Integrative Biology exploiting e-Science to combat fatal diseases

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Integrative Biology exploiting e-Science to
combat fatal diseases

• Damian Mac RandalCCLRC

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Overview of Talk

• Project background
• The scientific challenge
• The e-Scientific challenge
• Proposed system

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Scientific background

• Breakthroughs in biotechnology and IT have
  provided a wealth (mountain) of biological data
• Key post-genomic challenge is to transform this
  data into information that can be used to
  determine biological function
• Biological function arises from complex
  non-linear interactions between biological
  processes occurring over multiple spatial and
  temporal scales
• Gaining an understanding of these processes is
  only possible via an iterative interplay between
  experimental data (in vivo and in vitro),
  mathematical modelling, and HPC-enabled simulation

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e-Scientific background

• Majority of the first round of UK e-Science
  Projects focused primarily on data intensive
  applications (Data storage, aggregation, and
  synthesis)
• Life Sciences projects focused on supporting the
  data generation work of laboratory-based
  scientists
• In other scientific domains, projects such as
  RealityGrid, GEODISE, and gViz began to consider
  compute-intensive applications.

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The Science and e-Science Challenge

• To build an Integrative Biology Grid to support
  applications scientists addressing the key
  post-genomic aim of determining biological
  function
• To use this Grid to begin to tackle the two
  chosen Grand Challenge problems the in-silico
  modelling of heart failure and of cancer.
• Why these two? together they cause 61 of all
  deaths in the UK

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Courtesy of Peter Kohl (Physiology, Oxford)
Normal beating
Fibrillation
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Multiscale modelling of the heart
MRI image of a beating heart
Fibre orientation ensures correct spread of
excitation
Contraction of individual cells
Current flow through ion channels
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Heart modelling

• Typically solving coupled systems of PDEs (tissue
  level) and non-linear ODEs (cellular level) for
  the electrical potential
• Complex three-dimensional geometries
• Anisotropic
• Up to 60 variables
• FEM and FD approaches

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Details of test-run of Auckland heart simulation
code on HPCx

• Modelled 2ms of electrophysiological excitation
  of a 5700mm3 volume of tissue from the left
  ventricular free wall
• Noble 98 cell model used
• Mesh contained 20,886 bilinear elements (spatial
  resolution 0.6mm)
• 0.05ms timestep (40 timesteps in total)
• Required 978s CPU on 8 processors and 2.5 Gbytes
  of memory
• A complete simulation of the ventricular
  myocardium would require up to 30 times the
  volume and at least 100 times the duration
• Estimated max compute time to investigate
  arrhythmia 107s (100 days) requiring 100Gb of
  memory (compute time scales to the power 5/3)
• At high efficiency this scales to approximately 1
  day on HPCx

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Multiscale modelling of cancer
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Cancer modelling

• Focusing on avascular tumours
• Current models range from discrete
  population-based models and cellular automata, to
  non-linear ode systems and complex systems of
  non-linear PDEs
• Key goal is the coupling (where necessary) of
  these models into an integrated system which can
  be used to gain insight into experimental
  findings, to help design new experiments, and
  ultimately to test novel approaches to cancer
  detection, and new drugs and treatment regimes

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Summary of the scientific challenge

• Modelling and coupling phenomena which occur on
  many different length and time scales

• 1m person
• 1mm tissue morphology
• 1mm cell function
• 1nm pore diameter of a membrane protein
• Range 109

• 109 s (years) human lifetime
• 107 s (months) cancer development
• 106 s (days) protein turnover
• 103 s (hours) digest food
• 1 s heart beat
• 1 ms ion channel gating
• 1 ms Brownian motion
• Range 1015

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The e-Science Challenge

• To leverage the first round of e-Science projects
  and the global Grid infrastructure to build an
  international collaboratory which places the
  applications scientist within the Grid allowing
  fully integrated and collaborative use of
• HPC resources (capacity and capability)
• Computational steering, performance control and
  visualisation
• Storage and data-mining of very large data sets
• Easy incorporation of experimental data
• User- and science-friendly access
• gt Predictive in-silico models to guide
  experiment and, ultimately, design of novel
  drugs and treatment regimes

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Key e-Science Deliverables

• A robust and fault-tolerant infrastructure to
  support post-genomic research in integrative
  biology that is user and application driven
• 2nd Generation Grid bringing together components
  across range of current EPSRC pilot projects

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e-Science/Grid Research Issues

• Ability to carry out reliably and resiliently
  large scale distributed coupled HPC simulations
• Ability to co-schedule Grid resources based on a
  GGF-agreed standard
• Secure data management and access-control in a
  Grid environment
• Grid services for computational steering
  conforming to an agreed GGF standard
• Development of powerful visualisation and
  computational steering capabilities for complex
  models
• Contributing projects
• RealityGrid, gViz, Geodise, myGrid, BioSimGrid,
  eDiaMoND, GOLD, various CCLRC projects, .

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Service oriented Architecture

• The user-accessible services will initially be
  grouped into four main categories
• Job management
• including deployment, co-scheduling and workflow
  management across heterogeneous resources
• Computational steering
• both interactive for simulation
  monitoring/control and pre-defined for parameter
  space searching
• Data management
• from straightforward data handling and storage of
  results to location and assimilation of
  experimental data for model development and
  validation
• Analysis and visualization
• final results, interim state, parameter spaces,
  etc, for steering purposes

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Strawman Architecture
External Resources
Simulation Engine
IB Server
Data Management
Visualization
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Software architecture

• Underpinning development of the architecture are
  three fundamental considerations
• standardization, scalability and security
• Initially, Web service technology is being used
  for interactions between the system components
• Many of the underlying components are being
  adopted from previous projects, and adapted if
  necessary, in collaboration with their original
  developers
• Portal/portlet technology, integrated with the
  user's desktop environment, will provide users
  with a lightweight interface to the operational
  services
• The data management facilities are being built
  using Storage Resource Broker technology to
  provide a robust and scalable data infrastructure
• Security is being organized around Virtual
  Organizations to mirror existing collaborations
• A rapid prototyping development methodology is
  being adopted

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Demonstrators

• Objectives
• Immediate boost in size/complexity of problems
  scientists can tackle
• Validation of the Architecture
• Learning exercise, exploring new technology
• Introduce scientists to potential of advanced IT,
  so they can better specify requirements
• 4 Demonstrators, chosen for diversity

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Demonstrators

• Implementation of GEODISE job submission
  middleware (via MATLAB) using the Oxford JISC
  cluster on the NGS. (A simple cellular model of
  nerve excitation)
• MPI implementation of Jon Whiteley and Prasanna
  Pathmanathans soft tissue deformation code (for
  use in image analysis for breast disease). (FEM
  code, non-linear elasticity)
• MPI implementation of Alan Garnys 3D model of
  the SAN incorporating the ReG Steering Library
  (FD code for non-linear reaction-diffusion
  (anisotropic) plus an XML-based parser for
  cellular model definition)
• CMISS modelling environment for complex
  bioengineering problems - Peter Hunter, Auckland,
  NZ Production quality FE/BE library plus
  front/back ends)

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Resources

• Project manager, project architect, 7.5 post-docs
  and 6 PhD students broken down into three main
  teams
• Heart modelling and HPC 1.5 post-docs, 2 PhD
  students in Oxford, 0.5 post-doc at UCL. Led by
  Denis Noble and myself.
• Cancer Modelling 1 senior post-doc and 2 PhD
  students in Nottingham, 1 post-doc and 1 PhD
  student in Oxford, 1 PhD student in Birmingham.
  Led by Helen Byrne in Nottingham. (Several
  further PhD students have also been funded from
  other sources)
• Interactive services and Grid team Project
  architect plus 2 post-docs at CCLRC, 1 post-doc
  in Leeds, 0.5 post-doc at UCL
• Note well over half of the effort is dedicated
  to the science

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Current Status

• Official project start date 1/2/04, recruitment
  of staff now complete
• Initial project structure defined and agreed,
  initial requirements gathering and security
  policy exercises completed, initial architecture
  agreed
• Heart-modelling and cancer modelling workshops
  held in Oxford in June, with talks by user
  communities
• Cancer modelling meeting with all users in Oxford
  in July
• Full IB workshop with all stakeholders in Oxford,
  29th September
• Survey of capabilities of existing middleware
  under way (thanks to everyone who has given us
  lots of their time)
• Four demonstrators identified and development
  commenced

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Summary

• Science-driven project that aims to build on
  existing middleware to (begin to) prove the
  benefits of Grid computing for complex systems
  biology i.e. to do some novel science
• Huge and increasing (initial) buy-in from the
  user community
• Challenge is to develop sufficiently robust and
  usable tools to maintain that interest.

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