Thoralf Töpel

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/.

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.

RAMEDIS, the rare metabolic diseases database

UNLABELLED The RAMEDIS system is a platform-independent, web-based information system for rare diseases based on individual case reports. It was developed in close cooperation with clinical partners and collects information on rare metabolic diseases in extensive detail (e.g. symptoms, laboratory findings, therapy and genetic data). This combination of clinical and genetic data enables the analysis of genotype-phenotype correlations. By using largely standardised medical terms and conditions, the contents of the database are easy to compare and analyse. In addition, a convenient graphical user interface is provided by every common web browser. RAMEDIS supports an extendable number of different genetic diseases and enables cooperative studies. Furthermore, use of RAMEDIS should lead to advances in epidemiology, integration of molecular and clinical data, and generation of rules for therapeutic intervention and identification of new diseases. AVAILABILITY RAMEDIS is available from http://www.ramedis.de CONTACT Thoralf Töpel (thoralf.toepel@uni-bielefeld.de).

VINEdb: a data warehouse for integration and interactive exploration of life science data

Abstract Control of cell proliferation, differentiation, activation and cell removal is crucial for the development and existence of multi-cellular organisms. Apoptosis, or programmed cell death, is a major control mechanism by which cells die and is also important in controlling cell number and proliferation as part of normal development. Molecular networks that regulate these processes are critical targets for drug development, gene therapy, and metabolic engineering. The molecular interactions involved in this and other processes are analyzed and annotated by experts and stored as data in different databases. The key task is to integrate, manage and visualize these data available from different sources and present them in a user-comprehensible manner. Here we present VINEdb, a data warehouse developed to interact with and to explore integrated life science data. Extendable open source data warehouse architecture enables platform-independent usability of the web application and the underlying infrastructure. A high degree of transparency and up-to-dateness is ensured by a monitor component to control and update the data from the sources. Furthermore, the system is supported by a visualization component to allow interactive graphical exploration of the integrated data. We will use apoptotic pathway and caspase-3 as a case study to show capability and usability of our approach. VINEdb is available at http://tunicata.techfak.unibielefeld.de/VINEdb/.

RAMEDIS: a comprehensive information system for variations and corresponding phenotypes of rare metabolic diseases

RAMEDIS is a manually curated resource of human variations and corresponding phenotypes for rare metabolic diseases. The system is based on separate case reports that comprehensively describe various aspects of anonymous case study, e.g. molecular genetics, symptoms, lab findings, treatments, etc. Scientists are able to make use of the database by a simple and intuitive web‐based user interface with a common web browser. A registration or login is not necessary for a full reading access to the system content. Furthermore, a mutation analysis table summarizes the submitted variations per diagnosis and enables direct access to detailed information of corresponding case reports. Interested scientists may open an account to submit their case reports in order to share valuable genotype‐phenotype information efficiently with the scientific community. Currently, 794 case reports have been submitted, describing 92 different genetic metabolic diseases. To enhance the comprehensive coverage of available knowledge in the field of rare metabolic diseases, all case reports are linked to integrated information from public molecular biology databases like KEGG, OMIM and ENZYME. This information upgrades the case reports by related data of the corresponding diseases as well as involved enzymes, genes and metabolic pathways. Academic users may freely use the RAMEDIS system at http://www.ramedis.de.

A Medical Case-Based Reasoning Component for the Rare Metabolic Diseases Database Ramedis

In the area of diagnosis and therapy of inborn metabolic diseases, the investigation of genetic and clinical data has special importance. These data are the basis of further research and are available in large amounts in the form of case reports within relevant scientific publications and several mutation or disease-oriented databases. However, a successful evaluation of these information is difficult because of the large heterogeneity regarding availability, representation and comparability. Our approach supports clinical researchers of inborn metabolic defects by collecting relevant information of mutations and its corresponding phenotypes in the rare metabolic diseases database RAMEDIS and their analysis by using a medical case-based reasoning component. Today, on the basis of already over 750 stored case reports, the retrieval of similar cases is possible. The output of the CBR tool is a weighted list of relevant patient cases with according diagnosis and therapy information.

Reconstruction of biological networks based on life science data integration.

For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH--an integration toolkit for building life science data warehouses, CardioVINEdb--a information system for biological data in cardiovascular-disease and VANESA--a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.

Case-based support of information retrieval and analysis of molecular data

Nowadays databases offer great opportunities for collecting and analyzing data world wide via the Internet. Various databases are available, providing information about inborn errors of metabolism. Our system connects tools for data integration, combining data from these sources and preparing them in a well structured and uniform way, using the idea of federated database systems with methods of case-based analysis to detect inborn errors of metabolism.Nowadays databases offer great opportunities for collecting and analyzing data world wide via the Internet. Various databases are available, providing information about inborn errors of metabolism. Our system connects tools for data integration, combining data from these sources and preparing them in a well structured and uniform way, using the idea of federated database systems with methods of case-based analysis to detect inborn errors of metabolism.

A Customizable Framework to Access and Interconnect Clinical Patient Data and Molecular Biology Information

This work presents a customizable software framework that supports the analysis of correlations between genotype and phenotype within an integrated database in the context of metabolic disorders. The basis for the examination of relations between genotypes and phenotypes is the information from different life science data sources, merged by a mediator-based system in an integrated database. A graphical Web interface enables user queries to the integrated data and the trace of connections to corresponding data objects in different information domains. As a result of this work, a Web-based prototype of the system is presented. In the context of an example scenario, the framework was used to integrate clinical and molecular biology data for rare metabolic diseases. The framework offers software tools to integrate almost any life science data and to prepare the content for user-specific presentation.