Integrative Informatics Life Sciences Conference + Expo April 3 rd, 2006 – Boston, MA John...

Integrative Informatics

Life Sciences Conference + ExpoApril 3rd, 2006 – Boston, MA

John ReyndersInformation Officer - LRL Discovery and Development Informatics

Life Sciences C+E 2006, Boston MA Copyright © 2006 Eli Lilly and Company

Outline

Can’t we all just get along?

Navigating silos of silos



Rapid Application Development with the Parallel Object-Oriented

Methods and Applications (POOMA) Framework

Post-Doc Challenge:

Write a 3D Pseudo-Spectral code to simulate two colliding vortices using the Navier-Stokes Equations

Advanced Computing LabLos Alamos National Lab




Post-Doc Challenge:



On This:




Post-Doc Challenge:


Result:

One Post-Doc with no parallel experience wrote this application in 5 weeks with POOMA

Navier-Stokes simulation iso-surface of vorticity



Encapsulation in the POOMA FrameWorkEncapsulation in the POOMA FrameWork

STL ExpressionTemplates

Userthreads

RefCount &Data Pooling

MPI/PVMDomainDecomp

RTSSheduling

LoadBalancing

Fields Matrices

ParticlesMeshes

FFTEllipticSolvers

StencilOperations

DPMonteCarlo

ERPlasmas

DPHydro

EROcean

GlobalGlobal

AlgorithmAlgorithm

ComputerScience

StencilOperators Interpolators

Physics

ApplicationApplication

LocalLocal

ParallelParallel



Outline





The Problem: Silos of Silos

•Tools, application, and data are standalone with limited interaction•Scientists have great difficulty finding their data and associated tools•Asking cross-domain questions ( e.g. bio+chem ) very difficult•Support becoming very impractical – estimated 400+ individual tools across silos

LLYDB

BioSel

Jockyss

ELIAS

Beacon

ICARIS

ResultsStar

Jubilant

BioGeMs

Sig3

PathArt

TV-GAME

PubDBs

ProteomeXrep

Nautilus

Conformia

Intellichem

MCPACT

Watson

PRDB

LIMSIDW

Chem Bio PR&D/ADMET


PRESENTATION LAYER (.NET)

WORK FLOW / BUSINESS LAYER (Some . Net)

DATA LAYER

BioGems

Going from the vertical to the horizontal

DATA LAYER

Biosel/TINS

DATA LAYER

Process Tracking

DATA LAYER

Data Warehouse

DATA LAYER.Net?

...


Lilly Science Grid (LSG) Architecture - Systems

TDC-TAT

Plug-In Manager

Biology Chemistry Toxicology

BioGEMS TV-GAME BioSel System X

WS Provider

WS Consumer

WS Provider WS Provider

WS Cons

WS Provider

SAP Portfolio

Portfolio Sys

WS Provider

Plug-In A Plug-In B Plug-In N…

WS Cons WS Cons

Event Communication

TAO


LSG Architecture - Tools View

TDC-TAT

.NET API

SRS/Oracle Oracle Oracle

Perl CGI Perl CGI Perl CGI Java

SOAP::Lite

Perl

SOAP::Lite Axis

.NET Proxy

SOAP::Lite

Flat Files/Oracle

Java

Axis

.NETUser Ctrl

.NETUser Ctrl

.NET User Ctrl…

.NET Proxy .NET Proxy

.NET API

Oracle

.NET C#

IIS Web Server

Visual Studio

Apache Web Server

Linux

Tomcat Web Server

Linux

WSDL WSDL WSDL WSDL WSDL

Common XML Schema’s (XSD)


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Webparts Thin Client POC Interface

Portfolio WS

Spreadsheet of Portfolio

Active Cpds WS

BIOSEL/TINS

Prot. Express. WS

GeneAltas/BioGems

Prot. Express. WS

Proteome/BioGems

Prot. Express. WS

Proteome/BioGems

Prot. Express. WS

Proteome/BioGems

Data WebLinks. WS

BioGems/TV-GAMES …

Gene ID Mapping WS

Custom Spreadsheet

Data Integration/Mapping

Architecture enables encapsulation and division of labor


Grid Architecture Points: The User

MyScience: Enable Scientist to dynamically compose their environment from a set of components

Orchestration: Components communicate to enable an action/question in one component to yield results/answers from multiple components

Organic: New capabilities can be added by simply adding a new component

Scalable: The combinatorics of using 4 out of 12 components yields 495 configurations

• It is much easier to maintain a framework and 12 associated components than 495 separate tools!


Grid Architecture Points: The Developer

Get SEs and scientists out of silos and into layers so they may do what they do best

• Data, applications, algorithms, presentation

Plug-in architecture to factor business/science and framework development

Crisp abstraction barriers between and within layers to enable modular development

Rationalize tool set within layers to improve developer productivity


Some Observations/Thoughts

Where is the Value:• Data fusion in the military – information supremacy

– F15 heads-up display

– Aegis cruiser

– “Bob” and “Tom” from the NSA

• What makes the drug hunter effective in an “Informatics cockpit”– It’s partly the quality, speed, accuracy of any given tool ( e.g. the altimeter )

– It’s mostly how the instruments work as an integrated whole

• I can ask questions I could not ask before!– Integrate to this point of innovation – before spending significant time on optimizations

Some Lessons from Los Alamos:• How can one go wrong having an application framework built by a team of A+ students?

– By building a framework that can only be used by A+ students

• Surely everyone knowing as much as they can about all aspects of the framework will produce the best framework!

– Nope. By knowing the implementation behind the abstraction, a team fails to program through interface contracts

– Also, the team has challenges scaling in development efforts – because it is not functioning as a team ( everyone run to the soccer ball? )


The old vs. the new architecture

Benefits: rapid development, customizable environment, integration of tools for cross-domain inquiry, reduced support load…

plugin

DATA LAYER

Discovery Informatics Integration Kernel

plugin

DATA LAYER

plugin

DATA LAYER

plugin

DATA LAYER

Jubilant

BioGeMs

Sig3

PathArt

TV-GAME

PubDBs

ProteomeXrep


Web-Service Layer - LSG

Can accelerate staging the old into the Lilly Science Grid

Discovery Integration Kernel

plugin plugin pluginpluginplugin

Integrated Data Layer - LSG

BioGeMs PathArt


Web-Service Layer

Scaling efforts: Divide & ConquerThe Kernel

• Composability, Integration, Interaction, Scalability

• Clear contract with plug-ins

The Plug-inClear contract with kernel and web-service layer

• Domain-specific tool• Limited knowledge of Kernel required to build

plug-in

Web-Services• Clear contract with plug-ins and data-layer• Insert web-service layers into tools – preserving

legacy interface and creating service to build a plug-in

Integrated Data Layer• Clear contract with Web-Services• Design for integration first, optimization next• Automate ETL

Discovery Integration Kernel

plugin

Integrated Data Layer


Indications view


Drug Hunting Team view


Outline





Similarity?

~

Graph - yes.Text - yesAssay - yes


Similarity – and adding a magical Methyl

~

Graph - yes.Text - yesAssay - yes

~

Graph - yesText - maybeAssay - no


Gene Objects

•Representation• Name• String

•Filters• PathwaySet• GeneFamily• GO

•Measures• Alignment (Algorithm)• Text (DocumentSet)• GeneExpression (SampleSet, MoleculeSet )

ATGAGCCTCCCCAATTCCTCCTGCCTCTTAGAAGACAAGATGTGTGAGGGATGCCA


Protein Objects•Representation

• Gene• String• State ( e.g., Phosphorelated )

•Filters• GeneFamily• GO

•Measures• Alignment (Algorithm)• Pathway (PathwaySet)• Text (DocumentSet)• ProteinExpression (SampleSet, MoleculeSet )• Assay ( ExperimentSet )• 3D Structure (Algorithm)


SNP Objects

•Representation• Gene Locus

•Filters• GeneSet• SNP characterization

– Coding/Non-Coding– Blossum Score– Exon/Intron– Transcriptional

•Measures• Linkage disequilibrium

– D-Prime– R-Squared

• Haplotype Block Association• Text (DocumentSet)

ATGAGCCTCCCCAATTCCTCCTACCTCTTCGGAGACAAGATGTGTCAGGGATGCCA

ATGAGCCTCCCCAATTCCTCCTGCCGCTTCGAAGACAAGATGTGTCAGGGATGCCA

ATGAGCCTCCCCAATTCCTCCTACCTCTTAGGAGACAAGATGTGTCAGGGATGCCA


Image Objects

Law’s TextureConvolution

L5 = [ 1 4 6 4 1 ] E5 = [ -1 -2 0 2 1 ] S5 = [ -1 0 2 0 -1 ] W5 = [ -1 2 0 -2 1 ] R5 = [ 1 -4 6 -4 1 ]

Density FunctionalSignature ( DFS )

Target DFS + L2 Measure

Measure

Filter

Representation - 2D Matrix


Molecule Objects

•Representation• Name• Graph• 3D Structure

•Filters• Library Compounds• Similarity Search

•Measures• Text (DocumentSet)• Fingerprints (Algorithm)• 2D/3D Similarity (Algorithm)• HTS (GeneSet)


Targets/

Compoun

ds T1 T2 T3 T4 T5 T6 T7 logP Vdist

C1 8 4 >20 2

C2 5 7 >20 >20 60% 2340

C3 1.1 3 >20 >20 20% 100

C4 >20 >20 >20

C5 2 0.9

C6 >20 >20 0.1 0.09 90% 2500

C7 0.1 0.2 0.1 0.1 0.3 10% 400

C8 0.77 0.2 >20 >20 0

C9 0.57 0.27 >20 >20

… 0.2 >20 >20 >20 >20 >20

CompoundProfilingBioprint

Target Chemoprint

Chemogenomic Selectivity profilesMethod = Ward

GSK3B

PRKG1/PKG1

SRC

CDK2CDK4

PRKCA/PKCa

LYN

CDK5

CSF1R/FMS

CSNK1A1/CK1a

PRKCD/PKCd

KIT

PRKCE/PKCe

CHEK1/CHK1

PRKCG/PKCg

PRKCB1/PKCbPRKCH/PKCh

PDGFRAPDGFRB

LCK

ABL1FLT1FLT4

KDRMAPK1/Erk2

ERBB2/HER2/ErbB2

MAPK3/Erk1

FGFR1

WEE1

INSR

MAPK9/JNK2RAF1

EGFR

MAPK11/p38bMAPK14/p38a

CDC2

TEK/TIE2

IGF1R

FYN

MAP2K1

MYLK2/skMLCK

PRKACA/PKACaZAP70

Dendrogram

Hierarchical Clustering


Comparison of kinase dendograms Method = Ward

GSK3B

PRKG1/PKG1

SRC

CDK2

CDK4

PRKCA/PKCa

LYN

CDK5

CSF1R/FMS

CSNK1A1/CK1a

PRKCD/PKCd

KIT

PRKCE/PKCe

CHEK1/CHK1

PRKCG/PKCgPRKCB1/PKCb

PRKCH/PKCh

PDGFRAPDGFRB

LCKABL1

FLT1FLT4KDR

MAPK1/Erk2

ERBB2/HER2/ErbB2

MAPK3/Erk1

FGFR1

WEE1

INSR

MAPK9/JNK2

RAF1

EGFR


CDC2

TEK/TIE2

IGF1R

FYN

MAP2K1MYLK2/skMLCK

PRKACA/PKACa

ZAP70

Dendrogram


Method = Ward

GSK3B

PRKG1/PKG1

SRC

CDK2CDK4

PRKCA/PKCa

LYN

CDK5

CSF1R/FMS

CSNK1A1/CK1a

PRKCD/PKCd

KIT

PRKCE/PKCe

CHEK1/CHK1

PRKCG/PKCg

PRKCB1/PKCbPRKCH/PKCh

PDGFRAPDGFRB

LCK

ABL1FLT1FLT4

KDRMAPK1/Erk2

ERBB2/HER2/ErbB2

MAPK3/Erk1

FGFR1

WEE1

INSR

MAPK9/JNK2RAF1

EGFR


CDC2

TEK/TIE2

IGF1R

FYN

MAP2K1

MYLK2/skMLCK

PRKACA/PKACaZAP70

Dendrogram


AssaySim.

SequenceSim.


HTS as a Mapping Object

•Representation• ProteinSet• MoleculeSet• HTS Array

•Filters• Protein Filters• Molecule Filters

•Measures• Cluster Analysis• Self-Organizing Maps• Support Vector Machines• Neural Networks


Gene Expression as a Mapping Object

•Representation• GeneSet• SampleSet• 2D Expression Matrix

•Filters• Gene Filters• Sample Filters

•Measures• Cluster Analysis• Self-Organizing Maps• Support Vector Machines• Neural Networks


Text as a Mapping Object

•Representation• ObjectSet A• ObjectSet B• RDF Triplets

•Filters• DocumentSet• A Filters• B Filters

•Measures• QR Factorization• Text-based Classifiers


Pathway as a Mapping Object

•Representation• ProteinSet• MoleculeSet• VertexSet

•Filters• Protein Filters• Molecule Filters

•Measures• Graph Algorithms


Putting it all together…

Objects Measure

MTS Literature

Binding Coding

Clinical DB

Compounds

Images

Genes

SNPs

Expression

Linkage D

Signature

Fingerprint

Map 1 Map 2


The goal… find “wormholes”

9 10 11 12

5 6 7 8

1 2 3 4

13 14 15 16

16 Objects20 Text-based relations:

TextPathwayHTSExpressionImage

16 Objects120 heterogeneous relations:

Integrative Informatics Life Sciences Conference + Expo April 3 rd, 2006 – Boston, MA John...

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