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Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Molecular Networks in Mammals: Extraction from Literature and Microarray Analysis by Ilya.

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Presentation on theme: "Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Molecular Networks in Mammals: Extraction from Literature and Microarray Analysis by Ilya."— Presentation transcript:

1 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Molecular Networks in Mammals: Extraction from Literature and Microarray Analysis by Ilya Mazo, Ph.D.

2 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved It’s All About Pathways

3 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Promise of Systems Biology Understanding: Drug specificity Chemotherapy response Biomarker panels New target mechanisms

4 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Building Models Identify the elements of the system Describe the interactions/regulations between such elements Simplify the system by identifying components (functional modules or pathways) Integrate/validate with experimental data

5 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Available Pathway Information 0 2 mln 4 mln 6 mln 8 mln 10 mln 12 mln 14 mln 19651968197119741977198019831986198919921995199820012004 Year Abstract count PubMed

6 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved MedScan Information Extractor Reads >1000 abstracts per minute

7 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved How MedScan extracts facts from text? Sentence in PubMed: “ Axin binds beta-catenin and inhibits GSK-3beta.” Identify Proteins in Dictionary (in red): “ Axin binds beta-catenin and inhibits GSK-3beta.” Identify Interaction Type (in black): “ Axin binds beta-catenin and inhibits GSK- 3beta.” Extracted Facts: Axin - beta-cateninrelation: Binding Axin -> GSK-3betarelation: Regulation, effect: Negative Syntactic Layer Noun Phrase Verb Phrase Noun Phrase Semantic Layer ProteinProtein Relations Protein

8 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Overview of MedScan Architecture Input Text Tokenizer Semantic Interpreter Semantic tree Tagged Sentences Ontological interpreter Syntactic Parser Preprocessor Sequence of Words Sentence Structure Database of relations Grammar Lexicon Extraction rules Protein names dictionary Converter Extracted facts Dictionary-based Identifies proteins and small molecules Context-free grammar Grammar and lexicon are proprietary. They are domain- independent by design but focused on biomedical field. Rule-based Rules are equivalent to ontology Pattern Matcher Extraction patterns

9 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Database of Pathways >94 % precision >70 % recovery MedScan [Transcription] [factor] {7157=p53} [activates] [apoptosis] [in] [hepatocytes] ResNet Database PubMed – 7 mln abstracts 1,000,000 Facts

10 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Extracted Information Relation TypeCount Expression Control99,361 Binding50,812 Protein Modification25,368 Mol. Synthesis99,643 Mol. Transport48,423 Regulation675,539 Promoter Binding3,661 Total: protein relations1,002,807 1,002,807 relations (3.7 mil. findings extracted from 2005 Medline and 43 FTJ)

11 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Build Pathway (Find Neighbors) 2003 2005 2006 2004

12 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Mechanistic Model of Disease + genes harboring DAVs associated with Type 2 Diabetes Mellitus. (from Mol Cell Proteomics, Sharma et al 2005) ADCYAP1LEPR ADRB2LECAM-1 ADRB3NOS3 AGT NPY APM1NR3C1 CD38 NR3C1 FABP2 PC-1 GCGR PGC 1 GFPTPLA2G4A GYS1PON 1 HFE PON 2 HNF1a PPAR g2 HNF4a PPP1R3 ICAM1 PTPN1 INSR RAGE IRS 1 SOD2 IRS 2 TGF b KCNJ11 UCP 1 KCNJ11 UCP2

13 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Building Models Identify the elements of the system Describe the interactions/regulations between such elements Identify functional modules (pathways) Integrate/validate with experimental data

14 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Signaling Paths/Cascades Physical relations EGFR signaling including activation of Erk2 and the ELK-1 transcription factor The MAP and ERK kinase (MEK-1) is a dual specificity kinase that phosphorylates ERK1/2 on T-E-Y. ERK can phosphorylate and activate transcription factors such as TCF/ELK-1 Logical relations

15 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Inferring Cascades Simple protein classification schema and membrane-to- nucleus signaling paradigm can be applied - Receptor - Ligand - Extracellular - Transcription factor - Nuclear receptor - Effector. It allows for the network partitioning into several hundreds of “signaling cascades”.

16 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Regulomes as Canonical Pathways 700 inferred regulomes 200 textbook pathways 60% average overlap P<10-4

17 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Regulomes as Logical Models Use dependency relations to determine the “area of influence” for target proteins (receptors, kinases) 1) Both PP1 and expression of dominant negative c-Src inhibited PDGF-induced PI 3 kinase. 2) A pharmacologic inhibitor of c-Src, PP1  Logical Models: “what if?”

18 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Building Models Identify the elements of the system Describe the interactions/regulations between such elements Identify functional modules (pathways) Integrate/validate with experimental data

19 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Profiles to Pathways

20 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Find significant regulators Experimental dataset: melanoma, aggressive vs. non-aggressive cell lines, flat vs. 3D growth conditions. (Folberg and Arbieva, UIC) p=1e-5 p=0.0004 p=0.24

21 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Prediction of Activity Profiles Activity as a function of expression level and the ability to induce changes in the targets Random Markov fields formalism

22 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Combining the Approaches 1. Start with the global network of interactions 2. Add expert knowledge 3. Infer subnetworks (individual pathways) Signaling cascades and regulomes Phenotype or disease association Regulators and downstream targets Advanced models 4. Use available data (microarrays, proteomics) to screen for relevant pathways. 5. Add validated pathway libraries to the software package.

23 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Kinetic Models

24 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Integrated Systems Biology Platform Client PC Local DB PathwayStudio Tools Linux Server Oracle/PostgreSQL Tomcat, Java PathwayExpert Web Client Tools Central DB Tools

25 Copyright © 2003-2006 Ariadne Genomics, Inc. All Rights Reserved Published by Scientists

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