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Hans-Peter Lenhof

An extensive data set comprising 2660 unique protein identifications was obtained for the proteome of a human brain tumor (glioblastoma multiforme) by combining the results of two complementary analytical strategies based on two-dimensional chromatography and mass spectrometry. A bottom-up method, performing peptide separation in both chromatographic dimensions was employed as well as a semi-top-down method, in which intact proteins were separated in the first and tryptic peptides in the second dimension.
Computational molecular biology (bioinformatics) is a young research field that is rich in NP-hard optimization problems. The problem instances encountered are often huge and comprise thousands of variables. Since their introduction into the field of bioinformatics in 1997, integer linear programming (ILP) techniques have been successfully applied to many optimization problems. These approaches have added much momentum to development and progress in related areas. In particular, ILP-based approaches have become a standard optimization technique in bioinformatics.
Motivation: Deregulated signaling cascades are known to play a crucial role in many pathogenic processes, among them are tumor initiation and progression. In the recent past, modern experimental techniques that allow for measuring the amount of mRNA transcripts of almost all known human genes in a tissue or even in a single cell have opened new avenues for studying the activity of the signaling cascades and for understanding the information flow in the networks. Results: We present a novel dynamic programming algorithm for detecting deregulated signaling cascades.

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