We present a further development in the technology of sequencing by hybridization to oligonucleotide microchips (SHOM) and its application to diagnostics for genetic diseases. A robot has been constructed to manufacture sequencing "microchips." The microchip is an array of oligonucleotides immobilized into gel elements fixed on a glass plate. Hybridization of the microchip with fluorescently labeled DNA was monitored in real time simultaneously for all microchip elements with a two-wavelength fluorescent microscope equipped with a charge-coupled device camera. SHOM has been used to detect f3-thalassemia mutations in patients by hybridizing PCR-amplified DNA with the microchips. A contiguous stacking hybridization technique has been applied for the detection of mutations; it can simplify medical diagnostics and enhance its reliability. The use of multicolor monitoring of contiguous stacking hybridization is suggested for large-scale diagnostics and gene polymorphism studies. Other applications of the SHOM technology are discussed.

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The authors have previously applied two integrated platforms, MetaCore and MetaDrug, for the assembly and analysis of human biological networks as a useful method for the integration and functional interpretation of high-throughput experimental data. The present study demonstrates in detail the specific algorithms that are used in both software platforms. Using a standard set of genes as input, namely CYP3A4 (an enzyme), PXR (a nuclear hormone receptor), MDR1 (a transporter) and hERG (an ion channel) related to the absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) of xenobiotics, we have now generated networks with each algorithm. The relative advantages and disadvantages of these algorithms are explained using these examples as well as appropriate instances of utility to illustrate further the particular circumstances for their use. In addition, the benefits of the different network algorithms are identified when compared with algorithms available in other products, where this information is available.

Analysis of microarray, SNPs, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high-fidelity annotated knowledge base of protein interactions, pathways, and functional ontologies. This knowledge base has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here we present MetaDiscovery, an integrated platform for functional data analysis which is being developed at GeneGo for the past 8 years. On the content side, MetaDiscovery encompasses a comprehensive database of protein interactions of different types, pathways, network models and 10 functional ontologies covering human, mouse, and rat proteins. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for identification of over- and under-connected proteins in the data set, and a network module made up of network generation algorithms and filters. The suite also features MetaSearch, an application for combinatorial search of the database content, as well as a Java-based tool called MapEditor for drawing and editing custom pathway maps. Applications of MetaDiscovery include identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds, and clinical applications (analysis of large cohorts of patients and translational and personalized medicine).

ABSTRACT:The challenge of predicting the metabolism or toxicity of a drug in humans has been approached using in vivo animal models, in vitro systems, high throughput genomics and proteomics methods, and, more recently, computational approaches. Understanding the complexity of biological systems requires a broader perspective rather than focusing on just one method in isolation for prediction. Multiple methods may therefore be necessary and combined for a more accurate prediction. In the field of drug metabolism and toxicology, we have seen the growth, in recent years, of computational quantitative structure-activity relationships (QSARs), as well as empirical data from microarrays. In the current study we have further developed a novel computational approach, MetaDrug, that 1) predicts metabolites for molecules based on their chemical structure, 2) predicts the activity of the original compound and its metabolites with various absorption, distribution, metabolism, excretion, and toxicity models, 3) incorporates the predictions with human cell signaling and metabolic pathways and networks, and 4) integrates networks and metabolites, with relevant toxicogenomic or other high throughput data. We have demonstrated the utility of such an approach using recently published data from in vitro metabolism and microarray studies for aprepitant, 2(S)-((3,5-bis(trifluoromethyl)benzyl)-oxy)-3(S)phenyl-4-((3-oxo-1,2,4-triazol-5-yl)methyl)morpholine (L-742694), trovofloxacin, 4-hydroxytamoxifen, and artemisinin and other artemisinin analogs to show the predicted interactions with cytochromes P450, pregnane X receptor, and P-glycoprotein, and the metabolites and the networks of genes that are affected. As a comparison, we used a second computational approach, MetaCore, to generate statistically significant gene networks with the available expression data. These case studies demonstrate the combination of QSARs and systems biology methods.Predicting the metabolism and toxicity of a drug in humans can use resources that include in vivo animal models, in vitro systems, and high throughput genomics and proteomics methods (Gerhold et al., 2001;Thomas et al., 2001) to generate empirical data for analysis and decision making. The amount and complexity of the data being generated are increasing, requiring not only judicious decisions about which experimental methods to use but also novel tools for visualization and analysis. More recently, within drug disposition and toxicology, in vitro approaches for generating data with drug-metabolizing enzymes, transporters, ion channels, and receptors have been used for computational approaches, including quantitative structureactivity relationships (QSARs) (Ekins and Swaan, 2004). These methods have been used widely and applied for predicting absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) (Ekins et al., 2005d) properties, at the level of either the individual protein (e.g., P450s; Balakin et al., 2004a,b) or specific properties (e.g., absorption; Zhao et al., 2001;Niwa,...