Spatial stochastic modelling of the Hes1 gene regulatory network: intrinsic noise can explain heterogeneity in embryonic stem cell differentiation

Sturrock, Marc; Hellander, Andreas; Matzavinos, Anastasios; Chaplain, M. A. J.

doi:10.1098/rsif.2012.0988

Cited by 64 publications

(77 citation statements)

References 49 publications

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“…The values used are based on physiologically realistic parameters used for modelling similar gene regulatory network models (Sturrock et al, 2013;Monk, 2003;Terry et al, 2011). We neglect mRNA degradation in the nucleus as in Bahar Halpern et al (2015); Battich et al (2015).…”

Section: Parameter Values and Model Comparisonmentioning

confidence: 99%

“…This model was explored before in Sturrock et al (2013) and represents gene expression in a eukaryotic cell with negative feedback. The model has five discrete variables, the number of free promoters ( f ), mRNAs in the nucleus (m 1 ), mRNAs in the cytoplasm (m 2 ), proteins in the nucleus (n 1 ) and proteins in the cytoplasm (n 2 ).…”

Section: Modelmentioning

confidence: 99%

“…A spatio-temporal model of the Hes1 GRN, using a partial differential equation (PDE) approach, was presented in Sturrock et al (2011) and extensions of this model were considered in Sturrock et al (2012). A spatial stochastic model of the Hes1 GRN in embryonic stem cells was studied in Sturrock et al (2013) -this model was derived using mass action kinetics and its mean behaviour was studied in Sturrock et al (2014b). Wang et al considered a spatially-homogeneous model of a self-repressing gene without nonlinearity in regulation in Wang et al (2014), and were able to identify certain regions of the parameter space which yielded 'stochastic oscillations'.…”

Section: Introductionmentioning

confidence: 99%

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The influence of nuclear compartmentalisation on stochastic dynamics of self-repressing gene expression

Sturrock

Shahrezaei

2017

Journal of Theoretical Biology

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Gene expression is an inherently noisy process. This noise is generally thought to be deleterious as precise internal regulation of biochemical reactions is essential for cell growth and survival. Selfrepression of gene expression, which is the simplest form of a negative feedback loop, is commonly believed to be employed by cellular systems to decrease the stochastic fluctuations in gene expression. When there is some delay in autoregulation, it is also believed that this system can generate oscillations. In eukaryotic cells, mRNAs that are synthesised in the nucleus must be exported to the cytoplasm to function in protein synthesis, whereas proteins must be transported into the nucleus from the cytoplasm to regulate the expression levels of genes. Nuclear transport thus plays a critical role in eukaryotic gene expression and regulation. Some recent studies have suggested that nuclear retention of mRNAs can control noise in mRNA expression. However, the effect of nuclear transport on protein noise and its interplay with negative feedback regulation is not completely understood. In this paper, we systematically compare four different simple models of gene expression. By using simulations and applying the linear noise approximation to the corresponding chemical master equations, we investigate the influence of nuclear import and export on noise in gene expression in a negative autoregulatory feedback loop. We first present results consistent with the literature, i.e., that negative feedback can effectively buffer the variability in protein levels, and nuclear retention can decrease mRNA noise levels. Interestingly we find that when negative feedback is combined with nuclear retention, an amplification in gene expression noise can be observed and is dependant on nuclear translocation rates. Finally, we investigate the effect of nuclear compartmentalisation on the ability of self-repressing genes to exhibit stochastic oscillatory dynamics.

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Section: Parameter Values and Model Comparisonmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The influence of nuclear compartmentalisation on stochastic dynamics of self-repressing gene expression

Sturrock

Shahrezaei

2017

Journal of Theoretical Biology

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“…The assumptions concerning the molecular reactions in our GRN model follow previous modelling efforts [Sturrock et al, 2013, Wang et al, 2012. We assume mRNA is transcribed from a localised gene site within the cell nucleus and then diffuses from the nucleus across the nuclear membrane and into the cytoplasm where it is translated into protein.…”

Section: A Spatial Stochastic Model Of a Gene Regulatory Network Withmentioning

confidence: 99%

“…A spatio-temporal model of the Hes1 GRN, using a partial differential equation (PDE) approach, was presented in and extensions of this model were considered in Sturrock et al [2012]. A spatial stochastic model of the Hes1 GRN in embryonic stem cells was studied in Sturrock et al [2013] -this model was derived using mass action kinetics and is analysed further in this paper. Wang et al considered a spatially-homogeneous model of a self-repressing gene in Wang et al [2012], and were able to identify certain regions of the parameter space which yielded 'stochastic oscillations'.…”

Section: Introductionmentioning

confidence: 99%

Mean field analysis of a spatial stochastic model of a gene regulatory network

et al. 2014

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A gene regulatory network may be defined as a collection of DNA segments which interact with each other indirectly through their RNA and protein products. Such a network is said to contain a negative feedback loop if its products inhibit gene transcription, and a positive feedback loop if a gene product promotes its own production. Negative feedback loops can create oscillations in mRNA and protein levels while positive feedback loops are primarily responsible for signal amplification. It is often the case in real biological systems that both negative and positive feedback loops operate in parameter regimes that result in low copy numbers of gene products.In this paper we investigate the spatio-temporal dynamics of a single feedback loop in a eukaryotic cell. We first develop a simplified spatial stochastic model of a canonical feedback system (either positive or negative). Using a Gillespie's algorithm, we compute sample trajectories and analyse their corresponding statistics. We then derive a system of equations that describe the spatio-temporal evolution of the stochastic means. Subsequently, we examine the spatially homogeneous case and compare the results of numerical simulations with the spatially explicit case. Finally, using a combination of steady-state analysis and data clustering techniques, we explore model behaviour across a subregion of the parameter space that is difficult to access experimentally and compare the parameter landscape of our spatio-temporal and spatially-homogeneous models.

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Addressing Variability and Heterogeneity of Induced Pluripotent Stem Cell-Derived Cardiomyocytes

Biendarra-Tiegs

Secreto

Nelson

2019

Advances in Experimental Medicine and Biology

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Spatial stochastic modelling of the Hes1 gene regulatory network: intrinsic noise can explain heterogeneity in embryonic stem cell differentiation

Cited by 64 publications

References 49 publications

The influence of nuclear compartmentalisation on stochastic dynamics of self-repressing gene expression

The influence of nuclear compartmentalisation on stochastic dynamics of self-repressing gene expression

Mean field analysis of a spatial stochastic model of a gene regulatory network

Addressing Variability and Heterogeneity of Induced Pluripotent Stem Cell-Derived Cardiomyocytes

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