Modeling of stem cell self-renewal and regeneration


  • T. Borggrefe and F. Oswald. Keeping Notch Target Genes off: A CSL Corepressor Caught in the Act. Structure, 22, 3-5, 2014.
  • C. Jung, G. Mittler, F. Oswald and T. Borggrefe. RNA helicase Ddx5 and the noncoding RNA SRA act as coactivators in the Notch signaling pathway. Biochim Biophys Acta, 1833:1180-1189, 2013.
  • M.C. Florian, K. J. Nattamai, K. Dörr, G. Marka, B. Uberle, V. Vas, C. Eckl, I. Andrä, M. Schiemann, R.A. Oostendorp, K. Scharffetter-Kochanek, H.A. Kestler, Y. Zheng, H. Geiger. A canonical to non-canonical Wnt signalling switch in haematopoietic stem-cell ageing. Nature, 503:392-6, 2013.
  • F. Herrmann, A. Groß, D. Zhou, H. A. Kestler, and M. Kühl. A Boolean model of the cardiac gene regulatory network determining first and second heart field identity. PLOS ONE, 7(10):e46798, 2012.
  • J. Wang, Q. Sun, Y. Morita, H. Jiang, A. Groß, A. Lechel, K. Hildner, L. M. Guachalla, A. Gompf, D. Hartmann, A. Schambach, T. Wuestefeld, D. Dauch, H. Schrezenmeier, W. Hofmann, H. Nakauchi, Z. Ju, H. A. Kestler, L. Zender, and K. L. Rudolph. A Differentiation Checkpoint Limits Hematopoietic Stem Cell Self-Renewal in Response to DNA Damage. Cell, 148(5):1001-1014, 2012.

Molecular mechanisms


  • N. Johnsson. Analyzing protein-protein interactions in the post-interactomic era. Are we ready for the endgame? Biochemical and Biophysical Research Communications, 445(4):739-745, 2014.
  • C.E. Weidgang, R. Russell, P.R. Tata, S.J. Kühl, A. Illing, M. Müller, Q. Lin, C. Brunner, T.M. Boeckers, K. Bauer, A.E.R. Kartikasari, Y. Guo, M. Radenz, C. Bernemann, M. Weiß, T. Seufferlein, M. Zenke, M. Iacovino, M. Kyba, H. R. Schöler, M. Kühl, S. Liebau, A. Kleger.TBX3 Directs Cell-Fate Decision toward Mesendoderm. Stem Cell Reports, 1(3) 248-265, 2013.
  • D. Moreno, J. Neller, H. A. Kestler, J. Kraus, A. Dünkler, and N. Johnsson. A fluorescent reporter for mapping cellular protein-protein interactions in time and space. Molecular Systems Biology, 9:647, 2013.
  • H. Geiger and Y. Zheng. Cdc42 and aging of hematopoietic stem cells. Current Opinion in Hematology, 20(4):295-300, 2013.
  • H. Geiger, G. de Haan and M.C. Florian. The ageing haematopoietic stem cell compartment. Nature Reviews Immunology, 13:376-389, 2013.
  • H. M. Tauc, A. Tasdogan and P. Pandur. Isolating intestinal stem cells from adult Drosophila midguts by FACS to study stem cell behavior during aging. Journal of Visualized Experiments, 2014. Revised version submitted.

Computational Methods in Bioinformatics


  • J.M. Kraus, L. Lausser and H.A. Kestler. Exhaustive k-nearest neighbour subspace clustering. Journal of Statistical Computation and Simulation, 2014. Accepted 
  • A. Fürstberger, M. Maucher, H.A. Kestler. Extended pairwise local alignment of wild card DNA/RNA-sequences using dynamic programming. Journal of Statistical Computation and Simulation, 2014. Accepted
  • F. Schmid, L. Lausser, and H.A. Kestler. Three Transductive Set Covering Machines. In Spiliopoulou M.,Schmidt-Thieme L., Janning, R. Data Analysis, Machine Learning and Knowledge Discovery Studies in Classification, Data Analysis, and Knowledge Organization, pp. 303-311, Springer, 2014. 
  • L. Lausser, C. Müssel, A. Melkozerov, and H.A. Kestler. Identifying predictive hubs to condense the training set of k-nearest neighbour classifiers. Computational Statistics, 29:81-95, 2014.
  • L. Lausser, F. Schmid, M. Schmid, and H.A. Kestler. Unlabeling data can improve classification accuracy. Pattern Recognition Letters, 37:15-23, 2014.
  • L. Lausser, H.A. Kestler. Fold change classifiers for the analysis of gene expression profiles. In W. Gaul, A. Geyer-Schulz, Y. Baba, A. Okada, Hrsg., Proceedings volume of the German/Japanese Workshops in 2010 (Karlsruhe) and 2012 (Kyoto), Studies in Classification, Data Analysis, and Knowledge Organization, pp. 193-202, 2014.
  • M. Schmid, H.A. Kestler, and S. Potapov. On the validity of time-dependent AUC estimators. Briefings in Bioinformatics, 2013. in press
  • T. Schnattinger, U. Schöning, A. Marchfelder and H.A. Kestler. RNA-Pareto: interactive analysis of Pareto-optimal RNA sequence-structure alignments. Bioinformatics, 29(23):3102-3104, 2013.
  • T. Schnattinger, U. Schöning, H.A. Kestler. Structural RNA alignment by multi-objective optimization, Bioinformatics, 29(13):1607-1613, 2013.
  • G. Völkel and M. Maucher and H.A. Kestler. Group-based ant colony optimization. Proceedings of the 15th annual conference on Genetic and evolutionary computation, pages 121-128, 2013.

Computational methods in Systems Biology


  • M. Maucher, D.V. Kracht, S. Schober, M. Bossert, and H.A. Kestler. Inferring Boolean functions via higher-order correlations. Computational Statistics, 29(1-2):97-115, 2014.
  • A. Burkovski, L. Lausser, J.M. Kraus, and H.A. Kestler. Rank aggregation for candidate gene identification. In M. Spiliopoulou, L. Schmidt-Thieme, and R. Janning, editors, Data Analysis, Machine Learning and Knowledge Discovery, Studies in Classification, Data Analysis, and Knowledge Organization, pages 285-293, Heidelberg, 2014. Springer.
  • M. Hopfensitz, C. Müssel, M. Maucher, and H.A. Kestler. Attractors in Boolean networks - a tutorial. Computational Statistics, 28(1):19-36, 2013.
  • L. Lausser, F. Schmid, and H.A. Kestler. Multi-classifier systems incorporating meta information for the analysis of gene expression profiles. Proceedings of the GPSDAA 2013 Symposium in Dresden.