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Data standards for systems biology

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  • 1. Data Standards for Systems Biology Neil Swainston Manchester Centre for Integrative Systems Biology neil.swainston@manchester.ac.uk
  • 2. Introduction • Experimental standards • Proteomics • Metabolomics • Enzyme kinetics • Modelling standards • Models • Simulations • Results
  • 3. Why do we need standards? • Aids researchers by facilitating management of experimental data • Facilitates open-source software development and interoperability • Allows data to be shared • Increasingly becoming a requirement for journal submissions
  • 4. When are standards developed? • Standards generally are generated organically • Not for pioneers • When an experimental technique becomes established • Need for a standard becomes obvious
  • 5. Who develops standards? • Usually two or more academic groups • Commercial providers often less enthusiastic • Often formed by a Working Group • Proteome Standards Initiative • Metabolomics Standards Initiative • “Minimum information required” specification provided • Followed by data schema, XML standard
  • 6. MCISB project overview Enzyme kinetics Quantitative metabolomics Quantitative proteomics Model Parameters (KM, Kcat) Variables (metabolite, protein concentrations) PRIDE XML MeMo SABIO-RK Web serviceWeb serviceWeb service MeMo-RK Web service
  • 7. Proteomics • We wish to store: • Raw experimental mass spectrometry data • Protein / peptide identifications • Protein / peptide quantitations • Metadata (instrument, search algorithm, user, etc.)
  • 8. Mass spectrometry data • How do we represent the following?
  • 9. Mass spectrometry data • The simple approach:
  • 10. Mass spectrometry data • The simple approach does provide a list of masses and intensities, but… • What instrument was used? • Who ran the instrument? • What sample was used? • …etc. • The simple approach lacks metadata • Many simple approaches (formats) exist
  • 11. Mass spectrometry data • The less simple approach: mzData • Developed by the Proteome Standards Initiative, 2005 • Put together by Working Group of academics and commercial parties • Regular meetings, both real and virtual • Goal: unify the existing “simple” formats into one • Support “tagging” with metadata
  • 12. mzData • http://www.psidev.info/index.php?q=node/80#mzdata • XML format, includes… • Peak lists (mz / intensities) • Experimental protocols • Admin (Who? When?) • Instrument details • etc.
  • 13. Controlled vocabularies • Use of free text is “dangerous” • Non-standard, ambiguous terms • Difficult to match / compare • Controlled vocabularies • Collection of standardised terms • Organised into vocabularies or ontologies • Ontologies contain controlled terms and relationships between them (predicates)
  • 14. Controlled vocabularies • Ontology Lookup Service, EBI
  • 15. mzData
  • 16. Proteomics data • Proteomics data is not solely mass spectrometry data • Sample preparation protocol? • Peptide / protein identifications? • Post-translational modifications • Identification scores? • To support this, an extension is required • Extension based on defined set of “minimum requirements” • MIAPE
  • 17. MIAPE
  • 18. PRIDE • Proteomics identifications database – Both a format and a database – Centralised, standards compliant, open source, public data repository for proteomics data – Query, submit and retrieve proteomics data in standardized XML formats – Public version housed at the EBI – http://www.ebi.ac.uk/pride/
  • 19. PRIDE • Peptide / protein identifications
  • 20. PRIDE Converter • User interface • Usable by biologists • Interfaces with Ontology Lookup Service • Developed by EBI • Automatic upload to PRIDE database
  • 21. PRIDE database
  • 22. Future directions • PRIDE does NOT hold: • Protein and peptide quantitations • New approaches being developed • mzML – mass spectrometry format, enhancement of mzData, including support for richer datasets • mzIdentML – storage of protein and peptide identifications • mzQuantML – storage of protein and peptides quantitations
  • 23. Metabolomics • We wish to store: • Raw experimental mass spectrometry (and NMR) data • Metabolite identifications • Metabolite quantitations • Metadata (instrument, search algorithm, user, etc.)
  • 24. Metabolomics • Data standard does NOT currently exist • Core Information for Metabolomics Reporting • Metabolites Standard Initiative (MSI) • http://msi-workgroups.sourceforge.net/ • MetaboLights being developed at EBI • Not many details as yet • In the mean time… • MCISB has developed its own repository
  • 25. MeMo • Metabolomics Model database • Designed initially for metabolomics data • SQL / XML hybrid approach • Holds: – Experimental meta-data (submitter, lab, date) – Sample meta-data (including biological source) – Instrumentation meta-data – Mass spectra – Metabolite identifications
  • 26. MeMo
  • 27. MeMo web interface
  • 28. Enzyme kinetics • How fast does a given reaction occur? Enzyme A B • Determination of kinetic constants which define the kinetics of the reaction • Experimental approach: perform kinetic assays
  • 29. Enzyme kinetics • Many approaches: – Absorbance – Fluorescence – others • Currently concentrating on absorbance assays on BMG NOVOstar instrument • Requirement: determination of KM and kcat for a given reaction under particular conditions (pH and temperature)
  • 30. Enzyme kinetics: Michaelis-Menten • Traditionally, for each assay, initial rate, v is determined
  • 31. Enzyme kinetics: Michaelis-Menten • Performing this at various substrate concentrations allows KM and Vmax to be determined:
  • 32. STRENDA guidelines • Standards for Reporting Enzymology Data • http://www.beilstein-institut.de/en/projects/strenda/ • Specifies… • Reactants / products • Enzyme (wild-type, modified, purification, expressed in • Experimental conditions (pH, temperature, buffer) • Instrument, experiment type • Submitter (contact details)
  • 33. SABIO-RK • http://sabio.villa-bosch.de/ • Comprehensive collection of enzyme kinetic constants • Adheres to STRENDA recommendation • Harvested from literature • Searchable web interface
  • 34. SABIO-RK
  • 35. SABIO-RK
  • 36. SABIO-RK
  • 37. BRENDA • http://www.brenda-enzymes.org/ • Even more comprehensive • Slightly less well-curated • Again, searchable web interface
  • 38. BRENDA
  • 39. Other experimental standards • MIBBI: Minimum Information for Biological and Biomedical Investigations • http://mibbi.org/ • Over thirty recommendations for a range of experimental techniques
  • 40. Modelling standards
  • 41. MCISB project overview Enzyme kinetics Quantitative metabolomics Quantitative proteomics Model Parameters (KM, Kcat) Variables (metabolite, protein concentrations) PRIDE XML MeMo SABIO-RK Web serviceWeb serviceWeb service MeMo-RK Web service
  • 42. MCISB project overview Enzyme kinetics Quantitative metabolomics Quantitative proteomics Model Parameters (KM, Kcat) Variables (metabolite, protein concentrations) PRIDE XML MeMo SABIO-RK Web serviceWeb serviceWeb service MeMo-RK Web service
  • 43. Modelling • What is a model? • “An analytic or computational model proposes specific testable hypotheses about a biological system” • Mathematical / computational representation of a biological system • May allows computational simulations of the system
  • 44. Pathway databases • Building a model often starts with a topological description of a pathway or pathways • What reacts with what? • A number of existing data resources • Biochemical knowledge, curated from literature
  • 45. KEGG
  • 46. KEGG Metabolite Enzyme Reaction
  • 47. MetaCyc
  • 48. Reactome
  • 49. Simulation tools • The systems biology community has developed a strong software infrastructure • Many tools exist, including simulators • Several hundred • How do we link pathway databases to these simulators? • A standard: SBML • Systems Biology Markup Language • Recently celebrated its 10th birthday
  • 50. SBML • XML markup language describing models • Contains concepts such as… • compartments • species (metabolites, enzymes, RNA, etc.) • reactions • Similar to pathway databases • KEGG2SBML tool exists for converting KEGG pathway maps to SBML files
  • 51. Mathematical SBML • Also contains concepts allowing simulations • Many of these driven by experimental work • Specification of metabolite and enzyme concentrations • Specification of kinetic laws and kinetic parameters • Parameterised model = pathways + experimental data
  • 52. SBML
  • 53. SBML data resources • Biomodels.net • http://www.ebi.ac.uk/biomodels-main/ • Curated collection of biochemical models at EBI • JWS Online • http://jjj.mib.ac.uk/ • Also curated • BUT also includes an online simulator • You’ll learn more next month…
  • 54. SBML tools • Hundreds of ‘em (205) • http://sbml.org/SBML_Software_Guide • Different goals • Whole cell / single pathway • Deterministic / stochastic simulators • Different platforms / programming languages • Matrix exists, describing capabilities of each tool • http://sbml.org/SBML_Software_Guide/ SBML_Software_Matrix
  • 55. Making SBML models: CellDesigner
  • 56. Other model representations • CellML • http://www.cellml.org/ • Larger scale modelling • Inter-cellular, used in whole organ modelling • BioPAX • http://www.biopax.org/ • Similar goals to SBML • Overlap between “competing” representations is being reduced • Regular “COMBINE” meetings
  • 57. MIRIAM • Minimum Information Required in the Annotation of Models • http://www.ebi.ac.uk/miriam/ • Set of guidelines describing how to make models reusable • Specify model creator contact details • Ensure consistent annotation of terms with database resources • e.g. use UniProt identifiers for unambigous identification of enzymes
  • 58. SBML visualisation: SBGN • Until recently, no standardised way of viewing models • Systems Biology Graphical Notation • Attempts to generate standard “wiring-diagram” for biological representations
  • 59. Model simulation
  • 60. Model simulation • Many simulators exist • How do we tell a simulator what to simulate? • Simulation Experiment Description Markup Language (SED-ML) • Contains concepts… • Model (what to run the simulation on) • Simulation (define what to simulate, duration, step- size) • Data generation (post-processing normalisation) • Output (2D plot, 3D plot)
  • 61. Simulation results: SBRML • Simulation results are data too, and are represented by SBRML • Systems Biology Results Markup Language • Developed by Joseph Dada, et al. (Manchester) • Structured format for representing simulation results • Dada JO, et al. SBRML: a markup language for associating systems biology data with models. Bioinformatics 2010, 26, 932-938.
  • 62. SBRML
  • 63. Conclusion • Data standards greatly facilitate computational systems biology • Standards exist (and are being continually developed) for both experimental and modelling data • Provides a framework for data sharing and open-source software tool development
  • 64. Data Standards for Systems Biology Neil Swainston Manchester Centre for Integrative Systems Biology neil.swainston@manchester.ac.uk

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