A generalized moment-based method for estimating parameters of stochastic gene transcription

Liang, Chen; Yu, Jianshe

doi:10.1016/j.mbs.2022.108780

Cited by 18 publications

(29 citation statements)

References 37 publications

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“…Classical models of stochastic gene expression describe the fluctuations in copy numbers of mRNAs and proteins in single cells and tissues [1][2][3]. These models have been successful at fitting experimental copy number distributions to estimate rate constants [4][5][6]. They also have provided insight into the sources of intracellular fluctuations [7,8], in their control via feedback mechanisms [9][10][11] and in their exploitation to generate oscillations and multi-stable states [12,13].…”

Section: Introductionmentioning

confidence: 99%

Concentration fluctuations in growing and dividing cells: Insights into the emergence of concentration homeostasis

2022

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Intracellular reaction rates depend on concentrations and hence their levels are often regulated. However classical models of stochastic gene expression lack a cell size description and cannot be used to predict noise in concentrations. Here, we construct a model of gene product dynamics that includes a description of cell growth, cell division, size-dependent gene expression, gene dosage compensation, and size control mechanisms that can vary with the cell cycle phase. We obtain expressions for the approximate distributions and power spectra of concentration fluctuations which lead to insight into the emergence of concentration homeostasis. We find that (i) the conditions necessary to suppress cell division-induced concentration oscillations are difficult to achieve; (ii) mRNA concentration and number distributions can have different number of modes; (iii) two-layer size control strategies such as sizer-timer or adder-timer are ideal because they maintain constant mean concentrations whilst minimising concentration noise; (iv) accurate concentration homeostasis requires a fine tuning of dosage compensation, replication timing, and size-dependent gene expression; (v) deviations from perfect concentration homeostasis show up as deviations of the concentration distribution from a gamma distribution. Some of these predictions are confirmed using data for E. coli, fission yeast, and budding yeast.

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Section: Introductionmentioning

confidence: 99%

Concentration fluctuations in growing and dividing cells: Insights into the emergence of concentration homeostasis

2022

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“…To better depict the distribution of transcripts, we provided an effective method to calculate the skewness. For a further analysis, we will confirm all parameters related to transcription systems from experimental data with the help of appropriate algorithms [33,39,40] and parameter estimations [41,42]. These data help us to simulate and analyze behaviors of transcription.…”

Section: Discussionmentioning

confidence: 93%

Stochastic Transcription with Alterable Synthesis Rates

Chen

Sun

2022

Mathematics

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Background: Gene transcription is a random bursting process that leads to large variability in mRNA numbers in single cells. The main cause is largely attributed to random switching between periods of active and inactive gene transcription. In some experiments, it has been observed that variation in the number of active transcription sites causes the initiation rate to vary during elongation. Results: We established a mathematical model based on the molecular reaction mechanism in single cells and studied a stochastic transcription system consisting of two active states and one inactive state, in which mRNA molecules are produced with two different synthesis rates. Conclusions: By calculation, we obtained the average mRNA expression level, the noise strength, and the skewness of transcripts. We gave a necessary and sufficient condition that causes the average mRNA level to peak at a limited time. The model could help us to distinguish an appropriate mechanism that may be employed by cells to transcribe mRNA molecules. Our simulations were in agreement with some experimental data and showed that the skewness can measure the deviation of the distribution of transcripts from the mean value. Especially for mature mRNAs, their distributions were almost able to be determined by the mean, the noise (or the noise strength), and the skewness.

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“…The observed peaks are best explained by measurement noise that does not allow us to identify the exact protein numbers in the low copy number regime. Binning in this case, while potentially losing some information, renders the procedure more reliable than the alternative of discarding observations below the threshold ( Fu et al., 2022 ; Chen et al., 2022 ). As measurements at time

were almost entirely below the threshold we discarded that time point for inference purposes.…”

Section: Resultsmentioning

confidence: 99%

Approximating solutions of the Chemical Master equation using neural networks

2022

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A generalized moment-based method for estimating parameters of stochastic gene transcription

Cited by 18 publications

References 37 publications

Concentration fluctuations in growing and dividing cells: Insights into the emergence of concentration homeostasis

Concentration fluctuations in growing and dividing cells: Insights into the emergence of concentration homeostasis

Stochastic Transcription with Alterable Synthesis Rates

Approximating solutions of the Chemical Master equation using neural networks

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