Nonlinear dynamics and noise actuated by the cycle of gene inactivation in stochastic transcription

Ren, Jian; Jiao, Feng; Yu, Jianshe

doi:10.1016/j.cnsns.2020.105398

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…Solving this challenge requires rigorous but tedious mathematical analysis to establish bijection theory (table 1, appendix theorems A.1 and A.2) that shows one-to-one correspondence between parameter regions and M ( t ) dynamical features for each activation framework of the model. We noticed that the initial condition of the system could significantly influence M ( t ) dynamics [49]. Here we restricted our analysis under the condition of zero transcripts at time t = 0 (equation (2.11)), consistent with the basal expression level of genes under normal cellular growth conditions before adding external inductions [15,21,33].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Using average transcription level to understand the regulation of stochastic gene activation

Liang

Lin

2022

R. Soc. open sci.

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Gene activation is a random process, modelled as a framework of multiple rate-limiting steps listed sequentially, in parallel or in combination. Together with suitably assumed processes of gene inactivation, transcript birth and death, the step numbers and parameters in activation frameworks can be estimated by fitting single-cell transcription data. However, current algorithms require computing master equations that are tightly correlated with prior hypothetical frameworks of gene activation. We found that prior estimation of the framework can be facilitated by the traditional dynamical data of mRNA average level M ( t ), presenting discriminated dynamical features. Rigorous theory regarding M ( t ) profiles allows to confidently rule out the frameworks that fail to capture M ( t ) features and to test potential competent frameworks by fitting M ( t ) data. We implemented this procedure for a large number of mouse fibroblast genes under tumour necrosis factor induction and determined exactly the ‘cross-talking n -state’ framework; the cross-talk between the signalling and basal pathways is crucial to trigger the first peak of M ( t ), while the following damped gentle M ( t ) oscillation is regulated by the multi-step basal pathway. This framework can be used to fit sophisticated single-cell data and may facilitate a more accurate understanding of stochastic activation of mouse fibroblast genes.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Using average transcription level to understand the regulation of stochastic gene activation

Liang

Lin

2022

R. Soc. open sci.

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“…Many gene models, such as stochastic telegraph models [7][8][9], three-stage model [10], and gene models with feedback of various forms [11][12][13][14], have been proposed to study the stochastic mechanisms of gene expression from different viewpoints. Although these models have successfully interpreted some biological phenomena observed in experiments [12][13][14], they assume that the gene promoters have only one transcriptionally active (ON) state and one transcriptionally inactive (OFF) state and there are transitions between these states.…”

Section: Introductionmentioning

confidence: 99%

Calculation of Free Energy Consumption in Gene Transcription with Complex Promoter Structure

et al. 2020

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From the viewpoint of thermodynamics, gene transcription necessarily consumes free energy due to nonequilibrium processes. On the other hand, regulatory molecules present on the core promoter of a gene interact often in a dynamic, highly combinatorial, and possibly energy-dependent manner, leading to a complex promoter structure. This raises the question of how gene transcription with general promoter topology consumes free energy. We propose a biophysically intuitive approach to calculate energy consumption (quantified by the production rate of entropy) of a gene transcription process. Then, we show that the numbers of the ON and OFF states of a promoter can reduce energy consumption of the gene system and the Fano factor of mRNA, and in contrast to other regulatory ways, the cooperative binding of transcription factors to DNA sites always reduces energy consumption but amplifies the mRNA noise. While our proposed approach is general, our obtained qualitative results can in turn be used to the inference of complex promoter structure.

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Dynamical mRNA distribution regulated by multi-step gene activation

Chen

Gong

et al. 2021

AIP Advances

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Transcriptional bursting has been attributed to the random switch between the off and on states of genes. Previous studies modeled gene activation as a process involving ordered multiple rate-limiting steps. Here, we aimed to understand how multi-step gene activation regulates the dynamical mRNA distribution in isogenic cell populations. We focused on both inactive and active genes for which the gene shows a longer average duration in the off and on states, respectively. We developed a method to calculate the mRNA distribution and demonstrated the unstable dynamical bimodal distribution induced by multi-step activation. The suppression of bimodality requires only a few activation steps and is achieved by triggering more cells to express the gene. In the gene-expressing cell population, multi-step gene activation modulates the transcription distribution in a bidirectional manner: the inactive gene is transcribed with enhanced heterogeneity, whereas the active gene is facilitated to generate homogeneous transcription dynamics. These regulation scenarios guide the understanding of the network structure of signaling pathways that direct gene activation.

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Nonlinear dynamics and noise actuated by the cycle of gene inactivation in stochastic transcription

Cited by 3 publications

References 40 publications

Using average transcription level to understand the regulation of stochastic gene activation

Using average transcription level to understand the regulation of stochastic gene activation

Calculation of Free Energy Consumption in Gene Transcription with Complex Promoter Structure

Dynamical mRNA distribution regulated by multi-step gene activation

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