we investigate mice which lack of a glycoprotein receptor (RAGE). Besides obesity they additionally show resitance to tumors and decreased tumor growth. In the laboratory of our partners, experimental data is, i.e. gene expression microarray data, a broad range of physiological values and metabolite abundances using HPLC techniques. This is gathered for different juvenile stages (3, 6, 15 months after birth) and for a variety of organs and blood.
Within the project, an intelligent metabolic and signaling model needs to be developed that integrates this time and organ resolved data to track the origin of the metabolic dysfunction leading to obesity and which may also explain the cause for reduced cancer development. For this we will analyse gene expression data on networks using a variety of machine learning methods (see e.g. Plaimas, BMC Systems Biology, 2:67, 2008 for network anaylsis) and pattern recognition methods on networks (see e.g. Konig et al, BMC Bioinformatics, 7:119, 2006). Aim of the project is to come up with an analsis system that 1) tracks the substantial metabolic and signaling differences between the obesity mice and normal mice, and 2) generates predictions for the origin of the dysfunction (what developmental time, which tissue, which pathways?)
We are lookoing for physicists, computer scientists, biologigists and mathematicians with a lively interest in machine learning, pattern recognition and biological pathways.
Von:
Dr. rainer Koenig
r.koenig@dkfz.de
Universitaet Heidelberg
Heidelberg
Ansprechpartner: Dr. Rainer Koenig, r.koenig@dkfz.de
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