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dc.contributor.authorAltarawni, Ibrahimen
dc.contributor.authorSamarasinghe, Sandhyaen
dc.contributor.authorKulasiri, Gamalathge D.en
dc.contributor.editorElsawah, S.en
dc.date.accessioned2020-01-30T00:15:28Z
dc.date.available2019-12-01en
dc.date.issued2019-12en
dc.identifier.isbn9780975840092en
dc.identifier.urihttps://hdl.handle.net/10182/11370
dc.description.abstractIt has become very clear that stochasticity in biology is a rule rather than exception. Gillespie stochastic simulation algorithm (GSSA) (direct method) is the first algorithm proposed to model stochasticity in biochemical systems. However, the computational intractability of direct method has been identified as the main challenge for using it to model large biochemical systems. In this paper, a novel variant of the GSSA is proposed to address computational intractability of the direct method. The direct method is combined with a Mapping Reduction Method (MRM) to target a single run of the direct method to be accelerated by advancing the system through several reactions at each time step to replace the single reaction in GSSA. MRM is a framework for mimicking parallel processes occurring in large systems using a large number of threads that work together and seen as a single system. It is used for parallel problems to be processed across large datasets using a large number of nodes working together as a single system. Link between GSk3 and p53 in Alzheimer's disease (AD) is modelled using the proposed method and tested and validated by comparing it with the direct method.en
dc.format.extent1-7 (7)en
dc.language.isoenen
dc.publisherModelling and Simulation Society of Australia and New Zealanden
dc.relationThe original publication is available from - Modelling and Simulation Society of Australia and New Zealand - https://doi.org/10.36334/modsim.2019.a1.altarawni - https://mssanz.org.au/modsim2019/en
dc.relation.urihttps://doi.org/10.36334/modsim.2019.a1.altarawnien
dc.rights© The Authors and Modelling and Simulation Society of Australia and New Zealand Inc.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.source23rd International Congress on Modelling and Simulation (MODSIM2019)en
dc.subjectGSSAen
dc.subjectMRMen
dc.subjectAlzheimer's diseaseen
dc.subjectp53en
dc.subjectGSk3en
dc.titleAn improved stochastic modelling framework for biological networksen
dc.typeConference Contribution - Published
lu.contributor.unitLincoln Universityen
lu.contributor.unitFaculty of Agriculture and Life Sciencesen
lu.contributor.unitDepartment of Wine, Food and Molecular Biosciencesen
lu.contributor.unitFaculty of Environment, Society and Designen
lu.contributor.unitDepartment of Environmental Managementen
dc.identifier.doi10.36334/modsim.2019.a1.altarawnien
dc.subject.anzsrc080201 Analysis of Algorithms and Complexityen
dc.subject.anzsrc080205 Numerical Computationen
dc.subject.anzsrc060102 Bioinformaticsen
dc.relation.isPartOfMODSIM2019, 23rd International Congress on Modelling and Simulationen
pubs.finish-date2019-12-06en
pubs.notesTheme: Supporting evidence-based decision making: the role of modelling and simulation Publicly available for downloaden
pubs.organisational-group/LU
pubs.organisational-group/LU/Agriculture and Life Sciences
pubs.organisational-group/LU/Agriculture and Life Sciences/WFMB
pubs.organisational-group/LU/Faculty of Environment, Society and Design
pubs.organisational-group/LU/Faculty of Environment, Society and Design/DEM
pubs.organisational-group/LU/Research Management Office
pubs.organisational-group/LU/Research Management Office/QE18
pubs.publication-statusPublished onlineen
pubs.publisher-urlhttps://mssanz.org.au/modsim2019/en
pubs.start-date2019-12-01en
dc.rights.licenceAttributionen
lu.identifier.orcid0000-0001-8744-1578
lu.identifier.orcid0000-0003-2943-4331
lu.subtypeConference Paperen


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