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dc.contributor.authorMacnamara, Cicely K.
dc.contributor.authorMitchell, Elaine
dc.contributor.authorChaplain, Mark A. J.
dc.identifier.citationMacnamara , C K , Mitchell , E & Chaplain , M A J 2019 , ' Spatial-stochastic modelling of synthetic gene regulatory networks ' , Journal of Theoretical Biology , vol. 468 , pp. 27-44 .
dc.identifier.otherORCID: /0000-0003-4961-6052/work/54181495
dc.identifier.otherORCID: /0000-0001-5727-2160/work/55378841
dc.descriptionFunding: EPSRC Grant No. EP/N014642/1 (EPSRC Centre for Multiscale Soft Tissue Mechanics - With Application to Heart & Cancer) (MAJC,CKM).en
dc.description.abstractTranscription factors are important molecules which control the levels of mRNA and proteins within cells by modulating the process of transcription (the mechanism by which mRNA is produced within cells) and hence translation (the mechanism by which proteins are produced within cells). Transcription factors are part of a wider family of molecular interaction networks known as gene regulatory networks (GRNs) which play an important role in key cellular processes such as cell division and apoptosis (e.g. the p53-Mdm2, NFκB pathways). Transcription factors exert control over molecular levels through feedback mechanisms, with proteins binding to gene sites in the nucleus and either up-regulating or down-regulating production of mRNA. In many GRNs, there is a negative feedback in the network and the transcription rate is reduced. Typically, this leads to the mRNA and protein levels oscillating over time and also spatially between the nucleus and cytoplasm. When experimental data for such systems is analysed, it is observed to be noisy and in many cases the actual numbers of molecules involved are quite low. In order to model such systems accurately and connect with the data in a quantitative way, it is therefore necessary to adopt a stochastic approach as well as take into account the spatial aspect of the problem. In this paper, we extend previous work in the area by formulating and analysing stochastic spatio-temporal models of synthetic GRNs e.g. repressilators and activator-repressor systems.
dc.relation.ispartofJournal of Theoretical Biologyen
dc.subjectSynthetic gene regulatory networksen
dc.subjectActivator-repressor systemsen
dc.subjectSpatial modellingen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQH301 Biologyen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectSDG 3 - Good Health and Well-beingen
dc.titleSpatial-stochastic modelling of synthetic gene regulatory networksen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. Applied Mathematicsen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.description.statusPeer revieweden

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