Regulatory networks
In the context of Systems Biology the CMB group analyses and predicts regulatory networks comprising various biological entities. These graph based approaches allow for the integration of heterogeneous data, from single cell to genome-wide expression profiles. We generate regulatory models for the prediction and investigation of network interactions and network dynamics.
Gene Regulatory Network (GRN) inference based on Bayesian statistics
Main contact at CMB: Dominik Lutter
- We estimate GRN based on time dependent or steady state gene expression profiles (mRNA and miRNA).
- In general we here concentrate on semi-scale network sizes (about 10 to 15 genes). Edges in our networks refer to gene-gene interaction, but can also denote indirect interactions.
- To deal with common optimization issues like overfitting or parameter indeterminacies we here use an approach based on Bayesian statistics. Furthermore, this allows us to easily integrate prior knowledge.
- Using a sampling method based on MCMC methods we generate an ensemble of networks that allows us to estimate parameter probabilities and dependencies.
- Here we strongly collaborate with the groups of Heiko Lickert and Magdalena Götz within the CoReNe project.
Gene regulatory networks from time-resolved transcriptomics
Main contact at CMB: Nikola Müller
- Within the Lungsys consortium we are involved in the role of microRNA regulation in lung cancer. We extend mathematical signal transduction models by integrating microRNA influences and study their role in signal processing. We also perform matched mRNA and microRNA time-resolved expression analysis for NSCLC lung cancer cells.
- In the Virtual Liver consortium we are involved in projects studying the gene regulatory responses of several signaling molecules. The cytokine result in cellular phenotypes like proliferation, cell cycle arrest or apotosis. The goal is to predict downstream answers of the developed signaling models from the time-resolved microarray experiments.
- Collaboration partners include: Ursula Kingmüller, Hauke Busch, Michael Meister, Jens Timmer
Gene Regulatory Networks controlling skin aging
Main contact at CMB: Steffen Sass
- We are part of the GerontoSys consortium which aims to infer a gene-oriented stromal aging model that is able to explain regulatory mechanisms on different levels (mRNA, microRNA and proteomics)
- The application of biostatistics and machine learning approaches for the integration of heterogeneous data enables us to combine transcriptional and post-transcriptional regulation within gene regulatory networks
- In addition, we closely collaborate with the group of Prof.Reifenberger at the Heinrich Heine University Duesseldorf in order to reveal the impact of microRNA regulation in UV-induced and intrinsic skin aging.
MicroRNA target analyses
Main contact at CMB: Carsten Marr
MicroRNAs regulate cellular signal transduction via controlling mRNA levels and thus tuning protein abundance. We have developed several tools to analyze miRNA targets in a systematic fashion.
- miTALOS
miTALOS analyzes microRNA functional impact by mapping predicted microRNA targets onto signaling pathways (KEGG pathways). It uses a standard enrichment method as well as a novel proximity score that takes network structure into account.
As an additional feature, miTALOS considers the tissue-specific expression signatures of microRNAs and target transcripts to improve the analysis of microRNA regulation in biological pathways. Multiple microRNAs can be combined in a single analysis to highlight their combinatorial effects. A graphical visualization of microRNA targets is provided to illustrate their respective pathway context. - PhenomiR
PhenomiR links microRNAs into disease contexts. The PhenomiR database provides information about differentially regulated miRNA expression in diseases and other biological processes. The content of PhenomiR is completely generated by manual curation of experienced annotators.
References
D. Lutter*, P. Bruns* and FJ. Theis. An ensemble approach for inferring semi-quantitative regulatory dynamics for the differentiation of mouse embryonic stem cells using prior knowledge. Advances in Systems Biology, Igor I. Goryanin and Andrew B. Goryachev. Advances in Experimental Medicine and Biology; Volume 736, 2012, DOI *equal contribution
D. Lutter, C. Marr, J. Krumsiek, E. Lang and FJ. Theis. Intronic microRNAs support their host genes by mediating synergistic and antagonistic regulatory effects. BMC Genomics, 11(224), 2010. 10.1186/1471-2164-11-224. [ DOI | PubMed | .pdf ]
A. Kowarsch, F. Blöchl, S. Bohl, M. Saile, N. Gretz, U. Klingmüller and F. Theis. Knowledge-based matrix factorization temporally resolves the cellular responses to IL-6 stimulation. BMC Bioinformatics, 11(585), 2010. 10.1186/1471-2105-11-585. [ DOI | PubMed | .pdf ]
A. Kowarsch, M. Preusse, C. Marr and F. Theis. miTALOS: analyzing the tissue-specific regulation of signaling pathways by human and mouse microRNAs. RNA, 17(809-819), 2011. 10.1261/rna.2474511. [ DOI | PubMed ]