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Andreas Beyer - Cellular Networks & Systems Biology

Computational data analysis and model development

A growing number of technologies allow for the genome-scale measurement of biological properties such as protein and mRNA concentrations or phenotypic changes (e.g. response to RNAi knock-downs). The genome-wide nature of the available data facilitates a systems perspective: It becomes possible to go beyond individual genes or pathways and to study regulatory processes of the entire system ‘cell’. However, up to now the potential is by far not being fully exploited.
Thus, we develop computational tools to aid the processing and interpretation of large-scale biological data. Our group adopts a network perspective by studying relationships between proteins and other biomolecules (e.g. DNA, RNA) in silico to reveal the regulatory context of relevant genes.

Network Reconstruction
First, regulatory networks have to be uncovered, which we achieve by integrating a wide range of different data sets originating from public databases and from our collaborators (e.g. Trey Ideker). In cells many signals are transmitted or processed by protein complexes. Our algorithm for finding protein sub-complexes allows for the identification and scoring of all protein subcomplexes in large-scale measurements (Hollunder et al., 2005). These subunits represent important building blocks used by the cell to establish higher order molecular machines or signalling complexes. One important step in gene expression regulation is the interaction between transcription factors and their target genes. We presented the most comprehensive method for combining all possible experimental and computational evidences indicating an interaction between a transcription factor and a potential target gene (Beyer et al., 2006). In addition to identifying those interactions between transcription factors and their targets we also developed an algorithm to disclose the complex interaction among transcription factors (“combinatorial regulation”).

Post-transcriptional Regulation
Unlike many others we study gene expression regulation also at the post-transcriptional level. Previously, we demonstrated the importance of post-transcriptional regulation and we are pioneering new ways of analysing those processes at genomic scale (Beyer et al., 2004). We were able to link protein functions to specific regulatory patterns, such as ‘preferential transcriptional regulation’ or ‘preferentially regulated via protein turnover’, etc. Furthermore, we coined the term ‘translation on demand’, which refers to a mechanism by which cells can quickly increase the synthesis of specific proteins under stress (Beyer et al., 2004, Brockmann et al., 2007).

Endocytosis and Lysosome Biogenesis
Using experimental data from our collaborators (e.g. Bernard Hoflack, BIOTEC, Marino Zerial, MPI-CBG) we study the specific pathways and sub-networks that are activated during endocytosis and osteoclastogenesis. By linking those data together we gain new insights into the interactions between lysosomes, endosomes and the cellular environment. We apply our computational methods to determine cause-and-effect chains in those regulatory networks. The complex intra-cellular interaction between lysosomes, endosomes and the protein synthesis machinery (ER, golgi) requires the consideration of transcriptional and post-transcriptional regulation.

Pathways Controlling Adult Neurogeneis
In another project we are analysing expression QTL (eQTL) data from our collaborators Gerd Kempermann (CRTD) an Andrew Su (GNF, California) in order to reveal the pathway controlling adult neurogenesis in the hippocampus. Here we are applying network-based analysis methods to clean up the eQTL data and to mechanistically explain the causal relationships between significant loci and the respective target genes.

In the future we will specifically design experiments with our collaborators that will be perfectly tailored for our models. These data will integrated at a yet higher level in order to uncover the tight linking between transcriptional and post-transcriptional regulatory pathways in model species and human cell lines. This will address questions such as:

• How are different stress response pathways or developmental pathways interlinked?
• Many pathways control expression at different levels. Where are the 'branching points' of such pathways?
• How do small regulatory RNAs and transcription factors interact to control gene expression?

This network visualizes the complex hierarchical organization of transcriptional regulation in Saccharomyces cerevisiae. Each node represents a distinct set of transcription factors, where downstream modules (at bottom) are composed as combinations of upstream modules (at top).


Selected Publications

Suthram S, Beyer A, Ideker T. (2008) eQED: an efficient method for interpreting eQTL associations using protein networks. Molec. Syst. Biol.4:162

Beyer A, Bandyopadhyay S, Ideker T. (2007) Integrating physical and genetic maps: from genomes to interaction networks. Nat. Rev. Genet. 8(9):699-710.

R. Brockman, A.Beyer, J. Heinisch, T. Wilhelm (2007) Posttranscriptional expression regulation: what determines translation rates? PLoS Comput. Biol. 3(3):e57

A. Beyer, C. Workman, J. Hollunder, D. Radke, U. Möller, T. Wilhelm, T.G. Ideker (2006) Integrated assessment and prediction of transcription factor binding. PLoS Comput. Biol. 2(6):e70

J. Hollunder, A. Beyer, T. Wilhelm (2005) Identification and characterization of protein subcomplexes in yeast. Proteomics 5(8):2082-9

A. Beyer, T. Wilhelm (2005) Dynamic simulation of protein complex formation on a genomic scale. Bioinformatics 21(8):1610-6

A. Beyer, J. Hollunder, H.P. Nasheuer, T. Wilhelm (2004) Post-transcriptional expression regulation in the yeast Saccharomyces cerevisiae on a genomic scale Mol. Cell. Proteomics 3(11):1083-92