Our group develops and applies modern Bioinformatics techniques for different problem areas in the Life Sciences. A major focus are Bioinformatics approaches that relate to the study of human diseases and their therapies. From a methodological point of view, our research spans a broad range that encompasses, e.g., Computational Proteomics, Transcriptomics, Structural Bioinformatics, Visualization, and Image and Volume Analysis.
Biochemical Algorithms Library
Rapid Software Prototyping can significantly reduce development times in the field of Computational Molecular Biology and Molecular Modeling. BALL (Biochemical Algorithms Library) is an application framework in C++ that has been specifically designed for this purpose. It provides an extensive set of data structures as well as classes for Molecular Mechanics, advanced solvation methods, comparison and analysis of protein structures, file import/export, and visualization.
BALLView is our standalone molecular modeling and visualization application. Furthermore it is also a framework for developing molecular visualization functionality. It is available free of charge under the GPL for Linux, Windows and MacOS.
Biomedical image analysis
Magnetic Resonance Imaging (MRI) is an important diagnostic tool in modern medicine. The data acquisition, the storage and the statistical analysis of the data, the segmentation of images, the elimination of artefacts and noises are just some examples of issues for which mathematical and informatic tools should be considered and implemented.
After the image denoising, fiber tracking is performed to study the diffusion of water on a microscopic scale, analyzing the diffusion tensor and studying the relevant properties along the brain fibers (like Fractional Anisotropy, Mean, Radial and Axial Diffusivity). Statistical analylis is required to take into account correlations and spatial distributions, linking them with the other eventually relevant parameters like age and sex of people belonging to patients or controls subgroups.
Since many parameters are involved, a database approach is required, as well as a user interface to different tools, from visualization to statistical analysis and reporting; for this, galaxy has been taken into account.
Biomedical survival analysis of liver transplanted patients
The survival benefit of liver transplanted patients often varies remarkably. Thus, revealing genetic marker predicting the patients benefit of liver transplantations would be a mile stone for patients survival.
In a close cooperation with the AG Bioinformatik (Head: PD Dr. Dr. Teufel) and the surgical department (Head: Prof. Dr. Otto) of the Uniklinik Mainz, a dataset involving 101 liver transplanted patients were collected. This dataset includes clinical data of the donor and their corresponding recipients. Whereas clinical data of the recipients were monitored over a time period of five years. Further, biopsies were taken after the resection of the donor livers and directly after the transplantation into the recipient as well as following up biopsies after one, three and five years. Afterwards genetic profiles were created using the Illumina microarray technology.
One of the main goals of this research project is analyzing the clinical data to reveal potential clinical factors which closely correlated with the patient survival. A hypothesis for example could be: Did the cold ischemia time (CIT) affects the recipients survival? With respect to the individual genetic profiles, other goals are the identification of survival associated genes which could be used as diagnostic score to predict patients survival. Finally, time series analysis will be applied to the genetic profiles to evaluate genes affecting the longtime benefit of the liver transplantation.
CellNavigator – A workbench for cell biologists
CellNavigator will be a open-source web-based compendium of cell line expression profiles and query tools.
Its purpose will be dedicated to the web lab scientist working with cell lines. This innovative tool should help those scientists to pre-select specific cell lines for upcoming experiments to improve the chances of success. Therefore an intuitive query tool will be implemented allowing the user to analyze individual cell lines for specific genes, biological functions and expression or any combination of them. CellNavigator will hold genetic information on 318 individual cell lines, representing 54613 unique spots corresponding to 19798 unique genes. Additionally and to enlarge the scope of CellNavigator those data will be connected to common functional and genetic databases (e.g. NCBI, EnsEMBL, KEGG, etc.).
Computer aided prognosis of chronic liver diseases
Liver fibrosis is a connective tissue like alteration of the liver which raises from the chronic damage of liver tissues. Persistent damage leads from fibrosis to cirrhosis which further develops into serious liver damage and clinical symptoms. Thus, the development of a cirrhosis is involved in severe complications like liver failure or the emergence of liver cancer (hepatocellular carcinoma, HCC). In Germany, it is estimated that about 2.000.000 patients suffer from fatty liver disease and 400.000 patients are infected with chronic Hepatitis B and 600.000 with Hepatitis C. Furthermore with 500.000 alcohol-related liver cirrhosis as well as a significant number of patients with metabolic liver diseases, the emergence of fibrosis and the following cirrhosis is an essential health problem. The development of liver fibrosis results in an imbalance between the regulation of the expression and synthesis of collagens and reduced matrix degradation by collagenases. As a consequences there are chronic wounds and inflammations, necrosis as well as a massive expression of extra cellular matrix (ECM) proteins. If this imbalance remains over a longer time period, an alteration of healthy liver tissue into inoperative scar tissue is conducted. This cirrhotic modification can subsequently lead to HCC and despite the increasing incidence of HCC the treatment options are still insufficient.
The main focus of this research project is the implementation of a graph enrichment algorithm to score biological interaction networks for their relevance within pathological/biological states. The implementation basis of this algorithm is the UniPAX framework, a biological data warehouse for pathway based information. Another focus is devoted to the application of the graph enrichment algorithm to identify significant interaction networks with respect to patients suffering from liver fibrosis, liver cirrhosis and liver cancer and the evaluation of the most promising interaction networks for therapeutic targets. Additionally, this project is dedicated to provide a webapplication which deals with individual gene lists, uploaded by users, and output scored interaction networks for those gene lists.
G-protein coupled receptors (GPCRs), also known as seven transmembrane receptors (7TM receptors), are the largest family of α-helical transmembrane proteins. The functions of GPCRs are as diverse as their ligands are. They are responsible for several automatic body functions as blood pressure and heart rate, digestive processes and regulation of the immune system activity. Also many wide-spreaded and some serious diseases are related to dysfunctions of this receptors, e.g. hypertension, asthma, allergic reactions, schizophrenia, and Parkinson's disease, to mention only a few.
Because structural information for GPCRs is still limited (you can see the 7 available crystal structures on the right side, December 2011), but the demand is very high, we explore the applicability of in silico modeling strategies on this protein family.