Noise Contributions in an Inducible Genetic Switch: A Whole-Cell Simulation Study
Publication Date
March 10, 2011
Journal
PLOS Computational Biology
Authors
Elijah Roberts, Andrew Magis, Julio O. Ortiz, Wolfgang Baumeister, et al
Volume
7
Issue
3
Pages
e1002010
DOI
https://dx.plos.org/10.1371/journal.pcbi.1002010
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1002010
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/21423716
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3053318
Europe PMC
http://europepmc.org/abstract/MED/21423716
Web of Science
000288995500030
Scopus
79953666361
Mendeley
http://www.mendeley.com/research/noise-contributions-inducible-genetic-switch-wholecell-simulation-study
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Mendeley | Further Information

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/792381"], "description"=>"a<p>Based on data from Ridgway <i>et al.</i><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002010#pcbi.1002010-Ridgway1\" target=\"_blank\">[38]</a>.</p>b<p>Total occupied volume (excl. DNA) of 50%.</p>c<p>Per cylindrical persistence length 2 nm in diameter and 50 nm long.</p>", "links"=>[], "tags"=>["abundance", "spatial"], "article_id"=>462752, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.t002", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Obstacle_abundance_in_in_vivo_spatial_models_/462752", "title"=>"Obstacle abundance in <i>in vivo</i> spatial models.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-10 00:45:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/792020"], "description"=>"<p>(A) The distribution of LacY in (orange bars) 100 modeled <i>E. coli</i> cells at 13 TMG concentration compared with (green dotted) the PFB well-stirred distribution. (B) Mean number of LacY proteins in the (circles) PFB+IV and (green dotted) PFB models. (C) The noise in the distributions.</p>", "links"=>[], "tags"=>["molecular biology", "Computational biology", "cell biology", "biophysics", "physics", "mathematics"], "article_id"=>462381, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.g013", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_LacY_PFB_IV_in_vivo_distributions_/462381", "title"=>"LacY PFB+IV <i>in vivo</i> distributions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:39:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/792349"], "description"=>"a<p><i>S</i> = <i>in vivo</i> single molecule experiment, <i>K</i> = <i>in vitro</i> (kinetic) experiment, <i>M</i> = model parameter fit to single-molecule distributions.</p>b<p><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002010#pcbi.1002010-Goeddel1\" target=\"_blank\">[92]</a>,</p>c<p><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002010#pcbi.1002010-OGorman1\" target=\"_blank\">[74]</a>, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002010#pcbi.1002010-Dunaway1\" target=\"_blank\">[75]</a>,</p>d<p><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002010#pcbi.1002010-Maloney1\" target=\"_blank\">[69]</a>, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002010#pcbi.1002010-Chung1\" target=\"_blank\">[93]</a>,</p>e<p><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002010#pcbi.1002010-Dornmair1\" target=\"_blank\">[70]</a>.</p>", "links"=>[], "tags"=>["constants", "stochastic"], "article_id"=>462718, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.t001", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Reactions_and_rate_constants_used_in_the_stochastic_model_of_the_lac_circuit_/462718", "title"=>"Reactions and rate constants used in the stochastic model of the <i>lac</i> circuit.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-10 00:45:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/791252"], "description"=>"<p>(A) In the absence of inducer, the lac repressor (LacI) binds to the <i>lac</i> operator preventing transcription of genes in the <i>lac</i> operon. (B) Following an increase in the extracellular inducer concentration, inducer enters the cell via both diffusion across the membrane and active transport by lactose permease (LacY). Once inside, inducer binds free LacI molecules preventing them from binding to the operator. (C) After the intracellular inducer concentration reaches a threshold, any bound repressor is “knocked-off” the operator leading to expression of the <i>lac</i> genes. (D) At high intracellular inducer concentrations the genes for lactose metabolism are fully induced. (E) After inducer is removed, repressor rebinds to the operator preventing further expression of the <i>lac</i> operon and the enzymes for lactose metabolism are either degraded or diluted through cellular division.</p>", "links"=>[], "tags"=>["circuit"], "article_id"=>461619, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.g002", "stats"=>{"downloads"=>3, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overview_of_the_lac_genetic_circuit_in_E_coli_/461619", "title"=>"Overview of the <i>lac</i> genetic circuit in <i>E. coli</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:26:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/791620"], "description"=>"<p>(A) Fraction of operator regions bound by a repressor as a function of time following an increase of IPTG to the indicated concentration. In these simulations, . (B) Number of bursts over the mean protein lifetime as a function of inducer concentration for a variety of values of the parameter. x are data from Choi <i>et al. </i><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002010#pcbi.1002010-Choi1\" target=\"_blank\">[22]</a>.</p>", "links"=>[], "tags"=>["fitting"], "article_id"=>461996, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.g008", "stats"=>{"downloads"=>3, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameter_fitting_for_inducer_8211_repressor_8211_operator_interactions_/461996", "title"=>"Parameter fitting for inducer–repressor–operator interactions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:33:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/791882"], "description"=>"<p>(A) Mapping of the mean internal inducer concentration for a given external concentration for the (green) uninduced and (red) induced subpopulations. (black dotted) The values for the lac circuit without positive feedback are shown for reference. (B) The mean number of LacY in the subpopulations as a function of internal inducer concentration. (C) The noise in the LacY distribution.</p>", "links"=>[], "tags"=>["grf"], "article_id"=>462249, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.g011", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effect_of_positive_feedback_on_GRF_noise_/462249", "title"=>"Effect of positive feedback on GRF noise.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:37:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/791318"], "description"=>"<p>(A) Pseudo first order rate constants observed during stochastic simulations of IPTG binding to (blue) repressor and (red) repressor-operator complex. At each inducer concentration 1000 simulations starting with 2 free (or operator-complexed) repressor dimers in a volume of L were performed. The mean fraction of free repressor monomers as a function of time was fit to a single exponential to obtain the observed rate constant for binding at the inducer concentration. x and o are data from Dunaway <i>et al.. </i><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002010#pcbi.1002010-Dunaway1\" target=\"_blank\">[75]</a>. (B) Equilibrium binding of IPTG to (blue) repressor and (red) repressor-operator complexes. In a stochastic simulation at each inducer concentration, 20 free (or operator-complexed) repressor dimers in L were first equilibrated with inducer to reach the steady state. Following, 5 minutes of data were collected from which the equilibrium fraction of inducer bound repressor monomers was calculated. x and o are data from O'Gorman <i>et al.. </i><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002010#pcbi.1002010-OGorman1\" target=\"_blank\">[74]</a>.</p>", "links"=>[], "tags"=>["constants", "iptg", "binding"], "article_id"=>461682, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.g003", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fits_of_rate_constants_for_IPTG_binding_to_the_lac_repressor_/461682", "title"=>"Fits of rate constants for IPTG binding to the <i>lac</i> repressor.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:28:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/791763"], "description"=>"<p>(A) Probability density (arbitrary units, darker = higher) of the number of LacY in a cell over the course of 24 hours. Shown are representative responses for populations in the uninduced range (0–10 ; left), the bimodal range (10–25 ; center), and the concerted induction range (>25 ; right). Lines show the mean value of the (green) uninduced and (red) induced subpopulations. (B) Fraction of the cells in each of the subpopulations. (C) The (solid) mean and (dotted) variance of LacY in the uninduced subpopulation.</p>", "links"=>[], "tags"=>["uninduced", "pfb"], "article_id"=>462133, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.g010", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Response_of_an_uninduced_PFB_population_to_the_addition_of_external_inducer_/462133", "title"=>"Response of an uninduced PFB population to the addition of external inducer.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:35:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/396418", "https://ndownloader.figshare.com/files/396506", "https://ndownloader.figshare.com/files/396565"], "description"=>"<div><p>Stochastic expression of genes produces heterogeneity in clonal populations of bacteria under identical conditions. We analyze and compare the behavior of the inducible <em>lac</em> genetic switch using well-stirred and spatially resolved simulations for <em>Escherichia coli</em> cells modeled under fast and slow-growth conditions. Our new kinetic model describing the switching of the <em>lac</em> operon from one phenotype to the other incorporates parameters obtained from recently published <em>in vivo</em> single-molecule fluorescence experiments along with <em>in vitro</em> rate constants. For the well-stirred system, investigation of the intrinsic noise in the circuit as a function of the inducer concentration and in the presence/absence of the feedback mechanism reveals that the noise peaks near the switching threshold. Applying maximum likelihood estimation, we show that the analytic two-state model of gene expression can be used to extract stochastic rates from the simulation data. The simulations also provide mRNA–protein probability landscapes, which demonstrate that switching is the result of crossing both mRNA and protein thresholds. Using cryoelectron tomography of an <em>E. coli</em> cell and data from proteomics studies, we construct spatial <em>in vivo</em> models of cells and quantify the noise contributions and effects on repressor rebinding due to cell structure and crowding in the cytoplasm. Compared to systems without spatial heterogeneity, the model for the fast-growth cells predicts a slight decrease in the overall noise and an increase in the repressors rebinding rate due to anomalous subdiffusion. The tomograms for <em>E. coli</em> grown under slow-growth conditions identify the positions of the ribosomes and the condensed nucleoid. The smaller slow-growth cells have increased mRNA localization and a larger internal inducer concentration, leading to a significant decrease in the lifetime of the repressor–operator complex and an increase in the frequency of transcriptional bursts.</p> </div>", "links"=>[], "tags"=>["contributions", "inducible", "whole-cell", "simulation"], "article_id"=>138310, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002010.s001", "https://dx.doi.org/10.1371/journal.pcbi.1002010.s002", "https://dx.doi.org/10.1371/journal.pcbi.1002010.s003"], "stats"=>{"downloads"=>4, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Noise_Contributions_in_an_Inducible_Genetic_Switch_A_Whole_Cell_Simulation_Study/138310", "title"=>"Noise Contributions in an Inducible Genetic Switch: A Whole-Cell Simulation Study", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-03-10 02:18:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/791688"], "description"=>"<p>Distributions at inducer concentrations of (A) 0, (B) 100, and (C) 200 TMG. Shown are (gray bars) histograms from 10,000 Gillespie trajectories and (red dash) gamma distributions from Choi <i>et al. </i><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002010#pcbi.1002010-Choi1\" target=\"_blank\">[22]</a>. (D) Mean LacY as a function of inducer concentration along with 95% ranges. (E) The noise in the LacY distributions as quantified by the Fano factor (variance over the mean). (F) The fraction of time spent in the transcriptionally active state.</p>", "links"=>[], "tags"=>["lacy", "distributions", "well-stirred", "npf"], "article_id"=>462051, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.g009", "stats"=>{"downloads"=>4, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Steady_state_LacY_distributions_from_the_well_stirred_NPF_model_/462051", "title"=>"Steady state LacY distributions from the well-stirred NPF model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:34:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/791198"], "description"=>"<p>(A) Burst model in which transcription of the DNA is always active. (B) Two-state model in which the DNA switches with constant rates between active and repressed states. (C) Inducible genetic switch in which an inducer both controls the rate of switching between active and inactive transcription states and is also positively regulated by the protein product – a positive feedback loop (PFB). The gray dotted connection indicates a weak effect of the inducer in promoting the unbinding of repressor at high inducer concentrations.</p>", "links"=>[], "tags"=>["models", "stochastic"], "article_id"=>461566, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.g001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Three_models_for_stochastic_gene_expression_/461566", "title"=>"Three models for stochastic gene expression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:26:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/792195"], "description"=>"<p>(A and B) Parameter fits from the burst model. (C–F) Parameter fits from the two-state model. Shown are fits for (black dotted) NPF simulations, (green dotted) PFB simulations, and (orange circles) PFB+IV simulations. Also shown are (blue solid) actual parameter values calculated from the simulation data. Shaded areas indicate the 95% confidence intervals for ML fits using distributions from 50 and 200 NPF cells.</p>", "links"=>[], "tags"=>["fitting", "models", "stochastic", "simulations", "inducible"], "article_id"=>462562, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.g015", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Maximum_likelihood_fitting_of_two_models_for_gene_expression_to_stochastic_simulations_of_an_inducible_genetic_circuit_/462562", "title"=>"Maximum-likelihood fitting of two models for gene expression to stochastic simulations of an inducible genetic circuit.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:42:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/792104"], "description"=>"<p>(A) Slow growth <i>E. coli</i> cell model based in part on data from a tomographic reconstruction. Shown are (orange) ribosomes, (light gray) membrane, (dark grey) condensed nucleoid, and (red) <i>lac</i> operator. (B+C) Distribution of repressor–operator complex lifetimes for the fast and slow growth models, respectively. Curves show fits to an exponential distribution with the given mean. (D) Position of mRNA–membrane contact after diffusion of mRNA produced at the <i>lac</i> operon in (blue x) fast growth and (red o) slow growth models. Dotted lines show the length of the respective cells.</p>", "links"=>[], "tags"=>["cryoelectron", "tomography"], "article_id"=>462478, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.g014", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Analysis_of_cryoelectron_tomography_based_cell_model_/462478", "title"=>"Analysis of cryoelectron tomography based cell model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:41:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/791513"], "description"=>"<p>x are data from Choi <i>et al. </i><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002010#pcbi.1002010-Choi1\" target=\"_blank\">[22]</a>.</p>", "links"=>[], "tags"=>["inducer"], "article_id"=>461884, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.g006", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Linear_fit_of_burst_size_to_inducer_concentration_/461884", "title"=>"Linear fit of burst size to inducer concentration.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:31:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/792288"], "description"=>"<p>(A) Steady-state probability landscape (arbitrary units, darker = higher) for the NPF model at 500 TMG. The dotted line shows the trajectory of a representative cell during a ∼3 hour interval starting at the open circle and ending at the closed circle. (B) Probability landscape of the PFB circuit over a period of 24 hours following the addition of external TMG to 16 . The line follows a single cell switching from the uninduced to the induced state over the course of ∼13 hours.</p>", "links"=>[], "tags"=>["abundances", "inducible"], "article_id"=>462658, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.g016", "stats"=>{"downloads"=>2, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probability_landscape_of_protein_mRNA_abundances_in_the_inducible_lac_switch_model_/462658", "title"=>"Probability landscape of protein–mRNA abundances in the inducible <i>lac</i> switch model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:44:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/791571"], "description"=>"<p>The two-state process was described by: . Rate constants were chosen such that on average bursts of Z with a constant burst size were produced during Z's mean lifetime with the mean duration of each burst lasting for the indicated fraction of the lifetime. At each point, 250 stochastic simulations were run until the probability density was stationary and then the distributions of Z were fit to gamma distributions to obtain the and parameters. The ratios of (A) / and (B) / as a function of the burst duration show the range of burst durations for which a gamma distribution fit can reliably recover the original parameters. In this example and .</p>", "links"=>[], "tags"=>["stochastic", "simulations", "two-state"], "article_id"=>461939, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.g007", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Burst_analysis_of_stochastic_simulations_of_a_simple_two_state_process_/461939", "title"=>"Burst analysis of stochastic simulations of a simple two-state process.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:32:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/791940"], "description"=>"<p>Each line represents the mean of 5000 trajectories. (A) The observed diffusion coefficient, , as a function of time scale for a repressor diffusing in a volume with the indicated fraction occupied by <i>in vivo</i> obstacles. (B) –exponent arising from fitting to a model of anomalous diffusion, . (C) The probability for a repressor to rebind with the operator before diffusing into the bulk (64 nm from operator) following unbinding, as a function of the <i>in vivo</i> packing. (D) The distribution of escape times for repressors that diffuse to bulk rather than rebind, at three packing values.</p>", "links"=>[], "tags"=>["crowding", "repressor"], "article_id"=>462308, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.g012", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_effect_of_in_vivo_crowding_on_repressor_rebinding_/462308", "title"=>"The effect of <i>in vivo</i> crowding on repressor rebinding.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:38:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/791379"], "description"=>"<p>Under low-to-moderate inducer concentrations, a burst begins when the operator enters the state and ends when it transitions to a repressor bound state. .</p>", "links"=>[], "tags"=>["diagram", "transcriptional", "bursting"], "article_id"=>461746, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.g004", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Markov_diagram_for_transcriptional_bursting_in_the_lac_circuit_/461746", "title"=>"Markov diagram for transcriptional bursting in the <i>lac</i> circuit.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:29:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/791417"], "description"=>"<p>(A) Mean burst size as a function of inducer concentration for various values of , where . Parameters used were  =  M,  =  M,  = , and  = . (B) The rate of change in the burst size with respective to the inducer concentration.</p>", "links"=>[], "tags"=>["repressor", "binding", "parameter"], "article_id"=>461785, "categories"=>["Molecular Biology", "Physics", "Biophysics", "Mathematics", "Biological Sciences", "Cell Biology"], "users"=>["Elijah Roberts", "Andrew Magis", "Julio O. Ortiz", "Wolfgang Baumeister", "Zaida Luthey-Schulten"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002010.g005", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameter_space_of_repressor_binding_parameter_/461785", "title"=>"Parameter space of repressor binding parameter .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:29:45"}

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Relative Metric

{"start_date"=>"2011-01-01T00:00:00Z", "end_date"=>"2011-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[304, 568, 702, 818, 927, 1027, 1118, 1206, 1285, 1357, 1427, 1500, 1564, 1636, 1705, 1773, 1840, 1909, 1974, 2039, 2106, 2170, 2234, 2296, 2358, 2423, 2484, 2546, 2606, 2673, 2734, 2795, 2857, 2921, 2984, 3046, 3100]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[347, 582, 700, 805, 909, 997, 1061, 1147, 1220, 1282, 1350, 1411, 1471, 1532, 1600, 1665, 1726, 1790, 1868, 1927, 1986, 2048, 2105, 2166, 2229, 2288, 2347, 2407, 2463, 2525, 2589, 2650, 2704, 2755, 2806, 2854, 2896]}]}
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