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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

Kinetic scheme for the gene expression with general arrival time distributions.Bursts of mRNAs arrive with a general arrival time distributions f(t). Each mRNA produces proteins with rate kp and mRNAs and proteins decay with rates μm and μp, respectively.
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pcbi.1004292.g001: Kinetic scheme for the gene expression with general arrival time distributions.Bursts of mRNAs arrive with a general arrival time distributions f(t). Each mRNA produces proteins with rate kp and mRNAs and proteins decay with rates μm and μp, respectively.

Mentions: We consider a general model of gene expression [43] as outlined in Fig 1. In the model, mRNAs are produced in bursts, with f(t) representing a general arrival time distribution for mRNA bursts. The mRNA burst distribution can be arbitrary. Each mRNA then produces proteins with rate kp, and finally, both mRNAs and proteins decay with rates μm and μp, respectively. Note that the model also allows for post-transcriptional regulation since the protein burst distribution from each mRNA can be arbitrary; the only assumption is that each mRNA produces proteins independently.


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

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

Kinetic scheme for the gene expression with general arrival time distributions.Bursts of mRNAs arrive with a general arrival time distributions f(t). Each mRNA produces proteins with rate kp and mRNAs and proteins decay with rates μm and μp, respectively.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004292.g001: Kinetic scheme for the gene expression with general arrival time distributions.Bursts of mRNAs arrive with a general arrival time distributions f(t). Each mRNA produces proteins with rate kp and mRNAs and proteins decay with rates μm and μp, respectively.
Mentions: We consider a general model of gene expression [43] as outlined in Fig 1. In the model, mRNAs are produced in bursts, with f(t) representing a general arrival time distribution for mRNA bursts. The mRNA burst distribution can be arbitrary. Each mRNA then produces proteins with rate kp, and finally, both mRNAs and proteins decay with rates μm and μp, respectively. Note that the model also allows for post-transcriptional regulation since the protein burst distribution from each mRNA can be arbitrary; the only assumption is that each mRNA produces proteins independently.

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