Bioinformatics modelling and simulation of biological networks are one of the most important areas in system biology. Biological networks are powerful frameworks to integrate experimental as well as database knowledge in order to model biological interactions and systems. Therefore, different biological networks exist, such as protein-protein-interaction networks, signalling networks, gene-regulation-networks, and metabolic networks, among others. However, the fine- and fast-response regulation mechanisms within cells, like realized by microRNAs, have not been modelled, either simulated due to the lack of biological data.

MicroRNAs are one of the most important mechanisms in the fine-regulation of meany biological processes. Many reports have already shown, that this new –omic level opens the door to a completely new cell mechanisms. Although, the investigation on microRNAS is still in its beginning, it was possible to demonstrate, how big the impact of microRNA regulation can be. Right now, data about microRNA is very limited and not connected to other –omic level showing its regulation mechanisms. Therfore, we aim to build a new microRNA database, which can be linked to other life-schience databases in a datawarehouse approach. The aim is to reconstruct microRNA-metabolic networks showing the impact of various microRNA, whoich later on can be simulated.

Therefore, our project partner in Izmir will revise their existing RNA data and made this information accessible in a new relational database. This database will be the base for the modelling, analysis, and reconstruction of the biological networks in Bielefeld. Computer scientists from Bielefeld will integrate the new microRNA database into an existing database called DAWIS-M.D. where it will be semantically connected to other life-science databases, such as KEGG, HPRD, MINT, and IntAct, among others. This will result in biological correlation covering the most important –omic level.

Once integrated in the datawarehouse, the modelling tool VANESA will be extended in its functionality to reconstruct, analyse, and automatically simulated the microRNA-metabolic networks in the Petri net language. Thus, using VANESA scientists will have the possibility to predict complex networks from RNA particles, extend existing networks with RNA fragments, and/or extend already known networks from literature with RNA information. Besides the technical realization of this novel approach, we aim to investigate the microRNA-metabolic networks in the context of our cardiovascular networks, which were reconstructed without microRNA interference in previouse work. Up to now, it was not possible to integrate this new level of micro-RNA fine regulation.

Another goal we aim to realize is the tissue-specific reconstruction of those networks. With additional data from our partners from Izmir it is planned to reconstruct a 3D cell in the software application CellMicrocosmos, showing the regulation range of microRNA. Especially the membrane and nucleous transport is of great interest. With the data from the datawarehouse we are already able to determine the location of many different genes. In combination with the microRNA we will try to show the specific location of regulation and moreover the time of regulation. This will lead to new insights in fundamental research.

Das medizinische Personal und die Patienten haben im Zeitalter der Mehrfachmedikation die Herausforderung Wechsel- und Nebenwirkungen von Arzneimitteln zu minimieren, da diese sowohl in hohen und unvorhersehbaren Kosten, als auch in unerwünschten teils lebensbedrohlichen Konsequenzen resultieren. In dem geplanten Vorhaben wollen wir angelehnt an diese Problematik ein webbasiertes Informationssystem zur Identifikation von Arzneimittelwechselwirkungen und neuen unerwünschten Arzneimittelwirkungen realisieren. Dieses System weist nicht nur auf mögliche Risiken mit bereits bestehender Medikation hin, sondern auch auf mögliche Wechselwirkungen zwischen Medikamenten und Lebensmitteln. Zusätzlich ist es in der Lage, eine sinnvolle Alternativtherapie und homöopathische Mittel zu bestimmen. Außerdem ermöglicht ein integrierter Algorithmus eine beobachtete Nebenwirkung einem oder mehreren Bestandteilen der Medikation zuzuordnen. Auf diese Weise wird das Risiko in der Arzneimitteltherapie gemindert und eine Kosten- und Zeitersparnis ermöglicht. Zudem gewährt das System einen Einblick in die darunterliegenden biomedizinischen Netzwerke, die für die Wissensgewinnung und Aufklärung eine fundamentale Rolle spielen. Denn eine Krankheit wird nicht nur durch ein Bakterium, ein Virus oder auch ein Gen hervorgerufen. Vielmehr handelt es sich hier um das Zusammenspiel vieler komplexer biochemischer Netzwerke.

Run time: 2013 - 2014

AG Bioinformatik

Prof. Ralf Hofestädt
Dipl.-Inform. Alban Shoshi
Dipl.-Inform. Venus Ogultarhan
Dr. Sebastian Janowski

German Partners:

Prof. Dr. Ralf Hofestädt, Bielefeld University, Bioinformatics/Medical Informatics Department

Prof. Dr. Falk Schreiber, IPK Gatersleben, Plant Bioinformatics Group

Russian Partners:

Prof. Dr. Nikolay Kolchanov, Institute of Cytology & Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia

Dr. Ivanisenko, PBSoft Ltd., Novosibirsk Russia

German Partners:

Prof. Dr. Ralf Hofestädt, Bielefeld University, Bioinformatics/Medical Informatics Department

Russian Partners:

Prof. Dr. Nikolay Kolchanov, Institute of Cytology & Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia

Name: Bragin Anatoly Olegovich
Title: Development of database on protein structure-function relationships.
Abstract:
Structural similarities between proteins often define identity of their functions. Many proteins are polyfunctional and have major (well-known) and minor (poorly known) functions. In existing databases major protein functions are well described. Analysis of the human proteins will be made. Proteins, which have shared structural motives, were revealed. For the proteins with shared structural motives functional relationships will be defined. Search of the proteins functional features will be made with analysis of publications (text-mining method) and with database and protein structure mining.
Supervisor: Prof. Dr. N. Kolchanov, Dr. V. Ivanisenko, Prof. Dr. R. Hofestädt

Name: Tiys Evgeny Sergeevich
Title: Development of database on disease and protein mutations associations using text- and data-mining techniques.
Abstract:
Nowadays, there are many databases dedicated to phenotypical appearances of mutations. But, such databases don't contain information about potential molecular-genetic mechanisms of associations between mutations and diseases. Such information is distributed in heterogeneous type through many publications. For it's extraction from articles text-mining methods will be used. Additionally, data-mining methods of protein structure analysis and their sequences will be used for the detection of structure-property relationships. Received information will be integrated into the database.
Supervisor: Prof. Dr. N. Kolchanov, Dr. V. Ivanisenko, Prof. Dr. R. Hofestädt

Name: Ivanisenko Timofey Vladimirovich
Title: Automatic knowledge extraction from scientific publications, reconstruction of the associative networks and their deductive analysis.
Main tasks of the work:

1. Development of text-mining algorithms: Definition of the names of objects in texts Assessing the significance of recognition of object names in the texts Assessing the significance of establishing links between objects
2. Integration of the text-mining technology with GeneNet system for the automated knowledge extraction from the scientific publications in the field of molecular biology
3. Reconstruction and deductive analysis of the associative networks

Scientific objectives

The main goal of Systems Biology world-wide is to realize the implementation of a virtual cell. However, the electronic infrastructure for the realization of this fundamental idea does not yet exist.The main goal of this cooperative project is to develop and implement a tool for the semi-automatic implementation of complex metabolic Petri nets. Therefore, we have to implement a data integration tool, which will allow the automatic access to relevant metabolic databases (KEGG, BRENDA, TRANSFAC, TRANSPATH, BIND, etc.). Based on this integration layer, we will implement a visualization tool for complex metabolic networks and a Petri net transformation tool, which is able to translate selected metabolic networks into the language of Petri nets. In case of the Petri net simulation, the outstanding Petri net simulation tool Cell Illustrator will be integrated. Based on this tool, we will test and evaluate the system using data from rice (coming from the Chinese partner) and from crop (coming from IPK Gatersleben).

Project description

Today, the visualization and simulation of complex metabolic networks is of interest and importance for the future of Biotechnology (Systems Biology). The idea of this project is to bring together promising appendages coming from database integration, visualization and simulation. All partners have developed different tools. Prof. Hofestädt is an expert in integration and Petri net simulation of networks. Prof. Schreiber is an expert in the visualization of networks and Prof. Chen has a strong background in pathway prediction and alignment. By combining our tools and experience, we are able to implement a powerful tool for the analysis of metabolic pathways based on the Petri net analysis and simulation. This tool can also be the backbone of a virtual cell simulator. The realization of a virtual cell is the focus of various national and international projects. Until now, no qualified theoretical concept and tool could be presented.

Run time: 2008 - 2010

Project Members:

AG Bioinformatik: Prof. Ralf Hofestädt

IPK Gatersleben, Plant Bioinformatics Group: Prof. Dr. F. Schreiber, Dr. U. Scholz

Zhejiang University, College of Life Sciences: Prof. Dr. M. Chen, Prof. Dr. Wu

The cooperation between Prof. Kolchanov and Prof. Hofestädt has become a long tradition. Since 1998 we have various different seminars, workshops and summer schools together. One highlight of this cooperation is the foundation of the German/Russian network mentioned above. Furthermore, since 2000 Prof. Hofestädt has been the co-Chairman of the BGRS conference, which is organized regularly in Novosibirsk (next conference will be June 2008).

Reading the textbook of Prof. Ratner (Novosibrisk) Prof. Hofestädt began to model metabolic networks based on formal languages at the beginning of the 80’s (as a student). His Phd thesis and his Habilitation were based on the idea to use formal languages for the description of metabolic processes and networks. During the second half of the 90’s, when Prof. Hofestädt was Professor at the University of Magdeburg, he worked more closely with Prof. Kolchanov. Molecular databases and integration was becoming more and more the topic of our research. Both of us developed different database systems and started to develop integration tools for the automatic access and integration of molecular data. One result of the activities in Novosibirks is the ANDVisio tool, which we would like to further develop togehter in this project.

Bioinformatics, Systems Biology and Biotechnology are highly supported by DFG, BMBF and other national and international Foundations (VW-Stiftung etc.). However, we can see the same situation in Russia. During the last three years Prof. Hofestädt and Prof. Kolchanov founded the German/Russian Network Computational Systems Biology. All these activities were supported by BMB, Russian Ministry of Science and different other Partners.

The future of Biotechnology and Molecular Biology will be strongly affected by the development of Bioinformatics and Systems Biology. Today the implementation of tools for the user specific integration of databases, analysis tools and information systems is the most important topic of Bioinformatics and Systems Biology. Already in the world wide web we can see more than 1000 molecular information systems representing information about genes, proteins, enzymes, pathways, etc. Behind these database systems thousands of analysis tools are available in the internet. Based on these information systems and analysis tools the user is looking for integrative tools, which will help to realize the user specific application of selected databases and analysis tools.

The goal of our project seeks to offer a system, which is able to predict and analyse complex metabolic networks based on database integration and textmining tools.

Therefore, we will systematically extend the ANDVisio tool. One point is the user interface and the other is to use methods of deductive databases to support the automatic generation of new molecular knowledge. For the user interface we would like to implement a 3D visualisation tool for the analysis of predicted metabolic networks. For the generation of new knowledge we would like to implement a rule based systems for the automatic generation of metabolic networks.

We plan to cooperate from the beginning with Prof. Thiele (Bruker). He is also member of the German/Russian Network of Computational Systems Biology and interested in the results of this project. Few weeks ago Prof. Kolchanov presented ANDVisio in Bremen. Furthermore, we discussed the ideas of this project together in Bielefeld.

Run time: 2008 - 2009

Project Members:

AG Bioinformatik: Prof. Ralf Hofestädt, Dipl.-Inform. David Braun, Dipl.-Inform. Thorben Wallmeyer, M.A. Björn Sommer

Institute of Cytology and Genetics (Novosibirsk)

The intention of this work is to develop a basic simulation environment for simulating different types of bacterial behaviour for a better understanding of Quorum Sensing. Therefore a virtual world will be implemented with different configurable parameters. The bacteria will be represented as so called agents with a simplified cell-cycle, the ability of movement as well as detection of other bacterial agents in the immediate vicinity. Also a configurable list of given reactions will simulate a basic intracellular network for each agent.
There are many tools which simulate single aspects of cell behaviour like chemo taxis of E. Coli in a 3-dimensional media (AgentCell), population growth in 2-dimensional bio films (BacSim) or the intracellular pathways of a single cell (E-Cell). The approach of this work is to simplify and combine all necessary aspects of Quorum Sensing controlled cell behaviour in one tool.
Biologist will evaluate the models and simulation output to improve the quality of our work. With this project we will give computational guidance for designing biological experiments allow the discussion of the gene controlled cell-cell communication process by QS of V. salmonicida.

Partner

Prof. Dr. Nils Peder Willassen
Ass. Prof. Dr. Peik Haugen
Dipl.-Inform. Tim Kahlke (Ph.D. student in Tromsø, Diploma in Bielefeld)

University of Tromsø
Faculty of Medicine
Department of Molecular Biotechnology
Norway

People: Prof. Dr. Ralf Hofestädt, Dr. Thoralf Töpel. B.Sc. Sebastian Janowski

Der Schwerpunkt dieses Teilprojektes liegt in der projektübergreifenden Bereitstellung von Methoden aus dem Bereich Datenbanksysteme, dezentrale Datenhaltung und die Integration externer Daten-banken. Im Verbundprojekt werden Lösungen zur Datenvernetzung unter den Projektpartnern erarbei-tet, wodurch ein Kommunikations- bzw. Datennetzwerk aufgebaut wird, was die gezielte Integration von Daten und Analysemethoden im Projekt unterstützt. Die aus den MELK-basierten Proteom-Analysen und weiteren Laborexperimenten gewonnenen Daten werden in einer zentralen Datenbank gespeichert und gepflegt. Der automatische Zugang und die Analyse der Daten steht den Projektpart-nern zentral zur Verfügung.

Neben dem im Projekt gewonnen topologischen Proteomdaten präsentiert sich das heutige Wissen über molekulare Mechanismen in einem ständig wachsenden Datenbestand. Bereits heute sind über 300 molekularbiologische öffentliche Datenbanken via Internet verfügbar. In diesem Zusammenhang stellte die projektspezifische Nutzung dieser externen Datenbanken eine Möglichkeit dar, dieses Wis-sen in das Verbundprojekt einfließen zu lassen. Somit können die spezifischen Datenverarbeitungs- und Analysemethoden der Teilprojekte in einem weitaus globalerem Datenraum angewandt werden, was letztlich das Informationspotential erhöht. Die Berücksichtigung dieser externen Datenquellen er-höht die Aussage der Experimente und Analysen indem Querverbindungen und Zusammenhänge zu bereits vorhandenen Wissen aufgedeckt werden können. So können z.B. durch den automatischen Zugriff auf externe Datenbanken entsprechende Veröffentlichungen zu bestimmten Proteinstrukturen und -mustern, Signalketten, Vorhersagen von Proteintargets für bestimmte Krankheiten etc. ermittelt werden. Die relevanten Daten können dann anhand spezifischer Anforderungen extrahiert und bereit-gestellt werden. Die Notwendigkeit einer solchen Methode zur standardisierten Datenakquisition rückt verstärkt in den Vordergrund der aktuellen Forschungen der Bioinformatik. Neben der überwindung der physischen Datenverteilung und der damit verbundenen Heterogenität, ist die Berücksichtigung von nutzerspezifischen Anforderungen an integrierte Datenbestände ein wichtiger Aspekt. Dazu ge-hört auch die universelle Nutzbarkeit eines solchen Systems, was wiederum die Verwendung von standardisierten Schnittstellen und Anfragemöglichkeiten verlangt.

Diese beiden Aspekte werden im Kontext der Teilprojekte bearbeitet und in Werkzeugen resultieren, die eine zentrale Kommunikations-, Analyse- und Informationsverarbeitungsplattform für alle Projekt-partner zur Verfügung stellt.

Partner:

• BMBF Förderprogramm Proteomanalyse
• MelTec Multi-Epitope-Ligand-Technologies, Magdeburg

People:

• Prof. Dr. Ralf Hofestädt (Leader of the project)
• Dip. Sci. Susana Lin
• Dipl.-Inform. Alexander Rüegg
• Dipl.-Inform. Andreas Stephanik
• Dipl.-Inform. Thoralf Töpel

Number of grant (DFG): HO 1178/10-1

Project Leader: Ralf Hofestädt, Faculty of Technology, Bielefeld University, D-33619 Bielefeld

Scientists: Ming Chen, Andreas Freier, Matthias Lange, Jacob Köhler

Background of project:

Although substantial progress has been achieved within the application of database and internet technology in the field of molecular biology, the problem of integrating distributed data as a preprocessing step for the analysis of genomic datasetsis still a challenging task. Actually, there are hundreds of distributed and heterogeneous databases [Galperin], where genomic information is stored. However, existing file formats, interfaces and databases change over time, which makes it a permanent problem to access and integrate the recent datasources. To overcome this, universal and integrative tools are needed.

For the modelling and analysis of metabolic, protein and gene gene networks, access to different pathway, protein interaction, gene regulatory and other databases is essential. Partially overlapping datasets have to be fused in order to integrate networks of different types. For this reason, it is a typical data integration task.

In the broader sense, data integration is only one of the methods required for integrative modelling. At least, biochemical networks can be represented by different mathematical models. A lot of tools exist to design biochemical models interactively. The METABOLIKA simulation tool [Hofestaedt et al. ] is based on formal languages. GON [Nagasaki et al.] is a representative system with a powerful graphical interface which uses the Hybrid Petri Nets (HPN) approach.  Addicted to the dynamics of the field of application, a systematic approach is required for the automated derivation of models from distributed databases.

General references related to project:

• Galperin, M. Y. Molecular Biology Database Collection: 2004 update. Nucl. Acids. Res. 32(90001): D3--22, (2004).
• Hofestaedt, R. and Meineke, F. Interactive Modelling and Simulation of Biochemical Networks. Computers in Biology and Medicine, 25(3):321--334, (1995).
• Nagasaki et al. Genomic Object Net: I. A platform for modelling and simulating biopathways. Applied Bioinformatics 2(3): 181--184, (2003).

Aims of project:

The aim of the project was to develop a process to integratively design models of metabolic networks. Furthermore, a software platform needed to be implemented, providing generic tools to apply the developed methods to user-specific biological tasks. Our project places emphasis on two points:

1. Data integration
   Data integration is a user-specific and problem related task. In the process of modelling and animation of regulatory networks data integration is used to acquire information about the structure of regulatory networks and the properties of biochemical substances. A universal integration service should be developed, which is capable to fuse data from distributed and heterogeneous molecular data sources. The implemented integration service should allow the configuration of pluggable datasources and user-defined schemata.
2. Modelling and Animation
   A lot of different tools exist to simulate biological networks. Unfortunately, there is a lack of automated modelling capabilities, wherefore the user has to start modelling mostly from scratch. Consequently, the gap between the size of molecular databases and the size of networks modelled is growing even larger. Our aim is to combine data integration with modelling techniques to automate the modelling process.

Description of project results:

In the center of our approach we developed a process of what we call Integrative Modelling of Biochemical Networks. As an implementation of the process, we  implemented the software plattform MARGBench, which is named after this project. Figure 1 shows the projects overall architecture.

Figure 1

MARGBench includes the following four modules:

1. BioDataServer
   Modelling starts at the fusion of data from heterogeneous data sources. BioDataServer (BDS) is an integration service based on the mediator approach. The tool (see figure 2) remotely accesses distributed datasources using specifically implemented types of wrappers. Additional wrappers can be plugged into the system supporting new types of datasources.
   The user management of the system serves its functionality to different authorized users, allowing them to dynamically define schemata of virtual databases. When the user queries a virtual database, the query is decomposed into several subqueries. BioDataServer synchronizes the different tasks and joins the retrieved results. Standardized interfaces (JDBS/ODBC/XML) have been implemented to integrate the software into external applications.

   

   Figure 2

2. SEMEDA
   Data fusion can be supported by semantic data integration. The fact that the fusion of data from different databases often leads to semantic conflicts, e.g. homonyms and synonyms, makes it hard to model integrated schemata. The tool SEMEDA is a Semantic Meta Database, which is directly connected to the BioDataServer.
   It enables the user to query datasources using onthologies as well as controlled vocabularies. The system also supports collaborative editing of these elements, which makes it a powerful tool for the semantic annotation of molecular databases.

   

   Figure 3

3. PROTON
   The interactive modelling environment PROTON has been developed to reconstruct models from databases. The system is connected to the BioDataServer as well as native relational databases. It enables the user to model three aspects of regulatory networks: (1) static relationships (semantic modelling), (2) dynamic processes (mechanistic models) and (3) systems (dynamic behavoir). Each layer (see figure 4) provides several methods for its data-driven reconstruction and its analysis.

   

   Figure 4

   Configurable tools have been developed to apply data integration and database queries to the acquisition of molecular objects and the investigation of complex networks of relationships between them. A view concept allows interpretation of existing datasets as networks of molecular reactions of different types. Graphical methods provide the user with support in modelling the structure of regulatory networks. Finally, a simulation core provides the animation of the dynamic behavior of the structures as systems.

4. PathAligner
   PathAligner is designed to reconstruct/retrieve metabolic pathways and process alignments of them. It provides a simple interface to reconstruct metabolic pathways from rudimentary components such as metabolites, enzymes, genes, sequences, etc. It provides a navigation platform to investigate more related genetic/metabolic information. It also provides an alignment method to compare the similarity of metabolic pathways. PathAligner graphically presents the retrieved pathways and the alignment results (figure 5).

   

   Figure 5

Publications of project results:

• Chen M., Lin S. und Hofestädt R.: STCDB: Signal Transduction Classification Database, Nucleic Acids Research, 2004, 32(1): 456-458
• Chen M. und Hofestädt R.: WEB-Based Information retrieval System for the Prediction of Metabolic Pathways. IEEE Transaction on Nanobioscience, 2004, Vol.3, No. 3, 192-199
• Ming Chen and Ralf Hofestaedt. Quantitative Petri net model of gene regulated metabolic networks in the cell. In Silico Biology. 3, 0030 (2003).
• Köhler, S. Philippi and M. Lange. SEMEDA: Ontology Based Semantic Integration of Biological Databases Bioinformatics, Volume 19(18), pp. 2420-2427 (2003).
• A. Freier, R. Hofestädt and M. Lange. Integrative analysis of gene networks using dynamic process pattern modelling. Bioinformatics of Genome Regulation and Structure. R. Hofestaedt, N. Kolchanov (eds.), Kluwer Academic Publishers (2003).
• A. Freier, R. Hofestädt and M. Lange. iUDB: An object-oriented system for modelling, integration and analysis of gene controlled metabolic networks. In Silico Biology, 3 (0019), IOS Press, (2003).
• A. Freier, R. Hofestädt, M. Lange and U. Scholz. Information Fusion and Metabolic Network Control 
  In J. Collado-Vides and R. Hofestädt, Herausgeber, Gene Regulation and Metabolism - Post-Genomic Computational Approaches. Cambridge, MA: MIT Press, (2002).
• A. Freier, R. Hofestädt, M. Lange, U. Scholz und A. Stephanik. BioDataServer: A SQL-based service for the online integration of life science data. In Silico Biology, 2(0005), (2002).
• Ming Chen, Andreas Freier, Jacob Köhler, Alexander Rüegg. The Biology Petri Net Markup Language.
  In: Jörg Desel, Mathias Weske (Hrsg.), Lecture Notes in Informatics - proceedings of Promise'2002, Oct. 9-11, Potsdam, Germany, Vol. 21: 150-161 (2002).
• Ming Chen. Modelling and Simulation of Metabolic Networks: Petri Nets Approach and Perspective In the proceeding of "ESM 2002, 16th European Simulation Multiconference", 2002, June 3-5, Darmstadt, Germany, 441-444.
• A. Freier, R. Hofestädt und M. Lange. The electronical infrastructure for the analysis of metabolic networks. In Abstracts' Book of the Workshop on CORBA and XML: Towards a Bioinformatics Integrated Network Evironment, NETTAB 2001 - Network Tools and Applications in Biology, S. 38-43, (2001).
• R. Hofestädt, K. Lautenbach und M. Lange, Herausgeber. Modellierung und Simulation Metabolischer Netzwerke: DFG-Workshop im Rahmen des DFG-Schwerpunktes Informatikmethoden zur Analyse und Interpretation großer genomischer Datenmengen, Magdeburg, 19. - 20. Mai 2000, Preprint Nr. 10. Fakultät für Informatik, Universität Magdeburg, (2000).

Organized Workshops (DFG supported):

1. "Metabolic Pathways": Hofestädt R und K.Lautenbach, Magdeburg (2000)
2. "Integrative Bioinformatics": Hofestädt R., Kolchanov N. und T. Dandekar, Bielefeld (2003)

Grants (BMBF): 01KW9912 (01.11.1999-31.10.2002), 01KW0202 (01.11.2002-31.08.2004)

Project Leader:

Prof. Dr. Ralf Hofestädt, Bielefeld University, Bioinformatics/Medical Informatics Department

Scientists: Niels Grabe, Matthias Lange, Thoralf Töpel

Background:

Today we face a number of valuable data sources giving specialized views to highly specific aspects of biological systems ranging from genetic sequences up to pathological data. The same applies to the corresponding software where programs for analysis, modeling and simulation of individual aspects of biological data have been developed. These facts lead to the general task of integrating all this knowledge to make it biotechnologically and medically applicable.

Aim:

The goal of our project is to implement an information system which supports the analysis of gene controlled biochemical networks. Using and enhancing existing methods of bioinformatics, we plan to integrate different biochemical, molecular and medical databases which are important for the analysis of metabolic diseases. A special data integration system, partially based on the design of federated database systems and on the prototype of our Biobench system will be designed. As a second step methods for integration of tools, simulating gene controlled biochemical networks will enhance this system and lead to an integrated server for gene controlled biochemical networks.

Method:

Petrinet and rule-based simulation methods will be studied for modeling and simulation cellular signal pathways based on cell communication and gene regulation processes. Promoter detection methods will be used from the research partner T. Werner. Integration of databases will be done using FDBS techniques. While TRANSFAC contains knowledge about gene expression, biochemical reactions and signal pathways are supplied by KEGG and TRANSPATH. BRENDA stores behavior of enzymatic driven processes. For medical data METAGENE and MDDB are used. The simulation tools and databases will be combined into a gene regulation server accessible via web interface.

Result:

The developed Internet server establishes a widely usable system concerning gene regulation starting on the mere sequence level up to pathological data. The project reshapes the currently available biomedical knowledge by integrating it, eases its understanding and thus opens up the road for exploiting it for medical and pharmaceutical research.

• Computerunterstützte Diagnose von Stoffwechselerkrankungen
• Entwicklung eines Konzeptes zur Informationsfusion zum Wirkstoff-Pointing von Stoffwechselerkrankungen