ImmunoGrid Towards a Clinically Relevant Systems Biology Model for the Human Immune System Dr. Clare...

ImmunoGridImmunoGridTowards a Clinically RelevantTowards a Clinically Relevant

Systems Biology ModelSystems Biology Modelfor thefor the

Human Immune SystemHuman Immune System

Dr. Clare SansomDr. Clare SansomBedlewo, Poland May 2006Bedlewo, Poland May 2006

The Mammalian Immune The Mammalian Immune SystemSystem

• A complex and adaptive learning system

• Evolved to defend an individual against foreign invaders

• Operates at multiple levels: from molecule to cell, organ, organism and community

http://www.immunogrid.org

Immunology:Immunology:Successes and FailureSuccesses and Failure• Vaccines have been instrumental in

controlling many diseases– Eradication of smallpox– Near eradication of polio

• But many diseases are still poorly protected against– e.g. failure of the BCG vaccine against

TB in some communities


Vaccinomics:Vaccinomics:The Simplest ParadigmThe Simplest Paradigm

From genome sequenceto vaccine

Via data mining and bioinformatics


A Complex Combinatorial A Complex Combinatorial ProblemProblem

• The human immune system has immense diversity:– >1013 MHC class I haplotypes– 107-1015 different T-cell receptors – 1012 B-cell clonotypes in each individual – 1011 possible linear MHC-binding epitopes

composed of nine amino acids– >>1011 different conformational epitopes– >109 combinatorial antibodies


Problems of Complexity Problems of Complexity • Computational models are limited in

practical applications– Specific molecular functions are poorly

understood: examples include• Prediction of MHC binding and magnitude of

immune responses• Prediction of proteasomal cleavage• Integration of molecular and cellular level

models• Lack of appropriate “real life” data for

testing http://www.immunogrid.org

Enter the GRIDEnter the GRID• Modelling

combinatorial complexity requires immense computational power

• A Grid solution enables full use of the resources available in the community


Computation

Starlight (Chicago) Netherlight

(Amsterdam)

PSC

SDSC

UCL

Network PoP

NCSA

UKLight

US TeraGrid

UK NGS

All sites connected by production

network

DEISA

Visualization

Leeds

Manchester

Oxford

RAL

HPCx

New, State-of-the-art, proven to work

Run DL_Poly, NAMD, LAMMPS, LB3D, etc.. simulations

NGSGlobal Grid InfrastructureGlobal Grid Infrastructure

[Slide © P. Coveney, University College London, UK]

ImmunoGridImmunoGrid

“…a 3 year project funded by the European Union which will establish an infrastructure for the simulation of the immune system that integrates processes at molecular, cellular and organ levels.”

To be designed for applications that support clinical outcomes such as design of vaccines and immunotherapies and optimization of immunization protocols.”


Immunogrid: PartnersImmunogrid: Partners• CINECA, Bologna, Italy

(Project coordinator)• University of Queensland,

Australia (Scientific coordinator)

• CNR, Rome, Italy• CNRS, Montpellier, France• Technical University of

Denmark.• Birkbeck College,

University of London, UK• Department of

Experimental Pathology, University of Bologna, Italy

• University of Catania, Sicily


ImmunoGrid: AimsImmunoGrid: Aims

• Standardising immunological concepts and related bioinformatics tools and resources

• Combining data, tools and resources to develop a simulator and create models for the human immune system

• Pre-clinical testing• Dissemination to researchers and

clinicians


How will we implement How will we implement our aims?our aims?

concepts

molecular models

systemmodels

design

data collection

gridsetup

pre-clinical tests further tests

Design & data schema

simulator

concepts

feedback feedback

concepts

models & prototypes

dissemination

ImmunoGrid: ImmunoGrid: Birkbeck’s RoleBirkbeck’s Role• Lead partner for data collection and

integration– Repository of immunological data required for

simulations• Simulator design• Grid-based implementation

– ImmunologyGrid, APPP• Preclinical Tests

– Collaboration with Anthony Nolan Research Institute

• Dissemination, project management, promotion


Molecular LevelMolecular LevelSimulationsSimulations

Lead Partner:Lead Partner:Marie-Paule LefrancMarie-Paule LefrancMontpellier, FranceMontpellier, France

Molecular ImmunologyMolecular Immunology

• Parts of the immune system are well understood at a molecular level

• Reliable bioinformatics tools exist for– Modelling antibody-antigen interactions– Predicting protein localisation

• And thus visibility to the immune system

– Predicting MHC binding– (and now, proteasomal cleavage… up to

a point…)


The Adaptive ImmuneThe Adaptive ImmuneResponseResponse

Immunoglobulin

B cellT cell

T cell Receptor MHC

peptide

Trimolecular complex


Antigen Presentation on MHC class I and II(animation © Mark Halling-Brown, Birkbeck)

Molecules of theMolecules of theAdaptive Immune SystemAdaptive Immune System

Immunoglobulin IgG(From The Immunoglobin Factsbook2001)

MHC Class IICD4 T-cell responseEndocytosed antigensPredominantly bacterial

MHC Class ICD8 T-cell responseFree antigensPredominantly viral


Limits of KnowledgeLimits of Knowledge• Proteasomal

cleavage is still quite poorly understood

• Some programs exist but their precision is low

• “Vaccine design pipelines” need to be modified

Insertcleavage step


Integration of Molecular Integration of Molecular DataData• With data from

other providers– Many useful

simulation programs exist

– Need to avoid “reinventing the wheel”

– Need a universal database and molecular ontology

• In the simulator… with cellular and other data– Molecular data

must be incorporated into higher level simulations

– Need a database that can be read by all applications


FunctionalityfunctionalORFpseudogeneproductiveunproductive

Specieshumanmouse..

Gene typevariablediversityjoiningconstant

Configuration

germlinerearranged

Chain typeIg-HeavyIg-Light-LambdaTcR-AlphaTcR-Beta...

Structure typeregulartranslocated ...

ReceptorIgATcR gamma-delta

Molecule typegenomic DNAcDNAprotein..

IMGT-Ontology:IMGT-Ontology:IdentificationIdentification

SpecificityAnti-DNAAnti-HIV ...

http://imgt.cines.fr


[ membrane, IgM ]

Heavy chain

Light chain

Alpha - Beta

Gamma - Delta

Contribution of the

2 V-DOMAINs to the antigen binding

site

V-J-REGION

V-J-REGION

V-D-J-REGION

V-DJ-REGION

V-DOMAIN

V-DOMAIN


Immunoglobulin (IG) T cell receptor (TR)

IMGT-Ontology:IMGT-Ontology:DescriptionDescription

Human IGH locus

Chromosome 14q32.33


IMGT Repertoire, http://imgt.cines.fr

IMGT-Ontology: ClassificationIMGT-Ontology: Classification

Cell and OrganCell and OrganSimulationsSimulations

Lead Partner:Lead Partner:

Filippo Castiglione, RomeFilippo Castiglione, Rome

The Starting PointsThe Starting Points• C-ImmSim: An “Agent based”

simulator. Current version (v.6.2) available under GNU Public License.

http://www.iac.cnr.it/~filippo/cimmsim.html

• SimTriplex: An immune system – cancer – Triplex vaccine competition simulator.




C-ImmSim v.6.2C-ImmSim v.6.2• Able to simulate a

wide range of immunological phenomena

• Can handle up to 2^24 (~18 million) molecules

• Simple mathematical model Simulation of bacteria

growing on a grid


C-ImmSimC-ImmSim

Cell


T

B, MA, DC, …

Th, CTL

Self-peptides

ThymusThymocytes

Bone marrowAll cells

Simulation space (secondary organ)

Antigensnon-self

Virus, bacteria, …

B

CTL

DC

Th

Ag

MA

Positive/Negative selection



The “Triplex” VaccineThe “Triplex” VaccineDe Giovanni De Giovanni et al.et al., Cancer Res. 64: 4001, 2004, Cancer Res. 64: 4001, 2004

IL-12

p185neu

Allo-MHC (H-2q)

IL-12 genes


0 10 20 30 40 50 60 70 800

20

40

60

80

100Chronic

Very late

Late

Early

Untreated

Weeks of ageAtypicalhyper-plasia

CIS TumorMultiple

metastatictumors

Tu

mo

r-fr

ee (

%)

0 10 20 30 40 50 60 70 800

20

40

60

80

100Chronic

Very late

Late

Early

Untreated

Weeks of ageAtypicalhyper-plasia

CIS TumorMultiple

metastatictumors

Tu

mo

r-fr

ee (

%)

Triplex vaccine in real mice

SimTriplex in virtual mice

Simulating the Triplex VaccineSimulating the Triplex Vaccine(Pappalardo (Pappalardo et al.et al., Bioinformatics 21: 2891, 2005), Bioinformatics 21: 2891, 2005)


Using Simtriplex

To Find Optimal/ Minimal

Vaccination Schedules

1. Heuristic approach Based on the “Early” module, a posteriori driven by number of cancer cells.

Tumor-free mice at one year: 96%.

Number of vaccin-ations reduced by 27% in comparison to “Chronic” protocol.


Using Simtriplex

To Find Optimal/ Minimal

Vaccination Schedules

2. Genetic algorithm

Driven by SimTriplex outcome (survival >400 days).

Fitness function:- minimize number of vaccinations;

- keep Cancer Cells kinetics similar to “Chronic” schedule

Molecular matchingMolecular matching

The match is based on a simple binary representation

However, more complex procedures can “easily” be used


Introducing Introducing Molecular DetailMolecular Detail

• Introduce a pre-computed lookup table of affinities for each pair of peptides from a suitable set of peptides (basic components of cell receptors, antigens, MHC, etc)

(C-ImmSim)(SimTriplex)

IMMUNOGRIDPeptide set

database


F (sequence) = ImmSim parameter

GRID GRID ImplementationImplementation

A GRID Engine: EnginFrameA GRID Engine: EnginFrame

• EF (EnginFrame) is a Grid solution that provides an interface to applications and services

• Features Web and Web-Service interface• EF-Services can easily wrap system

commands:shell scripts, applications etc.

• Services are described as XML+XSL files.


• Can execute Services locally• No switch user •Web Authentication only

EnginFrame

Server

EnginFrame

Agent• Available plugins:

• OS• LSF

EnginFrame ServerEnginFrame Server


EnginFrame

Server

EnginFrame

Agent

EnginFrame

Agent

EnginFrame

Agent

EnginFrame AgentsEnginFrame Agents

•Agents can run on different hosts• Services are remotely executed • Switch to authenticated user for running the jobs

Available plugins:• OS• LSF• MetaFrame• Andrew FS• Globus• Sun Grid Engine

EnginFrame Server EnginFrame Server Architecture Architecture

To

mcat

Ap

ache

En

gin

Fram

e X

ML

Services

En

gin

Fram

e C

ore E

F

Au

tho

rities

EF

Co

ntexts

EnginFrame Server

EF Clients

Spoolerstorage

EF Agents

Authservices

HTTP RequestHTTP Request JavaJava

HTMLHTML XMLXML


EnginFrame Agent EnginFrame Agent Architecture Architecture

EnginFrame Agent Core

Scrip

ting

En

gin

e

EnginFrame Agent

EF Server

Spoolerstorage

Computingresource

JavaJava

XMLXML

Pre-Clinical Pre-Clinical TestingTesting

Will be carried out in the lab ofWill be carried out in the lab ofPier-Luigi Lollini, Bologna, ItalyPier-Luigi Lollini, Bologna, Italy

Developer of immunoprevection vaccines (Triplex)

Vehicle Triplex

Triplex Vaccine is proved Triplex Vaccine is proved effective on HER-2/neu effective on HER-2/neu transgenic mice using transgenic mice using a chronic schedule. a chronic schedule.

Triplex has been modeled by SimTriplex simulator.SimTriplex reproduce in vivo results and predicts new effects.

With SimTriplex one can predict effective schedules with reduced administrations.


Simtriplex Optimal Vaccination Scheduleswill be verified in vivo

0 10 20 30 40 50 60 70

Genetic

Heuristic

Chronic

Very late

Late

Early

Tumor-freemice

0%

0%

0%

85%-100%

75%-96%

84%-91%

In v

ivo

In s

ilic

o

Weeks of age

Vaccinations

12

12

12

60 (100%)

44 (-27%)

35 (-42%)

AcknowlegementsAcknowlegements

• Birkbeck– David Moss– Adrian Shepherd– Mark Halling-Brown

• Collaborators– Paul Travers, Anthony

Nolan Research Institute

– Darren Flower, Jenner Institute for Vaccine Design

• ImmunoGrid Partners– CINECA: Elda Rossi– Brisbane: Vladimir

Brusic– CNR: Filippo Castiglione– CNRS: Marie-Paule

Lefranc– DTU: Soren Brunak– Bologna: Pierre-Luigi

Lollini– Catania: Santo Motta


ImmunoGrid Towards a Clinically Relevant Systems Biology Model for the Human Immune System Dr. Clare...

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