A Toolbox for Discrete Modelling of Cell Signalling Dynamics

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cam.issuedOnline2018-05-22
dc.contributor.authorPaterson, Yasmin Z
dc.contributor.authorShorthouse, David
dc.contributor.authorPleijzier, Markus W
dc.contributor.authorPiterman, Nir
dc.contributor.authorBendtsen, Claus
dc.contributor.authorHall, BA
dc.contributor.authorFisher, J
dc.contributor.orcidShorthouse, David [0000-0002-3207-3584]
dc.contributor.orcidPleijzier, Markus [0000-0002-7297-4547]
dc.contributor.orcidHall, Benjamin [0000-0003-0355-2946]
dc.contributor.orcidFisher, Jasmin [0000-0003-4477-9047]
dc.date.accessioned2018-09-11T17:30:57Z
dc.date.available2018-09-11T17:30:57Z
dc.date.issued2018-06
dc.description.abstractIn an age where the volume of data regarding biological systems exceeds our ability to analyse it, many researchers are looking towards systems biology and computational modelling to help unravel the complexities of gene and protein regulatory networks. In order to make such techniques more accessible to mainstream researchers, tools such as the BioModelAnalyzer (BMA) have been developed to provide a user-friendly graphical interface for discrete modelling of biological systems. Here we use the BMA to build a library of target functions of known molecular interactions, translated from ordinary differential equations (ODEs). We then show that these BMA target functions can be used to reconstruct complex networks, which can correctly predict many known genetic perturbations. This new library supports the accessibility ethos behind the creation of BMA, providing a toolbox for the construction of complex cell signalling models without the need for extensive experience in computer programming or mathematical modelling, and allows for construction and simulation of complex biological systems with only small amounts of quantitative data.
dc.description.sponsorshipRoyal Society
dc.identifier.doi10.17863/CAM.27532
dc.identifier.eissn1757-9708
dc.identifier.issn1757-9694
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/280164
dc.publisherOxford University Press (OUP)
dc.publisher.urlhttp://dx.doi.org/10.1039/c8ib00026c
dc.subjectCell Cycle
dc.subjectComputational Biology
dc.subjectComputer Simulation
dc.subjectGene Regulatory Networks
dc.subjectHomeostasis
dc.subjectHumans
dc.subjectModels, Biological
dc.subjectOscillometry
dc.subjectSignal Transduction
dc.subjectSoftware
dc.subjectSystems Biology
dc.titleA Toolbox for Discrete Modelling of Cell Signalling Dynamics
dc.typeArticle
dcterms.dateAccepted2018-05-16
prism.endingPage382
prism.issueIdentifier6
prism.publicationDate2018
prism.publicationNameIntegrative Biology (United Kingdom)
prism.startingPage370
prism.volumeVolume 10
pubs.funder-project-idRoyal Society (Paul Instrument Fund) (UF130039)
pubs.funder-project-idMRC (unknown)
pubs.funder-project-idMedical Research Council (MC_UU_12022/9)
pubs.funder-project-idMRC
pubs.funder-project-idBiotechnology and Biological Sciences Research Council (1804772)
rioxxterms.licenseref.startdate2018-06
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review
rioxxterms.versionAM
rioxxterms.versionofrecord10.1039/C8IB00026C
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