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Science 339 (6119): 584-587

Copyright © 2013 by the American Association for the Advancement of Science

Systematic Identification of Signal-Activated Stochastic Gene Regulation

Gregor Neuert1,2,*, Brian Munsky3,*, Rui Zhen Tan1,5,6, Leonid Teytelman1, Mustafa Khammash4,7,{dagger}, and Alexander van Oudenaarden1,8,{dagger},{ddagger}

1 Departments of Physics and Biology and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
2 Department of Molecular Physiology and Biophysics, School of Medicine, Vanderbilt University, Nashville, TN 37232, USA.
3 Center for Nonlinear Studies and the Information Sciences Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
4 Department of Biosystems Science and Engineering, ETH-Zuerich, 4058 Basel, Switzerland.
5 Bioinformatics Institute, A*STAR, Singapore 138671, Singapore.
6 Harvard University Graduate Biophysics Program, Harvard Medical School, Boston, MA 02115, USA.
7 Center for Control, Dynamical Systems and Computation and Department of Mechanical Engineering, University of California, Santa Barbara, CA 93106, USA.
8 Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences and University Medical Center Utrecht, Uppsalalaan 8, 3584 CT, Utrecht, Netherlands.

Figure 1
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Fig. 1. Quantitative analysis of single-cell stochastic gene regulation. (A) Schematic of a generic signaling cascade in which a kinase enters the nucleus and interacts with transcription factors (TF) and chromatin modifiers (CM) to regulate gene expression. (B) Rapid, stochastic, and bimodal activation of endogenous STL1 mRNA expression is detected with single-molecule RNA-FISH [yeast cell: gray circle; DAPI (4',6-diamidino-2-phenylindole)–stained nucleus: blue; STL1 mRNA: green dots]. Scale bar: 2 μm.


Figure 2
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Fig. 2. Identifying a maximally predictive model structure. (A) Two- and multistate model structures that allow for kinase, transcription factor, and chromatin modifier–dependent activation of gene expression. (B) Relative likelihoods of best fit for different model structures at 0.4 M NaCl (red, left axis) and the resulting predictions at 0.2 M NaCl (green, right axis). Cross-validation at 0.4 M NaCl (27) is used to quantify predictive uncertainty (gray region, left axis) and yields excellent a priori knowledge of predictive power (compare blue and green lines). (C) mRNA expression distributions at two NaCl concentrations (black and blue lines) and best fit at 0.4 M (red line) and the corresponding prediction at 0.2 M NaCl (green line). The fit and predictions correspond to the four-state structure with one Hog1p dependency identified at 0.4 M NaCl in (fig. S7). The black arrow indicates the similar mRNA expression levels after an osmotic shock of 0.2 and 0.4 M NaCl. The purple star indicates the time point of gene expression deactivation.


Figure 3
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Fig. 3. Model structure validation. (A) Combined fit of the model structure identified (fig. S7) to different genetic mutations affecting STL1 expression at 0.4 M NaCl: wild-type (WT) (red), Hot1p 5x (blue), arp8{Delta} (black), and gcn5{Delta} (green). (B) Model prediction of CTT1 (cyan) and HSP12 (magenta) expression at 0.2 M NaCl. (C) Model prediction for HSP12 expression at 0.4 M in the arp8{Delta} strain.


Figure 4
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Fig. 4. Relating model structure to biological function. (A) Mutant and gene-specific rate changes relative to STL1. (B) Final model, in which Hog1p, Hot1p, Gcn5p, and Arp8p regulate transitions between states S1 and S2.


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