Presentation on theme: "The Biology of Ageing e-Science Integration and Simulation System Tom Kirkwood, Darren Wilkinson, Richard Boys, Colin Gillespie, Carole Proctor, Daryl."— Presentation transcript:
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:
DNA RNA PROTEIN Degradation or aggregation (e.g. amyloid) Antioxidants Modelling the ageing process Copying errors, Telomere shortening Mutations e.g. ROS Transcription errors Translation errors Damage, denaturing e.g. ROS Chaperones Refolding mtDNA ATP ROS ATP ROS, etc
Virtual Ageing Cell Telomere loss and oxidative stress: Proctor & Kirkwood Mech Ageing Dev Mitochondrial mutation: Kowald & Kirkwood J Theor Biol Somatic mutation: Kirkwood & Proctor Mech Ageing Dev 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 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
Hsp90 Model of Regulation of HSF1 Zou et al. (1998) Cell 94:
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 refolding binding aggregation degradation synthesis + folding into native state MisP Hsp90 AggP NatP ROS ADP ATP MisP Hsp90 Abbreviations: NatP native protein MisP misfolded protein AggP aggregated protein ROS reactive oxygen species misfolding
(ii) Autoregulation of Hsp90 Abbreviations: Hsf1 heat shock factor-1 DIH dimer of Hsf1 TriH trimer of Hsf1 HSE heat shock element Hsp90 Hsf1 Hsp90 Hsf1 binding degradation dimerisation synthesis TriH DiH trimerisation HSE TriH DNA binding
Model is coded in SBML.
Stochastic simulation refolding binding aggregation degradation synthesis + folding into native state MisP Hsp90 AggP NatP ROS ADP ATP MisP Hsp90 Abbreviations: NatP native protein MisP misfolded protein AggP aggregated protein ROS 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 MisP Ub ATPADP Proteasome MisP 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 Mitochondria model Chaperone model ROS ATP
BASIS: architecture User PC Internet (GRID) BASIS file server notification Web server CGI scripts Web browserBASIS client software Linux beowulf cluster Web services API Database Job Schedule r
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 urns, 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)
Whats new? More interaction with biologists –especially PhD students Virtual ageing cell –more computer resources needed – Grid Web services –import models from other databases
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 Acknowledgements