Noise in gene expression depends on the promoter class and on TF dynamics. Noise in gene expression depends on the promoter class and on TF dynamics. (A,

Slides:



Advertisements
Similar presentations
Nan Hao, Erin K O’Shea. + How is an environmental stimuli transmitted into a cell? + How a cell respond to a specific signal? – Here the signal can be.
Advertisements

Date of download: 7/8/2016 Copyright © 2016 SPIE. All rights reserved. ChR2-EYFP expression in transgenic mouse brain slices. (a) Fluorescent microscope.
Cluster analysis of 50 genes identified as affecting variability and or pheromone response output Cluster analysis of 50 genes identified as affecting.
Negative Feedback Synchronizes Islets of Langerhans
Tuning the response of the DPI
Xiao-yu Zheng, Erin K. O’Shea  Cell Reports 
Stability and Nuclear Dynamics of the Bicoid Morphogen Gradient
DNase‐HS sites are main independent determinants of DNA replication timing Simulations based on genome sequence features (GC content, CpG islands), or.
Periodic changes in distribution of ERK–GFP fusion protein in cells after stimulation with EGF. (A) Confocal image of cells expressing ERK–GFP both before.
Masahiro Ueda, Tatsuo Shibata  Biophysical Journal 
Hnf4g importance in human colon cancer organoids and regulation of the Hnf4a locus Hnf4g importance in human colon cancer organoids and regulation of the.
Most promoters preserve their relative activity levels across conditions. Most promoters preserve their relative activity levels across conditions. (A)
Sensitivity of RNA‐seq.
Wei-Hsiang Lin, Edo Kussell  Current Biology 
Motif detectability corresponds to the phylogenetic profile of the cognate transcription factor. Motif detectability corresponds to the phylogenetic profile.
Anders S. Hansen, Erin K. O’Shea  Current Biology 
Average Number of Photons
Predicted and measured double‐ring formation.
Computational Re-design of Synthetic Genetic Oscillators for Independent Amplitude and Frequency Modulation  Marios Tomazou, Mauricio Barahona, Karen.
A mathematical model for transcription factor‐activated gene expression allows clustering of promoters and detailed quantitative characterization. A mathematical.
Dose response of pAGA1 and pFIG1 induction
The human STRIPAK complex associates with RASF3 and MST1/2
Coupling immunophenotypes to Drop‐seq data
Volume 92, Issue 8, Pages (April 2007)
Intracellular noise in the cAMP circuit drives observed population behaviors Firing rate phase diagrams for single cells in a population (top) and the.
Evaluating CRISPR negative selection screens.
Marker reproducibility and metastasis prediction performance.
Negative Feedback Synchronizes Islets of Langerhans
The PRS is robust to changes in receptor abundance
Downstream antagonism.
Jianing Yu, David Ferster  Neuron 
Alignment time for Clustal Omega (red), MAFFT (blue), MUSCLE (green) and Kalign (purple) against the number of sequences of HomFam test sets. Alignment.
Dynamic map of the Nap1p/Kcc4p interaction.
Changes to the growth conditions break the circuit by changing host gene expression Changes to the growth conditions break the circuit by changing host.
Visualization of a global auxin‐response gradient in the root meristem
Effect of the loss of Kar4 on the induction of various promoters
Bisection mapping of T7. RNAP
Novel functional roles of uncharacterized genes as functional regulators of cellular cholesterol levels. Novel functional roles of uncharacterized genes.
NF-κB Dynamics Discriminate between TNF Doses in Single Cells
Dynamics and expression level of mating‐dependent promoters
The cell cycle influences circadian phase progression Circadian intervals with divisions (p1,d1,p2) last 21.95 ± 3.8 h (n = 1,926) and are significantly.
Alon Poleg-Polsky, Huayu Ding, Jeffrey S. Diamond  Cell Reports 
An Unstable Singularity Underlies Stochastic Phasing of the Circadian Clock in Individual Cyanobacterial Cells  Siting Gan, Erin K. O’Shea  Molecular.
Shared components define the ‘ancient’ phagosome.
Volume 12, Issue 3, Pages (March 2007)
ClpX facilitates FtsZ depletion during carbon starvation
Comparison of proteomics and RNA‐Seq data.
Determination of mRNA synthesis and decay rates.
Subnetworks enriched for the hallmarks of cancer.
Network modules correspond to known and novel functional distinctions between neuronal subtypes. Network modules correspond to known and novel functional.
PKA mediates the primary transcriptional response of cells to glucose.
Target-Specific Glycinergic Transmission from VGluT3-Expressing Amacrine Cells Shapes Suppressive Contrast Responses in the Retina  Nai-Wen Tien, Tahnbee.
The activity and orthogonality of ECF σs are shown.
A multitiered approach to characterize transcriptome structure.
Protein interactions at the nuclear pore.
Noise in hoxb1a/krox20 expression leads to boundary sharpening.
Correlations between metabolic pathway abundances and environmental conditions deduced from the ocean samples in this study, at various levels of model.
Dynamic transcriptome analysis in yeast.
Antisense expression associates with larger gene expression variability. Antisense expression associates with larger gene expression variability. (A–D)
tssRNA promoter analysis and function.
Interaction of Gpr1/Gpa2 and Sch9.
Dynamic regulatory map and static network for yeast response to AA starvation. Dynamic regulatory map and static network for yeast response to AA starvation.
Competence initiation during the progression to spore formation.
3D model and in vivo live imaging of dermal wound repair
Single‐cell RT–qPCR data
(A) Observed significant protein fold‐changes during the fed–fasting transition in C57BL/6J (B6) and 129Sv (S9) mice fed with a normal diet (T0). (A) Observed.
Sch9 plays a minor role in glucose signaling.
Christina Ketchum, Heather Miller, Wenxia Song, Arpita Upadhyaya 
Probing the Limits to Positional Information
Volume 12, Issue 23, Pages (December 2002)
Presentation transcript:

Noise in gene expression depends on the promoter class and on TF dynamics. Noise in gene expression depends on the promoter class and on TF dynamics. (A, B) Total noise (A) (σ2/μ2) and intrinsic (B) noise (defined in Materials and methods) is plotted against the Msn2 AUC (red, green, and blue denotes HS (SIP18, ALD3 and TKL2), RTN2, and LF (DDR2, DCS2, and HXK1) promoters, respectively). Each dot corresponds to the noise (mean noise across time points after gene expression has reached a plateau) for a single experiment: that is, a single Msn2 input for a single promoter. (C) TF dynamics and noise. The total noise for a 40‐min pulse at 690 nM 1‐NM‐PP1 (purple) is compared with the total noise for eight pulses with 5 min duration and interval at 690 nM 1‐NM‐PP1 (orange) such that the total Msn2 AUC is constant. (D, E) Single‐cell YFP time traces for DCS2 and SIP18 in response to a single 40‐min pulse at 690 nM 1‐NM‐PP1 corresponding to the orange bar graphs for DCS2 and SIP18 in (C) that are highlighted with an asterisk (*). The traces show raw single‐cell YFP data (smoothed by a 3‐point moving average). (F) YFP/CFP scatterplot. Each dot corresponds to the raw CFP (x axis) and YFP (y axis) fluorescence in a single cell at 150 min from (D) and (E). SIP18: red dots. DCS2: blue dots. Spread along the diagonal is due to extrinsic noise effects and spread orthogonal to the diagonal is due to intrinsic noise effects. See also Supplementary Figure S7 for examples of bimodal gene expression, noise versus mean, extrinsic noise and additional plots. Source data for this figure are available on the online Supplementary information page. Anders S Hansen, and Erin K O'Shea Mol Syst Biol 2013;9:704 © as stated in the article, figure or figure legend