Literature DB >> 18981421

Signatures of combinatorial regulation in intrinsic biological noise.

Aryeh Warmflash1, Aaron R Dinner.   

Abstract

Gene expression is controlled by the action of transcription factors that bind to DNA and influence the rate at which a gene is transcribed. The quantitative mapping between the regulator concentrations and the output of the gene is known as the cis-regulatory input function (CRIF). Here, we show how the CRIF shapes the form of the joint probability distribution of molecular copy numbers of the regulators and the product of a gene. Namely, we derive a class of fluctuation-based relations that relate the moments of the distribution to the derivatives of the CRIF. These relations are useful because they enable statistics of naturally arising cell-to-cell variations in molecular copy numbers to substitute for traditional manipulations for probing regulatory mechanisms. We demonstrate that these relations can distinguish super- and subadditive gene regulatory scenarios (molecular analogs of AND and OR logic operations) in simulations that faithfully represent bacterial gene expression. Applications and extensions to other regulatory scenarios are discussed.

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Year:  2008        PMID: 18981421      PMCID: PMC2582248          DOI: 10.1073/pnas.0809314105

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  44 in total

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Journal:  Proc Natl Acad Sci U S A       Date:  2002-09-17       Impact factor: 11.205

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8.  Predicting stochastic gene expression dynamics in single cells.

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9.  The logical analysis of continuous, non-linear biochemical control networks.

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Authors:  Y Setty; A E Mayo; M G Surette; U Alon
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  11 in total

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2.  Role of integrated noise in pathway-specific signal propagation in feed-forward loops.

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6.  Construction and analysis of an integrated regulatory network derived from high-throughput sequencing data.

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9.  Phase resetting reveals network dynamics underlying a bacterial cell cycle.

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10.  Dynamics of protein noise can distinguish between alternate sources of gene-expression variability.

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