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ImmunoGrid Towards a Clinically Relevant Systems Biology Model for the Human Immune System Dr. Clare Sansom Bedlewo, Poland May 2006.

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Presentation on theme: "ImmunoGrid Towards a Clinically Relevant Systems Biology Model for the Human Immune System Dr. Clare Sansom Bedlewo, Poland May 2006."— Presentation transcript:


2 ImmunoGrid Towards a Clinically Relevant Systems Biology Model for the Human Immune System Dr. Clare Sansom Bedlewo, Poland May 2006

3 The Mammalian Immune System 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

4 Immunology: Successes 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

5 Vaccinomics: The Simplest Paradigm From genome sequence to vaccine Via data mining and bioinformatics

6 A Complex Combinatorial Problem The human immune system has immense diversity: –>10 13 MHC class I haplotypes –10 7 -10 15 different T-cell receptors –10 12 B-cell clonotypes in each individual –10 11 possible linear MHC-binding epitopes composed of nine amino acids –>>10 11 different conformational epitopes –>10 9 combinatorial antibodies

7 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

8 Enter the GRID Modelling combinatorial complexity requires immense computational power A Grid solution enables full use of the resources available in the community

9 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 NGS Global Grid Infrastructure [Slide © P. Coveney, University College London, UK]

10 ImmunoGrid …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.

11 Immunogrid: 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

12 ImmunoGrid: 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

13 How will we implement our aims? concepts molecular models system models design data collection grid setup pre-clinical testsfurther tests Design & data schema simulator concepts feedback concepts models & prototypes dissemination

14 ImmunoGrid: Birkbecks 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

15 Molecular Level Simulations Lead Partner: Marie-Paule Lefranc Montpellier, France

16 Molecular 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…)

17 The Adaptive Immune Response Immunoglobulin B cellT cell T cell ReceptorMHC peptide Trimolecular complex

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

19 Molecules of the Adaptive Immune System Immunoglobulin IgG (From The Immunoglobin Factsbook 2001) MHC Class II CD4 T-cell response Endocytosed antigens Predominantly bacterial MHC Class I CD8 T-cell response Free antigens Predominantly viral

20 Limits of Knowledge Proteasomal cleavage is still quite poorly understood Some programs exist but their precision is low Vaccine design pipelines need to be modified Insert cleavage step

21 Integration of Molecular Data 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

22 Functionality functional ORF pseudogene productive unproductive Species human mouse.. Gene type variable diversity joining constant Configuration germline rearranged Chain type Ig-Heavy Ig-Light- Lambda TcR-Alpha TcR-Beta... Structure type regular translocated... Receptor IgA TcR gamma-delta Molecule type genomic DNA cDNA protein.. IMGT-Ontology:Identification Specificity Anti-DNA Anti-HIV...

23 [ 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-D-J-REGION V-DJ-REGION V-DOMAIN Immunoglobulin (IG) T cell receptor (TR) IMGT-Ontology:Description

24 Human IGH locus Chromosome 14q32.33 IMGT Repertoire, IMGT-Ontology: Classification

25 Cell and Organ Simulations Lead Partner: Filippo Castiglione, Rome

26 The Starting Points C-ImmSim: An Agent based simulator. Current version (v.6.2) available under GNU Public License. SimTriplex: An immune system – cancer – Triplex vaccine competition simulator.

27 C-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

28 C-ImmSim Cell

29 T B, MA, DC, … Th, CTL Self-peptides Thymus Thymocytes Bone marrow All cells Simulation space (secondary organ) Antigens non-self Virus, bacteria, … B CTL DCDC ThTh Ag MA Positive/Negative selection

30 The Triplex Vaccine De Giovanni et al., Cancer Res. 64: 4001, 2004 IL-12 p185 neu Allo-MHC (H-2 q ) IL-12 genes

31 Triplex vaccine in real mice SimTriplex in virtual mice Simulating the Triplex Vaccine (Pappalardo et al., Bioinformatics 21: 2891, 2005)

32 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.

33 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

34 Molecular matching The match is based on a simple binary representation However, more complex procedures can easily be used

35 Introducing Molecular 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) IMMUNOGRID Peptide set database F (sequence) = ImmSim parameter

36 GRID Implementation

37 A 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.

38 Can execute Services locally No switch user Web Authentication only EnginFrame Server EnginFrame Agent Available plugins: OS LSF EnginFrame Server

39 EnginFrame Server EnginFrame Agent EnginFrame Agent EnginFrame Agent EnginFrame 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

40 EnginFrame Server Architecture Tomcat Apache EnginFrame XML Services EnginFrame Core EF Authorities EF Contexts EnginFrame Server EF Clients Spooler storage EF Agents Auth services HTTP Request Java HTMLXML

41 EnginFrame Agent Architecture EnginFrame Agent Core Scripting Engine EnginFrame Agent EF Server Spooler storage Computing resource Java XML

42 Pre-Clinical Testing Will be carried out in the lab of Pier-Luigi Lollini, Bologna, Italy Developer of immunoprevection vaccines (Triplex)

43 Vehicle Triplex Triplex Vaccine is proved effective on HER-2/neu transgenic mice using 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.

44 Simtriplex Optimal Vaccination Schedules will be verified in vivo

45 Acknowlegements 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

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