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

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

Functionalities System properties System architecture Combined Workflow BASIS & CaliBayes

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

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

System architecture BASIS

System architecture BASIS

System interface Web services Web services BASIS user: BASIS user account registration, modification, etc. BASIS model: model upload, modification, share, etc. BASIS simulation: Runs simulation, checks job status, retrieves results, etc. BASIS SBML: SBML upload, modification, conversion, etc BASIS R portal: enables invocation with R script Web based tools on BASIS website Web based tools on BASIS website User account control Model editing Private and public model management Run simulations BASIS

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. 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. CaliBayes

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

System architecture CaliBayes

System interface CaliBayes web services: CaliBayes web services: Cohort simulation web services: Cohort simulation web services: CaliBayes

Integrated application

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

Step 2: prepare for calibration Prior distribution Prior distribution Experimental data Experimental data settings, etc settings, etcTools: calibayesR package calibayesR package Integrated application

Step 3: calibrate the model produce posterior distribution produce posterior distributionTools: CaliBayes web services CaliBayes web services calibayesR package calibayesR package Integrated application

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

FunctionalitiesDependability Scalability and capacity Security Rich Internet application Future work

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

Thanks