the biology of ageing e-science integration and simulation system tom kirkwood, darren wilkinson,...

The Biology of Ageing e-Science Integration and

Simulation System

Tom Kirkwood, Darren Wilkinson,

Richard Boys, Colin Gillespie,

Carole Proctor, Daryl Shanley

GRID-based research node to model/simulate hypotheses about mechanisms of ageing

Accessible and interactive

Nature Reviews Molecular Cell Biology 2003;4: 243 -249

www.basis.ncl.ac.uk

DNA

RNA

PROTEIN

Degradation oraggregation (e.g.amyloid)

Antioxidants

Modelling the ageing processCopying errors,Telomere shortening

Mutationse.g. ROS

Transcription errors

Translation errors

Damage,denaturinge.g. ROS

Chaperones

Refolding

mtDNA

ATP

ROS

ROS

ATP

ROS, etc

Virtual Ageing Cell• Telomere loss and oxidative stress: Proctor & Kirkwood Mech

Ageing Dev 2001.• Mitochondrial mutation: Kowald & Kirkwood J Theor Biol 2000.• Somatic mutation: Kirkwood & Proctor Mech Ageing Dev 2003.• Telomere capping: Proctor & Kirkwood Aging Cell 2003• Extrachromosomal DNA circles: Gillespie et al J Theor Biol

2004 • Genetic pathways: eg Sir2 gene action (in progress)• Protein turnover: Chaperones, ubiquitin-proteasome system

(Proctor et al. Mech Ageing Dev 2004 and in progress)• Antioxidant system: Shanley et al (in progress)• Network models:

• Mitochondrial mutation, oxidative stress, protein turnover (Kowald & Kirkwood Mutation Res 1996)

• Somatic mutation, telomere loss, mitochondrial mutation (oxidative stress (Sozou & Kirkwood JTheor Biol 2001)

A module of the virtual ageing cell: the action of chaperones

and their role in ageing

Proctor et al. 2004 Mechanisms in Ageing and Development

Cellular functions of chaperones

• Folding of nascent proteins• Assist in assembly of protein structures• Refolding of denatured proteins• Transport of proteins through cellular

membranes• Targeting of proteins for degradation• Prevention of protein aggregation

Protein model for quality control

Wickner et al. (1999) Science 286 1888-1893

Hsp90 Model of Regulation of HSF1

Zou et al. (1998) Cell 94:471-480

Steps in building and using a model

1. Draw a diagram of the system.2. Give values to the boxes representing

the number of molecules and to the arrows representing the reaction rates.

3. Use a software tool to translate the diagram into computer code.

4. Use the simulator to discover the dynamic behaviour of the system.

Building a model of the chaperone system

(i) The role of chaperones in preventing protein aggregation

refoldingbinding

aggregation

degradation

synthesis + folding into native state MisP

Hsp90

AggPNatP

ROS

ADP

ATP MisPHsp90

Abbreviations:NatP native proteinMisP misfolded proteinAggP aggregated proteinROS reactive oxygen species

misfolding

(ii) Autoregulation of Hsp90

Abbreviations:Hsf1 heat shock factor-1DIH dimer of Hsf1TriH trimer of Hsf1HSE heat shock element

Hsp90

Hsf1

Hsp90

Hsf1binding

degradation

dimerisation

synthesis

TriHDiH trimerisation

HSEHSE

TriH DNA binding

Model is coded in SBML<sbml xmlns="http://www.sbml.org/sbml/level2" version="1" level="2" ><model id="Hsp90model1" ><listOfCompartments><compartment id="cell" spatialDimensions="3" size=”1” name="cell" /></listOfCompartments><listOfSpecies><species id="NatP" compartment="cell" initialAmount="6000000.0" name=“NatP" /><species id=“Hsp90" compartment="cell" initialAmount=“30000.0" name=" Hsp90 " />

.</listOfSpecies><listOfParameters><parameter id="k1" value="7.04E-8" name=“k1" />

.</listOfParameters><listOfReactions><reaction id="protein_misfolding" reversible="false" ><listOfReactants><speciesReference species=“NatP" ></speciesReference></listOfReactants><listOfProducts><speciesReference species=“MisP" ></speciesReference></listOfProducts>

.</reaction>

.</listOfReactions></model></sbml>

Stochastic simulation

refoldingbinding

aggregation

degradation

synthesis + folding into native state MisP

Hsp90

AggPNatP

ROS

ADP

ATP MisPHsp90

Abbreviations:NatP native proteinMisP misfolded proteinAggP aggregated proteinROS reactive oxygen species

misfolding

• Reactions are picked at random according to their rates.

• After each reaction, the number of each species is updated.

Adding further detail to the model

degraded protein

Ub

Ub

Ub

Ub MisP

Ub

Ub Ub

Ub

ATP ADP

Proteasome

MisP

Ub

Ub

Ub

Ub

Ub = ubiquitin

ATP ADP

Combining models in the BASIS system

• Other components will include models of: the mitochondria; the antioxidant system; damage to nuclear DNA; telomere shortening; and signalling pathways.

• Combining the mitochondria and chaperone model via ROS and ATP

Mitochondriamodel

Chaperonemodel

ROS

ATP

BASIS: architecture

User PC

Internet (GRID)

BASISfile

servere-mail

notification

Web server

CGI scripts

Web browser

BASIS client software

Linux beowulf cluster

Web services

API

Database JobSchedul

er

BASIS: architecture

• Web server is running apache• Condor as a job scheduler• python as an all purpose glue• SBML is parsed and manipulated using

libSBML for C & python• postgresql for the database• graphviz for the visualisation of the SBML

models

BASIS: model repository

• Users have a private space for their models/simulations

• Once a model is made public it cannot be deleted– useful for the publication of models

• Models can be accessed through a web-service interface– other tools can access the models

• Models are referenced using urn’s, e.g. urn:basis.ncl:model:10

Example web-services#To put a model into your space

putModel(SId, sbml)

#Using libSBML & graphviz

visualiseSBMLReaction(sbml, #reaction)

What’s new?

• More interaction with biologists– especially PhD students

• Virtual ageing cell– more computer resources needed – Grid

• Web services– import models from other databases

BASIS TeamTom Kirkwood Darren Wilkinson Richard BoysColin Gillespie Carole Proctor Daryl Shanley

Collaborators at NewcastleThomas von Zglinicki David LydallGabriele SaretzkiTim Cowen (IAH/UCL)Doug TurnbullChris MorrisJohn MathersNeil Wipat

NE E-Science CentrePaul WatsonRob Smith

UnileverJanette JonesJonathan PowellFrans van der OuderaaBerlin (MPI Inst. Mol. Genet.)Axel KowaldUniversity of BolognaClaudio Franceschi Silvana Valensin Paolo TieriINSERM ParisFrancois TaddeiTufts University/USDAJose OrdovasUniversity of LiverpoolBrian MerryUniversity of SemmelweisCsaba SotiOttawa Regional Cancer CentreDoug Gray

Acknowledgements