Informatics In The Manchester Centre For Integrative Systems Biology
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Informatics In The Manchester Centre For Integrative Systems Biology Informatics In The Manchester Centre For Integrative Systems Biology Presentation Transcript

  • Informatics in the Manchester Centre for Integrative Systems Biology
    • Daniel Jameson, Neil Swainston
    • Manchester Centre for Integrative Systems Biology
    • SysMO-DB Workshop – Connecting Models and Data, Berlin
    • 23 November 2009
  • The MCISB
    • Currently employs 9.5 multidisciplinary people
      • All share same office , lab
    • Pioneer the development of new experimental and computational technologies in systems biology
    • Develop an annotated, kinetic model of yeast metabolism
  • Goals of the MCISB
    • Follow an integrative approach:
  • Goals of the MCISB
    • Follow an iterative approach:
  • Definition of the problem
    • Experimentalists generate data
    • Modellers require data
    • How do we pass data from the experimentalist to the modeller?
    • Traditional method
      • Experimentalist analyses data, produces spreadsheet
      • Experimentalists e-mails spreadsheet to modeller
      • Modeller cuts-and-pastes data into modelling tool
      • Do the experimentalist and the modeller speak the same language?
  • Informatics challenges
    • How do we map experimental data to models?
      • How do we know what data applies to what molecule or reaction ?
      • How do we identify molecules or reactions?
    • (Same problem in merging models)
    • Use names…?
  • Computers don’t like names … because they are non-unique / ambiguous / imprecise / etc.
  • (3R,4R,5S,6S)-6-(hydroxymethyl) oxane-2,3,4,5-tetrol Biochemists like names a little too much… Glucose Glc Anhydrous dextrose Cerelose 2001 Traubenzucker Staleydex 95M
  • Solution
    • Utilise unique, public identifiers for identifying molecules
      • Don’t re-invent your own…
      • Use ChEBI terms to uniquely identify metabolites
      • Use UniProt terms to uniquely identify enzyme
  •  
  • Solution
    • Further advantage:
        • Using links into existing databases (ChEBI, UniProt) provide additional information immediately
        • Chemical formulae, structures
        • Protein sequences, phosphorlyation sites, SNPs
      • Use unique, public IDs
  • But names are still important
    • Names are for humans (human-ish)
    • Unique ids (e-mail addresses, bank account numbers) are for computers (geek-ish)
    • BOTH are needed
  • But names are still important
  • Models
    • Useful to have a standard to allow models to be shared / re-used
      • Use SBML
      • Very well developed / supported
      • Tool set increasing all the time
    • Identifying metabolites / proteins in models?
      • Use MIRIAM standards
      • http://www.ebi.ac.uk/miriam/
      • Allows unique, public IDs to be embedded into SBML as annotations (along with human-readable names)
  • Models
    • Genome-scale SBML model of yeast metabolism
    • Annotated model
      • All >2000 molecules have unique database references
      • MIRIAM standards have been followed
      • Should be entirely unambiguous for third party users
      • Should be usable in third party tools
      • Should allow data to be imported “easily”
  •  
  • SBML annotation <species id=”glc&quot; name=&quot;D-Glucose&quot;> <annotation> <rdf:li rdf:resource=&quot;urn:miriam:obo.chebi:CHEBI:17634&quot;/> </annotation> </species>
  • Solution on the experimental side
    • Ensure that unique identifiers are captured and associated with data at the time of the experiment
      • BUT… this is all a bit geek-ish for biologists
    • So… generate intuitive tools to do this by stealth
  • KineticsWizard
  • Project overview Enzyme kinetics Quantitative metabolomics Quantitative proteomics SBML Model Parameters (K M , K cat ) Variables (metabolite, protein concentrations) PRIDE XML MeMo SABIO-RK Web service Web service Web service MeMo-RK Web service
  •  
  •  
  •  
  • CellDesigner plugins …eventually
  • But…
    • … MCISB has to manage “only” three types of experiment
      • Proteomics, metabolomics, enzyme kinetics
    • Informatics team share office with experimentalists and modellers
    • We’ve been doing this for years…
        • Lots of time, lots of people, lots of resource
        • Infrastructure development is part of our remit
  • And…
    • … SYSMO projects are far more diverse
    • Informatics team separated from experimentalists, who are separated from modellers
    • Less informatics resource
    • Heavyweight approach of MCISB ( bespoke tools for each experiment) probably not applicable
  • So…
    • … lightweight approach may be more suitable
    • Store only secondary data necessary for modelling
        • Not raw data
    • Daniel…
  • Einfach Klasse!
  • Modelling infrastructure
  • Taverna http://taverna.sourceforge.net
  • Taverna
  • Modelling life-cycle workflows
  • Model construction Input: list of ORFs Output: SBML file 1. Get reaction info 3. Create species 2. Create compartments 4. Create reactions Get annotations
  • Model construction
  • Model parameterisation
    • Data requirements
      • SBML model
      • Starting concentrations for enzymes and source metabolites
      • Key results database
      • Enzyme kinetics
      • SABIO-RK database web service
  • SABIO-RK web service
  • Model parameterisation
  • Model calibration
    • Data requirements
      • Parameterised SBML model
      • Experimental data
      • Metabolite concentrations from key results database
      • Calibration by COPASI web service
  • COPASI web service Design and Architecture of Web Services for Simulation of Biochemical Systems. Dada JO, Mendes P. Data Integration in the Life Sciences, Manchester, UK (2009).
  • Model calibration
  • Model simulation
    • Using COPASI web service
  • Conclusion
    • Integrating experimental data with models is “easy” and can be automated
      • If we adopt some standards
    • Data can be shared “easily” between groups
      • If we all adopt some standards
    • Lightweight approach more achievable
        • Key Results Database
  • Thanks…
  • Informatics in the Manchester Centre for Integrative Systems Biology
    • Daniel Jameson, Neil Swainston
    • Manchester Centre for Integrative Systems Biology
    • SysMO-DB Workshop – Connecting Models and Data, Berlin
    • 23 November 2009