“Understanding complex traits like aging necessarily requires a systems approach”
Prof. Andreas Beyer
Prof. Andreas Beyer studied Systems Science at the University of Osnabrück, where he received his PhD in 2002. He has held postdoctoral positions at the Leibniz Institute for Age Research in Jena (with Thomas Wilhelm) and at the University of California in San Diego (with Trey Ideker). From 2007 - 2012 he was an independent group leader at the BIOTEC, TU-Dresden. In 2013 he joined the Sybacol consortium as a professor for Systems Biology at the University of Cologne/University Hospital Cologne. His work focuses on the integration of functional genetics with interactome data in order to understand the functioning of cellular systems. Andreas Beyer serves as deputy coordinator responsible for the data-analysis/modelling projects of the Sybacol initiative.
- Network biology
- Statistical genetics
- Integration of different large-scale datasets into one model
Previous scientific achievements
Prof. Beyer has made seminal contributions to the development of new methods for the analysis of large scale network datasets. These datasets include various “omics” data, like transcriptomics, proteomics and interactomics. A special focus of his work lies on the integration of experimentally acquired and computationally derived data.
Within Sybacol, the Beyer group focuses on the integration of the datasets generated by the different Sybacol experimental groups into statistical models. The close interaction with experimental groups and theoretical groups and the understanding of their respective methods and requirements gives Prof. Beyer the role of a central hub within the Sybacol initiative.
Sikora-Wohlfeld, W., Ackermann, M., Christodoulou, E.G., Singaravelu, K., and Beyer, A. (2013). Assessing Computational Methods for Transcription Factor Target Gene Identification Based on ChIP-seq Data. PLoS Comput. Biol. 9, e1003342
Ackermann M, Sikora-Wohlfeld W, Beyer A. (2013). Impact of natural genetic variation on gene expression dynamics. PLoS Genet. 9, e1003514
Ackermann, M., and Beyer, A. (2012). Systematic detection of epistatic interactions based on allele pair frequencies. PLoS Genet. 8, e1002463.
Elefsinioti, A., Saraç, Ö.S., Hegele, A., Plake, C., Hubner, N.C., Poser, I., Sarov, M., Hyman, A., Mann, M., Schroeder, M., et al. (2011). Large-scale de novo prediction of physical protein-protein association. Mol. Cell. Proteomics 10, M111.010629.
Michaelson, J.J., Alberts, R., Schughart, K., and Beyer, A. (2010). Data-driven assessment of eQTL mapping methods. BMC Genomics 11, 502.
Picotti, P., Clément-Ziza, M., Lam, H., Campbell, D.S., Schmidt, A., Deutsch, E.W., Röst, H., Sun, Z., Rinner, O., Reiter, L., et al. (2013). A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis. Nature 494, 266–270.
Saraç, O.S., Pancaldi, V., Bähler, J., and Beyer, A. (2012). Topology of functional networks predicts physical binding of proteins. Bioinformatics 28, 2137–2145.