The route to transcription initiation determines the mode of transcriptional bursting in E. coli

Engl, Christoph; Jovanović, Goran; Brackston, Rowan D.; Kotta‐Loizou, Ioly; Buck, Martin

doi:10.1038/s41467-020-16367-6

Cited by 32 publications

(20 citation statements)

References 63 publications

(171 reference statements)

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“…shown 37,38 that it may arise from an extra number of gene states. However our results suggest that delay due to elongation (when the variability in elongation times is small) is another important source contributing to the zero-inflated distributions evident in RNA-seq data.…”

Section: Resultsmentioning

confidence: 99%

Neural network aided approximation and parameter inference of non-Markovian models of gene expression

et al. 2021

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Non-Markovian models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models, as well as the inference of their parameters from data, are fraught with difficulties because the dynamics depends on the system’s history. Here we use an artificial neural network to approximate the time-dependent distributions of non-Markovian models by the solutions of much simpler time-inhomogeneous Markovian models; the approximation does not increase the dimensionality of the model and simultaneously leads to inference of the kinetic parameters. The training of the neural network uses a relatively small set of noisy measurements generated by experimental data or stochastic simulations of the non-Markovian model. We show using a variety of models, where the delays stem from transcriptional processes and feedback control, that the Markovian models learnt by the neural network accurately reflect the stochastic dynamics across parameter space.

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Section: Resultsmentioning

confidence: 99%

Neural network aided approximation and parameter inference of non-Markovian models of gene expression

et al. 2021

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“…Although the authors of [ 67 ] do not address noise, they note enormous differences in mean expression levels when an identical construct is integrated at different genomic loci. The authors of [ 68 ] attribute noise and burstiness in their single-molecule mRNA data to the influence of different sigma factors, which is a reasonable conclusion from their data. Could the difference also be due to the different chromosomal locations of the two operons?…”

Section: Discussionmentioning

confidence: 81%

Reconciling kinetic and thermodynamic models of bacterial transcription

2021

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The study of transcription remains one of the centerpieces of modern biology with implications in settings from development to metabolism to evolution to disease. Precision measurements using a host of different techniques including fluorescence and sequencing readouts have raised the bar for what it means to quantitatively understand transcriptional regulation. In particular our understanding of the simplest genetic circuit is sufficiently refined both experimentally and theoretically that it has become possible to carefully discriminate between different conceptual pictures of how this regulatory system works. This regulatory motif, originally posited by Jacob and Monod in the 1960s, consists of a single transcriptional repressor binding to a promoter site and inhibiting transcription. In this paper, we show how seven distinct models of this so-called simple-repression motif, based both on thermodynamic and kinetic thinking, can be used to derive the predicted levels of gene expression and shed light on the often surprising past success of the thermodynamic models. These different models are then invoked to confront a variety of different data on mean, variance and full gene expression distributions, illustrating the extent to which such models can and cannot be distinguished, and suggesting a two-state model with a distribution of burst sizes as the most potent of the seven for describing the simple-repression motif.

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“…This phenomenon is commonly seen in single-cell RNA-seq data, and it is usually attributed to the expression drop-off caused by technical noises or sequencing sensitivity [28]. It has also been shown [29, 30] that it may arise from an extra number of gene states. However our results suggest that delay due to elongation (when the variability in elongation times is small) is another important source contributing to the zero-inflated distributions evident in RNA-seq data.…”

Section: Resultsmentioning

confidence: 99%

Neural network aided approximation and parameter inference of stochastic models of gene expression

Jiang

Yan

et al. 2020

Preprint

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Non-Markov models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models, as well as the inference of their parameters from data, are fraught with difficulties because the dynamics depends on the system's history. Here we use an artificial neural network to approximate the time dependent distributions of non-Markov models by the solutions of much simpler time-inhomogeneous Markov models; the approximation does not increase the dimensionality of the model and simultaneously leads to inference of the kinetic parameters. The training of the neural network uses a relatively small set of noisy measurements generated by experimental data or stochastic simulations of the non-Markov model. We show using a variety of models, where the delays stem from transcriptional processes and feedback control, that the Markov models learnt by the neural network accurately reflect the stochastic dynamics across parameter space.

show abstract

The route to transcription initiation determines the mode of transcriptional bursting in E. coli

Cited by 32 publications

References 63 publications

Neural network aided approximation and parameter inference of non-Markovian models of gene expression

Neural network aided approximation and parameter inference of non-Markovian models of gene expression

Reconciling kinetic and thermodynamic models of bacterial transcription

Neural network aided approximation and parameter inference of stochastic models of gene expression

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