Download The biology of ageing e-Science integration and simulation system

The Biology of Ageing
e-Science Integration and
Simulation System
Tom Kirkwood, Darren Wilkinson,
Richard Boys, Colin Gillespie,
Carole Proctor, Daryl Shanley
www.basis.ncl.ac.uk
GRID-based research node to model/simulate hypotheses
about mechanisms of ageing
Accessible and interactive
Nature Reviews Molecular Cell Biology 2003;4: 243 -249
Modelling the ageing process
Copying errors,
Telomere shortening
Mutations
e.g. ROS
ROS, etc
DNA
ATP
ROS
Transcription errors
mtDNA
RNA
Translation errors
ROS
Damage,
denaturing
e.g. ROS
PROTEIN
Refolding
Antioxidants
Chaperones
Degradation
or
aggregation (e.g.b-amyloid)
ATP
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
degradation
ROS
synthesis + folding
into native state
aggregation
NatP
MisP
AggP
misfolding
Hsp90
ADP
refolding
ATP
binding
Hsp90 MisP
Abbreviations:
NatP native protein
MisP misfolded protein
AggP aggregated protein
ROS reactive oxygen species
(ii) Autoregulation of Hsp90
Hsf1
dimerisation
DiH
trimerisation
TriH
binding
Hsf1
Hsp90
Hsp90
synthesis
TriH
HSE
DNA binding
HSE
degradation
Abbreviations:
Hsf1 heat shock factor-1
DIH dimer of Hsf1
TriH trimer of Hsf1
HSE heat shock element
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
degradation
ROS
synthesis + folding
into native state
aggregation
NatP
MisP
AggP
misfolding
Hsp90
ADP
refolding
ATP
binding
Hsp90 MisP
Abbreviations:
NatP native protein
MisP misfolded protein
AggP aggregated protein
ROS reactive oxygen species
• 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
Ub
Ub
Ub
Ub
Ub
Ub
Proteasome
Ub
Ub
Ub
Ub
Ub
MisP
MisP
Ub
ATP
ATP
Ub = ubiquitin
ADP
ADP
degraded protein
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
ROS
Mitochondria
model
ATP
Chaperone
model
BASIS: architecture
User PC
Web browser
BASIS client software
Internet (GRID)
BASIS
file
server
Web server
CGI scripts
Web services
API
Database
Job
Schedule
r
Linux beowulf cluster
e-mail
notification
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
Acknowledgements
BASIS Team
Tom Kirkwood
Darren Wilkinson
Richard Boys
Colin Gillespie
Carole Proctor
Daryl Shanley
Collaborators at Newcastle
Thomas von Zglinicki
David Lydall
Gabriele Saretzki
Tim Cowen (IAH/UCL)
Doug Turnbull
Chris Morris
John Mathers
Neil Wipat
NE E-Science Centre
Paul Watson
Rob Smith
Unilever
Janette Jones
Jonathan Powell
Frans van der Ouderaa
Berlin (MPI Inst. Mol. Genet.)
Axel Kowald
University of Bologna
Claudio Franceschi
Silvana Valensin
Paolo Tieri
INSERM Paris
Francois Taddei
Tufts University/USDA
Jose Ordovas
University of Liverpool
Brian Merry
University of Semmelweis
Csaba Soti
Ottawa Regional Cancer Centre
Doug Gray