Our research interests include the areas of computational biology and bioinformatics with a strong focus on applications in molecular biology and medicine. We are actively developing tools and databases to facilitate other researchers advanced investigations into complex molecular mechanisms. Ultimately, our goal is the construction of computational models bridging the various levels between elementary molecular processes and their physiological manifestations. To this end, an in-depth knowledge of gene expression and its regulation, protein function and interaction and cellular systems and their control is needed. Thus, pivotal lines of investigation in our research group are:
System-wide measurements of gene expression by microarray or next-generation sequencing technology have given us detailed pictures of the dynamical states of cells. However, the large amounts of generated data pose formidable challenges. For instance, it is well know that single transcriptome measurements can be sensitive to artefacts caused by the use of specific protocols and platforms. Meta-analysis can alleviate this problem and reveal more expression patterns. We have therefore developed software platforms for query and interactive analysis of integrated expression data that have been obtained in diverse experiments. Furthermore, we have therefore developed new methods for improved data pre-processing and robust detection of transcriptional patterns and regulatory motifs. To optimize utilization of gene expression data, we also work on their integration with complementary types of data and information. Such approaches will give us to new insights into the complex regulatory mechanisms inside cells.
Proteins are essential for various processes in cells. Most functions, however, are performed by individual proteins alone, but by the coordinated action of multiple proteins. Understanding the functions of a protein, thus, requires the knowledge of its various interactions to other proteins. To set the groundwork for future studies, we have started to integrate various molecular interaction maps and established several publicly accessible resources for network-oriented investigations such as the Unified Human Interactome (UniHI), StemCellNet, HeartmiR and HDNetDB. In cooperation with other research groups, we utilize these resources, for instance, to detect changes of network structures during disease development and to identify novel molecular targets for therapies.
Systems biology aims to capture the properties of biological systems that emerge from the complex interplay of the single components. Following this direction, we are studying the structure and function of regulatory and signalling networks for different biological processes. For this purpose, we combine comprehensive experimental profiling methods such as next-generation sequencing with in silico reverse engineering. The aim is to gain deeper insights into the dynamics of molecular systems as well as testable models for further experimental validation.
Realising the salient need for tools to perform efficiently network-based investigations, we have started to establish a web-based port for easy access and analyses of molecular networks: the Unified Human Interactome (UniHI) database. We are using this resource to study different physiological and pathophysiological processes. For instance, we have used interaction networks to identify novel modifier of Huntington's disease, a genetic and fatal neurodegenerative disorder (DFG-SFB618(A5)) and to dissect the underlying molecular processes (CHDI-A2666). This led to the construction of HDNetDB, a database customized for network-oriented investigations of the processes underlying Huntington's disease. We are also collaborating with local experimental groups in cancer research (SFRH/BPD/70718/2010).
Systems biology of stem cell maintenance and differentiation
To get a better understanding of these fundamental processes, we have collected and integrated numerous molecular interaction data for stem cells in StemCellNet - a web-server for network-oriented investigations in stem cell biology. Additionally, we curated a large number of genetic signatures for stemness - the defining characteristic of stem cells - and included them in our StemChecker resource. Finally, we integrated the transcriptomes for various types of stem cells and cell lineages in the StemMapper database. Using these resources, we seek to reveal the complex mechanisms underlying stem cell maintenance differentiation using a systems biology approach to address the question how a pluripotent cell determines its specific fate based on its internal state and external cues. Currently we are particular interested in differentiation of embryonic stem cells towards cardiomyocytes. To cope with the complexity of stem cell differentiation, the project integrates computational and experimental approaches including modeling and high-throughput sequencing (PTDC/BIA-GEN/116519/2010).
Regulatory networks in cyanobacteria
To fully understand their adaptive capacities as well as to efficiently utilize and modify cyanobacteria in biotechnology, a detailed knowledge of their gene regulatory networks is necessary. At present, however, such knowledge exists only in rudimentary form. To derive and validate detailed models of gene regulatory networks in cyanobacteria, we are combining experimental and computational approaches including transcriptome profiling and in silico reverse engineering. As basis, we have developed the CyanoEXpress database comprising the largest integrated gene expression dataset for the model cyanobacterium Synechocystis to date. We successfully used to the integrated data to elucidate the cellular homeostasis of iron - an essential, but often limiting, element especially for photosynthetic organims (PTDC/BIA-MIC/101036/2008). Currently, we are focusing the interconnections between transcriptional regulation, metabolism and photosynthesis, as they can serve as the basis for rational re-engineering of cyanobacteria using tools of synthetic biology with potential application such the production of bio-energy (PTDC/BIA-MIC/4418/2012).