ST. JOSEPH, MI, United States
ST. JOSEPH, MI, United States

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Szalma S.,Centocor R and D Inc. | Koka V.,Centocor R and D Inc. | Khasanova T.,Genego, Inc. | Perakslis E.D.,Centocor R and D Inc.
Journal of Translational Medicine | Year: 2010

Background: The growing consensus that most valuable data source for biomedical discoveries is derived from human samples is clearly reflected in the growing number of translational medicine and translational sciences departments across pharma as well as academic and government supported initiatives such as Clinical and Translational Science Awards (CTSA) in the US and the Seventh Framework Programme (FP7) of EU with emphasis on translating research for human health.Methods: The pharmaceutical companies of Johnson and Johnson have established translational and biomarker departments and implemented an effective knowledge management framework including building a data warehouse and the associated data mining applications. The implemented resource is built from open source systems such as i2b2 and GenePattern.Results: The system has been deployed across multiple therapeutic areas within the pharmaceutical companies of Johnson and Johnsons and being used actively to integrate and mine internal and public data to support drug discovery and development decisions such as indication selection and trial design in a translational medicine setting. Our results show that the established system allows scientist to quickly re-validate hypotheses or generate new ones with the use of an intuitive graphical interface.Conclusions: The implemented resource can serve as the basis of precompetitive sharing and mining of studies involving samples from human subjects thus enhancing our understanding of human biology and pathophysiology and ultimately leading to more effective treatment of diseases which represent unmet medical needs. © 2010 Szalma et al; licensee BioMed Central Ltd.


Nikolsky Y.,Genego, Inc.
Methods in molecular biology (Clifton, N.J.) | Year: 2010

Atherogenic lipids and chronic inflammation drive the development of cardiovascular disorders such as atherosclerosis. Many cardiovascular drugs target the liver which is involved in the formation of lipid and inflammatory risk factors. With robust systems biology tools and comprehensive bioinformatical packages becoming available and affordable, the effect of novel treatment strategies can be analyzed more comprehensively and with higher sensitivity. For example, beneficial as well as adverse effects of drugs can already be detected on the gene and metabolite level, and prior to their macroscopic manifestation. This chapter describes a systems approach for a prototype CV drug with established beneficial and adverse effects. All relevant steps, for example, experimental design, tissue collection and high quality RNA preparation, bioinformatical analysis of functional processes, and pathways (targeted and untargeted) are addressed.


Bessarabova M.,Russian Academy of Sciences | Kirillov E.,Russian Academy of Sciences | Shi W.,Genego, Inc. | Bugrim A.,Genego, Inc. | And 2 more authors.
BMC Genomics | Year: 2010

We identified a set of genes with an unexpected bimodal distribution among breast cancer patients in multiple studies. The property of bimodality seems to be common, as these genes were found on multiple microarray platforms and in studies with different end-points and patient cohorts. Bimodal genes tend to cluster into small groups of four to six genes with synchronised expression within the group (but not between the groups), which makes them good candidates for robust conditional descriptors. The groups tend to form concise network modules underlying their function in cancerogenesis of breast neoplasms. © 2010 Bessarabova et al; licensee BioMed Central Ltd.


Marcheva B.,Northwestern University | Ramsey K.M.,Northwestern University | Buhr E.D.,Northwestern University | Kobayashi Y.,Northwestern University | And 14 more authors.
Nature | Year: 2010

The molecular clock maintains energy constancy by producing circadian oscillations of rate-limiting enzymes involved in tissue metabolism across the day and night. During periods of feeding, pancreatic islets secrete insulin to maintain glucose homeostasis, and although rhythmic control of insulin release is recognized to be dysregulated in humans with diabetes, it is not known how the circadian clock may affect this process. Here we show that pancreatic islets possess self-sustained circadian gene and protein oscillations of the transcription factors CLOCK and BMAL1. The phase of oscillation of islet genes involved in growth, glucose metabolism and insulin signalling is delayed in circadian mutant mice, and both Clock and Bmal1 (also called Arntl) mutants show impaired glucose tolerance, reduced insulin secretion and defects in size and proliferation of pancreatic islets that worsen with age. Clock disruption leads to transcriptome-wide alterations in the expression of islet genes involved in growth, survival and synaptic vesicle assembly. Notably, conditional ablation of the pancreatic clock causes diabetes mellitus due to defective 2-cell function at the very latest stage of stimulusĝ€"secretion coupling. These results demonstrate a role for the 2-cell clock in coordinating insulin secretion with the sleepĝ€"wake cycle, and reveal that ablation of the pancreatic clock can trigger the onset of diabetes mellitus. © 2010 Macmillan Publishers Limited. All rights reserved.


The process of System Reconstruction is used to integrate sequence data, clinical data, experimental data, and literature into functional models of disease pathways. System Reconstruction models serve as informational skeletons for integrating various types of high-throughput data. The present invention provides the first metabolic reconstruction study of a eukaryotic organism based solely on expressed sequence tag (EST) data. System Reconstruction also provides a method for the identification of novel therapeutic targets and biomarkers using network analysis. The initial seed networks are built from the lists of novel targets for diseases with the high-throughput experimental data being superimposed on the seed networks to identify specific targets.


A system is provided for the prediction of human drug metabolism and toxicity of novel compounds. The system enables the visualization of pre-clinical and clinical high-throughput data in the context of a complete biological organism. Substructure and similarity structure searches can be performed using the underlying databases of xenobiotics, active ligands, and endobiotics. The system also has an analytical component for the parsing, integration, and network analysis of genomics, proteomics, and metabolomics high-throughput data. From this information, the system further generates networks around proteins, genes and compounds to assess toxicity and drug-drug interactions.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 336.19K | Year: 2010

DESCRIPTION (provided by applicant): In this project we propose to develop predictive regulation signatures which should overcome shortcomings of existing methods of molecular diagnostics. As a proof of concept, in Phase I we will develop regulation signatures for the sensitivity of cancer cell lines to the inhibitors of EGF receptor and will test their accuracy using publicly available datasets. This goal will be accomplished in two stages. First, we will build and optimize focused network model for global Erb family signaling using well-defined training data and knowledge content. In the second step we will test performance of the developed Erb signaling network model on publicly available sets of gene expression profiles from the variety of non-small cell lung carcinoma cell lines with variable drug sensitivity. Analysis will be performed to identify sets of key signaling proteins associated with drug resistance. Using these proteins predictive regulation signatures will be developed and their performance will be tested. If successful, the methodology could be replicated for developing predictive models for sensitivity to a broad range of targeted therapies, leading to a number of diagnostic applications such as specialized molecular tests, systems for formulating combination therapies and procedures for selecting patient cohorts for clinical trials. PUBLIC HEALTH RELEVANCE: In this project we will develop novel regulation signatures to predict sensitivity to the inhibitors of EGF receptor and identify mechanisms of drug resistance. Project will utilize public gene expression data in combination with knowledge base on protein interactions and our recently developed network analysis algorithm. If successful, the methodology could lead to a number of diagnostic applications.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 119.17K | Year: 2010

DESCRIPTION (provided by applicant): The goal of this project is to develop a database and data analysis platform specifically designed for the study of the causes and biological consequences of drug abuse. The product will comprise: i) a comprehensive disease ontology and database of associations between genes, their protein products and predisposition to drug abuse, development of dependency and pathologies associated with chronic drug abuse; ii) a collection of publicly-available systems biology datasets (gene-expression, proteomics, metabolomics) relating to drug abuse and it's affects on multiple organ systems; iii) a set of prebuilt maps and networks relating to drug dependency, and software tools that will enable the analysis of extant and newly-generated systems biology datasets, independently and in combination, to identify metabolic and signaling networks, pathways, and mechanisms of disease development, and potential strategies and targets for therapeutic interventions; iv) computational tools for the analysis of novel compounds to predict their potential for abuse and dependency. The product will be unique in the marketplace and of great interest to academic researchers, foundations and pharmaceutical companies performing research into the causes and treatment of alcoholism and alcohol- related disease and will be marketed and sold by GeneGo as part of the MetaDiscovery suite of products. PUBLIC HEALTH RELEVANCE: The aim of this grant is to develop a software database and analysis platform to enable a systems-level approach to understanding, preventing and treating drug abuse. The platform will provide a comprehensive knowledge-base, data analysis tools and predictive models to support research into the causes and treatment of drug abuse.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 1.20M | Year: 2010

DESCRIPTION (provided by applicant): A vast ocean of small experiments data and OMICs datasets is being accumulated in oncology research, and none of the currently available life sciences informatics platforms is capable of a comprehensive handling and meaningful functional analysis of these data. Here we propose to build such a system, MetaMiner (Oncology) on the base of our mature data analysis platform MetaCore. The system will have a comprehensive structured database of cancer domain knowledge and a toolkit for functional analysis (ontology enrichment, interactome, network tools). MetaMiner will be integrated with CaBIG, translational medicine databases and third party software. In Phase I, we developed several novel algorithms for quantitative functional analysis of large multi-patient datasets and offered new methods for cross-analysis of cancer datasets of different type. We have also designed a framework for manual annotation of cancer pathways, assays and gene-disease causative associations. In Phase II, we will implement the algorithms into a robust rich client analytical platform and complete annotation of cancer data. PUBLIC HEALTH RELEVANCE: We developed novel algorithms for quantitative functional analysis and cross-analysis of datasets of different type. We designed a framework for manual annotation of cancer pathways, assays and other data. In Phase II, we will implement the algorithms into a robust rich client platform and complete the annotation program


Trademark
Thomson Reuters and Genego, Inc. | Date: 2010-11-16

electronic database in the field of molecular biology, systems biology, chemoinformatics and bioinformatics recorded on computer media.

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