Research Themes

Our research interests include the areas of computational biology as well as of bioinformatics with a strong focus on applications in molecular biology and medicine. We are also 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. For that, an in-depth knowledge of gene expression and its regulation, protein function and interaction and cellular systems and their control is required. Thus, current pivotal lines of investigation in our research group are:

1. Gene Expression Analysis

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 to the use of specific protocols and platforms. Meta-analysis can alleviate this problem and reveal robust and potentially more reliable 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 its integration with complementary types of data and information. Such approaches will give us to new insights into the complex regulatory mechanisms inside cells.

2. Molecular Interaction Networks

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, 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.

3. Systems Biology

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.



Research Topics

Along our research themes, we have studied a variety of processes in different organims and are always open for new collaborations with other researchers. Currently, key research topics of the SysBioLab include:

Molecular networks in human health and disease

A comprehensive map of human molecular interaction or the so called human interactome promises to serve as foundation for a deeper understanding of cellular processes. Specially, molecular interaction networks will provide us with a new logical framework for analysis of the molecular machinery in health and disease. The interaction data are also useful to predict human disease genes, to define sets of biomarkers and to model biological processes on a systems level. In summary, network approaches are most promising advances towards the understanding of complex human diseases.

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). We are also collaborating with local experimental groups in cancer research (SFRH/BPD/70718/2010).

Systems biology of stem cell maintenance and differentiation

Stem cells have moved into the limelight of biomedical research due to their unique potential for regenerative medicine. For safe clinical application, it is important to thoroughly understand how stem cells maintain the identity and which factors controls their differentiation towards other cell types. Whereas the roles of single components have been intensively studied, many details are still missing how the stem cell maintenance and differentiation are executed on a systems level.

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. 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

Cyanobacteria form a versatile group of microorganisms, which have successfully adapted to various environmental conditions on Earth. Their living habitats range from soil and freshwater to the open sea; and they contribute to a significant proportion of the global biomass production. Besides their ecological importance, their potential in biotechnology has evoked increased interest in cyanobacteria over the recent years. As cyanobacteria have the distinct capability to utilize solar energy as power supply for their metabolic processes, they emerged as ideal production systems, combining plant-like photosynthesis with fast growth, small genome size and ease in genetic modification.

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).