(A-B) Heatmap of average mRNA at 24 hours (A) and fractional promoter-state probabilities in the initial state (B) for a range of feedback strengths. All other parameters are set to BIR = 0.1 hr-1, BTR = 1 hr-1, PBR = PPRR = 10 hr-1. Data was generated by stochastic simulation for 1,000 cells for each parameter combination. The feedback terms K (half max) and A (amplification factor) were varied over 5 orders of magnitude. (C) Feedback strength calculated for varied K values and plotted versus protein. (PDF)</p
<p>Four values of the control parameter , the R protein degradation rate, were used with the stochas...
<p>(A): mRNA (upper plots) and corresponding protein time courses (lower plots) of reaction systems ...
<p>(A) Correlation between simulated <i>T</i><sub>m</sub> and experimental <i>T</i><sub>m</sub>, ave...
(A) Updated three-state promoter system with HIV nucleosome remodeling, RelA recruitment, and Tat-me...
(A) Three representative fractional promoter-state probability pie charts with the addition of feedb...
(A) Three representative pie charts of fractional promoter-state probability of UP (blue), AP (teal)...
<p>(A) Single gene expression model with transcriptional self-repression. (B) Examples of protein di...
<p>Deterministic (A, C) and the corresponding stochastic (B, D) time evolution of RNA, protein and m...
<p>We tested how extrinsic parameter variations in the regulation of the transcriptionally controlle...
<p>(<b>A</b>) Numerical simulations showing the effect of increasing feedback gain on the magnitude ...
<p>All states (unit: molec./cell) converge to a stable steady state, while <i>g</i><sub>2</sub>, an...
Single-cell protein expression time trajectories provide rich temporal data quantifying cellular var...
(A-B) Average protein counts (A) and Fano factor (B) for the four activation options at 24 hours. Pr...
(A) Schematic of three-state transcriptional cycling model, including five species: an unavailable p...
Large stochastic fluctuation is fundamental in gene expression because the number of regulatory prot...
<p>Four values of the control parameter , the R protein degradation rate, were used with the stochas...
<p>(A): mRNA (upper plots) and corresponding protein time courses (lower plots) of reaction systems ...
<p>(A) Correlation between simulated <i>T</i><sub>m</sub> and experimental <i>T</i><sub>m</sub>, ave...
(A) Updated three-state promoter system with HIV nucleosome remodeling, RelA recruitment, and Tat-me...
(A) Three representative fractional promoter-state probability pie charts with the addition of feedb...
(A) Three representative pie charts of fractional promoter-state probability of UP (blue), AP (teal)...
<p>(A) Single gene expression model with transcriptional self-repression. (B) Examples of protein di...
<p>Deterministic (A, C) and the corresponding stochastic (B, D) time evolution of RNA, protein and m...
<p>We tested how extrinsic parameter variations in the regulation of the transcriptionally controlle...
<p>(<b>A</b>) Numerical simulations showing the effect of increasing feedback gain on the magnitude ...
<p>All states (unit: molec./cell) converge to a stable steady state, while <i>g</i><sub>2</sub>, an...
Single-cell protein expression time trajectories provide rich temporal data quantifying cellular var...
(A-B) Average protein counts (A) and Fano factor (B) for the four activation options at 24 hours. Pr...
(A) Schematic of three-state transcriptional cycling model, including five species: an unavailable p...
Large stochastic fluctuation is fundamental in gene expression because the number of regulatory prot...
<p>Four values of the control parameter , the R protein degradation rate, were used with the stochas...
<p>(A): mRNA (upper plots) and corresponding protein time courses (lower plots) of reaction systems ...
<p>(A) Correlation between simulated <i>T</i><sub>m</sub> and experimental <i>T</i><sub>m</sub>, ave...