Andreas Beyer - Cellular Networks and Systems Biology

Previous and current research

Our group develops computational tools for the analysis of large biological datasets. By integrating functional genetics data with interactomes we achieve substantially deeper insights into the functioning of cellular systems (Beyer et al. 2007). 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 �ecell�f. However, up to now the potential is by far not being fully exploited.

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. During the past years we contributed new computational methods for large-scale data integration, network biology, and statistical genetics. Even though we do not do any experiments ourselves, we have a tight network of experimental collaborators and together with them we develop experiments that support our computational analysis.

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 (Beyer et al. 2006, Elefsinioti et al. 2011). The experimental detection of protein-protein interactions is an important contribution to modern systems biology. Even though advanced technologies are used, it remains impossible to completely reveal the human �einteractome�f experimentally. Thus, we work on developing tools for the computational prediction of physical protein-protein interactions (Elefsinioti et al. 2011). Our interaction networks are subsequently used for guiding experimental efforts and for the integrated analysis with other genomic data.

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 �epreferential transcriptional regulation�f or �epreferentially regulated via protein turnover�f, etc. Furthermore, we coined the term �etranslation on demand�f, 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). Currently, we are studying the impact of natural genetic variation on post-transcriptional regulation.

Analysis of high-dimensional RNAi screens
An increasing number of RNAi screens is characterizing the knock-down phenotypes by many parameters. High-throughput technologies such as automated image analysis or FACS allow for the simultaneous measurement of several parameters for every single knock-down. However, the computational analysis of the resulting data is challenging. Further, the biological interpretation of the data is often elusive. We help by providing new methods that (1) removes noise especially due to off-target-effects, and (2) aid the identification of molecular pathways that mediate the observed phenotypes.

Explaining the impact of natural genetic variability on physiological phenotypes
Natural genetic variation is determining someone�fs eye and hair colour. Yet, other traits such as disease susceptibility are also affected by genetic variations. In order to improve our understanding of complex diseases and to support the development of new diagnostic methods and treatments, we develop systems biology methods for linking genetic variation to phenotypic variation (Suthram et al. 2008, Michaelson et al. 2009, Michaelson et al. 2010, Loguercio et al. 2010). Our ultimate goal is to understand the molecular mechanisms linking the two together.

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).

Future prospects and goals

In the future we will specifically design experiments with our collaborators that will be perfectly tailored for our models. These data will be 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 (transcription, RNA-turnover, translation, etc.). Where are the 'branching points' of such pathways?
How can we connect molecular phenotypes with physiological (disease) traits?

Selected publications