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Spatial-stochastic modelling of synthetic gene regulatory networks

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Spatial-stochastic modelling of synthetic gene regulatory networks. / Macnamara, Cicely K. ; Mitchell, Elaine; Chaplain, Mark A. J.

In: Journal of Theoretical Biology, Vol. In press, 10.02.2019.

Research output: Contribution to journalArticle

Harvard

Macnamara, CK, Mitchell, E & Chaplain, MAJ 2019, 'Spatial-stochastic modelling of synthetic gene regulatory networks', Journal of Theoretical Biology, vol. In press. https://doi.org/10.1016/j.jtbi.2019.02.003

APA

Macnamara, C. K., Mitchell, E., & Chaplain, M. A. J. (2019). Spatial-stochastic modelling of synthetic gene regulatory networks. Journal of Theoretical Biology, In press. https://doi.org/10.1016/j.jtbi.2019.02.003

Vancouver

Macnamara CK, Mitchell E, Chaplain MAJ. Spatial-stochastic modelling of synthetic gene regulatory networks. Journal of Theoretical Biology. 2019 Feb 10;In press. https://doi.org/10.1016/j.jtbi.2019.02.003

Author

Macnamara, Cicely K. ; Mitchell, Elaine ; Chaplain, Mark A. J. / Spatial-stochastic modelling of synthetic gene regulatory networks. In: Journal of Theoretical Biology. 2019 ; Vol. In press.

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@article{db2bab83d8954ce599224097db2e8856,
title = "Spatial-stochastic modelling of synthetic gene regulatory networks",
abstract = "Transcription 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.",
keywords = "Synthetic gene regulatory networks, Repressilators, Activator-repressor systems, Spatial modelling",
author = "Macnamara, {Cicely K.} and Elaine Mitchell and Chaplain, {Mark A. J.}",
note = "Funding: EPSRC Grant No. EP/N014642/1 (EPSRC Centre for Multiscale Soft Tissue Mechanics - With Application to Heart & Cancer) (MAJC,CKM).",
year = "2019",
month = "2",
day = "10",
doi = "10.1016/j.jtbi.2019.02.003",
language = "English",
volume = "In press",
journal = "Journal of Theoretical Biology",
issn = "0022-5193",
publisher = "ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Spatial-stochastic modelling of synthetic gene regulatory networks

AU - Macnamara, Cicely K.

AU - Mitchell, Elaine

AU - Chaplain, Mark A. J.

N1 - Funding: EPSRC Grant No. EP/N014642/1 (EPSRC Centre for Multiscale Soft Tissue Mechanics - With Application to Heart & Cancer) (MAJC,CKM).

PY - 2019/2/10

Y1 - 2019/2/10

N2 - Transcription 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.

AB - Transcription 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.

KW - Synthetic gene regulatory networks

KW - Repressilators

KW - Activator-repressor systems

KW - Spatial modelling

U2 - 10.1016/j.jtbi.2019.02.003

DO - 10.1016/j.jtbi.2019.02.003

M3 - Article

VL - In press

JO - Journal of Theoretical Biology

JF - Journal of Theoretical Biology

SN - 0022-5193

ER -

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