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Informaticsin the
Manchester Centre for Integrative Systems Biology
Daniel Jameson, Neil SwainstonManchester Centre for Integrative Systems Biology
SysMO-DB Workshop – Connecting Models and Data, Berlin23 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
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…
GlucoseGlcAnhydrous dextrose
Cerelose 2001TraubenzuckerStaleydex 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
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" name="D-Glucose">
<annotation>
<rdf:li rdf:resource="urn:miriam:obo.chebi:CHEBI:17634"/>
</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
Project overview
Enzyme kineticsQuantitativemetabolomics
Quantitativeproteomics
SBML Model
Parameters(KM, Kcat)
Variables(metabolite, proteinconcentrations)
PRIDE XML MeMo SABIO-RK
Web serviceWeb serviceWeb service
MeMo-RK
Web service
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!
Model construction
Input: list of ORFs
Output: SBML file
1. Get reaction info
3. Create species
2. Create compartments
4. Create reactions
Get
ann
otat
ions
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…
Informaticsin the
Manchester Centre for Integrative Systems Biology
Daniel Jameson, Neil SwainstonManchester Centre for Integrative Systems Biology
SysMO-DB Workshop – Connecting Models and Data, Berlin23 November 2009