Publikationsdatum:
2013-02-02
Beschreibung:
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.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3751578/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉 〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3751578/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Neuert, Gregor -- Munsky, Brian -- Tan, Rui Zhen -- Teytelman, Leonid -- Khammash, Mustafa -- van Oudenaarden, Alexander -- 1DP1OD003936/OD/NIH HHS/ -- DP1 CA174420/CA/NCI NIH HHS/ -- U54 CA143874/CA/NCI NIH HHS/ -- U54CA143874/CA/NCI NIH HHS/ -- New York, N.Y. -- Science. 2013 Feb 1;339(6119):584-7. doi: 10.1126/science.1231456.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Departments of Physics and Biology and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/23372015" target="_blank"〉PubMed〈/a〉
Schlagwort(e):
*Gene Expression Regulation, Fungal
;
Gene Regulatory Networks
;
Heat-Shock Proteins/metabolism
;
Membrane Transport Proteins/metabolism
;
*Models, Genetic
;
*Models, Statistical
;
Osmosis
;
Osmotic Pressure
;
Saccharomyces cerevisiae/*genetics/metabolism
;
Saccharomyces cerevisiae Proteins/metabolism
;
Signal Transduction
;
Single-Cell Analysis/*methods
;
Stochastic Processes
;
*Transcription, Genetic
;
*Transcriptional Activation
Print ISSN:
0036-8075
Digitale ISSN:
1095-9203
Thema:
Biologie
,
Chemie und Pharmazie
,
Informatik
,
Medizin
,
Allgemeine Naturwissenschaft
,
Physik
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