SLIDE 1
Introduction to the ImmunoGrid Simulators Grid Open Days Workshop - - PowerPoint PPT Presentation
Introduction to the ImmunoGrid Simulators Grid Open Days Workshop - - PowerPoint PPT Presentation
Introduction to the ImmunoGrid Simulators Grid Open Days Workshop Catania, 23rd January 2009 Adrian Shepherd Birkbeck College University of London Innate and Adaptive Immunity Immune System Cell Types Immune System Cell Types Inside the
SLIDE 2
SLIDE 3
Immune System Cell Types Immune System Cell Types
SLIDE 4
Inside the Cell...
Transport, cleavage, presentation, recognition
Inside the Cell…
SLIDE 5
Phases of Adaptive Immunity
SLIDE 6
Maturation of B and T Cells Maturation of B and T Cells
SLIDE 7
Immune System Characteristics
- Multi-level (from molecules to organs)
- Temporal (seconds to years)
- Spatial (signalling and diffusion)
- Diversity (molecules, cells, individuals)
Key modelling issue: Complexity versus simplification
The Challenge of Complexity
SLIDE 8
Cellular Automata
Conway’s Game of Life (1970)
Emergence of complex, unpredictable “behaviour” from simple rules
Cellular Automata
SLIDE 9
Cellular Automata Characteristics
- Simple, local rules
- Emergent behaviour
Contrast to differential equations:
– Individual agents, not average behaviour (enables us to model the life-history of individual cells) – Stochastic (potentially model the distribution of behaviours within a population) – Understandable – Easily extensible
Characteristics of CAs
SLIDE 10
The C-IMMSIM 2D Lattice
Agent based model: set of biological agents (cells and molecules) at a given location on lattice interacting probabilistically
T B Mϕ DC Tumor cell Ag MHCI +Pep MHCII IL-12 Abs TCR
In practice a hexagonal or triangular lattice is often used.
SLIDE 11
The ImmunGrid Simulators
Lattice-gas cellular automata
- f increasing complexity
B CTL DC Th Ag MA
The ImmunoGrid Simulators
SLIDE 12
Towards Models of Lymph Nodes
Lymph node Lymph node
Lymph channel
Modelling Lymph Nodes
SLIDE 13
Computational Requirements
- To run complex single simulations (large
cluster or supercomputer)
- To run large sets of simulations (explore
parameter space, investigate clinical scenarios for multiple individuals)
- To support smaller-scale simulations (e.g.
educational simulations using standard workstations)
SLIDE 14
Why Developed Our Own Grid Solution?
- Long-term access to national /international
production-quality Grids not guaranteed
- Consortium partners can contribute own local
resources (though no single partner has sufficient resources for whole project)
- We believe simple “home-made” Grid now both
feasible and effective solution
- Provides us with the control and flexibility we
desire
SLIDE 15
Web Service Local Resource DEISA GATEWAY CINECA GATEWAY Local Cluster FORK
Web Interface AHE Client AHE Server UNICORE
JSDL
Job launcher
NGS RSL
GLOBUS
NJS GridSAM GridSAM GridSAM
Our Grid Implementation
RSL - Resource Specification Language JSDL - Job Submission Description Language NJS - Network Job Supervisor
DESHL VM-Ware
PI2S2
Glite Virtual UI
JSDL JSDL JDL JDL – Job Description Language
SLIDE 16
The Triplex Vaccine
IL-12 p185neu Allo-MHC (H-2q)
IL-12 genes
HER-2/neu transgenic mouse mammary carcinoma
SLIDE 17
Vaccination Schedules
SLIDE 18
Schedules and Tumor Progression
SLIDE 19
Schedules and Tumor Progression
SLIDE 20
Schedules and Tumour Progression
Summary of experimental evidence
SLIDE 21
Simulation
SimTriplex vaccine in virtual mice Reproducing results of in vivo experiments
SLIDE 22
Acknowledgements
Birkbeck
- David Moss
- Mark Halling-
Brown
- Claire Sansom
- Masters and
PhD students
Elsewhere
- Vladimir Brusic (scientific coordination:
formerly U of Queensland, now Dana-Farber)
- Elda Rossi (CINECA)
- Filippo Castiglione (CNR)
- Santo Motta (U of Catania)
- Pierre-Luigi Lollini (U of Bologna)
- Marie-Paule Lefranc (IMGT, CNRS)
- Søren Brunak, Ole Lund (DTU)