Ariadne Genomics Inc.

Rockville, MD, United States

Ariadne Genomics Inc.

Rockville, MD, United States
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Yuryev A.,Ariadne Genomics Inc.
Expert Opinion on Drug Discovery | Year: 2011

Introduction: Current advances in software development and global molecular profiling technologies allow the development of holistic software solutions for drug discovery. Such solutions must streamline in silico drug and therapy development by integrating all types of data into one knowledge base and also by enabling continuous analysis workflows uninterrupted by manual restructuring of inputs and outputs from workflow components. They must provide a collaborative environment for data sharing between multiple users and allow importing of all types of experimental data for subsequent analysis. Areas covered: The reader is provided with a review of disparate software applications currently used in drug development and a discussion of existing organizational challenges for development of holistic software solutions. The reader is also provided with a proposed conceptual framework for integration of software components and some details for its implementation are suggested. Expert opinion: Holistic solutions can undoubtedly affect the speed, quality and cost of drug development and personalized therapy. However, it must be constantly evolved to rapidly adopt new experimental and statistical methods, incorporate advances in software technologies and allow perpetual optimization of its components. Perpetual improvements in data structure, data quality, statistical algorithms and other mathematical approaches for computer modeling can gradually shift financial and cultural emphasis in the pharmaceutical industry away from traditional experimental approaches and towards computational approaches. © 2011 Informa UK, Ltd.


Yuryev A.,Ariadne Genomics Inc.
Expert Opinion on Drug Discovery | Year: 2012

Introduction: The interpretation of high-throughput profiling data depends on the pathway analysis database. Currently, pathway analysis often has to rely on a set of interactions and pathways measured in every possible human tissue, due to insufficient knowledge about interactions and pathways in the context of the profiling experiment. However, a recent global scale analysis of human tissue proteomes and interactomes reveals significant differences among tissues, suggesting that interaction and pathway data that are used out of biological context are the major source of inaccuracies and noise in the analysis of profiling data. Areas covered: In this review, the major classes of biological context used for experimental detection of molecular interactions and pathways in molecular biology are described. Furthermore, the author reviews methods for predicting biological interactions in order to evaluate the applicability of various contextual interaction data in pathway analysis. Using the results from recent publications that study large-scale tissue composition, the article provides an estimation of the gain in pathway analysis accuracy if only the interactions predicted for the context of a molecular profiling experiment are used, relative to the analysis performed with a context-independent knowledge base. Expert opinion: It is of the author's opinion that the major source of inaccuracy in pathway analysis is the lack of knowledge about tissue-specific transcriptional regulation. It is therefore suggested that the accuracy of the analysis can be substantially improved if only context-specific interactions and pathways are used for interpretation. © 2012 Informa UK, Ltd.


Kotelnikova E.,Ariadne Genomics Inc. | Shkrob M.A.,Ariadne Genomics Inc. | Pyatnitskiy M.A.,Ariadne Genomics Inc. | Ferlini A.,University of Ferrara | Daraselia N.,Ariadne Genomics Inc.
PLoS Computational Biology | Year: 2012

Elucidation of new biomarkers and potential drug targets from high-throughput profiling data is a challenging task due to a limited number of available biological samples and questionable reproducibility of differential changes in cross-dataset comparisons. In this paper we propose a novel computational approach for drug and biomarkers discovery using comprehensive analysis of multiple expression profiling datasets. The new method relies on aggregation of individual profiling experiments combined with leave-one-dataset-out validation approach. Aggregated datasets were studied using Sub-Network Enrichment Analysis algorithm (SNEA) to find consistent statistically significant key regulators within the global literature-extracted expression regulation network. These regulators were linked to the consistent differentially expressed genes. We have applied our approach to several publicly available human muscle gene expression profiling datasets related to Duchenne muscular dystrophy (DMD). In order to detect both enhanced and repressed processes we considered up- and down-regulated genes separately. Applying the proposed approach to the regulators search we discovered the disturbance in the activity of several muscle-related transcription factors (e.g. MYOG and MYOD1), regulators of inflammation, regeneration, and fibrosis. Almost all SNEA-derived regulators of down-regulated genes (e.g. AMPK, TORC2, PPARGC1A) correspond to a single common pathway important for fast-to-slow twitch fiber type transition. We hypothesize that this process can affect the severity of DMD symptoms, making corresponding regulators and downstream genes valuable candidates for being potential drug targets and exploratory biomarkers. © 2012 Kotelnikova et al.


Kotelnikova E.,Ariadne Genomics Inc. | Ivanikova N.,Ariadne Genomics Inc. | Kalinin A.,Ariadne Genomics Inc. | Yuryev A.,Ariadne Genomics Inc. | Daraselia N.,Ariadne Genomics Inc.
PLoS ONE | Year: 2010

Microarray-based expression profiling of living systems is a quick and inexpensive method to obtain insights into the nature of various diseases and phenotypes. A typical microarray profile can yield hundreds or even thousands of differentially expressed genes and finding biologically plausible themes or regulatory mechanisms underlying these changes is a nontrivial and daunting task. We describe a novel approach for systems-level interpretation of microarray expression data using a manually constructed "overview" pathway depicting the main cellular signaling channels (Atlas of Signaling). Currently, the developed pathway focuses on signal transduction from surface receptors to transcription factors and further transcriptional regulation of cellular "workhorse" proteins. We show how the constructed Atlas of Signaling in combination with an enrichment analysis algorithm allows quick identification and visualization of the main signaling cascades and cellular processes affected in a gene expression profiling experiment. We validate our approach using several publicly available gene expression datasets. © 2010 Kotelnikova et al.


Saw J.H.W.,University of Hawaii at Manoa | Yuryev A.,Ariadne Genomics Inc. | Kanbe M.,Universiti Sains Malaysia | Hou S.,University of Hawaii at Manoa | And 4 more authors.
Standards in Genomic Sciences | Year: 2012

Saprospira grandis is a coastal marine bacterium that can capture and prey upon other marine bacteria using a mechanism known as 'ixotrophy'. Here, we present the complete genome sequence of Saprospira grandis str. Lewin isolated from La Jolla beach in San Diego, California. The complete genome sequence comprises a chromosome of 4.35 Mbp and a plasmid of 54.9 Kbp. Genome analysis revealed incomplete pathways for the biosynthesis of nine essential amino acids but presence of a large number of peptidases. The genome en-codes multiple copies of sensor globin-coupled rsbR genes thought to be essential for stress response and the presence of such sensor globins in Bacteroidetes is unprecedented. A total of 429 spacer sequences within the three CRISPR repeat regions were identified in the ge-nome and this number is the largest among all the Bacteroidetes sequenced to date.


Kotelnikova E.,Ariadne Genomics Inc. | Yuryev A.,Ariadne Genomics Inc. | Mazo I.,Ariadne Genomics Inc. | Daraselia N.,Ariadne Genomics Inc.
Journal of Bioinformatics and Computational Biology | Year: 2010

Heterogeneous high-throughput biological data become readily available for various diseases. The amount of data points generated by such experiments does not allow manual integration of the information to design the most optimal therapy for a disease. We describe a novel computational workflow for designing therapy using Ariadne Genomics Pathway Studio software. We use publically available microarray experiments for glioblastoma and automatically constructed ResNet and ChemEffect databases to exemplify how to find potentially effective chemicals for glioblastoma the disease yet without effective treatment. Our first approach involved construction of signaling pathway affected in glioblastoma using scientific literature and data available in ResNet database. Compounds known to affect multiple proteins in this pathway were found in ChemEffect database. Another approach involved analysis of differential expression in glioblastoma patients using Sub-Network Enrichment Analysis (SNEA). SNEA identified angiogenesis-related protein Cyr61 as the major positive regulator upstream of genes differentially expressed in glioblastoma. Using our findings, we then identified breast cancer drug Fulvestrant as a major inhibitor of glioblastoma pathway as well as Cyr61. This suggested Fulvestrant as a potential treatment against glioblastoma. We further show how to increase efficacy of glioblastoma treatment by finding optimal combinations of Fulvestrant with other drugs. © 2010 Imperial College Press.


Grant
Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: HEALTH-2009-2.4.4-1 | Award Amount: 7.54M | Year: 2009

The rapidly expanding knowledge of NMDs genetic diagnosis, pathogenesis and therapeutic possibilities has provided new targets for disease characterisation, early diagnosis, drug discovery and development as well as has raised many questions about how to translate this knowledge into clinical practice as (initial) clinical trials typically run for such a short time that clinical improvement can hardly be expected within that time frame. This militates for the discovery of surrogate endpoints for establishing the efficacy of clinical trials. The concept of biomarkers represents measurable bio-parameters able to flank the process of diagnosis, functional characterisation and therapy in NMDs. OMIC sciences (genomic, transcriptomics, proteomics) offer opportunities to identify biomarkers for finely defining and tuning the NMDs bases. This approach can make available non-invasive biomarkers, to be used for monitoring disease progression, prognosis and drugs response, therefore optimising the choice of appropriate and often personalised therapies. Validated biomarkers will increase therapy efficiency (meaning optimal dose of drug to get responders) and efficacy (responders vs non responders for example if we will identify genomic biomarkers linked to the lack of any therapeutic effect). In this case we could address a truly efficacious therapy (avoiding inefficacious treatment due to unfavourable genomic contexts). The new genomic and proteomic biomarkers discovered within BIO-NMD will be validated both in animal models and in human samples, before entering into a qualification process at the EMEA. The qualified biomarkers resulting from the BIO-NMD project will be ready for ongoing and further clinical trials for the patient benefit. This will increase the therapy efficacy and efficiency and also reduce adverse effects, with impact on patients quality of life with also economical relevance. The BIO-NMD consortium is led by the University of Ferrara, an internationally recognised university in the field of genomics of hereditary neuromuscular disorders. In addition the consortium is composed of 7 leading European academic partners bringing their expertise in all OMIC sciences as well as in bio-informatics and patient sample collection, 1 SME providing its skills in bio-informatics and 1 global company specialised in the development of patient samples screening.


Trademark
Elsevier and Ariadne Genomics Inc. | Date: 2011-07-28

Computer software for use by others for building and displaying molecular pathways and helping scientists build databases of information in the fields of processing, medical informatics, drug discovery, genetics, molecular biology, and Computer software for use by others for automated extraction of biological facts from biomedical abstracts, and internal text documents; Computer software for use by others that compiles information about molecular interactions in a cell; Computer software for use by others in the field of biotechnology to store, analyze, and manage information about nucleic acids.


Trademark
Elsevier and Ariadne Genomics Inc. | Date: 2010-06-29

Computer software for the automated extraction, searching, visualization and analysis of facts from scientific literature, biomedical abstracts, and internal text documents about the effect of industrial, pharmaceutical, and environmental chemicals on biological systems. Providing online computer databases enabling the searching, visualization and analysis of facts about the effects of industrial, pharmaceutical, and environmental chemicals on biological systems, for research purposes.


Trademark
Elsevier and Ariadne Genomics Inc. | Date: 2010-03-09

Computer software for use by others for automated extraction of statements about biological facts from scientific literature, biomedical abstracts, and internal text documents.

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