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Fedor Kolpakov Institute of Systems Biology Novosibirsk, Russia

BioUML – integrated platform for systems biology : From gene regulatory networks to modeling virtual cell and virtual physiological human. Fedor Kolpakov Institute of Systems Biology Novosibirsk, Russia. Alexander Kel geneXplain GmbH, Wolfenbuettel, Germany. BioUML platform.

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Fedor Kolpakov Institute of Systems Biology Novosibirsk, Russia

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  1. BioUML – integrated platform for systems biology:From gene regulatory networks to modeling virtual cell and virtual physiological human Fedor Kolpakov Institute of Systems BiologyNovosibirsk, Russia Alexander Kel geneXplain GmbH,Wolfenbuettel, Germany

  2. BioUML platform • BioUML is an open source integrated platform for systems biology that spans the comprehensive range of capabilities including access to databases with experimental data, tools for formalized description, visual modeling and analyses of complex biological systems. • Due to scripts (R, JavaScript) and workflow support it provides powerful possibilities for analyses of high-throughput data. • Plug-in based architecture (Eclipse run time from IBM is used) allows to add new functionality using plug-ins. BioUML platform consists from 3 parts: • BioUML server – provides access to biological databases; • BioUML workbench – standalone application. • BioUML web edition – web interface based on AJAX technology;

  3. BioUMLmain features • Supports access to main biological databases: • catalolgs: Ensembl, UniProt, ChEBI, GO… • pathways: KEGG, Reactome, EHMN, BioModels, SABIO-RK, TRANSPATH, EndoNet, BMOND… • Supports main standards used in systems biology: SBML, SBGN, CellML, BioPAX, OBO, PSI-MI… • database search: • full text search using Lucene engine • graph search • graph layout engine • visual modeling: • simulation engine supports (ODE, DAE, hybrid,1D PDE); • composite models; • agent based modeling; • parameters fitting; • genome browser (supports DAS protocol, tracks import/export); • data analyses and workflows – specialized plug-ins for microarray analysis, integration with R/Bioconductor, JavaScript support, interactive script console.

  4. BioUML workbench

  5. BioUML web edition

  6. BioUML web editiondedicated Amazon EC2 server: http://79.125.109.165/bioumlweb BioUML workbenchhttp://79.125.109.165/bioumlweb/biouml-install.jarPrerequisite: Java www.java.com/getjava/

  7. Notation Entities RNA Active monomer Inactive monomer Phosphorylated protein Heterodimer Homodimer Multimer Reactions Binary reaction Complex reaction

  8. Text search:universal full text search engine based on Apache Lucene technology

  9. Metaphor: biological systems reconstruction as solitaire (patience) game Desk – BioUML editor Solitaire – biological pathway Cards – biological objects(genes, proteins, lipids, etc.) Pack of cards – different biological databases

  10. Biomodels databaseSBGN Graph layoutVisual modelling

  11. N Source EPN pre:label LABEL \\ ? N val@var Target EPN LABEL Target PN Target PN Target PN LABEL Target PN N:5 N:5 LABEL LABEL LABEL Target PN e:INFO Target EPN LABEL N:2 N:2 LABEL LABEL Target EPN LABEL varW varZ INFO INFO LABEL LABEL AND EPN or CN LABEL B EPN or CN LABEL A OR varX EPN or CN varY C INFO NOT SBGN Process Description Diagram 1.1 Process Nodes Connecting Arcs Entity Pool Nodes Auxiliary Units consumption process unit of information unspecified entity uncertain process state variable production simple chemical omitted process modulation association macromolecule LABEL LABEL marker catalysis clone markers dissociation nucleic acid feature LABEL stimulation phenotype LABEL inhibition perturbing agent necessary stimulation Container Node source sink multimers Source EPN logic arc equivalence arc compartment Logical Operators Reference Nodes and operator LABEL or operator complex tag submap not operator

  12. Visual modeling

  13. Pane: model parmaters

  14. Pane: model variables

  15. Pane: model variables

  16. Pane: model simulation

  17. Reports (templates)

  18. Parameters fitting

  19. Main features Experimental data – time courses or steady states expressed as exact or relative values of substance concentrations Different optimization methods for analysis Multi-experimentsfitting Constraint optimization Local/global parameters Parameters optimization using java script

  20. Comparison with COPASI(10,000 simulations)

  21. Parameters fitting – user interface

  22. CD95L moduleand results of fitting its dynamics to experimental data Bentele M, 2004 Neumann L, 2010

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