A comparative analysis of noise properties of stochastic binary models for a self-repressing and for an externally regulating gene

Giovanini, Guilherme; Sabino, Alan U.; Barros, Luciana Rodrigues Carvalho; Ramos, Alexandre F.

doi:10.3934/mbe.2020295

Cited by 10 publications

(17 citation statements)

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“…The values of the parameters are selected for simulating treatment conditions which probability distributions governing expression of pre-treated cells indicate qualitatively distinguishable steady state regimens as recently classified [19]. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Finding building blocks having those features may provide useful insights on how to orchestrate the dynamics of a such a complex system [15]. For that, the exactly solvable stochastic model for transcription of a binary gene [16][17][18][19] is a good candidate to be used as a prototype for simulating enhanced treatment strategies.…”

Section: Introductionmentioning

confidence: 99%

“…The exact solutions of the model at constant kinetic rates have two characteristic time scales, one related with the promoter state switching and the second being the mRNA lifetime. Recently, we demonstrated that the ratio between those two time scales may be used to classify the qualitatively distinguishable noise regimens of gene transcription on the binary model [19,27] and the reliability of information about the promoter state that is transmitted by gene products [28]. Here, we assume that the addition of a drug cause the kinetic rates of the model to become time dependent and that this effect decays exponentially such that has up to four additional time scales for the problem.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Stochastic Binary Model for Regulation of Gene Expression to Investigate Treatment Effects Targeting RKIP

Giovanini¹,

Barros²,

Gama³

et al. 2021

Preprint

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In this manuscript we use an exactly solvable stochastic binary model for regulation of gene expression to analyse the dynamics of response to a treatment aiming to modulate the number of transcripts of RKIP gene. We demonstrate the usefulness of our method simulating three treatment scenarios aiming to reestablish RKIP gene expression dynamics towards pre-cancerous state: i. to increase the promoter’s ON state duration; ii. to increase the mRNAs’ synthesis rate; iii. to increase both rates. We show that the pre-treatment kinetic rates of ON and OFF promoter switching speeds and mRNA synthesis and degradation will affect the heterogeneity and time for treatment response. Hence, we present a strategy for reducing drug dosage by simultaneously targeting multiple kinetic rates. That enables a reduction of treatment response time and heterogeneity which in principle diminishes the chances of emergence of resistance to treatment. This approach may be useful for inferring kinetic constants related to expression of antimetastatic genes or oncogenes and on the design of multi-drug therapeutic strategies targeting master regulatory genes.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Stochastic Binary Model for Regulation of Gene Expression to Investigate Treatment Effects Targeting RKIP

Giovanini¹,

Barros²,

Gama³

et al. 2021

Preprint

Self Cite

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show abstract

“…Finding building blocks with those features may provide useful insights on how to modulate the dynamics of such a complex system [19]. For that, the exactly solvable stochastic model for transcription of a binary gene [20][21][22][23] is a good candidate to be used as a prototype for simulating enhanced treatment strategies.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Binary Model for the Regulation of Gene Expression to Investigate Responses to Gene Therapy

Giovanini

Barros

Gama

et al. 2022

Cancers

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In this manuscript, we use an exactly solvable stochastic binary model for the regulation of gene expression to analyze the dynamics of response to a treatment aiming to modulate the number of transcripts of a master regulatory switching gene. The challenge is to combine multiple processes with different time scales to control the treatment response by a switching gene in an unavoidable noisy environment. To establish biologically relevant timescales for the parameters of the model, we select the RKIP gene and two non-specific drugs already known for changing RKIP levels in cancer cells. We demonstrate the usefulness of our method simulating three treatment scenarios aiming to reestablish RKIP gene expression dynamics toward a pre-cancerous state: (1) to increase the promoter’s ON state duration; (2) to increase the mRNAs’ synthesis rate; and (3) to increase both rates. We show that the pre-treatment kinetic rates of ON and OFF promoter switching speeds and mRNA synthesis and degradation will affect the heterogeneity and time for treatment response. Hence, we present a strategy for reaching increased average mRNA levels with diminished heterogeneity while reducing drug dosage by simultaneously targeting multiple kinetic rates that effectively represent the chemical processes underlying the regulation of gene expression. The decrease in heterogeneity of treatment response by a target gene helps to lower the chances of emergence of resistance. Our approach may be useful for inferring kinetic constants related to the expression of antimetastatic genes or oncogenes and for the design of multi-drug therapeutic strategies targeting the processes underpinning the expression of master regulatory genes.

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“…Combining randomly timed and sized production bursts with deterministic decay leads to a Markovian drift-jump model of gene expression [7][8][9][10]. More fine-grained models of gene expression are based on a purely discrete [11][12][13][14][15] or a hybrid discrete-continuous state space [16][17][18][19][20]. The drift-jump model can be derived from the fine-grained processes using formal limit procedures [21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Heavy-tailed distributions in a stochastic gene autoregulation model

Bokes

2021

Preprint

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Synthesis of gene products in bursts of multiple molecular copies is an important source of gene expression variability. This paper studies large deviations in a Markovian drift--jump process that combines exponentially distributed bursts with deterministic degradation. Large deviations occur as a cumulative effect of many bursts (as in diffusion) or, if the model includes negative feedback in burst size, in a single big jump. The latter possibility requires a modification in the WKB solution in the tail region. The main result of the paper is the construction, via a modified WKB scheme, of matched asymptotic approximations to the stationary distribution of the drift--jump process. The stationary distribution possesses a heavier tail than predicted by a routine application of the scheme.

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A comparative analysis of noise properties of stochastic binary models for a self-repressing and for an externally regulating gene

Cited by 10 publications

References 1 publication

A Stochastic Binary Model for Regulation of Gene Expression to Investigate Treatment Effects Targeting RKIP

A Stochastic Binary Model for Regulation of Gene Expression to Investigate Treatment Effects Targeting RKIP

A Stochastic Binary Model for the Regulation of Gene Expression to Investigate Responses to Gene Therapy

Heavy-tailed distributions in a stochastic gene autoregulation model

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