Selecting VITA classical shades with the VITA 3D-master shade guide.

PURPOSE: Although the VITA 3D-Master (3D) shade guide offers improved shade-matching performance, many dental materials are only available in VITA Classical (VC) shades. This study aimed to clarify whether it is possible to convert 3D shades determined by observers into VC shades (indirect method) without adding a clinically significant error in comparison with direct shade determination using the VC shade guide. MATERIALS AND METHODS: Forty ceramic specimens were fabricated. L*a*b* values were recorded using a spectroradiometer. Sixty participants (35 dentists, 15 technicians, and 10 students) were recruited and asked to determine the shades of specimens using the VC and 3D shade guides under standardized conditions. Conversion tables were constructed by allocating the closest VC shade tab to every matched 3D shade and by use of an optimization algorithm (indirect methods). Differences between ΔE values for VC matches and for the indirect methods were evaluated using t tests. RESULTS: A mean ΔE (SD) of 4.34 (2.00) for VC and 4.22 (2.21) for 3D was observed (P = .040). Compared with direct shade matching using VC, the indirect method with the optimized tables resulted in a mean ΔE of 4.32 (1.96), which was not significantly different (P = .586). CONCLUSIONS: Within the limitations of this study, the conversion tables were suitable for the determination of tooth color using the 3D shade guide followed by conversion into VC shades without adding a clinically significant error.

Network topology-based detection of differential gene regulation and regulatory switches in cell metabolism and signaling.

BACKGROUND: Common approaches to pathway analysis treat pathways merely as lists of genes disregarding their topological structures, that is, ignoring the genes' interactions on which a pathway's cellular function depends. In contrast, PathWave has been developed for the analysis of high-throughput gene expression data that explicitly takes the topology of networks into account to identify both global dysregulation of and localized (switch-like) regulatory shifts within metabolic and signaling pathways. For this purpose, it applies adjusted wavelet transforms on optimized 2D grid representations of curated pathway maps. RESULTS: Here, we present the new version of PathWave with several substantial improvements including a new method for optimally mapping pathway networks unto compact 2D lattice grids, a more flexible and user-friendly interface, and pre-arranged 2D grid representations. These pathway representations are assembled for several species now comprising H. sapiens, M. musculus, D. melanogaster, D. rerio, C. elegans, and E. coli. We show that PathWave is more sensitive than common approaches and apply it to RNA-seq expression data, identifying crucial metabolic pathways in lung adenocarcinoma, as well as microarray expression data, identifying pathways involved in longevity of Drosophila. CONCLUSIONS: PathWave is a generic method for pathway analysis complementing established tools like GSEA, and the update comprises efficient new features. In contrast to the tested commonly applied approaches which do not take network topology into account, PathWave enables identifying pathways that are either known be involved in or very likely associated with such diverse conditions as human lung cancer or aging of D. melanogaster. The PathWave R package is freely available at http://www.ichip.de/software/pathwave.html.

Determination of VITA Classical shades with the 3D-Master shade guide.

OBJECTIVES: Because of its good matching performance the VITA 3D-Master shade guide (3D) is frequently used for determination of tooth color. Numerous composites/ceramics are, however, available in VITA Classical (VC) shades only. The objective of this study was to investigate the possibility of performing a shade match with 3D Master and converting this result via a table in a VC shade (indirect method) without this resulting in an apparent inferior shade matching in comparison with direct shade matching with the VC. METHODS: Experiments were performed with an artificial, computer-generated tooth color space. Conversion tables were generated by calculating the color difference (ΔE) between a 3D shade and the closest VC shade (simple conversion table) and with the aid of optimization procedures. Statistical differences between the direct and indirect methods and between the indirect methods were assessed by use of a U-test. RESULTS: Median ΔE was 2.38 for direct matching with the VC and 2.86 for indirect matching by use of a simple conversion table (p < 0.01). Optimized tables performed slightly better (median ΔE = 2.81). CONCLUSIONS: Within the limitations of the study, it is usually possible to determine tooth color with the 3D and convert it, via a table, into a VC shade without adding a clinically apparent error to the direct shade match with the VC.

Analyzing the regulation of metabolic pathways in human breast cancer.

BACKGROUND: Tumor 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. METHODS: For 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. RESULTS: Besides 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. CONCLUSION: We 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.

PathWave: discovering patterns of differentially regulated enzymes in metabolic pathways.

MOTIVATION: Gene expression profiling by microarrays or transcript sequencing enables observing the pathogenic function of tumors on a mesoscopic level. RESULTS: We investigated neuroblastoma tumors that clinically exhibit a very heterogeneous course ranging from rapid growth with fatal outcome to spontaneous regression and detected regulatory oncogenetic shifts in their metabolic networks. In contrast to common enrichment tests, we took network topology into account by applying adjusted wavelet transforms on an elaborated and new 2D grid representation of curated pathway maps from the Kyoto Enzyclopedia of Genes and Genomes. The aggressive form of the tumors showed regulatory shifts for purine and pyrimidine biosynthesis as well as folate-mediated metabolism of the one-carbon pool in respect to increased nucleotide production. We spotted an oncogentic regulatory switch in glutamate metabolism for which we provided experimental validation, being the first steps towards new possible drug therapy. The pattern recognition method we used complements normal enrichment tests to detect such functionally related regulation patterns. AVAILABILITY AND IMPLEMENTATION: PathWave is implemented in a package for R (www.r-project.org) version 2.6.0 or higher. It is freely available from http://www.ichip.de/software/pathwave.html.

Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling.

BACKGROUND: The reconstruction of gene regulatory networks from time series gene expression data is one of the most difficult problems in systems biology. This is due to several reasons, among them the combinatorial explosion of possible network topologies, limited information content of the experimental data with high levels of noise, and the complexity of gene regulation at the transcriptional, translational and post-translational levels. At the same time, quantitative, dynamic models, ideally with probability distributions over model topologies and parameters, are highly desirable. RESULTS: We present a novel approach to infer such models from data, based on nonlinear differential equations, which we embed into a stochastic Bayesian framework. We thus address both the stochasticity of experimental data and the need for quantitative dynamic models. Furthermore, the Bayesian framework allows it to easily integrate prior knowledge into the inference process. Using stochastic sampling from the Bayes' posterior distribution, our approach can infer different likely network topologies and model parameters along with their respective probabilities from given data. We evaluate our approach on simulated data and the challenge #3 data from the DREAM 2 initiative. On the simulated data, we study effects of different levels of noise and dataset sizes. Results on real data show that the dynamics and main regulatory interactions are correctly reconstructed. CONCLUSIONS: Our approach combines dynamic modeling using differential equations with a stochastic learning framework, thus bridging the gap between biophysical modeling and stochastic inference approaches. Results show that the method can reap the advantages of both worlds, and allows the reconstruction of biophysically accurate dynamic models from noisy data. In addition, the stochastic learning framework used permits the computation of probability distributions over models and model parameters, which holds interesting prospects for experimental design purposes.

Discovering functional gene expression patterns in the metabolic network of Escherichia coli with wavelets transforms.

BACKGROUND: Microarray technology produces gene expression data on a genomic scale for an endless variety of organisms and conditions. However, this vast amount of information needs to be extracted in a reasonable way and funneled into manageable and functionally meaningful patterns. Genes may be reasonably combined using knowledge about their interaction behaviour. On a proteomic level, biochemical research has elucidated an increasingly complete image of the metabolic architecture, especially for less complex organisms like the well studied bacterium Escherichia coli. RESULTS: We sought to discover central components of the metabolic network, regulated by the expression of associated genes under changing conditions. We mapped gene expression data from E. coli under aerobic and anaerobic conditions onto the enzymatic reaction nodes of its metabolic network. An adjacency matrix of the metabolites was created from this graph. A consecutive ones clustering method was used to obtain network clusters in the matrix. The wavelet method was applied on the adjacency matrices of these clusters to collect features for the classifier. With a feature extraction method the most discriminating features were selected. We yielded network sub-graphs from these top ranking features representing formate fermentation, in good agreement with the anaerobic response of hetero-fermentative bacteria. Furthermore, we found a switch in the starting point for NAD biosynthesis, and an adaptation of the l-aspartate metabolism, in accordance with its higher abundance under anaerobic conditions. CONCLUSION: We developed and tested a novel method, based on a combination of rationally chosen machine learning methods, to analyse gene expression data on the basis of interaction data, using a metabolic network of enzymes. As a case study, we applied our method to E. coli under oxygen deprived conditions and extracted physiologically relevant patterns that represent an adaptation of the cells to changing environmental conditions. In general, our concept may be transferred to network analyses on biological interaction data, when data for two comparable states of the associated nodes are made available.