Systematic Identification of Signal-Activated Stochastic Gene Regulation

Although much has been done to elucidate the biochemistry of signal transduction and gene regulatory pathways, it remains difficult to understand or predict quantitative responses. We integrate single-cell experiments with stochastic analyses, to identify predictive models of transcriptional dynamic...

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Bibliographic Details
Published in:Science (American Association for the Advancement of Science) 2013-02, Vol.339 (6119), p.584-587
Main Authors: Neuert, Gregor, Munsky, Brian, Tan, Rui Zhen, Teytelman, Leonid, Khammash, Mustafa, van Oudenaarden, Alexander
Format: Article
Language:English
Subjects:
Cellular biology
Chromatin
Gene expression
Gene Expression Regulation, Fungal
Gene Regulatory Networks
Genes
Heat-Shock Proteins - metabolism
Membrane Transport Proteins - metabolism
Messenger RNA
Modeling
Models, Genetic
Models, Statistical
Osmosis
Osmotic Pressure
Parametric models
Power lines
Predictive modeling
rev genes
Saccharomyces cerevisiae - genetics
Saccharomyces cerevisiae - metabolism
Saccharomyces cerevisiae Proteins - metabolism
Signal Transduction
Single-Cell Analysis - methods
Stochastic models
Stochastic Processes
Transcription, Genetic
Transcriptional Activation
Trucks
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Summary:Although much has been done to elucidate the biochemistry of signal transduction and gene regulatory pathways, it remains difficult to understand or predict quantitative responses. We integrate single-cell experiments with stochastic analyses, to identify predictive models of transcriptional dynamics for the osmotic stress response pathway in Saccharomyces cerevisiae. We generate models with varying complexity and use parameter estimation and cross-validation analyses to select the most predictive model. This model yields insight into several dynamical features, including multistep regulation and switchlike activation for several osmosensitive genes. Furthermore, the model correctly predicts the transcriptional dynamics of cells in response to different environmental and genetic perturbations. Because our approach is general, it should facilitate a predictive understanding for signal-activated transcription of other genes in other pathways or organisms.
ISSN:0036-8075
1095-9203
DOI:10.1126/science.1231456