Grilles informatiques en Europe, des sciences de la vie à la santé

V. Breton

Journée Génopôle

IRISA

Le concept d’infrastructure de grilles (1/2)

Internet met à disposition des informations… L’utilisateur doit tout faire lui-même

Mettre en forme les informations à partager (site web) Identifier, trier, analyser les données disponibles Limites : compétence, stockage, puissance de calcul

Evolution : sites web offrant des services spécialisés Limitations : ressources du site (compétence, CPU,

stockage) Notion d’infrastructure de grille : permettre à des

communautés d’utilisateurs de partager des ressources de calcul et de stockage et des services Mutualisation des compétences, du calcul et du stockage Traitement de l’information, Sécurité

Le Concept (2/2)

Une infrastructure de grille informatique permet à des communautés d’intérêt de partager de façon dynamique des ressources informatiques distantes et distribuées géographiquement pour le stockage de gros volumes de données et pour accroître les puissances de calcul

Une infrastructure de grille informatique comprend un ensemble hétérogène de calculateurs, de moyens de stockage, voire d ’instruments de mesure reliés entre eux par un réseau à haut débit et grâce à un middleware. Elle offre aux utilisateurs un accès aisé, transparent et sûr à l’ensemble de ces ressources hétérogènes.

Grid technology is promisingfor both computing intensive applications and

knowledge discovery

To connect databases of heterogeneous content (biology and medicine) enabling new knowledge discovery (research, drug design), better guidance and information (healthcare professionals)

To increase computing power for analysis, imaging, simulation and modelling thus allowing these fields to take into account more data and therefore to provide more accurate results.

To address security (integrity, confidentiality, authentication, authorization, non-repudiation, availability)

The challenges of a life science grid

Technical challenges data and tools integration : address data

heterogeneity and legacy of tools and standards provide the infrastructure to deploy biomedical

applications in a grid environment Human challenge : involve end users in the grid game

Grids are still very much in development and therefore user-unfriendly

Training and support to university hospitals, biology/medecine research centres

Projets de grille en bioinformatique

Projets nationaux en Europe France : GenoGRID, GRIPPS, Rugbi UK e-science : Mygrid Hollande : BioASP …

Projets américains : Encyclopedia of Life (EOL) North Carolina Biogrid project http://www.ncbiogrid.org/ …

Projets en Asie : Japon : OBIGrid, http://www.obigrid.org

Projets européens DataGrid (FP5) EGEE (FP6) Embrace (soumis en Novembre 2003) …

Phylojava, web portal for phylogeneticson DataGrid

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Datagrid EDG 1.4

SUN 900MHz

Bootstrapping : procedure to compute a consensus from a large number of independent phylogenetic tree calculations

Crédit : T. Silvestre, BBE Lyonhttp://pbil.univ-lyon1.fr/phylojava

Exemple de prise en charge de 450 jobs sur DataGrid

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Crédit : T. Silvestre, BBE Lyon

The Encyclopedia of Life (EOL)http://eol.sdsc.edu/

Collaborative global project designed to catalog the complete proteome of every living species in a flexible reference system.

Open collaboration led by the San Diego Supercomputer Center

Three major development areas: Creating protein sequence annotations using the

integrated genome annotation pipeline (iGAP). Storage of these annotations in a data warehouse

where they are integrated with other data sources A toolkit area that presents the data to users in the

presence of useful annotation and visualization tools.

Mygrid

myGrid offers service based middleware components Open source and free Open Grid Service Architecture-compliant

Allows the scientist to be at the centre of the Grid -- Personalisation Generic middleware that suits the creation of bioinformatics

applications Inclusion of rich semantics to facilitate the scientific process

42 months, 20 months in. Available from http://www.mygrid.org.uk

Prototype V0 technical and user requirements Prototype V1 Release Sept 2004, some services available now.

Les futurs projets en Europe

EGEE : infrastructure de production pour la recherche Suite de DataGrid 70 partenaires autour du CERN 32 Millions d’Euros Démarrage en Avril 2004 Domaines applicatifs privilégiés : Physique des particules et

biomédical Embrace : proposition de réseau d’excellence

17 partenaires autour d’EBI Developper les API pour intégrer les données biologiques et les

outils bioinformatiques dans un environnement de grille Soumis au 2ème appel (Nov. 2003)

Health + Grid = HealthGrid

Health:

All levels of data & information, from molecule to population needed to ensure better prevention, diagnosis

and treatment of the citizen.

Grid:

An environment, created through the sharing of resources, in which heterogeneous and dispersed data as well as applications can be accessed by different partners

according to their authorisation, without loss of information.

Draft Ideas, September 2002

INDIVIDUALISED HEALTHCAREMOLECULAR MEDECINE

Databases

Association

Modelling

Computation

HealthGRID

Computational recommandation

Public Health

Patient

Tissue, organ

Cell

MoleculePatient related data

Public Health

Patient

Tissue, organ

Cell

Molecule

S. NøragerY. PaindaveineDG-INFSO

A recent example of the potential grid impact

Last summer heat wave killed more than 10000 people in France

Mortality rate in excess of 10 to 50% in retirement homes and hospitals unnoticed for 2 weeks

A monitoring system could have raised the alarm much earlier Requirements : collect information from hospitals

and/or funeral services on the number of casualties Internet can do it through a centralized web portal Grid added value : database federation (data left in

hospitals, à la BIRN) + a grid service for mortality rate computation and monitoring

Grid technology allows to do it today…

UI

- PKI X.509 certificate keys- JDL files

Ordinateurs du médecin

enterGrid

enterGrid

enterGrid

enterGrid

UI WN

WN

WN

WN

WN

WNRB/II

CESESE

Machines de calcul

Machines de stockage de

données (images

radiologiques)

Conception d’une grille

Allow every physician to access a reliable grid for his daily practice

New actors : hospitals, physicians, healthcare administrations, big pharmas, SMEs

Technical issues Networking, User interfaceGrid quality of services (stability, scalability, security,…)

Legal/ethical issues : obey the laws of the European countries with respect to

personal data ownership and data transfer

Grid technology is not ready yet to address all these challenges, butIt is time to build bridges towards this vision

The challenges of an healthcare grid

In silico drug discovery

Goal : speed up the cycle for drug discovery Challenge : bridge gaps in the translation of basic

research through to drug development from the public to the private sector and in the feedback from the private sector of their results

The grid impact : high performance computing and data storage for

massive docking Collaborative environment for searching new targets

and sharing results while respecting privacy Short term perspective : a grid for neglected disease

Non profit drug discovery in a grid environment Technical issues : security, data management

Multi-site therapy monitoring

Goal : reduce time and cost to launch a drug on the market (100 million euros and 10 years)

Challenge : improve monitoring of multi-site clinical trials The grid impact : moving away from a single centralized

repository Technical issues : security, data management

Intensity Modulated Radiation Therapy

Goal : deliver a variable fluence (number of particles per unit square) using complex geometries adapted to the tumoral volume depending on the beam incidence

Challenge : necessity to simulate treatment through inverse dosimetry for each incidence of the beam and geometry of the multi-lames collimator and validate the dose delivered to the patient 30 beams x 2 minutes = 1 hour for each iteration of

treatment validation The grid impact : parallel execution of the different beam

configurations on a cluster Reduce time needed for treatment planning and increase

number of patients Technical issues : security, quality of service

Perform a trial for the introduction of the Grid

approach in the biotechnology industry

Biomolecular simulations

Some health related FP5 grid projects in Europe

Simulation/Imaging Software

Grid Software /solutions

Bio-numeric modelling

Medical Expertise

Legal Aspects

Medical simulation service+ networked compute resources

Pre- & Post-processing

User-site(SW installed)

GRID SW

GRID SW (interface)

Applications SW

GRID SW (service use)

Internetor Intranet

Internetor Intranet

Could also be movedto the services portal

Simulation Service System

Project Duration: 30 months, Commencement: 1.9.2002

http://www.gemss.de

GEMSS: GRID-enabled Medical Simulation Services

GEMSS - main goals

Main GEMSS Goals: Secure and lawful Grid provision of medical simulation

services, Build 6 Grid-enabled medical prototype applications, Build suitable middleware on top of common standards, Install and evaluate a GEMSS test-bed, Anticipate privacy, security and other legal concerns related

to providing medical services over the Internet.

Necessary Assumption:No special purpose network infrastructure

Logger

ComputeResource

VNC

DataStorage

QoS

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Err

orre

cove

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Web

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ConversationalAuthorisation

Keystore +Certificates

Web

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vice

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Keystore +Certificates

Bridge tolocal PKI ?

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App

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Wor

kflow

Bus

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LocalAuthorisation

RegistryCertification

UI

Ele

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Internet

Client ServerAppropriate User Interfaces& Applications Workflow

• Workflow Enactor

• Negotiation• Business

Processes

• Secure Transfer, Web Services Security, Logging

Negotiated Service Provision

GEMSS -Technical Goals & Challenges

GEMSS - outlook

Status of Work: GEMSS has finalised its design phase:

client-server arch. based on web services (OGSA-compliant).

Outlook: prototype system – Feb. 2004final GEMSS system – Aug. 2004

Contribution to Standardisation:GEMSS is assessing its involvement in GGF, IETF or W3C. Final Strategy has yet to be decided.

La téléradiologie aujourd’hui: une solution à

améliorer« Il ne suffit pas qu’un système de téléradiologie soit techniquement performant ni légalement installé pour garantir son succés pratique »

Franken et coll.Les difficultés mises à nu:

Délai constaté pour une interprétation d’image radiologique trop long (24 à 96h)

Les comptes rendus de téléradiologie mal adaptés

Nombreux problèmes de communication orale ou écrite, en particulier sur:

La qualité des images

Les renseignements cliniques

Expérience de téléradiologie entre 1992 et 1995 entre un hôpital rural de l’Arkansas et des radiologues universitaires de Iowa City (USA)

eDiamond Digital Mammogram National Database

• Fédérer des bases de données de mammographies• Aider au programme de détection du cancer du sein au Royaume Uni

• Buts: Outil d’apprentissage pour les radiologues: e-learning

Support au télédiagnostic

Outils pour l’aide au data mining et à l’épidémiologie

Outils pour contrôle de qualité automatisé

1.5M - examens en 2001-0265,000 – Rappelées pour 2ème contrôle8,545 – Cancers détectés300 – vies sauvées par an

230 – Radiologues “Double Lecture”

Film

Papier

230 - Radiologues “Double Lecture”50% - Croissance examens

2,000,000 – examens chaque année120,000 – Rappelées pour 2ème contrôle10,000 – Cancers détéctés1,250 – vies sauvées par an

Digital

Digital

Aujourd’hui Demain

Le dépistage du cancer du sein au Royaume Uni

Metadata Images

Logical View is One Resource

Grille

PatientPatient AgeAge …… ImageImage107258107258 5555 …… 1.dcm1.dcm236008236008 6262 …… 2.dcm2.dcm700266700266 5959 …… 3.dcm3.dcm895301895301 5858 …… 4.dcm4.dcm……………… …… …… ……..……..……………… …… …… ……..……..……………… …… …… ……..……..……………… …… …… ……..……..……………… …… …… ……..……..……………… …… …… ……..……..……………… …… …… ……..……..……………… …… …… ……..……..

DonnéesDonnéesDICOMDICOM

DICOMDICOM

DICOMDICOM

DICOMDICOM

CalculCalcul

StandardMammoFormat

StandardMammoFormat

DataMining

DataMining

CADeCADi

CADeCADi

92 centres de dépistage du

cancer du sein

Challenge: La normalisation des imagesDe nombreux paramètres influence l’apparence des images

Distribution de densité des tissus, tumeurs, microcalcifications

Voltage, temps d’exposition……

Solution : SMF Standard Mammogram Form

Le principe

Haut gauche: Image cranio-caudale(plus contour du sein et marques

Haut droit: Image médio-latérale oblique

Bas: Galerie d’images disponibles

Haut gauche: reconstruction 3D montrant la localisation de la tumeur

Bas gauche: SMF vue des différentes densités de surface

Droite :image normalisée

MammoGrid – European federated mammogram database

implemented on a GRID infrastructure

Main goals: Epidemiology of breast cancer from a European perspective

Open source architecture Use of Grid in developing quality control techniques for breast

cancer screening Development of some CADe techniques

http://lotus5.vitamib.com/hnb/mammogrid/mammogrid.nsf/Web/Frame?openform

University Database

Healthcare Institute

Hospital Italy

Hospital UK Shared meta-data

Analysis-specific data

•Knowledge is stored alongside data•Active (meta-)objects manage various versions of data and algorithms•Small network bandwidth required

Clinician’s Workstations

QueryResult

LocalQuery

LocalAnalysis

LocalAnalysis

LocalAnalysisLocal

Analysis

Massively distributed dataAND distributed analyses

GRIDLocalQuery

LocalQuery

LocalQuery

MammoGrid -Federated System Solution

MammoGrid -Grid challenges: database

Large federated databases Images and metadata

Ontologies and metadata Image formation parameters Image features Clinical information Demographic data

Effective data mining of a rapidly growing database Allow for complex queries involving executables Medical image analysis clients are not Grid experts!

MammoGridGrid challenges: communications

Legal restrictions on access to data Clinicians, researchers, developers, Govt, …

Data resides in hospitals Firewall protected

Combining several databases Secure file transfer Large images to be transferred Develop API for black box third party applications

Grids for medical development

Preparation and follow-up of medical missions in developing countries

Support to local medical centres in terms of second diagnosis, patient follow-up and e-learning

2 missions (Ibagué & Chuxiong) with the french NPO « Chaîne de l’Espoir » used as test cases

IbaguéHand surgery Medical centre

Clermont-Ferrand/Paris

Chuxiong

The grid impact :

•Improved telemedecine services

• Federation of patient databases

•Interactive e-learning (high bandwidth network required)

Request for

second diagnosis

Patient d

ata

consultatio

n

Second diagnostic

Patient follow-up

Patient dataRequest for

2nd diagnostic

Interactiv

e e-learn

ing

Video-conferences

DataGrid : status of biomedical applications

Bio-informatics Phylogenetics : BBE Lyon (T. Sylvestre) Search for primers : Centrale Paris (K. Kurata) Bio-informatics web portal : IBCP (C. Blanchet) Parasitology : LBP Clermont, Univ B. Pascal (N. Jacq) GRID platform for DNA microarray data analysis : Karolinska (R.

Martinez) Geometrical protein comparison : Univ. Padova (C. Ferrari)

Medical imaging MR image simulation : CREATIS (H. Benoit-Cattin) Medical data and metadata management : CREATIS (J. Montagnat) Mammographies analysis ERIC/Lyon 2 (S. Miguet, T. Tweed) Simulation platform for PET/SPECT based on Geant4 : GATE

collaboration (L. Maigne)

deployedtested on EDGunder preparation

Simulation Monte-Carlo sur grille

Credit : D. Hill L. Maigne R. Reuillot

Objectif : accélérer l’exécution de codes Monte-Carlo Méthode : étudier l’impact du déploiement sur grille de calculs Monte-Carlo Parallélisation étudiée : soumission de tâches avec des graines indépendantes

GATE , plate-forme de simulation Pour l’imagerie médicale nucléaire et la curie/radiothérapie

Impact du déploiement sur le temps de calcul

Credit : D. Hill L. Maigne R. Reuillot

Variation du temps de calcul en fonction du nombre de tâches soumises en parallèle

Variation du temps de calcul en fonction du jour du mois pour 100 tâches soumises en parallèle.

Simulated vascular reconstruction

Goals Supports the vascular surgeon in

placement of bypasses and stents Predicts blood flow before operation

Geometry obtained from medical scans

Add the proposed interventionMethod Interactive Virtual Reality Environment to

View scanned data Define proposed interventions View simulation results

Advanced fluid code to simulate flows Grid for data access and computational

resources

FP6 : the opportunities of a new paradigm

From pilot to production grid infrastructures (EGEE,…) committed to provide to users communities Training User support Access to resources

Need for collaborations with NoE and grid projects in the eHealth area to deploy large scale applications

Feedback eHealth specific requirements to middleware developers

Research infrastructures and testbeds

eHealth

Grids for complexProblem solving

eHealth

To widen the impact of the healthgrid cluster, the Healthgrid association

To disseminate information on grids for health Summaries and links to health related grid projects Available tools (software platforms, middleware,…) Tutorials Conferences

To foster exchange between projects, end users and technology developers To avoid reinventing the wheel To improve the take-up of grid technology

To promote standards Involvement in GGF Life Science Research group

Open to any new member Contact point : Y. Legrè (legre@clermont.in2p3.fr) Web site : http://www.healthgrid.org

eHealth

Healthgrid conferences

Jointly organised by CERN, CNRS and EMBnet in collaboration with the eHealth unit DG-INFSO

Meeting point for actors of grids for health End users = healthcare professionals / providers +

academic & industrial researchers and developers from bio-informatics and medical-informatics

Grid applications developers Technology developers

First conference in Lyon (January 2003) Next conference in Clermont-Ferrand (January 29-30

2004)

eHealth

HealthGrid 2004

January 29th - 30th 2004, Clermont-Ferrand, France

http://clermont2004.healthgrid.org

The aims of this conference are to reinforce and promote awareness of the possibilities and advantages linked to the deployment of GRID technologies in health. In this context "Health" does not involve only clinical practice but covers the whole range of information from molecular level (genetic and proteomic information) through cells and tissues, to the individual and finally the population level (social healthcare).

eHealth

Conclusion

Des grilles pilotes (FP5) aux grilles d’exploitation pour la recherche (FP6)

Grilles en bioinformatique Premiers portails prototype utilisant des grilles pour le

calcul distribué Projets de plate-forme pour déployer des expériences A faire : gestion des données hétérogènes distribuées (->

Embrace) Grilles pour la santé

Projets pilotes au niveau national et européen Initiative Healthgrid pour créer une communauté

( informaticiens, utilisateurs de grille, acteurs du monde de la santé)

goal : provide a GRID platform for DNA microarray data analysis and Gene Regulation Bioinformatics that permit predictions of involvement of genes in the pathogenesis of human diseases.