Informatics In The Manchester Centre For Integrative Systems Biology

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" 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

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

Informatics In The Manchester Centre For Integrative Systems Biology

by Neil Swainston

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