Stochastic analysis of genetic promoter architectures with memory

Singh, Abhyudai; Vargas, Cesar A.; Karmakar, Rajesh

doi:10.1109/cdc.2013.6761034

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…Experimental studies have directly probed fluctuations of protein levels across cells [4,5]. Theoretical models assuming specific promoter configurations have solved for the co-efficient of variance CV 2 of the mRNA and protein numbers, as well as their full steady state distributions [6][7][8][9][10]. Stochastic gene expression has been shown to be a fundamental property of living cells, affecting critical physiological processes of biological and biomedical importance [6].…”

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Exact Distribution of Threshold Crossing Times for Protein Concentrations: Implication for Biological Timekeeping

et al. 2022

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Stochastic transcription and translation dynamics of protein accumulation up to some concentration threshold sets the timing of many cellular physiological processes. Here we obtain the exact distribution of first threshold-crossing times of protein concentration, in either Laplace or time domain, and its associated cumulants: mean, variance and skewness. The distribution is asymmetric and its skewness non-monotonically varies with the threshold. We study lysis times of E-coli cells for holin gene mutants of bacteriophage-λ and find a good match with theory. Mutants requiring higher holin thresholds show more skewed lysis time distributions as predicted.

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confidence: 99%

Exact Distribution of Threshold Crossing Times for Protein Concentrations: Implication for Biological Timekeeping

et al. 2022

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“…In another experimental study of transcriptional bursting across the mammalian genome it was observed that the time spent in the inactive promoter state can be very long 35 . In these cases, the inactive state is better described by a refractory period in the “OFF” state modeled by two sequential processes, before the gene can be switched on again 36 . Including multiple inactive states via a three-state promoter system could provide a means to model a highly repressed stage characterized by hypermethylated CpG islands, which are considered distinct from the more reversible form of silencing mediated by the recruitment of histone deacetylases 16 ( Fig.…”

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confidence: 99%

Distinct promoter activation mechanisms modulate noise-driven HIV gene expression

Chavali

Wong

Miller‐Jensen

2015

Sci Rep

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Latent human immunodeficiency virus (HIV) infections occur when the virus occupies a transcriptionally silent but reversible state, presenting a major obstacle to cure. There is experimental evidence that random fluctuations in gene expression, when coupled to the strong positive feedback encoded by the HIV genetic circuit, act as a ‘molecular switch’ controlling cell fate, i.e., viral replication versus latency. Here, we implemented a stochastic computational modeling approach to explore how different promoter activation mechanisms in the presence of positive feedback would affect noise-driven activation from latency. We modeled the HIV promoter as existing in one, two, or three states that are representative of increasingly complex mechanisms of promoter repression underlying latency. We demonstrate that two-state and three-state models are associated with greater variability in noisy activation behaviors, and we find that Fano factor (defined as variance over mean) proves to be a useful noise metric to compare variability across model structures and parameter values. Finally, we show how three-state promoter models can be used to qualitatively describe complex reactivation phenotypes in response to therapeutic perturbations that we observe experimentally. Ultimately, our analysis suggests that multi-state models more accurately reflect observed heterogeneous reactivation and may be better suited to evaluate how noise affects viral clearance.

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Stochastic analysis of genetic promoter architectures with memory

Cited by 2 publications

References 27 publications

Exact Distribution of Threshold Crossing Times for Protein Concentrations: Implication for Biological Timekeeping

Exact Distribution of Threshold Crossing Times for Protein Concentrations: Implication for Biological Timekeeping

Distinct promoter activation mechanisms modulate noise-driven HIV gene expression

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