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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
http://www.immunogrid.org
Vaccinomics:Vaccinomics:The Simplest ParadigmThe Simplest Paradigm
From genome sequenceto vaccine
Via data mining and bioinformatics
http://www.immunogrid.org
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
http://www.immunogrid.org
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
http://www.immunogrid.org
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.”
http://www.immunogrid.org
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
http://www.immunogrid.org
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
http://www.immunogrid.org
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
http://www.immunogrid.org
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…)
http://www.immunogrid.org
The Adaptive ImmuneThe Adaptive ImmuneResponseResponse
Immunoglobulin
B cellT cell
T cell Receptor MHC
peptide
Trimolecular complex
http://www.immunogrid.org
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
http://www.immunogrid.org
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
http://www.immunogrid.org
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
http://www.immunogrid.org
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
http://www.immunogrid.org
[ 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
http://imgt.cines.fr
Immunoglobulin (IG) T cell receptor (TR)
IMGT-Ontology:IMGT-Ontology:DescriptionDescription
Human IGH locus
Chromosome 14q32.33
http://imgt.cines.fr
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.
http://www.immunogrid.org
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
http://www.immunogrid.org
C-ImmSimC-ImmSim
Cell
http://www.immunogrid.org
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
http://www.immunogrid.org
http://www.immunogrid.org
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
http://www.immunogrid.org
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)
http://www.immunogrid.org
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.
http://www.immunogrid.org
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
http://www.immunogrid.org
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
http://www.immunogrid.org
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.
http://www.immunogrid.org
• Can execute Services locally• No switch user •Web Authentication only
EnginFrame
Server
EnginFrame
Agent• Available plugins:
• OS• LSF
EnginFrame ServerEnginFrame Server
http://www.immunogrid.org
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
http://www.immunogrid.org
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.
http://www.immunogrid.org
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
http://www.immunogrid.org