Motivation: Understanding regulation of transcription is central for elucidating cellular regulation. Several statistical and mechanistic models have come up the last couple of years explaining gene transcription levels using information of potential transcriptional regulators as transcription factors (TFs) and information from epigenetic modifications. The activity of TFs is often inferred by their transcription levels, promoter binding and epigenetic effects. However, in principle, these methods do not take hard-to-measure influences such as post-transcriptional modifications into account.Results: For TFs, we present a novel concept circumventing this problem. We estimate the regulatory activity of TFs using their cumulative effects on their target genes. We established our model using expression data of 59 cell lines from the National Cancer Institute. The trained model was applied to an independent expression dataset of melanoma cells yielding excellent expression predictions and elucidated regulation of melanogenesis.Availability and implementation: Using mixed-integer linear programming, we implemented a switch-like optimization enabling a constrained but optimal selection of TFs and optimal model selection estimating their effects. The method is generic and can also be applied to further regulators of transcription.Contact: rainer.koenig@uni-jena.deSupplementary information: Supplementary data are available at Bioinformatics online.

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The aim of this investigation was to determine colour compatibility between dental shade guides, namely, VITA Classical (VC) and VITA 3D-Master (3D), and human teeth in quinquagenarians and septuagenarians. Tooth colour, described in terms of L*a*b* values of the middle third of facial tooth surface of 1391 teeth, was measured using VITA Easyshade in 195 subjects (48% female). These were compared with the colours (L*a*b* values) of the shade tabs of VC and 3D. The mean coverage error and the percentage of tooth colours being within a given colour difference (DeltaE(ab)) from the tabs of VC and 3D were calculated. For comparison, hypothetical, optimized, population-specific shade guides were additionally calculated based on discrete optimization techniques for optimizing coverage. Mean coverage error was DeltaE(ab) = 3.51 for VC and DeltaE(ab) = 2.96 for 3D. Coverage of tooth colours by the tabs of VC and 3D within DeltaE(ab) = 2 was 23% and 24%, respectively, (DeltaE(ab)

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BackgroundTumor therapy mainly attacks the metabolism to interfere the tumor's anabolism and signaling of proliferative second messengers. However, the metabolic demands of different cancers are very heterogeneous and depend on their origin of tissue, age, gender and other clinical parameters. We investigated tumor specific regulation in the metabolism of breast cancer.MethodsFor this, we mapped gene expression data from microarrays onto the corresponding enzymes and their metabolic reaction network. We used Haar Wavelet transforms on optimally arranged grid representations of metabolic pathways as a pattern recognition method to detect orchestrated regulation of neighboring enzymes in the network. Significant combined expression patterns were used to select metabolic pathways showing shifted regulation of the aggressive tumors.ResultsBesides up-regulation for energy production and nucleotide anabolism, we found an interesting cellular switch in the interplay of biosynthesis of steroids and bile acids. The biosynthesis of steroids was up-regulated for estrogen synthesis which is needed for proliferative signaling in breast cancer. In turn, the decomposition of steroid precursors was blocked by down-regulation of the bile acid pathway.ConclusionWe applied an intelligent pattern recognition method for analyzing the regulation of metabolism and elucidated substantial regulation of human breast cancer at the interplay of cholesterol biosynthesis and bile acid metabolism pointing to specific breast cancer treatment.

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