Project application, 2006


HepatoSys - Kompetenznetz Systembiologie des Hepatozyten


HepatoPath platform: Building comprehensive regulatory network of hepatocytes through integration of hormonal, gene regulatory, signal transduction and metabolic pathways.


Table of contents

Project application, 2006_ 1

HepatoSys - Kompetenznetz Systembiologie des Hepatozyten 1

HepatoPath platform: Building comprehensive regulatory network of hepatocytes through integration of hormonal, gene regulatory, signal transduction and metabolic pathways. 1

Objectives. 4

Participants 4

1. Topic and aim of the project 4

Steps of the planned approach: 6

2. State of science and technology and previous work of the consortia partners 6

Gene regulatory networks involved in liver differentiation, regeneration, detoxification and lipid metabolism. 7

Signal transduction pathways 8

Hormonal regulatory network of liver cells 8

The innate immune system in liver disease. 9

Analysis of regulatory interactions and network structure 9

3. Contribution of the project to the main focus of the program "Systeme des Lebens - Systembiologie" of the German Government 10

4. Novelty and attractiveness of the product/method of resolution. Innovation aspects 10

5. Economic impact and market potential 10

6. Applicant, contributing partners and their expertise 11

Partner 1: BIOBASE GmbH (Kel) 11

Partner 2: Department of Bioinformatics, University Göttingen (Wingender) 13

Partner 3: Fraunhofer Institute of Toxicology and Experimental Medicine, Hannover, Germany (Borlak) 14

Partner 4: Institute of Clinical Chemistry and Laboratory Medicine, University of Regensburg (Schmitz) 15

Partner 5: Iinstitute of Biochemistry, University of Cologne (Schomburg) 18

Partner 6: Department of Gastroenterology & Endocrinology, University of Göttingen (Ramadori) 19

Partner 7: Dept. Clinical and Experimental Endocrinology, University of Goettingen (W. Wuttke) 22

Partner 8: Department of Bioinformatics, University Freiburg (Backofen) 23

7.         Project structure (coordination and composition of the competencecluster, connections with relevant research institutions) 25

8. Detailed description of the work plan and the contribution of each working group_ 25

WP1. Gene expression analysis tools 25

WP2. Signal transduction and transcription regulation network_ 26

WP3. Metabolic and hormonal network_ 27

WP4. Network analysis and integration. 27

9.         Detailed costs projection (3 years) 28

10. Project time plan 28

11. Prospects of success (economic, scientific and/or technical success chances; scientific and economic utilization chance and continuation capability with dates) 28

12. Exploitation plan 29

Exploitation Strategy 29

Dissemination and standardization activities 29




Modeling the integral activity of huge regulatory networks of multicellular organisms is a great challenge of modern systems biology. Many advanced techniques have been employed in the last years to model and simulate genetic regulatory systems, including: Boolean  and Bayesian networks, their generalization - Petri nets, differential equations, stochastic equations, the pi-calculus as a formal language, and rule-based formalisms (reviewed in de Jong, 2002). However, any simulation attempts hardly make any sense if the regulatory network behind is not complete in their essential elements. To achieve the ultimate goal of systems biology of building the virtual representation of the cell and the whole organism in order to be able to perform the "computational experiments" we must reconstruct the core regulatory system in its maximal possible comprehensiveness. So, the goal of this project is to build a most comprehensive regulatory network of the hepatocyte that will include, first of all, gene regulation network, signal transduction and metabolic as well as hormone and cytokine network of hepatocytes in various developmental stages and physiological situations of normal and pathological states of the organism.  The regulatory networks reconstructed in this project will provide the basis for building the quantitative models and dynamic simulation in close cooperation with modeling teams of the HepatoSys research framework.

Being the biggest endocrine gland in our body, the liver is a control station of energy homeostasis, metabolism/detoxification potential and the hormone system. HepatoSys research framework puts its focus on modeling of two important processes in liver: detoxification and regeneration. The ultimate goal is to perform quantitative modeling of molecular-genetic mechanisms controlling these processes in the liver cells to be able to understand their integral activity in normal physiological situations as well as under metabolic pathophysiological transdifferentiation and deregulated endocrine signaling processes characteristic for various liver diseases.

As an integral part of HepatoSys framework, the goal of our HepatoPath platform will be to build the integrated regulatory networks of liver cells and populate it with quantitative data using unique combination of modern experimental and computational techniques. Starting from high throughput experimental technologies of profiling the transcriptome, proteome, lipidome and metabolome, we are going to contribute a novel approach to perform knowledge driven reconstruction of integrity of hormonal, gene regulatory, signal transduction and metabolic networks of hepatocytes. The core computational biology technologies in our hands are the world leading databases: TRANSFAC on transcription factors and their target sites, TRANSPATH on signal transduction pathways (, BRENDA on enzymes functional data and metabolic reactions ( as well as novel databases: EndoNet ( on endocrine networks and miRNAdb on gene regulation via micro RNA (under construction). The databases are accompanied by highly effective bioinformatics tools for promoter analysis and network reconstruction. The project will be also in close collaboration with international consortia in the field of metabolomics such as the European Lipidomics Initiative (

First of all, in the HepatoPath platform, we are going to extend the central intracellular regulatory pathways important for hepatocyte regeneration and detoxification, such as,  JAK/STAT, SMAD, NF-kappaB, Wnt/beta-catenin, AhR/ARNT,  and regulatory networks of cell cycle and apoptosis. Four extension frontiers will be in our focus, that are absolutely essential for comprehensiveness of pathways and the realistic chances for successful quantitative modeling: 1) hormonal, cytokine and chemokine dependent regulation of the entry points of the signaling pathways; 2) transcriptional regulation of the genes encoding the components of the pathways and the mostly undiscovered multiple target genes for the transcription factors involved - the source of often unconsidered multiple feedback loops in the signaling pathways; 3) regulated metabolic pathways leading to the catabolism and anabolism of hormones and energy; 4) regulation through micro-RNAs. 

Four main methods will be used by the members of the consortia to achieve the goal of reconstructing the most comprehensive regulatory network of hepatocytes: 1) modern high-throughput techniques to measure the dynamics of transcriptome, proteome, lipidome and metabolome; 2) expert annotation of the pathways through text mining and manual curation of the vast body of scientific literature; 3) applying the most advanced molecular biological techniques to detect protein-protein and protein-DNA interaction in vivo, such as chromatin IP, and knowledge-driven ChIP-on-chip approaches; 4) sophisticated computational methods, based on machine learning techniques, to discover signals in gene regulatory regions and algorithms of network analysis to find key nodes of the pathways.



1.      To build the most comprehensive regulatory network of the hepatocytes including gene regulation, signal transduction and metabolic as well as hormone and cytokine network of hepatocytes in the developmental stages and physiological situations of liver detoxification response. Extension of the network towards liver-specific pathological phenotypes such as steatosis/non-alcoholic steatohepatitis and toxic liver injury and de-differentiation/regeneration.

2.      To develop a novel approach for modelling of complex biological systems by integration of intracellular regulatory and metabolic pathways with the network of communication between different populations of liver cells and soluble components of the blood compartment in the context of the endocrine system of the whole organism.

3.      To build a new knowledge-driven and data-rich platform that expands the logic of the current HepatoSys research framework and provides network data to the consortia in order to facilitate the quantitative modeling of hepatocytes.



P1       BIOBASE GmbH, Wolfenbüttel (Dr. A. Kel).

P2       UKG-G, University of Göttingen, Dept. Bioinformatics, Göttingen (Prof. Dr. E. Wingender).

P3       ITEM, The Fraunhofer Institute of Toxicology and Experimental Medicine, Dept. Drug Research and Medical Biotechnology, Hannover (Prof. Dr. J. Borlak).

P4       UREG, Institute for Clinical Chemistry and Laboratory Medicine University Hospital Regensburg, Regensburg (Prof. Dr. G. Schmitz)

P5       IBUC, Institute of Biochemistry, University of Cologne (Prof. Dr. D. Schomburg,)

P6       PUKG-G, University of Göttingen, Dept. Gastroenterology and Endocrinology, Göttingen (Prof. Dr. G. Ramadori)

P7       UKG-E, University of Göttingen, Dept. Clinical and Experimental Endocrinology, Göttingen (Prof. Dr. W. Wuttke).

P8       IIF, Institute of Informatics, University of Freiburg, Freiburg (Prof. Dr. R. Backofen).


1. Topic and aim of the project


Nowadays, we experience a shift in system biology research from the study of single signal transduction pathways to increasingly complex regulatory networks (Bornholdt, 2005). Detailed predictive models of large regulatory networks could revolutionize our understanding of complex biological systems like liver cell and would tremendously facilitate the study of complex diseases, yet such models are not fissile to create. One reason is that experimental data for large genetic systems are incomplete; another is that large genetic systems require novel modeling approaches.  Therefore, to bring the dream of system biology closer to reality, the aim of the HepatoPath project is twofold.

First, to collect all available data and generate missing experimental data on the molecular genetic processes we are going to model. This includes, characterizing the circuit wiring on all levels of regulations of cellular processes: reception of extracellular hormone and cytokine signals provided by other cells of the organism; transduction of the signals to the nucleus leading to regulation of transcription, processing and translation of genes involved in further regulatory processes and in regulation of metabolic pathways providing cellular response to the signal and communication of the cell with other cells of the organism. We are going to collect all this types of data for regulatory pathways important for hepatocyte regeneration and detoxification, centered around such important signal transduction molecules and transcription factors as, JAK/STAT, SMAD, NF-kappaB, Wnt/betta-Catenin, AhR/ARNT, and on regulatory networks of cell cycle and apoptosis. We will vastly extend the currently used ,rather limited, textbook knowledge on these pathways at the "top" and at the "bottom": by adding hormonal regulation and transcription regulation respectively. We will link these pathways to the network of the metabolism of energy, toxins and hormones resulting in the most comprehensive network of hepatocytes. We will also complement the study of intracellular networks of hepatocytes with the analysis of circulating peripheral blood monocytes as the hematopoietic percursor cells for Kupffer cells and the plasma components. They will be used as correlating biological sources for liver energy metabolism, detoxification of endogenous and foreign compounds and immune mechanisms related to liver regeneration. We will generate the quantitative and semi-quantitative data as far as possible in order to support maximally the efforts on dynamic modeling of the networks. To do that will apply most modern methods of experimental molecular biology and biochemistry for generation and validation of the qualitative and quantitative network data. We will use a combination of modern functional genomic techniques including gene expression analysis, proteomics analysis, mediator metabolomics, analysis of chromatin structure using significantly improved "ChIP-on-chip" method, analysis of protein-protein interactions and most importantly innovative knowledge-based bioinformatic techniques based on databases and methods of artificial intelligence for computational identification of missing components of the pathways followed up by experimental validation.

Second aim of HepatoPath project is to extend existing and develop novel approaches suitable for modeling of large regulatory networks. In the course of realization of this project, powerful algorithms and tools will be developed to automate analysis of data coming from DNA-microarrays, proteomics, metabolomics and lipidomics experiments in order to extended the metabolic and signal transduction networks and populate them with quantitative data. In the current project we will link the intracellular networks with the hormonal, cytokine/chemokine and small molecule mediator networks of the whole organism (Fig.1). By means of topological graph analysis and information flow analysis of the network we will identify the key controlling components and the key circuit motifs of the system.

HepatoPath platform will provide all this information on the reconstructed networks and key node prediction to all members of HepatoSys consortia. The predictions will be further validated by the modeling approaches developed in the HepatoSys modeling platform. We will use selected endocrine disruptors on liver function and apply RNAi in order to bring disturbances to the networks and to validate the dynamic modeling results. Modelling of how the liver processes integrate with all other organs of the organism by exchanging molecular signals is a novel concept which will enhance the power of the already running HepatoSys program in regard to the aims of Systems Biology


Fig. 1. Performing knowledge-driven analyses of expression array data, we detect in-silico those molecular changes that are not seen directly on the arrays.

Steps of the planned approach:


1.      Build comprehensive regulatory network of signal transduction integrated with metabolic and hormonal and cytokine network by collecting all available data.

2.      Analysis of transcriptomics and proteomics data (using mircroarrays macroarrays, SAGE, differential display and 2D display).

3.      Prediction of transcription factor binding sites and composite modules in the promoters of gene regulatory clusters.

4.      Prediction of key nodes, by searching for common key regulators in the regulatory network upstream of the differentially expressed genes and their transcription factors.

5.      Experimental validation of the TF-binding sites ChIP on chip and validation of key nodes by RNAi.



2. State of science and technology and previous work of the consortia partners


Integral activity of all processes in hepotocytes are controlled by large regulatory network of tightly interlinked gene regulatory, signal transduction, metabolic and hormonal pathways. A lot of details on the structure of these networks have been elucidated in thousands of experimental works in the previous years, still the are gaps in the knowledge limiting modeling efforts of system biology.


Gene regulatory networks involved in liver differentiation, regeneration, detoxification and lipid metabolism.

Gene expression is mainly regulated at the transcriptional level through sequence-specific binding of transcription factors (TFs) to their target sites in regulatory regions of genes, where the combination of these sites and bound TFs and co-regulatory proteins as well as the basal transcriptional machinery provide the required specificity.

TFs involved in liver-specific gene regulation vary in terms of their structure and function. Some TFs are liver-enriched, others are ubiquitous; some of TFs are activated in response to extracellular stimuli; others are constitutive. Cooperative action of a great number of TFs provides combinatorial transcriptional regulation of gene expression in hepatocytes.

A simplified view on transcription regulatory networks in liver differentiation, regeneration and detoxification is provided on Fig. 2.C/EBPs and HNFs are master-regulators  of hepatocyte differentiation and regeneration.TFs of the AP-1 s are important regulators of cell cycle and are closely involved in differentiation and regeneration as well as detoxification. Tumor suppressor p53, via transcriptional control of a great number of target genes regulates DNA repair, cell cycle checkpoints, apoptosis, reversible and irreversible cell cycle arrests.

Fig. 2. C/EBP, HNF and the nuclear receptor gene regulatory networks involved in the regulation of differentiation, regeneration and detoxification in liver.


The best studied TFs in toxicology are Ah-receptor (AHR), a member of helix-loop-helix-PAS domain TFs, and several members of the nuclear receptor family including the pregnane-X-receptor (PXR), constitutive androstane receptor (CAR), liver X receptor (LXR), farnesoid X receptor (FXR), PPAR, ROR. These factors coordinately regulate gene batteries of cellular defence mechanisms and all three phases of detoxification.AHR is known to function as a heterodimer with Arnt; members of the nuclear receptor family form heterodimers with RXR.PPARs, RXRs, the bile acid activated FXR, Arnt as well as sterol-regulatory element binding proteins (SREBPs), are involved in the regulation of lipid metabolism.


Signal transduction pathways

Being the dominant cell type in the liver, hepatocytes exist and function in the context of other cells of the liver as well as cells of many other organs. In response to various extracellular signaling molecules such as hormones, cytokines, chemokines, a number of signal transduction pathways are actived in hepatocytes. On the Fig. 3 we present principle schemas of two signal transduction pathways important for liver regeneration and detoxification taken from the TRANSPATH(R) database (Krull et al., 2006).


Fig. 3. a) TNF-alpha pathway; b) AhR-signaling / Xenobiotic pathway



Hormonal regulatory network of liver cells

The liver is an important control site of hormone homeostasis. Several hormones are degraded (e.g. insulin, glucagon, thyroids and steroids) upon passage through the liver. Some hormones are activated (e.g., conversion of T4 to T3 by hepatic deiodases), or synthesized (e.g., insulin-like growth factors, IGFs, and several of their binding proteins, IGFBPs) in the liver. The metabolic and regulatory pathways serving for all these functions are subject to control by metabolite levels, and circulating hormones. These different levels of control are strongly interlinked in many ways. o model how the liver and hepatocytes processes integrate with all other organs by exchanging molecular signals, we would like to combine different experimental approaches with systematic collecting the data in a proper database, i.e. EndoNet database (Potapov et al., 2006) on intercellular regulatory pathways.


Fig. 4 Being the key player in maintenance of the metabolic balance, detoxification, hormone homeostasis, exocrine and endocrine activities in a human body, the liver is in the centre of intensive cell-to-cell and tissue-to-tissue communications which are provided via numerous signaling molecules (hormones, cytokines, chemokines, etc.) and their specific receptors.


The innate immune system in liver disease.

Liver contains multiple cell populations, which are key components of the innate immune system: resident macrophage populations termed Kupffer cells, NK cells, T-cells, and lymphocytes that coexpress T- and NK- cell receptors. Hepatic Tcells produce IFN-gamma, TNF-alpha, IL-2, and/or IL-4, but little or no IL-5, while NK-cells produce IFN-gamma and/or TNF-alpha only. Kupffer cells are derived from circulating blood monocytes that arise from bone marrow progenitors. Kupffer cells also generate various immune defence products, including cytokines, prostanoids, nitric oxide, and reactive oxygen intermediates. These molecules autoregulate the phenotypic characteristics of Kupffer cells and also activate signal transduction pathways in other liver cells.


Analysis of regulatory interactions and network structure

New experimental high-throughput approaches have been introduced to study the gene regulatory (and other molecular) mechanisms of complex diseases. Among them are the ChIP (Chromatin Immuno Precipitation)-on-chip method for the identification of in vivo target genes for various TFs and RNAi approaches (gene silencing by small double-stranded interfering RNAs) for the functional elucidation of selected genes. For analysis of microarray data, various computational methods such as hierachical clustering allow to allocate genes which are coregulated in time or in response to specific treatments, into expression groupings called regulons. Mapping of the clusters onto known metabolic, gene regulatory or signalling pathways helps to reveal functional effects of the observed changes in gene expression. In the work of P1, P2 and P5 over the many years the data of the regulatory pathways are systematically collected in specialized databases (TRANSFAC, TRANSPATH, TRANSCompel, BRENDA) that put the cornerstones in this field (Matys et al., 2006; Krull et al., 2006; Schomburg et al., 2004). Recently P2 came up with a new important database on intercellular endocrine communication networks (EndoNet) ( (Potapov et al., 2006).

The results from clustering analysis are limited since the correlation of gene expression does not reveal "causality" in regulatory mechanisms. Posttranscriptional and posttranslational regulation must be taken into account as well. Novel methods of causal interpretation of gene expression data are necessary to draw biological conclusions on the mechanisms and to select reasonable target genes. This can be done by analysis of promoters and other regulatory regions of the genes in co-expressed clusters. In the work of P1, P2 and P8 novel algorithms have been developed for analysis of promoter sequences and for identification of TF binding sites and their specific compositions using positional weigh matrix (PWM) and HMM approaches, various structural and contextual features of promoter DNA and by applying sophisticated machine learning techniques and genetic algorithm [Kel2005]. Since all site recognition algorithms suffer from high prediction errors, additional evidence for the predictions can be obtained by comparative analysis of genomic sequences, phylogenetic footprint which is developed by P1 and P2 [Cheremushkin2003; Sauer2006]. Further improvement of these methods are required by combining it with ChIP-on-chip data.

In order to model dynamics of regulatory systems one partly requires information about binding affinity of transcription factor to promoter sequences of particular genes. A couple of computational approaches have been developed to model quantities of binding affinities: [Udalova2002], that requires a dissimilarity measure for known binding sequences, [Stormo2000], that uses PWMs as are good estimations for binding affinity. P8 is working now to extend current solutions with additional binding site features, such as local structural parameters, to access the binding affinity.

Analysis of the structure of large regulatory network using graph theoretical algorithms are very promising for revealing key regulatory components of the network. Still the problems arises due to the extremely high false positive rate of the computationally reconstructed paths through the networks. P1 has developed powerful algorithms of structural analysis of the network by modelling of the chains of consecutive reactions using Markov models [Kel-Margoulis In: Information Processing and Living Systems2005].


3. Contribution of the project to the main focus of the program "Systeme des Lebens - Systembiologie" of the German Government


This project contributes to the aims of the program by providing the new platform HepatoPath. This platform will significantly help to understand the biological modelsystem of hepatocytes. The focus of this platform is to provide understanding of the regulatory network of liver cells with respect to the integration of intracellular and intercellular regulatory pathways. It should be pointed out that the extension to the modeling of the liver cells in consideration of their tasks with other parts of the organism (the intercellular pathways) brings a new quality and novel concept into the HepatoSys program and fills a gap to the higher level of systems biology. The program is thereby supplemented with a better understanding of the central role and the functions this modeling system plays in higher organisms. During this approach new concepts and methods are developed that can be applied later to other modeling systems of systems biology that will facilitate their understanding.


4. Novelty and attractiveness of the product/method of resolution. Innovation aspects


A number of cutting edge methods and tools is available in this consortium for the generation of a large amount of data on of gene regulatory, signal transduction, metabolic and hormonal networks. The challenge is now to bring these different methods together and to combine them in a way that data generation and analysis becomes most cost- and time-efficient. Our approach is based on combination of the most innovative knowledge-based bioinformatic techniques based on databases and methods of artificial intelligence and modern experimental methods of analysis of chromatin structure and massive gene expression analysis. In the course of realization of this project, powerful algorithms and tools will be developed to automate analysis of data coming from microarrays and other high throughput experiments. On the other side, experiments will be planned on the basis of computational sequence analysis and modeling of the gene regulatory networks. The intimate cooperation and quick feedback between experimental and in silico approaches will allow us to direct and specify experiments according to the hypothesis generated by bioinformatics studies, and finally to make them more precise and less time-consuming. This is a clear step forward beyond the nowadays practice of “hypothesis pure – data rich” studies. 

Among the main innovations that we will develop during this project is the significant improvement of the “ChIP-on-chip” method by exploiting the advanced in silico site recognition methods. This will allow us to construct genomic microarrays in a very efficient way and to plan experiments on total investigation of genomic in vivo targets for many different transcription factors. Our new method will increase significantly the scope of these ChIP-on-chip experiments and make them really high precision and genome-wide.


5. Economic impact and market potential


The workflow proposed here comprises all major steps in the early phase of drug development. Thus, if successful, the whole package, including individual and integrated tools as well as the corresponding services provided by the partners individually or in cooperation with each other, will significantly speed up drug development in the pharmaceutical industry. In the past, it was to be observed that many pure bioinformatics approaches raised too high expectations about what could be done for drug development and finally failed, frequently because of much too narrow focussing on, e.g., modelling metabolic effects, rather than developing a scientifically systemic view and a methodologically comprehensive approach as we aim at with this proposal.

The approach can be easily applied to other areas. Thus, all big pharmaceutical companies as well as smaller companies involved in drug development form the potential market for the envisaged offerings.


6. Applicant, contributing partners and their expertise


Partner 1: BIOBASE GmbH (Kel)

Since its creation in 1997, BIOBASE's core business is to maintain and distribute databases on gene expression regulation. Its main product is TRANSFACâ, a database on eukaryotic transcription factors (TF), their genomic binding sites and DNA-binding profiles (PWMs). TRANSFACâ is accompanied by the sequence analysis tools MATCH(tm) and PATCH(tm). TRANSCompelâ  is a collection of experimentally proven composite regulatory elements. TRANSPATHâ is a signal transduction pathways database. It comes along with the tools PathwayBuilder(tm) and ArrayAnalyzer(tm). PathwayBuilder(tm) provides the possibility to compose signaling paths, pathways and networks out of the fragmented information stored in the databases and to visualize them. ArrayAnalyzer(tm) is is a tool for the analysis of data coming from microarray experiments. BIOBASE has proven expertise in the prediction of binding sites for TFs, and also in the analysis of experimental data. Researchers at BIOBASE have long standing experience in modeling of gene networks involved in regulation of mammalian cell cycle. The model is enriched by protein-DNA interactions that are predicted from the promoter analysis and identification of TF binding sites. Modeling of such in-silico enriched networks provides the possibility to validate the predicted regulatory interactions and to generate new hypotheses.

Dr. Alexander Kel is Senior Vice-President Research and Development of BIOBASE. He has more than 20 years research experience in Bioinformatics. He worked almost in all branches of current Bioinformatics. He was involved in founding and developing several databases on gene regulation and signal transduction (TRANSFAC, TRANSPATH, TRANSCompel, TRRD), developing tools for analysis of gene regulatory regions and networks of signal transduction. Currently, he is a scientific coordinator of INTAS project funded by EU.

Selected publications:

1.      Kel A.E., Kel-Margoulis OV, Farnham PJ, Bartley SM, Wingender E. and Zhang MQ. (2001) Computer-assisted Identification of Cell Cycle-related Genes: New Targets for E2F Transcription Factors. J Mol Biol. 309: 99-120.

2.      Kel A, Reymann S, Matys V, Nettesheim P, Wingender E and Borlak J. (2004) A novel computational approach for the prediction of networked transcription factors of Ah-receptor regulated genes. Mol Pharmacol. 66: 1557-72.

3.      Kel A, Konovalova T, Valeev T, Cheremushkin E, Kel-Margoulis O and Wingender E. (2005) Composite Module Analyst: A Fitness-Based Tool for Prediction of Transcription Regulation. Lecture Notes in Informatics, Proceedings of the German Conference on Bioinformatics 2005 (GCB 2005), Torda A, Kurtz  S and Rarey M (eds.), Gesellschaft für Informatik, Bonn, 63-75.

4.      Matys V, Kel-Margoulis O, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I, Chekmenev D, Krull M, Hornischer K, Voss N, Stegmaier P, Lewicki-Potapov B, Saxel H, Kel A and Wingender E. (2006) TRANSFAC(r) and its module TRANSCompel(r): transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 34, D108-D110.

5.      Krull M, Pistor S, Voss N, Kel A, Reuter I, Kronenberg D, Michael H, Schwarzer K, Potapov A, Choi C, Kel-Margoulis O and Wingender E. (2006) TRANSPATH(r): An Information Resource for Storing and Visualizing Signaling Pathways and their Pathological Aberrations. Nucleic Acids Res. 34, D546-D551.


Partner 2: Department of Bioinformatics, University Göttingen (Wingender)

This group has long-standing experience in network modeling and sequence analysis of promoter regions. Promoter models are constructed that comprise defined cis-acting sequence elements characterizing the promoter(s) of a certain group of genes. Treating reported results of microarray analyses together with other available information about the expressed genes, we search for distinguishing features of the promoters of co-expressed genes. The application of such promoter models enables to identify additional candidate genes belonging to the same regulatory network. We investigated several defensive eukaryotic systems: (1) antibacterial response of human lung epithelial cells to P. aeruginosa binding; (2) LPS-triggering (early response genes); (3) MyD88-independent TLR4-triggered pathway; (4) MALP-2-triggered pathway. For all considered systems the promoter models were provided. We developed several new methods for promoter model construction and complemented them by novel developments on phylogenetic footprinting approaches. In addition, the group has accrued expertise in database development, in the integrative modeling of different networks, and in establishing corresponding ontologies. Recently, a database on intercellular communication networks (EndoNet) has been made available by the group (  EndoNet focuses on the endocrine cell-to-cell signaling and enables the analysis of human intercellular regulatory pathways. It aims at bridging the existing gap between known genotypes and their molecular and clinical phenotypes, thus allowing utilization of EndoNet in medical research.

Prof. Dr. Edgar Wingender has coordinated and contributed to several national and international bioinformatics projects since the start of bioinformatics funding by BMFT/BMBF in 1993. Having established and headed the Research Group Bioinformatics at GBF until 2002, he then accepted the call to the University of Göttingen where he is now heading the Department of Bioinformatics at the Medical School. He is founder and Scientific Director of BIOBASE GmbH. His expertise is in bioinformatics of gene regulation and signal transduction.

Selected publications:

1.      Shelest E, Kel A E, Goessling E and Wingender E. (2003) Prediction of potential C/EBP/NF-kappaB composite elements using matrix-based search methods. In Silico Biol. 3: 71-79.

2.      Potapov AP, Voss N, Sasse N and Wingender E. (2005) Topology of mammalian transcription networks. Genome Inf Ser. 16, 270-278.

3.      Shelest E and Wingender E. (2005) Construction of predictive promoter models on the example of antibacterial response of human epithelial cells. Theor. Biol. Med. Model. 2, 2.

4.      Chen X, Wu JM, Hornischer K, Kel A and Wingender E. (2006) TiProD: The Tissue-specific Promoter Database. Nucleic Acids Res. 34, D104-D107.

5.      Potapov A, Liebich I, Dönitz J, Schwarzer K, Sasse N, Schoeps T, Crass T and Wingender E. (2006) EndoNet: An information resource about endocrine networks. Nucleic Acids Res. 34, D540-D545.

6.      Sauer T, Shelest E and Wingender E (2006) Evaluating phylogenetic footprinting for human-rodent comparisons. Bioinformatics, in press.


"Übersicht über bewilligte Drittmittelprojekte der Jahre 2002-2005":


The department participates in two German government-financed projects: Helmholz Open Bioinformatics Technology (HOBIT, since 2003) as well as the National Genome Research Network (NGFN2, since 2005). It takes part in two projects finaced by the EU: COMBIO (since 2004) and TEMBLOR (2002 – 2005).



Partner 3: Fraunhofer Institute of Toxicology and Experimental Medicine, Hannover, Germany (Borlak)

The Center of Drug Research and Medical Biotechnology was founded in 1998 and now consists of 9 postdoctoral fellows (3 men and 6 women), 3 doctoral fellows (3 men) and 12 technicians (2 men and 10 women). In this Center a wide range of in vivo and in vitro systems are available which enable problem solving and a mechanistic approach in pharmacology, toxicology and cancer. Alongside routine tests in molecular dosimetry, biochemical toxicology, xenobiochemistry, and some specialized studies in endocrine toxicology, gene expression patterns are also investigated in pathology and pharmacological and toxicological assessments using advanced molecular biology techniques. 

We have gained in depth diligence in the fields of genomics, proteomics, metabonomics, cell culture, ChIP-on-chip technology and bioinformatics (in silico analyses of gene expression data). We collaborate with different European clinical centers and are able to get hold of different patient tissues.


Selected publications:

1.      ·Borlak J, Meier T, Halter R, Spanel R, Spanel-Borowski K. (2005) Epidermal growth factor-induced hepatocellular carcinoma: gene expression profiles in precursor lesions, early stage and solitary tumours. Oncogene. 2005 Mar 10;24(11):1809-19.

2.      ·Niehof M, Borlak J. (2005) RSK4 and PAK5 are novel candidate genes in diabetic rat kidney and brain. Mol Pharmacol.2005 Mar;67(3):604-11.

3.      ·Kel, A., Reymann, S., Matys, V., Nettesheim, P., Wingender, E. and Borlak, J. (2004) A novel computational approach for the prediction of networked transcription factors of Ah-receptor regulated genes. Mol Pharmacol. 2004 Dec;66(6):1557-72

4.      ·Schrem H, Klempnauer J, Borlak J. (2004) Liver-enriched transcription factors in liver function and development. Part II: the C/EBPs and D site-binding protein in cell cycle control, carcinogenesis, circadian gene regulation, liver regeneration, apoptosis, and liver-specific gene regulation. Pharmacol Rev. 56:291-330.

5.      ·Thum T, Borlak J. (2004) Mechanistic role of cytochrome P450 monooxygenases in oxidized low-density lipoprotein-induced vascular injury: therapy through LOX-1 receptor antagonism? Circ Res. Jan 9;94(1):e1-13.

6.      ·Rütters, H., Zürbig, P., Halter, R. and Borlak, J. (2006) Towards a lung adenocarcinoma proteome map – studies with SP-C/c-raf transgenic miceProteomics, accepted for publication


Partner 4: Institute of Clinical Chemistry and Laboratory Medicine, University of Regensburg (Schmitz)

The research of the Institute of Clinical Chemistry and Laboratory Medicine is directed towards the molecular analysis of metabolic diseases with focus on lipid homeostasis, organ transdifferentiation, cellular detoxification and the central role of monocytes/macrophages in the pathogenesis of high triglyceride/ low HDL syndromes. Our strategy combines basic research with clinical studies enabling a rapid transfer of newly identified gene candidates and biomarkers from basic science to clinical diagnostics. The clinical part is related to the Regensburg Diabetes Endpoint Prediction and Prevention Study (REDEPPS), an interdisciplinary project that enables the establishment of large sample and data banks from patients with different endpoints or co-morbidities of type 2 diabetes including liver diseases such as steatohepatitis under GLP conditions. REDEPPS is embedded in the Danubian Biobank Consortium (SSA DanuBiobank) that has been initiated by partner 4. Both initiatives are members of the global P3G consortium for standardization of biobanking. The group has a special interest and expertise in transcriptional regulatory networks and bioinformatics of lipid homeostasis and complex diseases in close collaboration with the newly founded Institute of Functional Genomics (Prof. Oefner) and the Bavarian Genome Project (BayGene). The available technologies include high throughput genotyping, DNA-microarrays and Taqman PCR for genetic analysis and MALDI-TOF and the multicolour 2D-Gel Typhoon system for proteomics. The center has established an elaborated lipidomics platform with high performance gel-filtration chromatography methods, tandem mass spectrometry (ESI-MS/MS, GC-MS), capillary isotachophoresis and gradient gel analysis for lipoprotein subspecies and lipid analysis. The institute is cofounder and partner of the European Lipidomics Initiative (SSA ELIfe).

Sophisticated cellular life imaging techniques (Discovery 1 high content screening system, Leica fluorescence life time imaging and confocal microscopy) have been established and the group has developed a series of multiparameter flow cytometric assays including flow-FRET to study innate immunity receptors clusters on monocytes. In this context the group has identified the ATP binding cassette transporter ABCA1 as a major regulator of raft-microdomain dynamics and HDL metabolism in macrophages, hepatocytes and enterocytes. Work from the SFB/Transregio 13 (Membrane microdomains in disease) has lead to the discrimination of sphingomyelin/cholesterol rich from cermaide/cholesteril rich  raft microdomains in monocytes of patients with lipid disorders, and lipidomic characterization of plasma from sepsis patients revealed ceramide and lysophosphatidylcholine as novel sepsis mortality parameters. Furthermore, the group has extensively studied epithelial cell homeostasis in inflammatory bowel disease (IBD) and has identified a loss of detoxification potential and reduced expression levels of the master detoxification transcription factor PXR (pregnane-X-receptor) in the colon of IBD patients. Mediator lipidomics and metabolomics techniques for quantification of transcription factor ligands including nuclear receptor cofactors such as bile acids, oxysterols and fatty acid derivatives have been successfully developed over the last years.

Prof. Dr. med. Gerd Schmitz, is the director of the Institute of Clinical Chemistry and Laboratory Medicine and together with Prof. Dr. med R. Andreesen runs the local stem cell center and immunehematological diagnostic center of the university hospital. His research is directed towards the molecular analysis of metabolic and aging disorders with focus on lipid homeostasis and the central role of epithelial cells and blood cells in the pathogenesis of complex diseases. Our strategy combines basic research with clinical research enabling a rapid transfer of newly identified gene candidates and biomarkers from basic science to clinical diagnostics. We are founding member of the Regensburg Diabetes Endpoint Prediction and Prevention Study. The group has expertise in transcriptional regulatory networks and bioinformatics of lipid homeostasis, transdifferentiation and detoxification in complex diseases. The Institute has established a state of the art transcriptomics and lipidomics platform and is cofounder of the European Lipidomics Initiative (

Selected publications:

1.      Bodzioch M, Orso E, Klucken J, Langmann T, Bottcher A, Diederich W, Drobnik W, Barlage S, Buchler C, Porsch-Ozcurumez M, Kaminski WE, Hahmann HW, Oette K, Rothe G, Aslanidis C, Lackner KJ, and Schmitz G. (1999) The gene encoding ATP-binding cassette transporter 1 is mutated in Tangier disease. Nat Genet. 22: 347-51.

2.      Langmann T, Liebisch G, Moehle C, Schifferer R, Dayoub R, Heiduczek S, Grandl M, Dada A, Schmitz G. Gene expression profiling identifies retinoids as potent inducers of macrophage lipid efflux. Biochim Biophys Acta. 2005 May 30;1740(2):155-61.

3.      Liebisch G, Drobnik W, Lieser B, Schmitz G. High-throughput quantification of lysophosphatidylcholine by electrospray ionisation tandem mass spectrometry. Clin Chem 2002;48: 2217-24.

4.      Langmann T, Mauerer R, Zahn A, Moehle C, Probst M, Stremmel W, Schmitz G. Loss of detoxification in inflammatory bowel disease: dysregulation of pregnane X receptor target genes. Gastroenterology 2004;49:230-8.

5.      Orsò E, Broccardo C, Kaminski WE, Bottcher A, Liebisch G, Drobnik W, Gotz A, Chambenoit O, Diederich W, Langmann T, Spruss T, Luciani MF, Rothe G, Lackner KJ, Chimini G, Schmitz G. Transport of lipids from golgi to plasma membrane is defective in tangier disease patients and Abc1-deficient mice. Nat Genet. 2000;24:192

6.      Drobnik W, Liebisch G, Audebert FX, Frohlich D, Gluck T, Vogel P, Rothe G, Schmitz G. Plasma ceramide and lysophosphatidylcholine inversely correlate with mortality in sepsis patients. J Lipid Res. 2003 Apr;44(4):754-61.


"Übersicht über bewilligte Drittmittelprojekte der Jahre 2002-2005":




Zuwendungsgeber, Aktenzeichen



Quantifizierung von bioaktiven Lipiden des Sphingo- und Glycerophospholipidstoffwechsels mittels tandem-Massenspektrometrie

G. Liebisch

DFG: LI 923/2-1

ab Jan 2004 für 2(+1) Jahre

BAT IIa/2, 20.000,- € (+10.000 €) Sachmittel

Konjugierte Linolsäuren und verzweigtkettige Fettsäuren als Lipidantagonisten der genexpression und therapeutische Targets in Darmepithelzellen und Monozyten

G. Schmitz

DFG: SCHM 654/9-2 (im Verbundprojekt Lipide)

ab April 2005 für 2(+1) Jahre

BAT IIa, BAT IIa/2, 37.000 € (18.500 €)

Charakterisierung von ZNF202 im Lipidmetabolismus

S. Heimerl, G. Schmitz

DFG: HE 4727/1-1

ab Aug 2005 für 2 Jahre

BAT Va/b, 32.100 € Sachmittel

Charakterisierung der Expression, morphologischen Verteilung und Regulation von ATP-binding-cassette Transportern in der Darmmukosa von Patienten mit entzündlichen Darmerkankungen

T. Langmann, G. Schmitz

DFG: SFB 585/A1 (Regulation von Immunfunktionen im Verdauungstrakt)

01.01.2002 - 30.06.2005

BATIIa/2, BATVb, 40.000 DM Sachmittel pro Jahr

Die Bedeutung von „Lamellar Bodies“ bei Störungen der intestinalen Membranintegrität im Rahmen chronisch-entzündlicher Darmerkrankungen

E. Orso, G. Schmitz

DFG: SFB 585/A4 (Regulation von Immunfunktionen im Verdauungstrakt)

01.01.2002 - 31.12.2005

BATIIa/2, BATVb, 20.000 DM Sachmittel pro Jahr

DNA/RNA Analytik und Arraytechnologie

G. Schmitz

DFG: SFB 585/Z2 (Regulation von Immunfunktionen im Verdauungstrakt)

01.01.2002 - 31.12.2005

BATIIa, 25.000 DM Sachmittel pro Jahr

Analysis of ABCA1 interactive proteins and raft domain association depending on genetic factors and pre-beta-HDL composition

G. Schmitz

DFG: SFB TR13/A3 (Membrane Microdomains and Their Role in Human Disease)

01.01.2004 - 31.12.2007

2 BATIIa/2, 12.800 € pro Jahr

Bedeutung von humanen Monozyten/Makrophagen für die Immunpathogenese fakultativ intrazellulärer Erreger am Modell von Francisella tularensis


Bundesministerium für Verteidigung

01.08.04 - 31.07.06

BATVb, 130.000 Sachkosten

Central Facility for the Production of  Stabilised Cellular Reference Standards and External Quality Assessment in Clinical Flow Cytometry (EuroStandards)

G. Rothe

EU: QLRI-CT-2000-00436

01.09.00 - 28.02.04

BATIIa, 12.000 € Reisen, 39.000 € consumables

Dietary Lipids as Risk Factors in Development (DLARFID)

G. Schmitz, T. Langmann

EU: QLK1-CT-2001-00183

01.01.02 - 31.12.04

BATIIa, BAT Vb, 10.000 € travel, 60.000 € consumables

The European Lipidomics Initiative; Shaping the life sciences (ELife)

G. Schmitz

EU: SSA 013032

01.01.05 - 31.12.06




Partner 5: Iinstitute of Biochemistry, University of Cologne (Schomburg)

The University of Cologne is one of the premier German institutions in the fields of protein bio-chemistry and genetics. In Prof. Schomburg's group at the Institut für Biochemie research is performed in the areas of enzymology, structural biochemistry and bioinformatics. The Iinstitute of Biochemistry is also responsible for the development, maintenance and curation of the BRENDA enzyme function database. The enzyme information system BRENDA (, started in 1987, is the world's most comprehensive enzyme function and property database and is made available to the scientific community via a complex query system on the Internet and is cu-rated with close links to the user community. The BRENDA site registers more than 2 million hits per month and is queried by ca. 1000 different scientists per day.

The group is also actively involved in research on enzyme function. The contribution of the University of Cologne will rely on its long-standing experience in development and maintenance of BRENDA as well as on its active involvement in standardisation of biochemical terminology and promotion of recommended scientific nomenclature.

Selected publications:

1.      Hofmann, O. & Schomburg, D. (2005), 'Concept-based annotation of enzyme classes.', Bioinformatics 21(9), 2059--2066.

2.      Rahman, S.A.; Advani, P.; Schunk, R.; Schrader, R. & Schomburg, D. (2005), 'Metabolic pathway analysis web service (Pathway Hunter Tool at CUBIC).', Bioinformatics 21(7), 1189--1193.

3.      Fleischmann, A.; Darsow, M.; Degtyarenko, K.; Fleischmann, W.; Boyce, S.; Axelsen, K.B.; Bai-roch, A.; Schomburg, D.; Tipton, K.F. & Apweiler, R. (2004), 'IntEnz, the integrated relational en-zyme database.', Nucleic Acids Res 32(Database issue), D434--D437.

4.      Heuser, P.; Wohlfahrt, G. & Schomburg, D. (2004), 'Efficient methods for filtering and ranking fragments for the prediction of structurally variable regions in proteins.', Proteins 54(3), 583--595.

5.      Schomburg, I.; Chang, A.; Ebeling, C.; Gremse, M.; Heldt, C.; Huhn, G. & Schomburg, D. (2004), 'BRENDA, the enzyme database: updates and major new developments.', Nucleic Acids Res 32(Database issue), D431--D433.

6.      Ehrentreich, F. & Schomburg, D. (2003), 'Dynamic generation and qualitative analysis of metabolic pathways by a joint database/graph theoretical approach.', Funct Integr Genomics 3(4), 189--196.


"Übersicht über bewilligte Drittmittelprojekte der Jahre 2002-2005":


Prof. Schomburg obtained a grant for the establishment of the "Cologne University Bioinformatics Centre (CUBIC)", an "Exploratory Project" grant in the framework of NGFN2 on metabolomics/systems biology, an EU grant on the establishment of an integrated enzyme database, is a member of two EU

networks of excellence, obtained a GIF grant on protein structure determination, is a member of a Max-Planck research school, obtained a grant from the Helmholtz-strategy fund as well as DFG-grants.




Partner 6: Department of Gastroenterology & Endocrinology, University of Göttingen (Ramadori)

The Dept. of Gastroenterology & Endocrinology has a long tradition in basic liver cell research. The group has a broad experience in the isolation and purification of the different liver cell populations including hepatocytes, sinusoidal endothelial cells, hepatic stellate cells, Kupffer cells, and inflammatory mononuclear cells. A series of studies has established an important role of the non-parenchymal cells in disease processes of the liver, particularly in pathomechanisms of toxic liver injury and development of liver fibrosis. These works were funded by DFG, SFB 402.  Currently, the research is directed towards gene expression analysis in different developmental stages of the liver and in different animal models of  liver diseases with a focus on liver regeneration. To this aim, several animal models have been established at the Dept. including acute and chronic liver injury (rat CCl4-model), gamma-irradiation of the rat liver, liver regeneration after partial hepatectomy in rats and mice (incl. sham operations), acute phase reaction (turpentine-oil rat model), liver regeneration via hepatic stem cells (rat oval cell model by the modified Solt-Farber protocol, AAF administration and partial hepatectomy). Hepatic gene expression was studied in these models by a wide variety of analysis tools, including SAGE analysis, micro- and macroarray analyses and differential display. Resulting data were confirmed on a single gene basis by Real-time PCR and Northern Blot analysis. Analyzing and comparing hepatic gene expression under different in vivo conditions provides us the unique possibility to characterize distinct genetic pathways involved in health, liver injury and/or regeneration.

Prof. Dr. G. Ramadori worked in internal medicine at the Free University Berlin and at the University of Mainz with main scientific focus on basic liver research. He runs the department of Gastroenterology at the University of Göttingen since 1992 and he is one of the initiators of the Sonderforschungsbereich 402 (SFB 402, Collaborative Research Center 402) in 1993, and serves as the director of the SFB since 2002. He is also co-founder of the Graduiertenkolleg 335 (GRK 335) at the University of Göttingen. He is member of the Editorial Board of Laboratory Investigation and of BMC-Hepatology, and he is an appointed reviewer for the Deutsche Forschungsgemeinschaft (DFG), NIH, Wellcome Trust, MRC, SNRC (Swiss National Research Council), Austrian Council for Research and Technology Development .

Selected publications:

1.      Armbrust T, Kreissig M, Tron K, Ramadori G (2004) Modulation of fibronectin gene expression in inflammatory mononuclear phagocytes of rat liver after acute liver injury. J Hepatol 40:638-645

2.      Batusic DS, Armbrust T, Saile B, Ramadori G (2004) Induction of Mx-2 in rat liver by toxic injury. J Hepatol 40:446-453

3.      Batusic DS, Cimica V, Chen Y, Tron K, Hollemann T, Pieler T, Ramadori G (2005) Identification of genes specific to "oval cells" in the rat 2-acetylaminofluorene/partial hepatectomy model. Histochem Cell Biol 124:245-260

4.      Christiansen H, Batusic DS, Saile B, Hermann RM, Dudas J, Rave-Frank M, Hess CF, Schmidberger H, Ramadori G (2005) Identification of genes early responsive to gamma-irradiation in rat hepatocytes by cDNA array gene expression analysis. Radiation Research


5.      Christiansen H, Saile B, Neubauer-Saile K, Tippelt S, Rave-Frank M, Hermann RM, Dudas J, Hess CF, Schmidberger H, Ramadori G (2004) Irradiation leads to susceptibility of hepatocytes to TNF-alpha mediated apoptosis. Radiother Oncol 72:291-296

6.      Cimica V, Batusic D, Hollemann T, Chen Y, Pieler T, Ramadori G (2004) Transcriptome Analysis of Early Stage of Rat Liver Regeneration In the Model of Oval Hepatic Stem Cells. Biochem Biophys Res Commun

7.      Cimica V, Batusic D, Chen Y, Hollemann T, Pieler T, Ramadori G (2005) Transcriptome analysis of rat liver regeneration in a model of oval hepatic stem cells. Genomics 86:352-364

8.      Dudas J, Papoutsi M, Hecht M, Elmaouhoub A, Saile B, Christ B, Tomarev SI, von Kaisenberg CS, Schweigerer L, Ramadori G, Wilting J (2004) The homeobox transcription factor Prox1 is highly conserved in embryonic hepatoblasts and in adult and transformed hepatocytes, but is absent from bile duct epithelium. Anat Embryol (Berl) 208:359-366

9.      Haralanova-Ilieva B, Ramadori G, Armbrust T (2005) Expression of osteoactivin in rat and human liver and isolated rat liver cells. J Hepatol 42:565-572

10.  Mihm S, Frese M, Meier V, Wietzke-Braun P, Scharf JG, Bartenschlager R, Ramadori G (2004) Interferon type I gene expression in chronic hepatitis C. Lab Invest 84:1148-1159

11.  Novosyadlyy R, Tron K, Dudas J, Ramadori G, Scharf JG (2004) Expression and regulation of the insulin-like growth factor axis components in rat liver myofibroblasts. J Cell Physiol 199:388-398

12.  Ramadori G, Saile B (2004a) Inflammation, damage repair, immune cells, and liver fibrosis: specific or nonspecific, this is the question. Gastroenterology 127:997-1000

13.  Ramadori G, Saile B (2004b) Portal tract fibrogenesis in the liver. Lab Invest 84:153-159

14.  Ramadori G, Saile B: Hepatocytes. Hepatology, 2005, pp 1-31

15.  Saile B, Eisenbach C, Dudas J, El-Armouche H, Ramadori G (2004a) Interferon-gamma acts proapoptotic on hepatic stellate cells (HSC) and abrogates the antiapoptotic effect of interferon-alpha by an HSP70-dependant pathway. Eur J Cell Biol 83:469-476

16.  Saile B, DiRocco P, Dudas J, El-Armouche H, Sebb H, Eisenbach C, Neubauer K, Ramadori G (2004b) IGF-I induces DNA synthesis and apoptosis in rat liver hepatic stellate cells (HSC) but DNA synthesis and proliferation in rat liver myofibroblasts (rMF). Lab Invest 84:1037-1049

17.  Tron K, Novosyadlyy R, Dudas J, Samoylenko A, Kietzmann T, Ramadori G (2005) Upregulation of heme oxygenase-1 gene by turpentine oil-induced localized inflammation: involvement of interleukin-6. Lab Invest 85:376-387


"Übersicht über bewilligte Drittmittelprojekte der Jahre 2002-2005"


  1. Sonderforschungsbereich 402 (SFB 402), Molekukare und zelluläre Hepatogastroenterologie, 3. Förderperiode ab 1.1.2004 -31.12.2006


Projekt: „Reparaturprozesse in der geschädigten Leber - Einfluss von Zellinteraktionen auf die Überlebensmechanismen von aktivierten hepatischen Sternzellen und Leber Myofibroblasten“ Ramadori, Saile

Projekt: „Entwicklung und Regeneration der Leber: Molekulare Mechanismen bei Proliferation und Differenzierung von Hepatozyten im Verlauf der embryonalen Lebereintwicklung und bei der Regeneration der adulten Leber in Vertebraten
Pieler, Ramadori


  1. Graduiertenkolleg 335 (GRK 335)

“Molecular, Cellular, and Clinical Biology of Internal Organs”


Projekt: “Molecular mechanisms of the cytokine-dependent induction of the heme oxygenase-1 gene: In vivo and in vitro studies


Projekt: “Role of hepatic IGF-I in liver fibrogenesis.”


  1. HepNet (Kompetenznetz Hepatitis, 2. Förderperiode, ab 1.1.2005)


Projekt: „Prognostische Marker und Mechanismen der Fibrogenese und Strategien für deren Inhibition“ Ramadori


  1. Deutsche Krebshilfe

Projekt „Molekulare Mechanismen der akuten und chronischen strahlen-induzierten Leberschädigung“, Christiansen, Schmidberger, Saile, Ramadori; ab 1.01.2006



Partner 7: Dept. Clinical and Experimental Endocrinology, University of Goettingen (W. Wuttke)

Experience in the co-ordination of EU- and German Research Society (DFG) funded projects, referee for numerous national (DFG, ministry of science) and international (NIH, GIF, NSF, MRE) funding agencies.

More than 30 years of experience in endocrine research with animal models and primary and permanent cell culture systems suitable to study genomic and non-genomic actions of steroids and their naturally occurring or synthetic analogues in steroid receptive organs including the liver.

Methods to assess liver function at transcriptional and functional levels are established in the laboratory (e. g. DNA-microarrays and Taqman realtime RT-PCR , immunohistochemistry, RIA, ELISA)

Selected publications:

1.      Seidlova-Wuttke D, Christoffel J, Rimoldi G, Jarry H, Wuttke W. Comparison of effects of estradiol with those of octylmethoxycinnamate and 4-methylbenzylidene camphor on fat tissue, lipids and pituitary hormones. Toxicol Appl Pharmacol. 2005 Dec 17; [Epub ahead of print]

2.      Supornsilchai V, Svechnikov K, Seidlova-Wuttke D, Wuttke W, Soder O. Phytoestrogen resveratrol suppresses steroidogenesis by rat adrenocortical cells by inhibiting cytochrome P450 c21-hydroxylase.Horm Res. 2005; 64:280-286

3.      Klammer H, Schlecht C, Wuttke W, Jarry H. Multi-organic risk assessment of estrogenic properties of octyl-methoxycinnamate in vivo A 5-day sub-acute pharmacodynamic study with ovariectomized rats.Toxicology. 2005;215: 90-96.

4.      Seidlova-Wuttke D, Jarry H, Christoffel J, Rimoldi G, Wuttke W. Effects of bisphenol-A (BPA), dibutylphtalate (DBP), benzophenone-2 (BP2), procymidone (Proc), and linurone (Lin) on fat tissue, a variety of hormones and metabolic parameters: a 3 months comparison with effects of estradiol (E2) in ovariectomized (ovx) rats.Toxicology. 2005; 213: 13-24.

5.      Schlecht C, Klammer H, Jarry H, Wuttke W. Effects of estradiol, benzophenone-2 and benzophenone-3 on the expression pattern of the estrogen receptors (ER) alpha and beta, the estrogen receptor-related receptor 1 (ERR1) and the aryl hydrocarbon receptor (AhR) in adult ovariectomized rats. Toxicology. 2004; 205:123-130.

Partner 8: Department of Bioinformatics, University Freiburg (Backofen)


The group has generally experience with developing and applying bioinformatics methods for the detection of regulatory sequences. A particularly relevant work of this group is the stochastic modeling of transcription factor binding sites. We have developed a flexible modeling framework which is based on Bayesian networks and thus able to integrate various biological features (like chromatin structure) of binding sites and interdependencies among these features. The distinguishing features are retrieved using feature subset selection (FSS) algorithms which take a set of TRANSFAC binding sites as input. The resulting binding site models have shown to have a better predicting performance compared to common positional weight matrices. We are currently developing a stochastic reasoning approach which combines the prediction results of a set of single binding site models with an expectation to find such hits. This expectation is based on evaluating arbitrary biological data such as tissue information or neighboring binding sites for co-acting factors, thus favoring binding site clusters. The underlying reasoning machine is a specially designed Bayesian network which possesses properties of Boolean networks.

Beside the transcriptional regulation, another extensively researched topic of the group is the development of (non-EST-based) approaches for detecting alternative splice forms. Alternative splicing is one important way of post-transcriptional regulation. Within the SFB 604 “Multifunctional Signaling Proteins”, we are currently investigating how alternative splicing works as an modulator for signal transduction. The group is using its modeling expertise to determine signals which favor alternative splicing events, such as RNA sequence/structure motifs. Sequence and RNA structure information together is used to discover novel motifs from a set of input sequences (e. g. SELEX data).


Prof. Dr. Rolf Backofen, established and headed the Chair in Bioinformatics at the University of Jena, where he is participating in several projects in the Jena Centre of Bioinformatics. He is also part of the SFB "Multifunctional Signalling Proteins", the EU-network of excellence "REWERSE", and EU-STREP project "EMBIO". In 2005, the accepted the offer for a chair in bioinformatics from the University of Freiburg. His expertise is in algorithmic bioinformatics.


Publications 2002-2005


1.      Backofen R. and Will S. (2006) A constraint-based approach to fast and exact structure prediction in three-dimensional protein models. Journal of Constraints, 11(1) To appear.

2.      Backofen R. and Siebert S. (2005) Fast detection of common sequence structure patterns in RNAs. Journal of Discrete Algorithms, To appear.

3.      Hiller M., Huse K., Platzer M., and Backofen R. (2005) Creation and disruption of protein features by alternative splicing - a novel mechanism to modulate function. Genome Biol, 6(7):R58.

4.      Busch A., Will S., and Backofen R. (2005) SECISDesign: a server to design SECIS-elements within the coding sequence. Bioinformatics, 21(15):3312-3.

5.      Hiller M., Huse K., Platzer M. and Backofen R. (2005) Non-EST based prediction of exon skipping and intron retention events using Pfam information. Nucleic Acids Research, 33(17): 5611-21.

6.      Siebert S. and Backofen R. (2005) MARNA: multiple alignment and consensus structure prediction of RNAs based on sequence structure comparisons. Bioinformatics, 21(16):3352-9.

7.      Pudimat R., E.G. Schukat-Talamazzini E.G., and Backofen R. (2005) A multiple feature framework for modelling and predicting transcription factor binding sites. Bioinformatics, 21(14):3082-8.

8.      Hiller M., Huse K., Szafranski K., Jahn N, Hampe J., Schreiber S., Backofen R., and Platzer M (2004) Widespread occurrence of alternative splicing at NAGNAG acceptors contributes to proteome plasticity. Nat Genet. 36(12), 1255-7.

9.      Backofen R., Will S. (2004)  Local sequence-structure motifs in RNA. JBCB 2(4), 681 - 698

10.  Hiller M., Backofen R., Heymann S., Busch A., Glaesser T.M., Freytag J.C.. (2004) Efficient prediction of alternative splice forms using protein domain homology. In Silico Biology, 4(2), 0017

11.  Backofen R. and Siebert S. Fast detection of common sequence structure patterns in RNAs. In Symposium on String Processing and Information Retrieval 2004 (SPIRE 2004).

12.  Backofen R. and Busch A. Computational design of new and recombinant selenoproteins. In Proc. of the 15th Annual Symposium on Combinatorial Pattern Matching (CPM2004).

13.  Backofen R. and Will S. (2003) A constraint-based approach to structure prediction for simplified protein models that outperforms other existing methods. In Proceedings of the 19th International Conference on Logic Programming (ICLP 2003), pages 49-1.

14.  Backofen R. (2004) A polynomial time upper bound for the number of contacts in the HP-model on the face-centered-cubic lattice (FCC). Journal of Discrete Algorithms, 2(2), 161-206

15.  Backofen R. and Sebastian Will S. (2003). A constraint-based approach to structureprediction for simplified protein models that outperforms other existing methods. In Proceedings of the 19th International Conference on Logic Programming (ICLP 2003), pages 49-71, 2003.


"Übersicht über bewilligte Drittmittelprojekte der Jahre 2002-2005":


·        BMBF (FKZ 0312704K): Stochastic Constraint-based Description of Regulatory Sequences, PI: Rolf Backofen, 2003-2007, € 298.007

·        BMBF (FKZ 031652C), subproject D6: Population genetic variability of alternative NAGNAG splice acceptors, PIs: Rolf Backofen and Matthias Platzer, 2005-2007, € 98.928 (part Backofen)

·        BMBF  (FKZ 031652C), subproject D1A: Integrative analysis of complex networks of gene regulation and signal transduction in cells from patient with rheumatic diseases. PIs: Rolf Backofen and R. Guthke , 2005-2007, € 51.464 (part Backofen)

·        EU Framework 6, NEST-2003-Path-1 Contr. No. 12835: Emergent organisation in complex biomolecular systems (EMBIO), PI: Rolf Backofen, € 124.473

·        EU Framework 6, Network of Excellence, Project ref. 506779: REWERSE: Reasoning on the Web with Rules and Semantics, PI: Rolf Backofen, 2004-2008.

·        DFG Sonderforschungsbereich (SFB) 604 “Multifunctional Signaling Proteins”: Alternative Splicing as a Modulator for Signal transduction, PI: Rolf Backofen, 1 Postdoc position, 2005-2008.

·        DFG priority program “Selenoproteins”: Replacing cysteine by selenocysteine in proteins: an algorithmic, bioinformatic approach, PI: Rolf Backofen, 1 Postdoc position, 2001-2003.




7.         Project structure (coordination and composition of the competencecluster, connections with relevant research institutions)



Eight project partners from different disciplines will cooperate on this project. The HepatoPath project is organized in 4 Workpackages (WP). The key tasks and interrelationships of the WPs are depicted in the following schema.

The workflow of WPs is organized in a working loop, so the results of the work of the HepatoPath project will be provided as a iteratively enriching platform for all partners of the HepatoSys research framework.

The partners have had extensive collaborations, especially in several joint interdisciplinary research programs founded by BMBF and EU: e.g. Partners 1 and 2 in the BMBF Bioinformatics Competence Center Braunschweig "Intergenomics" and EU project "COMBIO"; Partners 1 and 3 and 1 and 4 cooperate on the gene expression studies of toxicity mechanisms, cancer and cell cycle and other human disorders.


8. Detailed description of the work plan and the contribution of each working group


WP1. Gene expression analysis tools

WP1 will provide bioinformatics software for the analysis of gene expression and proteomics data. These tools will be used in WP2 and WP4 for building the gene regulatory networks and reconstructing of their functional dynamic modes in particular physiological and pathological states. The tasks are:

1.      Development of novel, high precision methods for predicting TF binding sites in DNA: multidomain structure of sites, local context feature on flanks, repeated and symmetrical structure, improving the feature selection algorithms, application of machine learning techniques, HMMs, genetic algorithms, semi-supervised learning techniques (Kel, Backofen).

2.      Development of tools for creating promoter models and composite clusters of cis-elements. Extend the Boolean promoter models to the models of cis-regulatory logic (promoter programs). (Kel, Wingender, Backofen).

3.      Development of a statistical modeling approach for estimating binding affinity of TFs to their target sites by learning stochastic models which consider both, sequence data and quantitative affinity data (Backofen, Kel).

4.      Development of tools for causal analysis of gene expression and proteomics data and reconstruction of functional gene regulatory networks using integration of reverse engineering approaches and TF site prediction and phylogenetic footprinting (Kel, Wingender)

5.      Development of tools for analysis of regulation of post-transcriptional processes, especially effects of regulated alternative splicing and tools for modelling of regulatory effects of micro RNAs (Kel, Backofen)

6.      Development of an integrated HepatoPath database for storing of the high throughput transcriptome, proteome and metabolome data, all generated qualitative and quantitative data on the regulatory networks of hepatocytes and related cells, using the the ExProfile database structure as a prototype. (Kel)


WP2. Signal transduction and transcription regulation network

In WP2 we will experimentally generate and collect from the literature data on signal transduction and transcription regulatory networks of hepatocytes in the processes of detoxification, dedifferentiation, regeneration and related liver pathological states. In WP2 we will create the enriched regulatory networks and deliver them to other WPs and to the consortia.

1.      Updating of the BIOBASE Knowledge Library (including the databases TRANSFAC, TRANSPATH, TRANSCompel, HumanPSD and others) with project-relevant signal transduction and gene regulatory data by manual annotation of the scientific literature and with the use of text-mining tools. Development of the database structure and recording of the quantitative regulatory information (Kel).

2.      Development of the novel knowledge-driven ChIP-on-chip method combined with gene expression microarray analysis. Generation of experimental data on in-vivo genomic targets of several TFs, important for the hepatocyte regulation, such as, C/EBP, PPAR, HNFs, NF-kappaB, AhR, PXR, ERalpha, p53, AP-1. (Kel, Borlak, Schmitz, Backofen).

3.      High-throughput transcriptomic and proteomic profiling of the hepatocyte in the processes of detoxification and regeneration: cell cultures (provided by HepatoSys Cell Biology platform), different animal models (liver development, liver injury and regeneration) and primary patient materials (peripheral blood monocytes, plasma samples and liver biopsy samples from controls, patients with high triglycerdie/low HDL syndromes (BMI groups), steatosis/NASH patients, and patients with liver toxicity (cholestasis, alcohol, drug abuse)). (Schmitz, Borlak, Wuttke, Ramadori).

4.      Experimental studies of signal transduction networks and generation of qualitative and quantitative data. Analysis of signalling wiring, chaining and kinetic of a selected set of signal transduction pathways involved in detoxification, cell cycle, dedifferentiation and regeneration including: MAPK (Ras/Raf/MEK/ERK), TNF-alpha, Wnt/beta-catenin pathways. (Borlak, Wuttke, Ramadori).

5.      Use advanced tools developed in WP1 to analyze the gene expression data, to identify DNA binding sites and composite regulatory modules in the promoters of target genes of transcription factors – master regulators of the processes under study. In-silico enrichment of the transcription regulatory networks. Validation of the prediction by various experimental methods (Kel, Wingender, Backofen, Borlak, Wuttke).


WP3. Metabolic and hormonal network

In WP3 we will experimentally generate and collect from the literature quantitative data on enzymes, data on metabolic pathways and data on network of hormones, growth factors and cytokines that involve hepatocytes and other cell populations of liver in the processes of detoxification, dedifferentiation, regeneration and related liver pathologies. In WP3 we will create the enriched metabolic and hormonal networks and deliver them to other WPs and to the consortia.

1.      Updating of the BRENDA database with project-relevant data on enzymes and metabolic pathways by manual annotation of the scientific literature and with the use of text-mining tools. (Schomburg, Wingender).

2.      High-throughput profiling of metabolome and lipidome of the hepatocyte in the processes of detoxification and regeneration, using the same biological material as in WP2, task 4. Storing of the data in the HepatoPath database (Schmitz, Kel)

3.      Experimental studies of hormonal network and generation of qualitative and quantitative data: a) Animal models of liver injury and regeneration (Ramadori); b) Analysis of Comparison of effects of selected endocrine disruptors on liver function with focus on cross-talk of nuclear receptors and gender differences (Wuttke);

4.      Updating of the EndoNet database with project-relevant data on endocrine communication between different cell types within the liver as well as links between the liver and the rest of the organism. Further development of the EndoNet structure in order to enable the collection of quantitative data and representing particular phenotypes. Populating Endonet with quantitative data (amount, expression, half-life, affinity, binding constant). (Wingender).


WP4. Network analysis and integration.

In WP4 we will develop concepts and tools for integration and structural and analysis of the signal transduction, gene regulatory, metabolic and hormonal pathways. WP4 will deliver the integrated comprehensive regulatory network of hepatocytes.

1.      Development of the conceptual schema and of the integration of the four different types of regulatory pathways, integration of wiring qualitative and quantitative data. (Kel, Wingender)

2.      Further development of the data exchange formats. Creation of the integrated network of the hepatocytes and delivery to the HepatoSys Modeling platform (Kel, Wingender).

3.      Development of tools for network analysis and semi-quantitative modelling (using Petri Nets). Extend the algorithms to identify key regulators in networks to the weighted graphs. Incorporation of the information about reaction chaining. Integration with algorithms of topology and dynamic analysis of networks from other partners. (Kel, Wingender, Schomburg, Backofen).

4.      Case studies: Application of the constructed integrated regulatory network and network analysis tools to analyze experimental data in HepatoSys project for identification of key regulatory circuits involved in detoxification and regeneration mechanisms in the liver. (Kel, Wingender, Schomburg, Backofen, Borlak, Schmitz, Wuttke, Ramadori).


9.         Detailed costs projection (3 years)










Requested Sum




















































































10. Project time plan


The project duration is considered as three years. During the first year we will focus on the development of tools and database structure and establishing the experimental set-up. In the second year we will generate the main amount of data. During the third year we will construct the integrated network, analyze the structure and provide it for further dynamic simulations.


11. Prospects of success (economic, scientific and/or technical success chances; scientific and economic utilization chance and continuation capability with dates)


The scientific aim of this project is ambitious though not unrealistic. First, the problems tackled have to be dealt with now, since the time is ripe to do so after we entered the "postgenomics" and "systems biology" era. Second, all required prerequisites are present: (1) solid data which are properly structured in databases, knowledge in establishing the still missing components, and the expertise to integrate and link all these components into an integrated signaling network; (2) mathematical tools and modeling concept have been principally established; (3) experiments will be designed to generate all necessary data and to validate computational predictions. Finally, many of the project partners including the coordinator have long-standing experience in many of the related areas and have worked closely together in such an interdisciplinary setting and even on related projects. The project is primarily built on existing infrastructure and expertise. These facts should warrant a successful carrying-out of the project.

The economic value of many of the already existing bioinformatics tools has been successfully exploited by the industrial partner BIOBASE, and the expected results of this project may even exceed this success. No commercially competing initiatives are to be expected since there are no competitors for the underlying databases. Thus, in case of even partial success there will be a strong interest to transfer the results into commercial exploitation, BIOBASE being one candidate to do so.

The achievements of the project can be flexibly marketed, depending on the specific needs of potential customers: The commercial partner of the consortium BIOBASE may offer the products and services resulted from the realization of the project individually or jointly, since the synergism between the partners will have been proven at the end of the project. Wherever necessary, individual contracts will specify the details of the usage of shared IP and exploitation of the IP of the academic by the commercial partner. E. g., there is already a technology transfer agreement in place between the partners UKG-G and BIOBASE.


12. Exploitation plan


Exploitation Strategy

·        Through the industrial partner BIOBASE, as well as through the technology transfer office of the research centers, we will identify those technologies, and novel methods that could be patented, as well as possible partners to licenses them.

·        The partners will reach an agreement on product ownership of the software produced and on new insight into the networks studied that could lead to new treatments.

·        The partners will shape the results of the project into altered or improved marketable products and services. This activity will be done mainly by industrial partner in the project involving their own marketing and RTD resources.


Dissemination and standardization activities

This task will focus on the exploitation and dissemination of the underlying scientific and technological ideas and results: by demonstrations and supporting papers at targeted conferences, at appropriate public events, and of publications in scientific journals of high quality. Another medium for dissemination will be the presentation on public WWW servers.