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Transcriptional Bursting in Gene Expression: Analytical Results for General Stochastic Models.

Kumar N, Singh A, Kulkarni RV - PLoS Comput. Biol. (2015)

Bottom Line: To address this issue, we invoke a mapping between general stochastic models of gene expression and systems studied in queueing theory to derive exact analytical expressions for the moments associated with mRNA/protein steady-state distributions.These results are then used to derive noise signatures, i.e. explicit conditions based entirely on experimentally measurable quantities, that determine if the burst distributions deviate from the geometric distribution or if burst arrival deviates from a Poisson process.The proposed approaches can lead to new insights into transcriptional bursting based on measurements of steady-state mRNA/protein distributions.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, University of Massachusetts Boston, Boston, Massachusetts, United States of America.

ABSTRACT
Gene expression in individual cells is highly variable and sporadic, often resulting in the synthesis of mRNAs and proteins in bursts. Such bursting has important consequences for cell-fate decisions in diverse processes ranging from HIV-1 viral infections to stem-cell differentiation. It is generally assumed that bursts are geometrically distributed and that they arrive according to a Poisson process. On the other hand, recent single-cell experiments provide evidence for complex burst arrival processes, highlighting the need for analysis of more general stochastic models. To address this issue, we invoke a mapping between general stochastic models of gene expression and systems studied in queueing theory to derive exact analytical expressions for the moments associated with mRNA/protein steady-state distributions. These results are then used to derive noise signatures, i.e. explicit conditions based entirely on experimentally measurable quantities, that determine if the burst distributions deviate from the geometric distribution or if burst arrival deviates from a Poisson process. For non-Poisson arrivals, we develop approaches for accurate estimation of burst parameters. The proposed approaches can lead to new insights into transcriptional bursting based on measurements of steady-state mRNA/protein distributions.

No MeSH data available.


Related in: MedlinePlus

Schematic representation of the general kinetic scheme with promoter switching.Thick line from inactive state D0 to active state Da represents a general kinetic scheme with g(t) as the waiting-time distribution for the promoter to switch to the ON state.
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pcbi.1004292.g003: Schematic representation of the general kinetic scheme with promoter switching.Thick line from inactive state D0 to active state Da represents a general kinetic scheme with g(t) as the waiting-time distribution for the promoter to switch to the ON state.

Mentions: We begin by considering the general kinetic scheme shown in Fig 3. This form for the kinetic scheme is supported by recent experiments in mammalian cells which suggest the presence of multiple rate-limiting steps between transition of the promoter from OFF to ON state [45, 57]. However, as observed in these experiments, a promoter in the ON state switches to the OFF state by a single rate-limiting step. We model the promoter switching from OFF to ON state by a general waiting-time distribution, g(t). The switching rate from ON to OFF state is given by β.


Transcriptional Bursting in Gene Expression: Analytical Results for General Stochastic Models.

Kumar N, Singh A, Kulkarni RV - PLoS Comput. Biol. (2015)

Schematic representation of the general kinetic scheme with promoter switching.Thick line from inactive state D0 to active state Da represents a general kinetic scheme with g(t) as the waiting-time distribution for the promoter to switch to the ON state.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4608583&req=5

pcbi.1004292.g003: Schematic representation of the general kinetic scheme with promoter switching.Thick line from inactive state D0 to active state Da represents a general kinetic scheme with g(t) as the waiting-time distribution for the promoter to switch to the ON state.
Mentions: We begin by considering the general kinetic scheme shown in Fig 3. This form for the kinetic scheme is supported by recent experiments in mammalian cells which suggest the presence of multiple rate-limiting steps between transition of the promoter from OFF to ON state [45, 57]. However, as observed in these experiments, a promoter in the ON state switches to the OFF state by a single rate-limiting step. We model the promoter switching from OFF to ON state by a general waiting-time distribution, g(t). The switching rate from ON to OFF state is given by β.

Bottom Line: To address this issue, we invoke a mapping between general stochastic models of gene expression and systems studied in queueing theory to derive exact analytical expressions for the moments associated with mRNA/protein steady-state distributions.These results are then used to derive noise signatures, i.e. explicit conditions based entirely on experimentally measurable quantities, that determine if the burst distributions deviate from the geometric distribution or if burst arrival deviates from a Poisson process.The proposed approaches can lead to new insights into transcriptional bursting based on measurements of steady-state mRNA/protein distributions.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, University of Massachusetts Boston, Boston, Massachusetts, United States of America.

ABSTRACT
Gene expression in individual cells is highly variable and sporadic, often resulting in the synthesis of mRNAs and proteins in bursts. Such bursting has important consequences for cell-fate decisions in diverse processes ranging from HIV-1 viral infections to stem-cell differentiation. It is generally assumed that bursts are geometrically distributed and that they arrive according to a Poisson process. On the other hand, recent single-cell experiments provide evidence for complex burst arrival processes, highlighting the need for analysis of more general stochastic models. To address this issue, we invoke a mapping between general stochastic models of gene expression and systems studied in queueing theory to derive exact analytical expressions for the moments associated with mRNA/protein steady-state distributions. These results are then used to derive noise signatures, i.e. explicit conditions based entirely on experimentally measurable quantities, that determine if the burst distributions deviate from the geometric distribution or if burst arrival deviates from a Poisson process. For non-Poisson arrivals, we develop approaches for accurate estimation of burst parameters. The proposed approaches can lead to new insights into transcriptional bursting based on measurements of steady-state mRNA/protein distributions.

No MeSH data available.


Related in: MedlinePlus