Holimap: an accurate and efficient method for solving stochastic gene network dynamics

Jia, Chen; Grima, Ramon

doi:10.1101/2024.02.25.581947

Cited by 3 publications

(2 citation statements)

References 61 publications

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“…For simplicity and analytical tractability, we here only derive the switching time distributions in an autoregulatory feedback loop. We anticipate that the results in the present paper can be generalized to more complex gene regulatory networks [26, 41]. In addition, while we have an implicit effective description of mRNA and protein dilution due to cell division, via the effective mRNA and protein decay rates, it has recently been shown that, in some parameter regimes, this type of model cannot capture the stochastic dynamics predicted by models with an explicit description of the cell cycle [42, 43].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Exact switching time distributions for autoregulated gene expression models with mRNA and protein descriptions

Liu,

Wu,

Jia

2024

Preprint

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In this study, we obtain the exact switching time distributions between the free and bound gene states for a detailed stochastic model of an autoregulatory genetic feedback loop with transcription, translation, mRNA and protein decay, as well as protein-gene interactions. The analytical solution generalizes and corrects the previous ones obtained in [Phys. Rev. Lett. 101, 118104 (2008)] and [Nat. Commun. 9, 3305 (2018)] for a reduced model of an autoregulatory loop that ignores the mRNA dynamics. We find that when the mRNA dynamics is modelled explicitly, the holding time in the free gene state can produce three shapes of steady-state distributions (decaying, bell-shaped, and bimodal). In particular, the detailed model with both mRNA and protein descriptions can produce a distribution shape that the reduced model fails to capture — the detailed model of a negative (positive) feedback loop can display a bimodal (bell-shaped) holding time distribution, while the reduced model cannot. Interestingly, we also find that an autoregulatory loop can produce a heavy-tailed holding time distribution and the origin of this heavy-tailed phenomenon is clarified using our analytical solution. Finally, we investigate how the distribution shape is affected by the type of feedback, the binding and unbinding rates, and the transcription rates.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Exact switching time distributions for autoregulated gene expression models with mRNA and protein descriptions

Liu,

Wu,

Jia

2024

Preprint

Self Cite

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show abstract

“…Despite the potential risks, we found that fitting these complex models to the telegraph model still provide a large amount of valuable information. In fact, the idea of using the distribution of the telegraph model to approximate that of a complex model has been applied in previous studies using analytical PLOS COMPUTATIONAL BIOLOGY methods such as linear mapping or moment matching [89][90][91]. Here we evaluate the performance of the effective telegraph model using statistical and computational methods.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

What can we learn when fitting a simple telegraph model to a complex gene expression model?

Jiao,

Li,

Liu

et al. 2024

PLoS Comput Biol

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In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise.

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What can we learn when fitting a simple telegraph model to a complex gene expression model?

Jiao

Liu

et al. 2023

Preprint

Self Cite

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In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and degradation of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective rate parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective rate parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can be also applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for {\it{E. coli}} and mammalian cells.

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Holimap: an accurate and efficient method for solving stochastic gene network dynamics

Cited by 3 publications

References 61 publications

Exact switching time distributions for autoregulated gene expression models with mRNA and protein descriptions

Exact switching time distributions for autoregulated gene expression models with mRNA and protein descriptions

What can we learn when fitting a simple telegraph model to a complex gene expression model?

What can we learn when fitting a simple telegraph model to a complex gene expression model?

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