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Using gene expression noise to understand gene regulation

Using gene expression noise to understand gene regulation

http://www.wikidata.org/entity/Q24617979

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Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity Mathematics in modern immunology Modeling for (physical) biologists: an introduction to the rule-based approach Molecular mechanisms of robustness in plants Single molecule fluorescence approaches shed light on intracellular RNAs Stochastic effects in adaptive reconstruction of body damage: implied the creativity of natural selection Heterogeneity in immune responses: from populations to single cells Clk post-transcriptional control denoises circadian transcription both temporally and spatially.A single-molecule view of transcription reveals convoys of RNA polymerases and multi-scale bursting.A critical review of perfluorooctanoate and perfluorooctanesulfonate exposure and immunological health conditions in humans New methods to image transcription in living fly embryos: the insights so far, and the prospects Quantifying intrinsic and extrinsic variability in stochastic gene expression models Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories Only accessible information is useful: insights from gradient-mediated patterning Deciphering Transcriptional Dynamics In Vivo by Counting Nascent RNA Molecules Noise in biology Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction The Effect of Gap Junctional Coupling on the Spatiotemporal Patterns of Ca2+ Signals and the Harmonization of Ca2+-Related Cellular Responses Tackling Drug Resistant Infection Outbreaks of Global Pandemic Escherichia coli ST131 Using Evolutionary and Epidemiological Genomics.Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles.Stochastic profiling of transcriptional regulatory heterogeneities in tissues, tumors and cultured cells Long-term model predictive control of gene expression at the population and single-cell levels.Lattice Microbes: high-performance stochastic simulation method for the reaction-diffusion master equation.A simple add-on microfluidic appliance for accurately sorting small populations of cells with high fidelity.Population transcriptomics with single-cell resolution: a new field made possible by microfluidics: a technology for high throughput transcript counting and data-driven definition of cell types Mechanism of transcriptional bursting in bacteria.Dynamic regulation of eve stripe 2 expression reveals transcriptional bursts in living Drosophila embryos.Cdk1 promotes cytokinesis in fission yeast through activation of the septation initiation network.Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data A structured population modeling framework for quantifying and predicting gene expression noise in flow cytometry data.The assembly of miRNA-mRNA-protein regulatory networks using high-throughput expression data.Single-site transcription rates through fitting of ensemble-averaged data from fluorescence recovery after photobleaching: a fat-tailed distribution.Finite state projection based bounds to compare chemical master equation models using single-cell data.FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data Robust classification of single-cell transcriptome data by nonnegative matrix factorization.LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering.SCALE: modeling allele-specific gene expression by single-cell RNA sequencing.Effect of transcription factor resource sharing on gene expression noise Simulation of reaction diffusion processes over biologically relevant size and time scales using multi-GPU workstations.

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P2860

Using gene expression noise to understand gene regulation

http://www.wikidata.org/entity/Q24617979

description

2012 nî lūn-bûn

@nan

2012 թուականի Ապրիլին հրատարակուած գիտական յօդուած

@hyw

2012 թվականի ապրիլին հրատարակված գիտական հոդված

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2012年の論文

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2012年論文

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2012年論文

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2012年論文

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2012年論文

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2012年論文

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2012年论文

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name

Using gene expression noise to understand gene regulation

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Using gene expression noise to understand gene regulation

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Using gene expression noise to understand gene regulation

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type

label

Using gene expression noise to understand gene regulation

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Using gene expression noise to understand gene regulation

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Using gene expression noise to understand gene regulation

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prefLabel

Using gene expression noise to understand gene regulation

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Using gene expression noise to understand gene regulation

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Using gene expression noise to understand gene regulation

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Using gene expression noise to understand gene regulation

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Gregor Neuert

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P2860

Stochastic mRNA synthesis in mammalian cells

Nature, nurture, or chance: stochastic gene expression and its consequences

Single-RNA counting reveals alternative modes of gene expression in yeast

Single-cell dissection of transcriptional heterogeneity in human colon tumors

Intrinsic noise in gene regulatory networks

Super-resolution fluorescence microscopy

Genomic analysis at the single-cell level

Epigenetic and conventional regulation is distributed among activators of FLO11 allowing tuning of population-level heterogeneity in its expression

mRNA-Seq whole-transcriptome analysis of a single cell

Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations

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Alexander van Oudenaarden

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