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.

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.

CardioVINEdb: a data warehouse approach for integration of life science data in cardiovascular diseases.

One of the major challenges in bioinfomatics is to integrate and manage data from different sources as well as experimental microarray data and present them in a user-friendly format. Therefore, we present CardioVINEdb, a data warehouse approach developed to interact with and explore life science data. The data warehouse architecture provides a platform independent web interface that can be used with any common web browser. A monitor component controls and updates the data from the different sources to guarantee up-todateness. In addition, the system provides a "static" and "dynamic" visualization component for interactive graphical exploration of the data.

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: 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.

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/.

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).

Supporting genotype-phenotype correlation with the rare metabolic diseases database Ramedis.

To gain further knowledge about rare genetic diseases, a world wide method for data collection via the Internet has been established. This new approach will improve collecting valuable data from single case reports. Ramedis saves standardised patient data which will be usable for statistics, longitudinal examinations and cooperative studies in future time. Embedded in the scene of the German Human Genome Project, Ramedis directly will enable phenotype-genotype correlations. Beside the better characterisation of clinical heterogeneity of rare metabolic diseases, there may be a great benefit for the treatment of these patients in whom prospective studies are otherwise expensive and difficult to perform. This contribution presents the motivation for this system, introduces features, current state and the future of the project. Additionally, first experiences of using Ramedis by health professionals are explained.