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CaliBayes and BASIS: e-Science applications for Systems Biology research

CaliBayes and BASIS: e-Science applications for Systems Biology research. Yuhui Chen. Institute for Ageing and Health Centre for Integrated Systems Biology of Ageing and Nutrition Newcastle University, UK. Introduction. BASIS Biology of Ageing e-Science Integration and Simulation System

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CaliBayes and BASIS: e-Science applications for Systems Biology research

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  1. CaliBayes and BASIS: e-Science applications for Systems Biology research Yuhui Chen Institute for Ageing and Health Centre for Integrated Systems Biology of Ageing and Nutrition Newcastle University, UK

  2. Introduction • BASIS • Biology of Ageing e-Science Integration and Simulation System • Web-based Systems Biology application that provides high performance computing power for dynamic biological simulation. • www.basis.ncl.ac.uk • CaliBayes • A powerful tool based on advanced Bayesian statistical inference technology for inferences about kinetic parameters within deterministic and stochastic SBML models • www.calibayes.ncl.ac.uk

  3. BASIS & CaliBayes • Functionalities • System properties • System architecture • Combined Workflow

  4. BASIS • Functionalities • SBML model tools • Build • Store • Share (private and public user space) • Dynamic stochastic simulation • Gillespie stochastic simulator • SBML Model and simulation data publishing • Web based interface

  5. BASIS • System properties • High throughput computing (HTC) • Condor HTC framework • High performance computing • CISBAN computer cluster (96 CPUs) • Parallel computing software framework • User friendly interface • Web based tools with RIA (Rich Internet Application) • Web service client libraries (R, Java, Python, Taverna, etc) • Dependability • Dependable system architecture • Security • User account control • Interoperability • WS-I compliant web services

  6. BASIS • System architecture

  7. BASIS • System architecture

  8. BASIS • System interface • Web services • BASIS user: BASIS user account registration, modification, etc. https://basis1.ncl.ac.uk:8181//BasisWebServices/BasisUserService?WSDL • BASIS model: model upload, modification, share, etc. https://basis1.ncl.ac.uk:8181//BasisWebServices/BasisModelService?WSDL • BASIS simulation: Runs simulation, checks job status, retrieves results, etc. https://basis1.ncl.ac.uk:8181//BasisWebServices/BasisSimulationService?WSDL • BASIS SBML: SBML upload, modification, conversion, etc https://basis1.ncl.ac.uk:8181//BasisWebServices/BasisSBMLService?WSDL • BASIS R portal: enables invocation with R script https://basis1.ncl.ac.uk:8181//BasisWebServices/BasisWSRPortalService?WSDL • Web based tools on BASIS website • User account control • Model editing • Private and public model management • Run simulations

  9. CaliBayes • Aim • To provide a powerful new tool for the academic community based on advanced Bayesian statistical inference technology, which enables inferences to be made about kinetic parameters within large and complex deterministic and stochastic network models of biochemical pathways and cell signalling systems.

  10. CaliBayes • System properties • HTC • Dedicated HTC software framework • HPC • Powerful computer cluster • Dependability • Dependable system architecture • Fault tolerance • Interoperability • WS-I compliant SOAP web services • User friendly • Web service client libraries (Java, R, etc) • Grid computing • Simulation web services: Fern, Copasi, BASIS, etc • Free CaliBayes Java API

  11. CaliBayes • System architecture

  12. CaliBayes • System interface • CaliBayes web services:https://calibayes2.ncl.ac.uk:8181//CalibayesWS/CalibayesService?WSDL • Cohort simulation web services:https://calibayes2.ncl.ac.uk:8181//CalibayesWS/SimCohortService?WSDL#

  13. Integrated application

  14. Integrated application • Step 1: Create model • Tools: • BASIS SBML web services • BASIS web-based mod2sbml converter • basisR (package)

  15. Integrated application • Step 2: prepare for calibration • Prior distribution • Experimental data • settings, etc • Tools: • calibayesR package

  16. Integrated application • Step 3: calibrate the model • produce posterior distribution • Tools: • CaliBayes web services • calibayesR package

  17. Integrated application • Step 4: in silico experiments with the model • Tools: • BASIS Forward simulation web services • basisR (package) • CaliBayes Cohort simulation web services • calibayesR (package)

  18. Future work • Functionalities • Dependability • Scalability and capacity • Security • Rich Internet application

  19. TEAM • CaliBayes • Colin Gillespie • Conor Lawless • Jake Wu • Darren Wilkinson • Richard Boys • Tom Kirkwood • BASIS • Conor Lawless • Carole Proctor • Colin Gillespie • Daryl Shanley • Darren Wilkinson • Richard Boys • Tom Kirkwood • Acknowledgement • Daniel Swan, Anthony Youd, Michael Beaty • BBSRC

  20. Thanks

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