SlideShare a Scribd company logo
1 of 30
TheSystems Biology Software Infrastructure
‘A new infrastructure to streamline the  connection between data, models, and analysis,  allowing the updating of large scale data, models  and analytic tools with greatly reduced overhead’ SBSI objective
Systems Biology Research, CSBE view Network Inference Process Algebras Model analysis Graphical Notation Systems Biology Software Infrastructure™ Existing knowledge Static models Kinetic models New knowledge High-throughput data High-resolution data Kinetic Parameter Facility Her2/ERK signalling Circadian clock RNA metabolism
Current people involved in SBSI Core developers Biopepa integration      Adam Duguid Project management Test Models and  Evaluation  Requirements & Numerics People previously  involved with SBSI Shakir Ali Anatoly Sorokin TreenutSaithong Stuart Moodie Igor Goryanin Nikos Tsorman Neil Hanlon Richard Adams Galina Lebedeva AlexeyGoltsov Circadian clock modellers Azusa Yamaguchi OzgurAkman Carl Troein Stephen Gilmore            PI EPCC Andrew Millar Kevin Stratford
SBSI goals 2008-2009 Parallelized global parameter optimization – for everyone! Develop client application   Integrate at least 1  external software package
Parameter Estimation Problem ,[object Object]
Parameter estimation – critical stage in model development
Multiple data sets for model calibration
Global optimization needed due to complex cost landscapes
Genetic /evolutionary techniques perform well.
Circadian clock modellers have existing high-quality time-series data to fit.,[object Object]
Performance scales well with increasing processor cores
Testing, testing, testing…. Rastrigin ‘abc_1’ VderPol Goldbeter clock Biomodels clock  models
Multi-objective optimisation
Optimizing Circadian Clock models  with experimental data Locke 2 loop model from Biomodels (57 params, 13 species) Using BG/L 128 nodes,  it finished at 63140th  generation by  non-improvement criteria. Run-time 46 hours.  0-6740 :FFT +Chi-squared 674o – end : Chi-squared
Outline of SBSI design SBSI  clients Integration of other CSBE projects BioPepa✔  EPE SBSI Visual  ✔ Desktop application ✔ Upload and edit SBML models ✔ Run simulations ✔ Interact with external repositories ✔ Visualisation of data and results SBSI Web  Interface ✔Command         line SBSI  Dispatcher (Task Manager) ,[object Object]
Submit jobs to HPC✔Retrieve results ✔Provide job status SBSI Numerics Numerical algorithms and  Frameworks for  ,[object Object],-Sensitivity analysis ,[object Object],core Eddie (ECDF)
Outline of SBSI design SBSI  clients Integration of other CSBE projects BioPepa✔  EPE SBSI Visual  ✔ Desktop application ✔ Upload and edit SBML models ✔ Run local and remote simulations ✔ Interact with external repositories ✔ Visualisation of data and results SBSI Web  Interface Command  line SBSI  Dispatcher (Task Manager) ,[object Object]
Submit jobs to HPC✔Retrieve results ✔Provide job status SBSI Numerics Numerical algorithms and  Frameworks for  ,[object Object],-Sensitivity analysis ,[object Object],core Eddie (ECDF) SBSI repository Models (SBML) Data ( SBSI standard format): -experimental data -simulation results Plasmo, Robust
Aims early 2010 Move  all code to SourceForge, encourage open-source access Publish SBSI paper  Integrate Edinburgh Pathway Editor Develop plugin mechanism for SBSI Dispatcher to connect to other HPCs, Grid?
SBSI resources www.sbsi.ed.ac.uk http://sourceforge.net/projects/sbsi/
Availability SBSI Numerics Numerical algorithms and  Frameworks for  ,[object Object],-Sensitivity analysis ,[object Object],Command line on local machine, Bluegene, or ECDF
Availability SBSI Visual  ✔ Desktop application ✔ Upload and edit SBML models ✔ Run simulations ✔ Interact with external repositories ✔ Visualisation of data and results Available for Windows XP/Vista,  MacOSX10.5, 64bit Linux . Access to local or remote SBSINumerics
Availability Deployed on SBSI server. SBSI  Dispatcher (Task Manager) ,[object Object]
Submit jobs to HPC✔Retrieve results ✔Provide job status Access to test server, Bluegene
Systems Biology Software Infrastructure overview

More Related Content

Viewers also liked (7)

Informe jefe de salon
Informe jefe de salonInforme jefe de salon
Informe jefe de salon
 
Isab 11 for_slideshare
Isab 11 for_slideshareIsab 11 for_slideshare
Isab 11 for_slideshare
 
Plastic Pollution Presentation By AnkitMishra
Plastic Pollution Presentation By AnkitMishraPlastic Pollution Presentation By AnkitMishra
Plastic Pollution Presentation By AnkitMishra
 
What is big data?
What is big data?What is big data?
What is big data?
 
Big Data
Big DataBig Data
Big Data
 
What is Big Data?
What is Big Data?What is Big Data?
What is Big Data?
 
Big data ppt
Big  data pptBig  data ppt
Big data ppt
 

Similar to Systems Biology Software Infrastructure overview

Saving resources with simulation webinar 092011
Saving resources with simulation webinar 092011Saving resources with simulation webinar 092011
Saving resources with simulation webinar 092011
Scott Althouse
 
GRIFFOR_OxfordU CPS 20Mar2017.pptx
GRIFFOR_OxfordU CPS 20Mar2017.pptxGRIFFOR_OxfordU CPS 20Mar2017.pptx
GRIFFOR_OxfordU CPS 20Mar2017.pptx
DAYARNABBAIDYA3
 
Tony Reid Resume
Tony Reid ResumeTony Reid Resume
Tony Reid Resume
storyhome
 

Similar to Systems Biology Software Infrastructure overview (20)

TiMetmay10
TiMetmay10TiMetmay10
TiMetmay10
 
Ti met may10
Ti met may10Ti met may10
Ti met may10
 
Eclipse Meets Systems Biology
Eclipse Meets Systems BiologyEclipse Meets Systems Biology
Eclipse Meets Systems Biology
 
Tool-Driven Technology Transfer in Software Engineering
Tool-Driven Technology Transfer in Software EngineeringTool-Driven Technology Transfer in Software Engineering
Tool-Driven Technology Transfer in Software Engineering
 
SSE Practices Overview
SSE Practices OverviewSSE Practices Overview
SSE Practices Overview
 
Uses of Data Lakes
Uses of Data Lakes Uses of Data Lakes
Uses of Data Lakes
 
Easygenomics ISCB Cloud section 2012
Easygenomics ISCB Cloud section 2012Easygenomics ISCB Cloud section 2012
Easygenomics ISCB Cloud section 2012
 
Saving resources with simulation webinar 092011
Saving resources with simulation webinar 092011Saving resources with simulation webinar 092011
Saving resources with simulation webinar 092011
 
GRIFFOR_OxfordU CPS 20Mar2017.pptx
GRIFFOR_OxfordU CPS 20Mar2017.pptxGRIFFOR_OxfordU CPS 20Mar2017.pptx
GRIFFOR_OxfordU CPS 20Mar2017.pptx
 
Closing the Design Cycle Loop with Executable Requirements and OSLC - IBM Int...
Closing the Design Cycle Loop with Executable Requirements and OSLC - IBM Int...Closing the Design Cycle Loop with Executable Requirements and OSLC - IBM Int...
Closing the Design Cycle Loop with Executable Requirements and OSLC - IBM Int...
 
Tony Reid Resume
Tony Reid ResumeTony Reid Resume
Tony Reid Resume
 
Applying linear regression and predictive analytics
Applying linear regression and predictive analyticsApplying linear regression and predictive analytics
Applying linear regression and predictive analytics
 
Transform 2014: Best Practices in Integrating Analytics into Your Environment
Transform 2014: Best Practices in Integrating Analytics into Your EnvironmentTransform 2014: Best Practices in Integrating Analytics into Your Environment
Transform 2014: Best Practices in Integrating Analytics into Your Environment
 
Agile Big Data Analytics Development: An Architecture-Centric Approach
Agile Big Data Analytics Development: An Architecture-Centric ApproachAgile Big Data Analytics Development: An Architecture-Centric Approach
Agile Big Data Analytics Development: An Architecture-Centric Approach
 
Jonathon Wright - Intelligent Performance Cognitive Learning (AIOps)
Jonathon Wright - Intelligent Performance Cognitive Learning (AIOps)Jonathon Wright - Intelligent Performance Cognitive Learning (AIOps)
Jonathon Wright - Intelligent Performance Cognitive Learning (AIOps)
 
RuaumokoSuite
RuaumokoSuiteRuaumokoSuite
RuaumokoSuite
 
Deep learning in manufacturing predicting and preventing manufacturing defect...
Deep learning in manufacturing predicting and preventing manufacturing defect...Deep learning in manufacturing predicting and preventing manufacturing defect...
Deep learning in manufacturing predicting and preventing manufacturing defect...
 
Big Data LDN 2017: How Big Data Insights Become Easily Accessible With Workfl...
Big Data LDN 2017: How Big Data Insights Become Easily Accessible With Workfl...Big Data LDN 2017: How Big Data Insights Become Easily Accessible With Workfl...
Big Data LDN 2017: How Big Data Insights Become Easily Accessible With Workfl...
 
Predicting Medical Test Results using Driverless AI
Predicting Medical Test Results using Driverless AIPredicting Medical Test Results using Driverless AI
Predicting Medical Test Results using Driverless AI
 
SSE Technical Overview
SSE Technical OverviewSSE Technical Overview
SSE Technical Overview
 

Recently uploaded

Structuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessStructuring Teams and Portfolios for Success
Structuring Teams and Portfolios for Success
UXDXConf
 
Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...
FIDO Alliance
 

Recently uploaded (20)

The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
 
Structuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessStructuring Teams and Portfolios for Success
Structuring Teams and Portfolios for Success
 
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
 
WebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM PerformanceWebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM Performance
 
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
 
ADP Passwordless Journey Case Study.pptx
ADP Passwordless Journey Case Study.pptxADP Passwordless Journey Case Study.pptx
ADP Passwordless Journey Case Study.pptx
 
Portal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russePortal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russe
 
2024 May Patch Tuesday
2024 May Patch Tuesday2024 May Patch Tuesday
2024 May Patch Tuesday
 
AI mind or machine power point presentation
AI mind or machine power point presentationAI mind or machine power point presentation
AI mind or machine power point presentation
 
State of the Smart Building Startup Landscape 2024!
State of the Smart Building Startup Landscape 2024!State of the Smart Building Startup Landscape 2024!
State of the Smart Building Startup Landscape 2024!
 
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
 
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on ThanabotsContinuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
 
ERP Contender Series: Acumatica vs. Sage Intacct
ERP Contender Series: Acumatica vs. Sage IntacctERP Contender Series: Acumatica vs. Sage Intacct
ERP Contender Series: Acumatica vs. Sage Intacct
 
Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...
 
Event-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingEvent-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream Processing
 
TopCryptoSupers 12thReport OrionX May2024
TopCryptoSupers 12thReport OrionX May2024TopCryptoSupers 12thReport OrionX May2024
TopCryptoSupers 12thReport OrionX May2024
 
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
(Explainable) Data-Centric AI: what are you explaininhg, and to whom?
 
Your enemies use GenAI too - staying ahead of fraud with Neo4j
Your enemies use GenAI too - staying ahead of fraud with Neo4jYour enemies use GenAI too - staying ahead of fraud with Neo4j
Your enemies use GenAI too - staying ahead of fraud with Neo4j
 
Design Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptxDesign Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptx
 
Easier, Faster, and More Powerful – Notes Document Properties Reimagined
Easier, Faster, and More Powerful – Notes Document Properties ReimaginedEasier, Faster, and More Powerful – Notes Document Properties Reimagined
Easier, Faster, and More Powerful – Notes Document Properties Reimagined
 

Systems Biology Software Infrastructure overview

  • 2. ‘A new infrastructure to streamline the connection between data, models, and analysis, allowing the updating of large scale data, models and analytic tools with greatly reduced overhead’ SBSI objective
  • 3. Systems Biology Research, CSBE view Network Inference Process Algebras Model analysis Graphical Notation Systems Biology Software Infrastructure™ Existing knowledge Static models Kinetic models New knowledge High-throughput data High-resolution data Kinetic Parameter Facility Her2/ERK signalling Circadian clock RNA metabolism
  • 4. Current people involved in SBSI Core developers Biopepa integration Adam Duguid Project management Test Models and Evaluation Requirements & Numerics People previously involved with SBSI Shakir Ali Anatoly Sorokin TreenutSaithong Stuart Moodie Igor Goryanin Nikos Tsorman Neil Hanlon Richard Adams Galina Lebedeva AlexeyGoltsov Circadian clock modellers Azusa Yamaguchi OzgurAkman Carl Troein Stephen Gilmore PI EPCC Andrew Millar Kevin Stratford
  • 5. SBSI goals 2008-2009 Parallelized global parameter optimization – for everyone! Develop client application Integrate at least 1 external software package
  • 6.
  • 7. Parameter estimation – critical stage in model development
  • 8. Multiple data sets for model calibration
  • 9. Global optimization needed due to complex cost landscapes
  • 11.
  • 12. Performance scales well with increasing processor cores
  • 13. Testing, testing, testing…. Rastrigin ‘abc_1’ VderPol Goldbeter clock Biomodels clock models
  • 14.
  • 16. Optimizing Circadian Clock models with experimental data Locke 2 loop model from Biomodels (57 params, 13 species) Using BG/L 128 nodes, it finished at 63140th generation by non-improvement criteria. Run-time 46 hours. 0-6740 :FFT +Chi-squared 674o – end : Chi-squared
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24. Aims early 2010 Move all code to SourceForge, encourage open-source access Publish SBSI paper Integrate Edinburgh Pathway Editor Develop plugin mechanism for SBSI Dispatcher to connect to other HPCs, Grid?
  • 25. SBSI resources www.sbsi.ed.ac.uk http://sourceforge.net/projects/sbsi/
  • 26.
  • 27. Availability SBSI Visual ✔ Desktop application ✔ Upload and edit SBML models ✔ Run simulations ✔ Interact with external repositories ✔ Visualisation of data and results Available for Windows XP/Vista, MacOSX10.5, 64bit Linux . Access to local or remote SBSINumerics
  • 28.
  • 29. Submit jobs to HPC✔Retrieve results ✔Provide job status Access to test server, Bluegene

Editor's Notes

  1. Good morningMy name is Richard Adams & for the last year I’ve project managedthe development of the SBSI.My background is as a cell biologist, but for the last 7 years I’ve been writing software for bioinformatics and systems biology. Today I’ll give a brief introduction to SBSI, and review progress over the last year
  2. Read out quoteThere is scope for a program that will link models, with experimental data that is perhaps in remote repositories, to the latest analytic tools, in a way that is straightforward for modellers to use.
  3. SBSI has a broad set of aims, we have initially chosen to focus on a key set that would be of early benefit.Client application easy to useIntegration point for other software projects
  4. Whyaer we tacklingparmeter estimation first?Predictive models are desirable e.g., for P4 medicineSearch space dimensionality increases with each new parameter to fitLocal minima are a big problem , therefore need global algorithms
  5. Testing very important to establish legitimacy and encourage user uptake.Important part of 2009’s activities.Developing models, performance benchmarking.Testing is an important part of dvpt process – unit testing, GUI testing, written systems tests as well.
  6. We’ are now using optimisation framework to fir real datamodelsBlue line is simulation using parameters from published modelGreen line is after fitting. (data is red spots)Fitted parameters reproduce the decaying oscillation in the data
  7. Screenshot of applicationWorkspace – allows management of project based resourcesViews & editors for simulation, optimisation etc.,Biopepa integration: SBSI benefits – stochastic solvers, Biopepa benefit – can optimise their models, Loosely coupled via SBML filesBased on Eclipse development environment, using SBML as modelRobust plugin model, any developer can add plugin that uses SBML
  8. Job tracker – hides complexity from userKeeps UI biologically focussedCan track running jobs, download interim results etc.,