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M o d e l l i n g o f B i o l o g i c a l N e t w o r k s 8 “New therapeutic approaches for cancer must take network structures into account.” Stefan Legewie Education 2004 Diploma in Biochemistry, University of Witten/Herdecke 2008 PhD in Biophysics, Humboldt University Berlin Positions held 2008 - 2009 Postdoctoral Researcher, Institute for Theoretical Biology, Humboldt University Berlin 2009 - 2010 Group Leader ‘Theoretical Systems Biology’, Department of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg Since 2010 Group Members Group Leader, Institute of Molecular Biology (<strong>IMB</strong>), Mainz Stephan Baumgärtner / PhD student; since 11/2011 Bhashar Gosh / Visiting Postdoc; 12/2012 - 02/2013 Lu Huang / Postdoc; since 10/2012 Matthias Jeschke / Postdoc; since 07/2011 Monika Kuban / Research Assistant; since 10/2012 Tamara Milhaljev / Postdoc; since 11/2010 Uddipan Sarma / Postdoc; since 08/2012 Research Overview Our group employs mathematical modelling to gain insights into the dynamics of biological networks. Data-based models are developed in close collaboration with experimental partners, and model predictions are verified using wet lab experiments. Our research focusses on cellto-cell variability in cellular signal transduction and on quantitative modelling of gene expression responses. Research Highlights Modelling gene regulatory networks One of the major focuses of our group is the quantitative modelling of gene regulation. We use various modelling approaches to describe different aspects of this process. Network models taking into account the wiring of many genes are derived based on large-scale datasets. We also study small genetic modules, and use models to describe mechanistic details at the single promoter level. Gene expression responses to external stimulation are coordinated by complex networks of transcription factors. The wiring and the dynamics of such networks are incompletely understood. We characterized a medium-scale transcription factor response that mediates oncogenic transformation upon overexpression of oncogenic Ras (Stelniec-Klotz et al. 2012). The network dynamics were analysed by systematically perturbing transcription factor levels, and subsequently measuring gene expression responses. A mathematical model was applied to distinguish direct and indirect perturbation effects. As a result, regulatory interactions within the network could be reconstructed from the data and the wiring of the network controlling tumour growth clarified. The model-predicted network structure was validated experimentally using double perturbation experiments as well as phenotypic analyses. Contrary to current assumptions, the results show that no superordinate transcription factor exists that controls the activity of other factors as a master regulator. Instead, two hierarchical groups of interacting factors exist. Each of them activates gene sets needed for the growth and cancer-specific properties of the cells. The results indicate that new therapeutic approaches against tumours must target multiple rather than single factors and consider network structures.
I M B a n n u a l R e p o r t 2 0 1 2 Genes are often subject to complex combinatorial control by multiple transcription factors. Mathematical modelling approaches at the single-promoter level are required to quantitatively understand the dynamics of signal integration. Hepcidin, the central regulator of systemic iron homeostasis, is transcriptionally controlled by BMP/SMAD and IL6/STAT signalling cascades. Deregulation of either signalling pathway alters hepcidin expression, thereby causing iron storage diseases such as haemochromatosis and anaemia of inflammation. We quantitatively analysed how BMP and IL6 signal co-ordinately to control hepcidin expression in human hepatoma (HuH7) cells. We measured reporter gene expression and transcription factor phosphorylation under co-stimulation conditions, perturbed the promoter by systematic mutagenesis and applied mathematical modelling. Our results revealed that hepcidin cross-regulation primarily arises at the level of combinatorial SMAD/STAT transcription factor binding to the promoter, whereas signalling crosstalk plays only a minor role. We found that hepcidin expression responds with high sensitivity towards BMP stimulation owing to the simultaneous presence of two BMPresponsive elements in the promoter. Using mathematical modelling, we demonstrated that such a steep promoter response optimises the performance of the iron/BMP/hepcidin negative feedback loop controlling systemic iron homeostasis. These results reveal that the hepcidin promoter loses the ability to sense iron blood level changes in disease conditions like anaemia of inflammation, and that this leads to a disturbance of systemic iron homeostasis. Gene expression is frequently controlled at the post-transcriptional level. We recently studied post-transcriptional regulation by small RNAs, and found that they are particularly efficient in robustly coordinating the expression of multiple target mRNAs (Schmiedel et al. 2012). Control of cell-to-cell variability in signalling Cellular signalling networks must function reliably despite noise from intracellular events and fluctuating environments. Conversely, signalling systems may also exploit noise to ensure that only a fraction of the cell population enters a new fate. It is thus important to understand how the heterogeneity of cellular signalling can be modulated. Molecular noise arises from stochastic dynamics of signal transduction processes ("intrinsic noise") or from cell-to-cell variability in the copy number of signalling proteins ("extrinsic noise"). Intrinsic fluctuations in signal transduction typically occur on a time scale of minutes, and are thus too fast to affect downstream cell fate decisions. Our research thus focusses on cell-to-cell variability due to noise in signalling protein concentrations. In cooperation with the Niehrs group, we previously showed that negative feedback loops in the BMP signalling cascade expand the dynamic range of the system and at the same time suppress fluctuations (Paulsen et al. 2011; Blüthgen et al. 2012). We confirmed the robustness-promoting role of this feedback in vivo by showing that BMP-dependent morphological features of Xenopus embryos are significantly more variable when the feedback loops were perturbed. In our current research efforts we are analysing how variability can be modulated in more complex, multi-step signalling cascades like the mammalian and yeast MAPK pathways (Blüthgen et al. 2012; manuscript in preparation). We are applying this theory to interpret live-cell imaging data of yeast MAPK signalling networks. Table of Contents Foreword Research Groups Core Facilities Facts and Figures 9 Future Directions Figure 1. Transcription factor network that mediates oncogenic transformation by a constitutively active RAS mutant. RAS triggers the activation of AKT and ERK signalling pathways which in turn control the gene regulation network transcriptionally and posttranscriptionally (only a subset of regulatory interactions is shown). The transcription factor network decomposes into two hierarchical layers, each of which controls different aspects of oncogenic transformation. We recently extended our gene expression analyses at the single promoter level to the temporal dynamics of transcription in collaboration with the Reid group. Specifically, we are interested in how transcriptional bursts at the single-cell level give rise to population-level cycling dynamics, and how these phenomena relate to the transcriptional readout. Selected Publications Schmiedel J, Axmann I and Legewie S (2012). Multi-target regulation by small RNAs synchronizes gene expression thresholds and may enhance ultrasensitive behavior. PLoS ONE, 7, e42296. Stelniec I*, Legewie S*, Tchernitsa O, Bobbe S, Sers C, Herzel H, Blüthgen N and Schäfer R (2012). Reverse engineering a hierarchical regulatory network downstream of oncogenic KRAS. Mol Syst Biol, 8, 601 (* joint first authors). Paulsen M*, Legewie S*, Eils R, Karaulanov E and Niehrs C (2011). Negative feedback in the bone morphogenetic protein 4 (BMP4) synexpression group governs its dynamic signalling range and canalizes development. Proc Natl Acad Sci USA, 25, 10202-10207 (* joint first authors).
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