Telomeres are nucleoprotein structures protecting the eukaryotic chromosomal ends against fusion, degradation and unwanted DNA double-strand break repair mechanisms. They shorten with each cell division and this eventually triggers cell growth arrest. Thus, telomere length maintenance is essential for indefinite cell proliferation and survival. Cancer cells overcome this restriction by maintaining their telomeres by either re-expressing telomerase or employing mechanisms based on homologous recombination, called alternative lengthening of telomeres (ALT). Hence, identifying and intervening regulation of telomere maintenance is a promising research topic for translational oncology.
In this project we focus on the development of mathematical models to explain telomere maintenance mechanisms in different tumor types. To study the regulation of the telomerase in Saccharomyces cerevisiae we developed the Mixed Integer linear Programming based Regulatory Interaction Predictor (MIPRIP). MIPRIP was then used to identify the most important regulators of the human telomerase reverse transcriptase (TERT) gene in different cancer types. We are now extending MIPRIP to a regulatory network to find compact regulatory moduls that best explain the regulation of TERT including also gene-gene interactions.
For pediatric glioblastoma we further developed the 'Predicting ALT IN Tumors' (PAINT) classifier by combining specific cytogenetic TMM features, genetic variants and RNA-Seq data to distinguish between ALT positive and ALT negative samples.
- since 10/2014 PhD student at the University Hospital Jena in the “Systemsbiology of sepsis” group headed by Prof. Dr. Rainer König
- 11/2013 – 06/2014: Master thesis “Identifying regulators of the telomerase in Saccharomyces cerevisiae employing Mixed Integer Linear Programming approaches” in the Division of Theoretical Bioinformatics at the German Cancer Research Center (Network Modeling Group, Prof. Dr. Rainer König)
- 10/2011 – 08/2014: University of Heidelberg - Master program of Molecular Biotechnology (major: Bioinformatics)
- 10/2008 – 09/2011: University of Heidelberg - Bachelor program of Molecular Biotechnology
- Poos AM, Maicher A, Dieckmann AK, Oswald M, Eils R, Kupiec M, Luke B, Konig R (2016) Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast. Nucleic Acids Res 44: e93 Doi 10.1093/nar/gkw111
DKFZ & BioQuant Center
Division of Chromatin Networks
Im Neuenheimer Feld 267-BQ24
BioQuant room 641
Fax: +49 6221 54 51487
e-mail: a.poos (at) dkfz.de