Re-classification of chronic inflammatory diseases using network signatures derived from high-throughput data
Project Outline:
The evidence that network architecture determines aspects of biological function, both on the levels of gene regulation and metabolism, is incontrovertible. Furthermore, a wide range of abstract model studies has demonstrated that network architecture shapes dynamical processes.
Here we will analyze and interpret the diverse high-throughput data available for various aspects of chronic inflammatory disorders by contextualizing them within given biological networks.
Data will be provided by our collaborators on the clinical side (the Cluster of Excellence “Inflammation at Interfaces” in Kiel).
The network methods established and applied here offer the possibility of identifying coherent patterns in transcriptome data and other high-throughput data sets. Put together with predictions of metabolic fluxes and with network- and flux-based microbiome modeling, these approaches will serve as a foundation for better classifying and interpreting patient subgroups, disease progressions and treatment responses.
For further information on the topic, see:
Sonnenschein, N., Golib Dzib, J.F., Lesne, A., Boulkroun, S., Zennaro, M.-C., Benecke, A. and Hütt, M.-Th. (2012) A network perspective on metabolic inconsistency. BMC Systems Biology 6, 41.
Beber, M., Fretter, C., Jain, S., Sonnenschein, N., Müller-Hannemann, M. and Hütt, M.-Th. (2012) Artifacts in statistical analyses of network motifs: General framework and application to metabolic networks. Roy. Soc. Interface 7, 3426-3435.
Sonnenschein, N., Geertz, M., Muskhelishvili, G. and Hütt, M.-Th. (2011) Analog regulation of metabolic demand. BMC Systems Biology 5, 40.
Marr, C., Theis, F.J., Liebovitch, L.S. and Hütt, M.-Th. (2010) Patterns of subnet usage in the transcriptional regulatory network of Escherichia coli. PLoS Computational Biology 6, e1000836.
Candidates should have a background or strong interest in Computational Systems Biology, Bioinformatics or Statistical Physics of Complex Networks and solid programming skills (e.g., Matlab/Mathematica/C++/Python).
How to apply:
Interested candidates are encouraged to submit a CV, contact details of two references and a short statement of research interest electronically to:
m.huett@jacobs-university.de
To be assured of full consideration, applications must arrive by August 18, 2013