC2 are known as genes with cell growth and/or maintenance activity. Transport activity genes and signal transduction genes are LCN2, GJB2 and CEACAM6. Three cell communication genes were identified; one CDH19 and two from the S100 family. Conclusions This study demonstrates that the expression profile of cholesteatoma is similar to a metastatic tumour and chronically inflamed tissue. Based on the investigated profiles we present novel protein-protein interaction and signal transduction networks, which include cholesteatoma-regulated transcripts and may be of great value for drug targeting and therapy development.

Visualization and analysis of a cardio vascular disease- and MUPP1-related biological network combining text mining and data warehouse approaches.

Detailed investigation of socially important diseases with modern experimental methods has resulted in the generation of large volume of valuable data. However, analysis and interpretation of this data needs application of efficient computational techniques and systems biology approaches. In particular, the techniques allowing the reconstruction of associative networks of various biological objects and events can be useful. In this publication, the combination of different techniques to create such a network associated with an abstract cell environment is discussed in order to gain insights into the functional as well as spatial interrelationships. It is shown that experimentally gained knowledge enriched with data warehouse content and text mining data can be used for the reconstruction and localization of a cardiovascular disease developing network beginning with MUPP1/MPDZ (multi-PDZ domain protein).

CancerNet: a database for decoding multilevel molecular interactions across diverse cancer types

Protein–protein interactions (PPIs) and microRNA (miRNA)–target interactions are important for deciphering the mechanisms of tumorigenesis. However, current PPI databases do not support cancer-specific analysis. Also, no available databases can be used to retrieve cancer-associated miRNA–target interactions. As the pathogenesis of human cancers is affected by several miRNAs rather than a single miRNA, it is needed to uncover miRNA synergism in a systems level. Here for each cancer type, we constructed a miRNA–miRNA functionally synergistic network based on the functions of miRNA targets and their topological features in that cancer PPI network. And for the first time, we report the cancer-specific database CancerNet (http://bis.zju.edu.cn/CancerNet), which contains information about PPIs, miRNA–target interactions and functionally synergistic miRNA–miRNA pairs across 33 human cancer types. In addition, PPI information across 33 main normal tissues and cell types are included. Flexible query methods are allowed to retrieve cancer molecular interactions. Network viewer can be used to visualize interactions that users are interested in. Enrichment analysis tool was designed to detect significantly overrepresented Gene Ontology categories of miRNA targets. Thus, CancerNet serves as a comprehensive platform for assessing the roles of proteins and miRNAs, as well as their interactions across human cancers.

SUBCELLULAR LOCALIZATION CHARTS: A NEW VISUAL METHODOLOGY FOR THE SEMI-AUTOMATIC LOCALIZATION OF PROTEIN-RELATED DATA SETS

The CELLmicrocosmos PathwayIntegration (CmPI) was developed to support and visualize the subcellular localization prediction of protein-related data such as protein-interaction networks. From the start it was possible to manually analyze the localizations by using an interactive table. It was, however, quite complicated to compare and analyze the different localization results derived from data integration as well as text-mining-based databases. The current software release provides a new interactive visual workflow, the Subcellular Localization Charts. As an application case, a MUPP1-related protein-protein interaction network is localized and semi-automatically analyzed. It will be shown that the workflow was dramatically improved and simplified. In addition, it is now possible to use custom protein-related data by using the SBML format and get a view of predicted protein localizations mapped onto a virtual cell model.

Web-based information retrieval system for the prediction of metabolic pathways

Analysis of metabolic pathways is a central topic in understanding the relationship between genotype and phenotype. The rapid accumulation of biological data provides the possibility of studying metabolic pathways both at the genomic and metabolic levels. Our motivation is to develop a conceptual framework and computational system that will allow retrieval of metabolic information and prediction of metabolic pathways. In this paper, we introduce a metabolic pathway prediction framework that extracts metabolic information from biological databases via the Internet, and builds metabolic pathways with data sources of genes, sequences, enzymes, metabolites, etc. It provides an easy-to-use interface to retrieve, display, and manipulate metabolic information. The system has been implemented into PathAligner, available at http://bibiserv.techfak.uni-bielefeld. de/pathaligner/.