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

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…

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

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 kineticsQuantitativemetabolomics

Quantitativeproteomics

SBML Model

Parameters(KM, Kcat)

Variables(metabolite, proteinconcentrations)

PRIDE XML MeMo SABIO-RK

Web serviceWeb serviceWeb 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

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