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Title: National Center for Biomedical Computing at


1
  • National Center for Biomedical Computing at
  • Columbia University

2
Mission
  • Basic Science To study the organization of the
    complex networks of biochemical interactions
    whose concerted activity determines cellular
    processes at increasing levels of granularity.
  • Software Tools To provide an integrative
    computational framework to organize molecular
    interactions in the cell into manageable context
    dependent components.
  • Biomedical Applications To develop interoperable
    computational models and tools that can leverage
    such a map of cellular interactions to elucidate
    important biological processes and to address a
    variety of biomedical applications.

3
MAGNet Organization
4
Core I Computational Sciences
Coordinator Waltz Proj. Lead Leslie, Wiggins,
Friedman, Califano, Yemini Invest. Servedio,
Lussier, Kaiser, Ofran, Ross
  • Machine Learning - Classification, Network
    analysis, Functional analysis.
  • NLP - Analysis of Literature for biomedical
    content (genotytic/phenotypic)
  • Software Design - BISON, an ontology for
    bioinformatics interoperability
  • Biomedical Database Integration - GeneTegrate a
    semantic layer for bioinformatics data integration

5
Core II Bioinformatics
Coordinator Rost Leaders Honig, Bussemaker,
Califano, Rzhetsky, Lussier Invest. Yemini,
Ofran, Petrey, Long, Anastassiou, Leslie,
Pavlidis, Wiggins, Friedman
  • Protein Structure and Function - Sequence and
    structure based annotation of protein function
    (specifically protein-protein interactions)
  • Reverse Engineering of Cellular Networks - An
    integrated knowledge-base of Cellular
    interactions in human B lymphocytes
  • Cellular and Molecular Context - Using cellular
    and molecular phenotypes for context filtering
  • MAGNet Tools - Software platform (geWorkbench)

Hot Topic
Hot Topic
6
Core III Driving Biological Projects
Coordinator Califano Leaders Shapiro, Dalla
Favera, Gilliam Invest.
  • Cell Adhesion - Structural and energetic basis of
    cadherin binding specificity A combined
    computational and experimental study
  • Pathway Dysregulation - Regulatory Modules in
    Normal and Transformed B-Cells
  • Complex Diseases - Genomic and Bioinformatics
    Solutions to the Search for Genetic Determinants
    of Common, Heritable Disorders Alzheimers
    Disease and Autism.

Hot Topic
7
Core IV Infrastructure (cont.)
MAGNet/C2B2
8
Hot Topic B-Cell Knowledge Base
Basic Science
  • Everything you always wanted to know about B
    Cells but were afraid to ask

H. Bussemaker A. Califano R. Dalla Favera C.
Leslie A. Rzhetsky C. Wiggins
9
Knowledge Base for Human B Lymphocytes
  • Integrative
  • Bayesian Evidence integration of pairwise
    interactions
  • Protein-Protein, Protein-DNA
  • Context Specific
  • ARACNE, GeneWays, REDUCE
  • B-Cell data or B-cell specific criteria
  • Linked to one of the largest B-Cell expression
    profiles microarray dataset, ChIP-Chip assays
    (MYC/BCL6), miRNA profiles, and Literature
  • Captures Multi-variate dependencies
  • Three-way interactions via MINDY and MATRIXReduce
  • Post-translational modulation of transcriptional
    regulation
  • Combinatorial transcriptional regulation
  • Signal transduction control of Transcriptional
    Regulation I.e. the Transferome meets the
    Transcriptome
  • Links to literature, via GeneWays

10
Knowledge Base for Human B Lymphocytes V1.0
  • Integrative
  • Bayesian Evidence integration of pairwise
    interactions
  • Protein-Protein, Protein-DNA
  • Context Specific
  • ARACNE, GeneWays, REDUCE
  • B-Cell data or B-cell specific criteria
  • Linked to one of the largest B-Cell expression
    profiles microarray dataset, ChIP-Chip assays
    (MYC/BCL6), miRNA profiles, and Literature
  • Captures Multi-variate dependencies
  • Three-way interactions via MINDY and MATRIXReduce
  • Post-translational modulation of transcriptional
    regulation
  • Combinatorial transcriptional regulation
  • Signal transduction control of Transcriptional
    Regulation I.e. the Transferome meets the
    Transcriptome
  • Links to literature, via GeneWays

11
Integrating protein-DNA and protein-protein
Interactions via Naïve Bayes Classification
  • Protein-Protein Interactions (PPIs)
  • Human PPI databases
  • Human Protein Reference Database (HPRD)
  • Biomolecular Interaction Network Database (BIND)
  • Database of Interacting Proteins (DIP and IntAct)
  • Y2H Studies (2in human)
  • Eukaryotic PPI via hortologous genes
    (Inparanoid)
  • MIPS, BIND, IntAct.
  • GeneWays Predictions (context-specific literature
    analysis)
  • Co-expression analysis (Mutual Information)
  • Gene Ontology classification (biological
    process/compartment)
  • Protein-DNA Interactions (PDIs)
  • Human PDI databases
  • TRANSFAC, BIND, MycDB
  • Mouse PDI databases (TRANSFAC, BIND via
    orthologous genes (Inparanoid)
  • ARACNE (bootstrap-TF)
  • GeneWays predictions (context-specific literature
    analysis)
  • 49,719 interactions (4,944 genes)
  • 27,705 PPIs (4,209 genes)
  • 22,014 PDIs (3,216 genes/457 TFs)

12
Network Motifs
Protein complexes
Regulatory Motifs
13
Definition of a Modulator
  • Modulator genes capable of modulating the
    activity of transcription factors at
    post-transcriptional levels, i.e. without
    affecting its mRNA concentration (e.g. activating
    Kinase, co-factor, etc.)

14
Algorithm Workflow
  • Statistical Tests
  • The gene gm is a modulator of the Interaction
    gTF ? gt if
  • A modulator has sufficient expression range
  • Modulators need to be statistically
    independent of the TF
  • i.e., it does not condition the TF expression
    range
  • Conditional MI difference is statistically
    different from zero

15
An Example JUN as Cofactor of MYC
DNA Binding sites -2kb,2kb
Mutual Information
Candidate Targets
16
MYC Modulation by Transferases
17
MYC Modulation by co-Transcription Factors
18
Hot Topic Caherins
Biomedical Applications
  • Elucidating Cadherins binding specificity

L. Shapiro B. Honig
19
Differential expression of cadherins is critical
for vertebrate development
20
Cadherins structure
21
Electron tomography reconstructions of
desmosomes.
He et al., Science, 302, 109-113, 2003.
22
23
8
Asparagine Arginine
Serine Glutamine
23
(No Transcript)
24
(No Transcript)
25
8NgtS, 23RgtQ N-Cadherin
26
Homophilic adhesion is retained in the N-8/23
mutant
N-cadherin
N- 8/23 mutant
E-cadherin
27
But adhesive specificity is converted to that of
E-cadherin
N-cad
E-cad
N- 8/23
E-cad
N-8/23
N-cad
4X
10X
28
Hot Topic geWorkbench
MAGNet Software Tools
  • An interoperable platform for integrative
    genomics research

Columbia University A. Califano A.
Floratos The BROAD Institute (GenePattern)
Jill Mesirov Michael Reich
29
geWorkbench (genomic Workbench)
  • Based on caWorkbench, an NCI/caBIG-funded effort
  • Open source, Java based platform
  • Integrated Genomics Platform
  • Support for gene expression data, sequences,
    pathways, structure, etc. (40 visualization and
    analysis modules).
  • Access to local and remote data sources and
    analytical services.
  • Support for workflow scripting.
  • Integration with caGRID.
  • Development framework
  • Open source development.
  • Modular/extensible architecture, supporting
    pluggable components with configurable user
    interface.
  • Formal (caBIG-registered) data models for
    multitude of bioinformatics concepts.
  • Easy integration of 3rd party components.

30
geWorkbench
  • Major effort on making the platform broadly
    available to and extensible by the biomedical
    research community
  • Main Vehicle for Integration and Dissemination of
    MAGNet Tools
  • B cell Interaction Knowledge base
  • ARACNE
  • REDUCE
  • MEDUSA
  • GeneWays
  • Protein Structure Pipeline
  • JMol (Open source Molecular Viewer)
  • Functional Annotation Pipeline
  • Etc.

31
Integration of 3rd party components
Cytoscape
GenePattern
MatrixREDUCE
GoMiner
32
BISON Biomedical Informatics Structured Ontology
Component A
_at_Publish public DSDataSet publish(. . .)
DSDataSet dataSet // do some work that
assigns a value to dataSet. return
dataSet _at_Subscribe public void
receive(DSDataSet dataSet, Object source)
// Consume the argument dataSet, as
appropriate
Component B
  • Provide re-usable models of common bioinformatics
    concepts
  • Data sequence, expression, genotype, structure,
    proteomics
  • Complex data structures patterns, clusters,
    HMMs, PSSMs, alignments
  • Algorithms Clustering, matching, discovery,
    normalization, filtering
  • Provide a foundation for the development of
    interoperable geWorkbench components
  • Endorsed by multiple communities (caBIG, AMDeC,
    NCBCs)

33
geWorkbench Resources
http//www.geworkbench.org/
34
The DREAM Project
35
Web Resources
  • Center Overview
  • magnet.c2b2.columbia.edu
  • geWorkbench
  • www.geworkbench.org
  • DREAM Project
  • www.nyas.org/ebriefreps/splash.asp?intEbriefID534
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