Journal: Nature Communications

Article Title: In vitro implementation of robust gene regulation in a synthetic biomolecular integral controller

doi: 10.1038/s41467-019-13626-z

Figure Lengend Snippet: a, b TXTL deGFP measurement of the response of the integral controller in the a open-loop and b closed-loop configurations at different initial concentrations of P X (0.1–0.7 nM) while initial \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Y^{{\mathrm{tot}}}$$\end{document} P Y tot and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Z^{{\mathrm{tot}}}$$\end{document} P Z tot were both 1 nM each. In the open-loop, instead of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Y^{{\mathrm{tot}}}$$\end{document} P Y tot , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{YC}^{{\mathrm{tot}}}$$\end{document} P Y C tot was added. c, d The slopes of measured deGFP responses for the c open-loop and d closed-loop operations and the corresponding summary in e and f at 8 h respectively. To disable the feedback in the open-loop case \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Y^{{\mathrm{tot}}}$$\end{document} P Y tot was replaced by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{YC}^{{\mathrm{tot}}}$$\end{document} P Y C tot , which expresses a protein that cannot sequester with X . g, h Summary of the deGFP slopes of the controller at 8 h for a step change in P X for the g open-loop and h closed-loop operations. P X was increased from 0 nM to different concentrations (0.1–0.7 nM) after 4 h of the reaction in the presence of initial 1 nM of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Y^{{\mathrm{tot}}}$$\end{document} P Y tot and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Z^{{\mathrm{tot}}}$$\end{document} P Z tot each. Note that the lower GFP slope values in g, h than e, f are due to the shorter active reaction time. Error are shown in the shaded region and were determined using the standard error of the mean of three or more repeats. A linear regression with zero intercept was used to fit the deGFP slopes and the corresponding R -square values are e 0.71, f 0.98, g 0.84, and h 0.98. A calibration factor was used to convert the measured deGFP fluorescent signal into the concentration. Before calculating deGFP slopes, measured deGFP responses were smoothed-out using the rloess smoothing method in MATLAB. Source data are provided as a Source Data file.

Article Snippet: Before calculating deGFP slopes, measured deGFP responses were smoothed-out using the rloess smoothing method in MATLAB.

Techniques: Concentration Assay

Journal: Nature Communications

Article Title: In vitro implementation of robust gene regulation in a synthetic biomolecular integral controller

doi: 10.1038/s41467-019-13626-z

Figure Lengend Snippet: a, b Predicting the a open-loop and b closed-loop controller response for a step change in P X . P X was increased from 0 nM to different concentrations (0.1–0.7 nM) after 2 h of incubation in the presence of initial 0.7 nM of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Y^{{\mathrm{tot}}}$$\end{document} P Y tot and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Z^{{\mathrm{tot}}}$$\end{document} P Z tot each. (Recall that fitting was done under different conditions, namely 1 nM of P YC tot (or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{Y}^{\mathrm{tot}}$$\end{document} P Y tot ) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{Z}^{\mathrm{tot}}$$\end{document} P Z tot each.) The ODE model shown in Fig. was used to determine the response with parameters shown in Table . c Simplified ODE model of the controller. d Approximate analytical solution for deGFP slopes (time derivative of G ) for the open and closed-loop cases. e , f Comparing the measured responses of the controller shown in Fig. , d with the response determined using the approximate analytical solution for the e open-loop and f closed-loop cases respectively. The data shown in Fig. 4a, b are compared with the analytical solution results in Supplementary Fig. . Before calculating deGFP slopes, measured deGFP responses were smoothed-out using the rloess smoothing method in MATLAB. Error bars are from the SEM of at least three repeats. Source data are provided as a Source Data file.

Article Snippet: Before calculating deGFP slopes, measured deGFP responses were smoothed-out using the rloess smoothing method in MATLAB.

Techniques: Incubation

Journal: Nature Communications

Article Title: In vitro implementation of robust gene regulation in a synthetic biomolecular integral controller

doi: 10.1038/s41467-019-13626-z

Figure Lengend Snippet: a, b Measured deGFP response of the controller in the presence of disturbances in the concentration of P YC tot (or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{Y}^{{\mathrm{tot}}}$$\end{document} P Y tot ) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Z^{{\mathrm{tot}}}$$\end{document} P Z tot (0.2–0.7 nM) for the a open-loop and b closed-loop cases while initial P X was 0.2 nM. The error bars are shown in the shaded region and were determined using the standard error of the mean of three or more repeats. c , d Summary of the deGFP slopes of the controller at 8 h for the c open-loop and d closed-loop operations. Error bars are from the SEM of at least three repeats. To disable the feedback in the open-loop case \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Y^{{\mathrm{tot}}}$$\end{document} P Y tot was replaced by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{YC}^{{\mathrm{tot}}}$$\end{document} P Y C tot , which expresses a protein that cannot sequester with X . e, f Measured response of the controller when the disturbance in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Y^{{\mathrm{tot}}}$$\end{document} P Y tot and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Z^{{\mathrm{tot}}}$$\end{document} P Z tot was added in a step manner for the e open-loop and f closed-loop cases. Additional \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Y^{{\mathrm{tot}}}$$\end{document} P Y tot and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Z^{{\mathrm{tot}}}$$\end{document} P Z tot were added (0.1–0.5 nM) after 4 h of the reaction in the presence of initial 0.2 nM of P X , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Y^{{\mathrm{tot}}}$$\end{document} P Y tot and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Z^{{\mathrm{tot}}}$$\end{document} P Z tot each. g , h Summary of the normalized deGFP slopes of the controller at 8 h for the g open-loop and h closed-loop operations. Normalization was done with respect to the first slope value for each variation in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Y^{{\mathrm{tot}}}$$\end{document} P Y tot and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Z^{{\mathrm{tot}}}$$\end{document} P Z tot . The predicted response for each case was determined using the ODE model shown in Fig. with parameters shown in Table . Before calculating deGFP slopes, measured deGFP responses were smoothed-out using the rloess smoothing method in MATLAB. Source data are provided as a Source Data file.

Article Snippet: Before calculating deGFP slopes, measured deGFP responses were smoothed-out using the rloess smoothing method in MATLAB.

Techniques: Concentration Assay

Journal: Nature Communications

Article Title: In vitro implementation of robust gene regulation in a synthetic biomolecular integral controller

doi: 10.1038/s41467-019-13626-z

Figure Lengend Snippet: a , b Measured response of the controller at three different external constant change in the reaction temperatures for the a open-loop ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{YC}^{{\mathrm{tot}}}$$\end{document} P Y C tot and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Z^{{\mathrm{tot}}}$$\end{document} P Z tot were both 0.1 nM) and b closed-loop ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Y^{{\mathrm{tot}}}$$\end{document} P Y tot and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Z^{{\mathrm{tot}}}$$\end{document} P Z tot were both 1 nM each) cases while initial P X was 0.1 nM. The error bars are shown in the shaded region and were determined using the standard error of the mean of three or more repeats. c , d Summary of the normalized deGFP slopes of the controller at 8 h for the c open-loop and d closed-loop operations. Error bars are from the SEM of at least three repeats. To disable the feedback in the open-loop case, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_Y^{{\mathrm{tot}}}$$\end{document} P Y tot was replaced by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{YC}^{{\mathrm{tot}}}$$\end{document} P Y C tot , which expresses a protein that cannot sequester with X . The responses shown in c , d were normalized with respect to the deGFP slope value recorded at 29 °C. Plate readers were calibrated at 29 °C, 33 °C, and 37 °C separately to a standard curve of GFP to ensure fluorescence variation reflects protein concentration variation (see Supplementary Fig. ). Before calculating deGFP slopes, measured deGFP responses were smoothed-out using the rloess smoothing method in MATLAB. Source data are provided as a Source Data file.

Article Snippet: Before calculating deGFP slopes, measured deGFP responses were smoothed-out using the rloess smoothing method in MATLAB.

Techniques: Fluorescence, Protein Concentration

Journal: PLoS ONE

Article Title: A Daily Oscillation in the Fundamental Frequency and Amplitude of Harmonic Syllables of Zebra Finch Song

doi: 10.1371/journal.pone.0082327

Figure Lengend Snippet: A ) The FF for 1 syllable across 4 days of singing, each point represents one rendition of the syllable. The black lines are RLOESS fits. B ) The amplitude for the same syllable and same renditions as in A.

Article Snippet: The daily time course of the mean fundamental frequency and amplitude of each syllable was calculated using the MATLAB smoothing curve function rloess, a non-parametric regression method related to loess regression, which decreases the weighting of outlying data points allowing better curve fits when there are long gaps between data points (for example when there is an hour of no singing in the day).

Techniques:

Journal: PLoS ONE

Article Title: A Daily Oscillation in the Fundamental Frequency and Amplitude of Harmonic Syllables of Zebra Finch Song

doi: 10.1371/journal.pone.0082327

Figure Lengend Snippet: Plotted are the slopes of the RLOESS fits for the first three hours of the day and the last three hours of the evening- each point represents the slope of 1 syllable from 1 animal from one morning or evening. Note the apparent ‘v’ shape of the data is an artifact of the algorithm used to make all symbols visible on the graph and does not connote information.

Article Snippet: The daily time course of the mean fundamental frequency and amplitude of each syllable was calculated using the MATLAB smoothing curve function rloess, a non-parametric regression method related to loess regression, which decreases the weighting of outlying data points allowing better curve fits when there are long gaps between data points (for example when there is an hour of no singing in the day).

Techniques:

Journal: PLoS ONE

Article Title: A Daily Oscillation in the Fundamental Frequency and Amplitude of Harmonic Syllables of Zebra Finch Song

doi: 10.1371/journal.pone.0082327

Figure Lengend Snippet: Each symbol represents the slope of the RLOESS curve of FF or amplitude of one syllable from one animal on one day. A ) There was a trend for evening slope of FF to become positive immediately following 5-HT lesion (while song was recovering but 5-HT was still depleted, p=0.0582 when compared to baseline slope). Oscillations were indistinguishable from baseline condition by 5 days after lesion surgery, at which point 5-HT levels continued to be drastically reduced. Saline controls showed no similar effects. B ) Evening slope of amplitude increased significantly after 5-HT lesion (p=0.0075 when compared to baseline slope). Similarly to FF, the trend of the evening slope of amplitude to become positive ceased to persist 5 days after lesion surgery.

Article Snippet: The daily time course of the mean fundamental frequency and amplitude of each syllable was calculated using the MATLAB smoothing curve function rloess, a non-parametric regression method related to loess regression, which decreases the weighting of outlying data points allowing better curve fits when there are long gaps between data points (for example when there is an hour of no singing in the day).

Techniques: Saline

Journal: Human Brain Mapping

Article Title: Memory training impacts short‐term changes in aging white matter: A Longitudinal Diffusion Tensor Imaging Study

doi: 10.1002/hbm.21370

Figure Lengend Snippet: Anterior–posterior effect gradient. The figure shows an rLOESS curve fit (red) for mean t‐values at each integer MNI y‐coordinate. The distribution of mean t‐values after testing for longitudinal changes in MD suggests an increasing gradient from posterior to anterior segments. All WM‐skeleton t‐values were first extracted from the raw statistical t‐map output testing for MD increases over time across all participants. T‐values at each y‐coordinate were averaged and analyzed using rLOESS curve fitting in Matlab. A sagittal section of the MNI‐template T1‐brain is used as background and aligned so that the anatomy corresponds to the respective MNI y‐coordinates on the x‐axis of the diagram. Note that neither the y‐axis nor the x‐axis of the diagram starts at the conventional 0 levels.

Article Snippet: T ‐values at each y ‐coordinate were averaged and analyzed using rLOESS curve fitting in Matlab.

Techniques: