Virtual Organizations: Building Interdisciplinary Collaborations

Dan Reedreed@renci.org

Chancellor’s Eminent ProfessorVice Chancellor for IT

University of North Carolina at Chapel Hill

Director, Renaissance Computing Institute

Acknowledgments

• Funding agencies– NIH

• Carolina Center for Exploratory Genetic Analysis (CCEGA)

– NSF• TeraGrid Science Gateways

– State of North Carolina• RENCI and ancillary Bioportal support

• RENCI staff– Alan Blatecky, Kevin Gamiel, Xiaojun Guan– Clark Jefferies, Howard Lander– John Magee, Ruth Marinshaw, Jeff Tilson– Lavanya Ramakrishnan

• And a host of others …

21st Century Challenges• The three fold way

– theory and scholarship– experiment and measurement– computation and analysis

• Supported by– distributed, multidisciplinary teams– multimodal collaboration systems– distributed, large scale data sources– leading edge computing systems– distributed experimental facilities

• Socialization and community– multidisciplinary groups– geographic distribution– new enabling technologies– creation of 21st century IT infrastructure

• sustainable, multidisciplinary communities

• “Come as you are” response

Th

eory

Exp

erim

ent

Computation

Exemplar 21st Century Challenges

• Population growth in sensitive areas– severe weather sensitivity

• national impact– geobiology and environment– economics and finance– sociology and policy

• Economics and health care– longitudinal public health data

• environmental interactions– genetic susceptibility

• heart disease, cancer, Alzheimer's– privacy and insurance– public policy and coordination

Mean Onset of Alzheimer’s Disease• apolipoprotein (apo)

– apoE2, apoE3 and apoE4 alleles• on chromosome 19

– apoE4 allele• 40% to 60% of Alzheimer's patients• not the only cause for Alzheimer’s

• apo gene inheritance– ~25% inherit 1 copy of apoE4 allele

• Alzheimer's risk increases 4X

– 2% inherit 2 copies of apoE4 allele• Alzheimer's risk increases 10X

60 65 70 75 80 85

1.0

0.8

0.6

0.4

0.2

0P

ropo

rtio

n of

eac

hge

noty

pe u

naffe

cted

Age at onset

2/3

2/43/3

3/4

4/4

Source: Alan Roses, GSK

Big QuestionsDNA

sequenceProtein

structure

Homology basedprotein structure

prediction

Protein sequence and regulation

SequenceAnnotation

Q

Y

R

CGT

TAC

CAG

TATAP

rom

ote

rM

es

sa

ge

Protein/enzymefunction

Molecularsimulations

Bacteria and cells

Pathwaysimulations

Metabolic pathwaysand regulatory networks

Networkanalysis

Dataintegration

Organs, Organisms and Ecologies

Multi-proteinmachines

Identify Genes

Phenotype 1 Phenotype 2 Phenotype 3 Phenotype 4

Predictive Disease Susceptibility

Physiology

Metabolism Endocrine

Proteome

Immune Transcriptome

BiomarkerSignatures

Morphometrics

Pharmacokinetics

EthnicityEnvironment

AgeGender

Genetics and Disease Susceptibility

Source: Terry Magnuson, UNC

PITAC Report Contents• Computational Science: Ensuring

America’s Competitiveness 1. A Wake-up Call: The Challenges to U.S.

Preeminence and Competitiveness2. Medieval or Modern? Research and Education

Structures for the 21st Century3. Multi-decade Roadmap for Computational Science4. Sustained Infrastructure for Discovery and

Competitiveness5. Research and Development Challenges

• Two key appendices– Examples of Computational Science at Work– Computational Science Warnings – A Message

Rarely Heeded

• Available at www.nitrd.gov

Life Science Lessons from Astronomy

• Historically, discoveries accrued to those– with access to unique data– who built next generation telescopes

• Two things changed– growing costs and complexity of telescopes– emergence of whole sky surveys

• The result – virtual astronomy– discovering significant patterns

• analysis of rich image/catalog databases

– understanding complex astrophysical systems • integrated data/large numerical simulations

{Inter}national Virtual Observatory

Cluster Galaxy Morphology Analysis Portal

clusters

Chandra SIA

Skyview SIA

DSS SIA

2. Look up clusterin internally storedcatalog

1.User’s Machine

webbrowser

User selectsa cluster

3. X-ray and Optical Images retrieved via SIA interface

4. User launchesdistributed analysis

NED Cone Search

CADC CNOC Cone Search

5. Initial Galaxy Catalog generated via Cone Search

DSS SIA

CNOC SIA

6. Image cutout pointers merged into catalog

Morphology CalculationService

7.Morphological parameters calculatedon grid for each galaxy

8.User downloads finaltable and images for analysis & visualization

Source: Ray Plante, NCSA

The Bioinformatics Challenge• Challenge

– the rise of quantitative biology• burgeoning bioinformatics data

– complex analysis and modeling problems– education and training in new technologies

• Reality– diverse tools with idiosyncratic interfaces

• steep learning curves– software development by diverse groups– distributed, databases with diverse metadata

• Need– integrated, easy-to-use toolset with standard interfaces– extensible mechanisms that hide idiosyncrasies– tool and bioinformatics training

• The solution– bioinformatics infrastructure and coupled training

Need: Simple, Easy-To-Use Tools

“Genome. Bought the book. Hard to read.”

Eric Lander

Web and Social Processes• Google

– it’s a search engine, it’s a verb, …

• Blogs– published self-expression

• Instant Messenger– social networks

• Wireless messaging– semi-synchronous

• Internet commerce– the dot.com boom/bust– EBay, Amazon

• Spam, phishing, …– anti-social behavior

Benefits of Standards

• Interoperability• Separation of concerns• Reuse• Independence• Dependability• Sharing• Commonality• Shared knowledge base

– knowledge reuse– simplification (one hopes)

Grids of All Flavors

What’s A Grid/Web Service?

http://http://

http://http://

Web: Uniform access to documents

Grid/Web Services: Flexible, high-performance access to resources and services for distributed communities

Sensors andinstruments

Data archives

Computers

Softwarecatalogs

Colleagues

              

It’s been 12 years!

Grid History: I-Way at SC’95• A prototype national infrastructure

– 17 sites, connected by • vBNS and six other ATM networks

– 60 applications

• Features– I-POPs for site access– Kerberos authentication– manual scheduling– distributed communication libraries

• Experiences– led to Globus Grid toolkit

• Concurrent industry needs– led to web services for B2B interoperation

Web Services: “Commercial Grids”

• From browser-centric to service-centric– from human-computer to computer-computer– structured negotiation and response

• Workflow creation and management– end-to-end service negotiation– inter-organizational interaction

• Prerequisites– metadata standard for service descriptions– standard communication mechanisms– resource discovery and registration

eBay Web Services Architecture

• Over 40% of eBay's listings are now via API calls

Source: IBM

Web Services: A DefinitionA web service is … designed to support interoperable machine-to-machine interaction over a network. It has an interface described in a machine-processable format (specifically WSDL). Other systems interact … [using] its description using SOAP-messages, … using HTTP with an XML serialization ....

W3C Working Draft, August 2003

• SOAP (Simple Object Access Protocol)

ServiceProvider

ServiceConsumer

ServiceBroker

Publish

LocateInvoke

SOAPSOAP

SOAP

WSDL

• WSDL (Web Services Description Language)

UDDI

• UDDI (Universal Description, Discovery and Integration)

Technology Push

Source: Gartner Group

European myGrid Architecture

Source: www.mygrid.org

The Bioinformatics Challenges• Complex, multilevel models

– integration and in silico designs• Information visualization

– complexity and scale• Data models and ontologies

– community definition• Data federation, storage and management

– shared access and support• User access portals

– web-based tool and service interfaces • Packaging, distribution and deployment

– community building

Multilevel Cellular Models• Signaling networks

– environmental triggers and behavior• e.g., cell lifecycle

– different pathways in each tissue type • Metabolic networks

– measurable products in pathway – many systems are steady state– negative feedback leads to stabilization

• Protein interaction networks– localization of proteins that interact for function– protein-protein interactions for specific actions

• Gene regulatory networks– many things affect gene product concentration– nucleic-nucleic, protein-nucleic interactions

• Computing, physics, engineering and biology– control theory, mathematical models, phase spaces– from biological cartoons to predictive models

• e.g., microRNAs and gene expression controls

Biological Models• Simulation and prediction

– structures and dynamics

• Reasoning and discovery– reverse engineering

10-12

Bond Motion Catalysis

Diffusion

TranscriptionTranslation

Growth &Division

10-9 10-6 10-3 100 103 106

100

Metabolites Proteins Ribosomes Prokaryotes Eukaryotes

102 104 106 108 1010 1012

Temporal (seconds)

Spatial (nM3)

Biophysical and Environmental Modeling

Genomics

Proteomics

Cell biochemistryand structure

Cilia

Mucus

Airway/flow

Source: Ric Boucher, UNC

Data Heterogeneity and Complexity

DiseaseDisease

DiseaseDrug

DiseaseClinical

trialPhenotype

ProteinProtein

StructureProtein

SequenceP-P

interactions

Proteome

Gene sequenceGenome

sequence

Gene expressionGene

expression

homology

Genomic, proteomic, transcriptomic, metabalomic, protein-protein interactions, regulatory bio-networks, alignments, disease, patterns and motifs, protein structure, protein classifications, specialist proteins (enzymes, receptors), …

Source: Carole Goble (Manchester)

Source: Robert Morris, IBM

Sensor Data Overload

• High resolution brain imaging– 4.5 petabytes (PB) per brain

Source: Chris Johnson, Utah Art Toga, UCLA

RENCI: What Is It?• Statewide objectives

– create broad benefit in a competitive world– engage industry, academia, government and citizens

• Four target areas– public benefit

• supporting urban planning, disaster response, …– economic development

• helping companies and people with innovative ideas– research engagement across disciplines

• catalyzing new projects and increasing success• building multidisciplinary partnerships

– education and outreach• providing hands on experiences and broadening participation

• Mechanisms and approaches– partnerships and collaborations– infrastructure as needed to accomplish goals

Extant Data Models

Faculty, Staff & Students

Virtuous Cycle

InterdisciplinaryResearch & Education

Carolina Center for Exploratory Genetic Analysis (CCEGA)

Statistical &Computational

Techniques

ExperimentalGenetics Portal

Driving Problems

Analysis Techniques

PromotingMutual

Awareness

InteroperableData

Management

CCEGA Participants• Coordination team

– Dan Reed, RENCI– Terry Magnuson, CCGS– Alan Blatecky, RENCI– Kirk Wilhelmsen, CCGS

• Eleven departments/institutes– Biostatistics– Cancer Center– Genetics– Computer Science– Epidemiology– Genetics– Health Science Library– Information and Library Science– Pharmacy– RENCI– Statistics

• Campus wide support– from many sources

• Project participants– Brad Hemminger, Information & Library Science– James Evans, Genetics– Kevin Gamiel, RENCI– Xiaojun Guan, RENCI– Barrie Hays, Health Science Library– Clark Jefferies, RENCI– Ethan Lange, Genetics– Andrew Nobel, Statistics– Karen Mohlke, Genetics– Kari North, Epidemiology– Susan Paulsen, Computer Science– Fernando Manuel Pardo, Genetics– Charles Perou, Cancer Center– Lavanya Ramakrishnan, RENCI– Jan Prins, Computer Science– Patrick Sullivan, Genetics– Lisa Susswein, Cancer Center– David Threadgill, Genetics– Alexander Tropsha, Pharmacy– K.T.L. Vaughan, Health Science Library– Fred Wright, Biostatistics– Wei Wang, Computer Science– Fei Zou, Biostatistics

Data: From Lab and Clinic to Analysis• Independent data management

– data security– version control– redundancy– controlled access

Clinical

LaboratoryAnalysis

ELSI

Source: Brad Hemmenger, UNC

• NIH CCEGA– Carolina Center for Exploratory Genetic Analysis

Analysis

LAB

ELSI

Integration &Informatics

Clin

ic

Analysis

Data Management and Information Viz

…..

Information MiningModule

Information MiningModule

Information VisualizationModule

Information VisualizationModule

GenBank

Taxonomy Annotation

Taxonomy Annotation

Ontology AnnotationOntology

Annotation

Annotated Domain Literature

Annotated Domain Literature

Published Domain Literature

DB Schema Ontology

Annotation

From SNPs to HapMap

• Single Nucleotide Polymorphisms (SNPs)– one in ~1200 bases differ across individuals– SNPs act as markers to locate genes

• Common groups of SNPs are shared – i.e., form a haplotype

• HapMap data sources– 90 Yoruba individuals (30 trios) from Nigeria (YRI)– 90 individuals (30 trios) of European descent from Utah (CEU)– 45 Han Chinese individuals from Beijing (CHB)– 45 Japanese individuals from Tokyo (JPT)

• ~3,500,000 SNPs typed– basis for association studies for disease identification

CCEGA HapMap Simulator

• Synthetic data– disease models– model testing

• mining bakeoffs

Carolina Bioportal• Three overlapping target groups

– undergraduate education– graduate education and research– academic/industrial research

• Features– access to common bioinformatics tools– extensible toolkit and infrastructure

• OGCE and National Middleware Initiative (NMI)• leverages emerging international standards

– remotely accessible or locally deployable– packaged and distributed with documentation

• National reach and community– TeraGrid deployment

• science gateway• Education and training

– hands-on workshops• clusters, Grids, portals and bioinformatics

Distributed Grid and Web Services

Resource Layer(from PCs to Supercomputers)

Grid Portals

Launch, configureand control Application Interface

Workflow service

App InstanceApp InstanceApp Instance

SecuritySecurity

Data ManagementServiceData Management

Service

AccountingServiceAccounting

ServiceLogging

Logging

Event/MessageServiceEvent/Message

Service

PolicyPolicy

Administration& MonitoringAdministration

& MonitoringGrid Orchestration

Grid Orchestration

Registries andName bindingRegistries and

Name binding

Reservations And SchedulingReservations

And Scheduling

Open Grid Service Architecture Layer

Open Grid Service Infrastructure (web service component model)

Online instruments

Source: Dennis Gannon, Indiana

PISEApplication

XML Description

HTML Files

Bioportal

GatekeeperGridFTP MyProxy

OGCE User Databases

Job History Database

Application Processing

InterfaceGenerator

VelocityFiles

ApplicationProcessing

CommandFiles

Authentication,Grid Credential

User Profile

Job SubmissionJob

Records

RemoteFile

Access

Bioportal Architecture

www.ncbioportal.org

ApplicationDatabases

Localcluster• OGCE toolkit

– used by cyberinfrastructure projects• LEAD, NEES, PACI, DOE, TeraGrid …

Putting the Technologies Together

NC Bioportal

OGCE Toolkit (Grid middleware)

Chef (collaboration/standard portlets)

Velocity(templateengine)

Jakarta Jetspeed(enterprise portal)

Turbine(web app

framework)

Tomcat(Apacheservlet

container)

GridPortlets,

CoG

Databases

BioApplications

PISE(XML

Wrapper)

VMC

Community Software Toolkit: Lessons

• NSF PACI Alliance “In a Box” toolkits– cluster software (aka OSCAR)– Grid infrastructure (aka NMI)– Access Grid for distributed collaboration– tiled display walls for visualization

• Distribution materials– software and training materials

• CDs and web• Community workshops and training

– Linux Clusters Institute– MSI HPC workshops– hands on training

• Lowering the entry barrier– usage and deployment

• Bioportal distribution– workshops, tutorials– training materials– road shows

NC Bioportal: What’s Next• Engagement

– workshops, experiences and deployments• Infrastructure

– dynamic job scheduling across multiple sites– migration to OGCE 2.0– fully automated database updates– workflow construction and processing

• Portal tool suite– expanded applications and databases

• phylogeny, morphology, microarray analysis, …

• Training materials– additional modules based on user feedback– workshop materials packaged for self-study

• Leverage national presence– TeraGrid/NCSA bioinformatics portal

The Vision of Grid/Web Services

“… Behold, the people is one, and they have all one language; and this they begin to do: and now nothing will be restrained from them, which they have imagined to do.”– Book of Genesis

Peter BruegelThe Tower of Babel (1563)

Interdisciplinary Collaborations

• Appropriate reward structures– well-matched time constants

• Intellectual equality– balanced recognition of contributions

• Research/infrastructure distinctions– timelines and people needs differ

• Confidentiality and openness– academic/industry collaboration perspectives

• Intellectual property– background IP and differential disciplinary models

Some Thoughts on the Future• Grids/web services are not a panacea

– we have seen this movie before• standards debates can be endless• make new mistakes, not the same old ones

– code is shifted from modules to interfaces

• Danger of “Death by CS Abstraction”– “all problems can be solved by another level of indirection”

• Appropriate decomposition is a challenge– performance, usability, flexibility

• Generality and extensibility really matter– incremental aggregation and interoperability– data management and federation

• Better questions, not just private capabilities– limited by creativity not resources

The Cambrian Explosion

• Most phyla appear– sponges, archaeocyathids, brachiopods– trilobites, primitive mollusks, echinoderms

• Indeed, most appeared quickly!– Tommotian and Atdbanian – as little as five million years

• Lessons for computing– it doesn’t take long when conditions are right

• raw materials and environment

– leave fossil records if you want to be remembered!

Thanks for the Invitation!