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multiple gaussian fitting routine  (MathWorks Inc)


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    MathWorks Inc multiple gaussian fitting routine
    Complexity of thin filament activation. a, kymograph in sub-maximal activation conditions ( p Ca 6, 15 n m myosin, 0.1 μ m ATP) shows a continuous patch of activation that moves both toward the plus and minus ends of actin. The lower kymograph has the peak intensities labeled (using ImageJ skeletonize); this allow a clearer view of how these active regions collide and collapse catastrophically. The skeletonization was used only for visual purposes and not for the fitting used in b . This image is the first real time single molecule view of how the thin filament both activates and deactivates. b, center of a number of active regions was analyzed by measuring their positional displacement over a single frame and plotted as a histogram. This histogram follows a <t>Gaussian</t> distribution indicative of diffusion; the mean position (−0.88 ± 1.11 (S.D.) px) suggests the diffusion is unbiased. One pixel = 80 nm. c, global fitting of the intensity histogram data ( , b , d, and f ) across a number of conditions. The data in blue diamonds (10 fps) was obtained at a faster frame rate than those in red squares (3.8 fps) and were therefore fit separately; the results in either condition provide the same value for d of 11. The quality of the fits both validate the choice of model and provide an activation distance for the open state of 11 myosin-binding sites on actin. The remaining parameters used in this model are provided in . d, sensitivity was tested for each the parameters stated in used in the fitting for c . The value for each parameter was first halved, and the resulting ΔS.D. of the fit was squared and then summed with the ΔS.D. obtained when the same parameter was doubled. This root of this value is plotted as the root mean square ΔS.D. value (or sensitivity) for each parameter. A more sensitive value indicates the parameter is fit with better precision.
    Multiple Gaussian Fitting Routine, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/multiple gaussian fitting routine/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    multiple gaussian fitting routine - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "Using Fluorescent Myosin to Directly Visualize Cooperative Activation of Thin Filaments * ♦ "

    Article Title: Using Fluorescent Myosin to Directly Visualize Cooperative Activation of Thin Filaments * ♦

    Journal: The Journal of Biological Chemistry

    doi: 10.1074/jbc.M114.609743

    Complexity of thin filament activation. a, kymograph in sub-maximal activation conditions ( p Ca 6, 15 n m myosin, 0.1 μ m ATP) shows a continuous patch of activation that moves both toward the plus and minus ends of actin. The lower kymograph has the peak intensities labeled (using ImageJ skeletonize); this allow a clearer view of how these active regions collide and collapse catastrophically. The skeletonization was used only for visual purposes and not for the fitting used in b . This image is the first real time single molecule view of how the thin filament both activates and deactivates. b, center of a number of active regions was analyzed by measuring their positional displacement over a single frame and plotted as a histogram. This histogram follows a Gaussian distribution indicative of diffusion; the mean position (−0.88 ± 1.11 (S.D.) px) suggests the diffusion is unbiased. One pixel = 80 nm. c, global fitting of the intensity histogram data ( , b , d, and f ) across a number of conditions. The data in blue diamonds (10 fps) was obtained at a faster frame rate than those in red squares (3.8 fps) and were therefore fit separately; the results in either condition provide the same value for d of 11. The quality of the fits both validate the choice of model and provide an activation distance for the open state of 11 myosin-binding sites on actin. The remaining parameters used in this model are provided in . d, sensitivity was tested for each the parameters stated in used in the fitting for c . The value for each parameter was first halved, and the resulting ΔS.D. of the fit was squared and then summed with the ΔS.D. obtained when the same parameter was doubled. This root of this value is plotted as the root mean square ΔS.D. value (or sensitivity) for each parameter. A more sensitive value indicates the parameter is fit with better precision.
    Figure Legend Snippet: Complexity of thin filament activation. a, kymograph in sub-maximal activation conditions ( p Ca 6, 15 n m myosin, 0.1 μ m ATP) shows a continuous patch of activation that moves both toward the plus and minus ends of actin. The lower kymograph has the peak intensities labeled (using ImageJ skeletonize); this allow a clearer view of how these active regions collide and collapse catastrophically. The skeletonization was used only for visual purposes and not for the fitting used in b . This image is the first real time single molecule view of how the thin filament both activates and deactivates. b, center of a number of active regions was analyzed by measuring their positional displacement over a single frame and plotted as a histogram. This histogram follows a Gaussian distribution indicative of diffusion; the mean position (−0.88 ± 1.11 (S.D.) px) suggests the diffusion is unbiased. One pixel = 80 nm. c, global fitting of the intensity histogram data ( , b , d, and f ) across a number of conditions. The data in blue diamonds (10 fps) was obtained at a faster frame rate than those in red squares (3.8 fps) and were therefore fit separately; the results in either condition provide the same value for d of 11. The quality of the fits both validate the choice of model and provide an activation distance for the open state of 11 myosin-binding sites on actin. The remaining parameters used in this model are provided in . d, sensitivity was tested for each the parameters stated in used in the fitting for c . The value for each parameter was first halved, and the resulting ΔS.D. of the fit was squared and then summed with the ΔS.D. obtained when the same parameter was doubled. This root of this value is plotted as the root mean square ΔS.D. value (or sensitivity) for each parameter. A more sensitive value indicates the parameter is fit with better precision.

    Techniques Used: Activation Assay, Labeling, Diffusion-based Assay, Binding Assay

    Analyzing the interactions between myosin and actin. a, vertical box is drawn one pixel wide and scanned along the time axis. Each box contains all of the fluorescence intensity information for a single time point in the movie along the length of a thin filament. In this example, one vertical slice is rotated 90° and plotted as a graph of intensity versus position along the thin filament ( lower graph in a ). This graph shows two peaks with a full width half-maximum of ∼300 nm (>100 actin monomers) as would be expected for a point source. The intensity of the peaks provides information on the number of myosins bound. Therefore, each vertical slice is fitted to a sum of Gaussian distributions with unconstrained intensities (shown as the fit line). The non-zero baseline represents the background noise, which is removed as a consequence of the fitting, which was performed using a custom-written Matlab routine. b, peak intensity values from all of the fitted Gaussian distributions were then plotted as a histogram ( blue squares ), and in this case the conditions are 15 n m myosin at p Ca 5 with 0.5 μ m ATP. As a result of this treatment, the histogram now represents steady-state intensities and has no spatial and temporal information from the kymograph. At low myosin concentrations without regulatory proteins, only single myosins were seen to bind actin resulting in a single peak for the intensity histogram (data not shown). This peak corresponds to the profile of a single eGFP. The next stage of analysis was to determine the number of intensity subpopulations that constitute a multiple myosin intensity histogram. This was achieved by fitting the histogram to multiple Gaussians, each with the standard deviation of a single eGFP until the fit could no longer be improved.
    Figure Legend Snippet: Analyzing the interactions between myosin and actin. a, vertical box is drawn one pixel wide and scanned along the time axis. Each box contains all of the fluorescence intensity information for a single time point in the movie along the length of a thin filament. In this example, one vertical slice is rotated 90° and plotted as a graph of intensity versus position along the thin filament ( lower graph in a ). This graph shows two peaks with a full width half-maximum of ∼300 nm (>100 actin monomers) as would be expected for a point source. The intensity of the peaks provides information on the number of myosins bound. Therefore, each vertical slice is fitted to a sum of Gaussian distributions with unconstrained intensities (shown as the fit line). The non-zero baseline represents the background noise, which is removed as a consequence of the fitting, which was performed using a custom-written Matlab routine. b, peak intensity values from all of the fitted Gaussian distributions were then plotted as a histogram ( blue squares ), and in this case the conditions are 15 n m myosin at p Ca 5 with 0.5 μ m ATP. As a result of this treatment, the histogram now represents steady-state intensities and has no spatial and temporal information from the kymograph. At low myosin concentrations without regulatory proteins, only single myosins were seen to bind actin resulting in a single peak for the intensity histogram (data not shown). This peak corresponds to the profile of a single eGFP. The next stage of analysis was to determine the number of intensity subpopulations that constitute a multiple myosin intensity histogram. This was achieved by fitting the histogram to multiple Gaussians, each with the standard deviation of a single eGFP until the fit could no longer be improved.

    Techniques Used: Fluorescence, Standard Deviation



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    multiple gaussian fitting routine  (MathWorks Inc)
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    MathWorks Inc multiple gaussian fitting routine
    Complexity of thin filament activation. a, kymograph in sub-maximal activation conditions ( p Ca 6, 15 n m myosin, 0.1 μ m ATP) shows a continuous patch of activation that moves both toward the plus and minus ends of actin. The lower kymograph has the peak intensities labeled (using ImageJ skeletonize); this allow a clearer view of how these active regions collide and collapse catastrophically. The skeletonization was used only for visual purposes and not for the fitting used in b . This image is the first real time single molecule view of how the thin filament both activates and deactivates. b, center of a number of active regions was analyzed by measuring their positional displacement over a single frame and plotted as a histogram. This histogram follows a <t>Gaussian</t> distribution indicative of diffusion; the mean position (−0.88 ± 1.11 (S.D.) px) suggests the diffusion is unbiased. One pixel = 80 nm. c, global fitting of the intensity histogram data ( , b , d, and f ) across a number of conditions. The data in blue diamonds (10 fps) was obtained at a faster frame rate than those in red squares (3.8 fps) and were therefore fit separately; the results in either condition provide the same value for d of 11. The quality of the fits both validate the choice of model and provide an activation distance for the open state of 11 myosin-binding sites on actin. The remaining parameters used in this model are provided in . d, sensitivity was tested for each the parameters stated in used in the fitting for c . The value for each parameter was first halved, and the resulting ΔS.D. of the fit was squared and then summed with the ΔS.D. obtained when the same parameter was doubled. This root of this value is plotted as the root mean square ΔS.D. value (or sensitivity) for each parameter. A more sensitive value indicates the parameter is fit with better precision.
    Multiple Gaussian Fitting Routine, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/multiple gaussian fitting routine/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    multiple gaussian fitting routine - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Complexity of thin filament activation. a, kymograph in sub-maximal activation conditions ( p Ca 6, 15 n m myosin, 0.1 μ m ATP) shows a continuous patch of activation that moves both toward the plus and minus ends of actin. The lower kymograph has the peak intensities labeled (using ImageJ skeletonize); this allow a clearer view of how these active regions collide and collapse catastrophically. The skeletonization was used only for visual purposes and not for the fitting used in b . This image is the first real time single molecule view of how the thin filament both activates and deactivates. b, center of a number of active regions was analyzed by measuring their positional displacement over a single frame and plotted as a histogram. This histogram follows a Gaussian distribution indicative of diffusion; the mean position (−0.88 ± 1.11 (S.D.) px) suggests the diffusion is unbiased. One pixel = 80 nm. c, global fitting of the intensity histogram data ( , b , d, and f ) across a number of conditions. The data in blue diamonds (10 fps) was obtained at a faster frame rate than those in red squares (3.8 fps) and were therefore fit separately; the results in either condition provide the same value for d of 11. The quality of the fits both validate the choice of model and provide an activation distance for the open state of 11 myosin-binding sites on actin. The remaining parameters used in this model are provided in . d, sensitivity was tested for each the parameters stated in used in the fitting for c . The value for each parameter was first halved, and the resulting ΔS.D. of the fit was squared and then summed with the ΔS.D. obtained when the same parameter was doubled. This root of this value is plotted as the root mean square ΔS.D. value (or sensitivity) for each parameter. A more sensitive value indicates the parameter is fit with better precision.

    Journal: The Journal of Biological Chemistry

    Article Title: Using Fluorescent Myosin to Directly Visualize Cooperative Activation of Thin Filaments * ♦

    doi: 10.1074/jbc.M114.609743

    Figure Lengend Snippet: Complexity of thin filament activation. a, kymograph in sub-maximal activation conditions ( p Ca 6, 15 n m myosin, 0.1 μ m ATP) shows a continuous patch of activation that moves both toward the plus and minus ends of actin. The lower kymograph has the peak intensities labeled (using ImageJ skeletonize); this allow a clearer view of how these active regions collide and collapse catastrophically. The skeletonization was used only for visual purposes and not for the fitting used in b . This image is the first real time single molecule view of how the thin filament both activates and deactivates. b, center of a number of active regions was analyzed by measuring their positional displacement over a single frame and plotted as a histogram. This histogram follows a Gaussian distribution indicative of diffusion; the mean position (−0.88 ± 1.11 (S.D.) px) suggests the diffusion is unbiased. One pixel = 80 nm. c, global fitting of the intensity histogram data ( , b , d, and f ) across a number of conditions. The data in blue diamonds (10 fps) was obtained at a faster frame rate than those in red squares (3.8 fps) and were therefore fit separately; the results in either condition provide the same value for d of 11. The quality of the fits both validate the choice of model and provide an activation distance for the open state of 11 myosin-binding sites on actin. The remaining parameters used in this model are provided in . d, sensitivity was tested for each the parameters stated in used in the fitting for c . The value for each parameter was first halved, and the resulting ΔS.D. of the fit was squared and then summed with the ΔS.D. obtained when the same parameter was doubled. This root of this value is plotted as the root mean square ΔS.D. value (or sensitivity) for each parameter. A more sensitive value indicates the parameter is fit with better precision.

    Article Snippet: To calculate the number of myosins bound for all clusters on individual thin filaments, we analyzed kymographic data directly by taking consecutive vertical slices through the kymograph and fitting each one with a custom-written (Matlab) multiple Gaussian fitting routine ( a ).

    Techniques: Activation Assay, Labeling, Diffusion-based Assay, Binding Assay

    Analyzing the interactions between myosin and actin. a, vertical box is drawn one pixel wide and scanned along the time axis. Each box contains all of the fluorescence intensity information for a single time point in the movie along the length of a thin filament. In this example, one vertical slice is rotated 90° and plotted as a graph of intensity versus position along the thin filament ( lower graph in a ). This graph shows two peaks with a full width half-maximum of ∼300 nm (>100 actin monomers) as would be expected for a point source. The intensity of the peaks provides information on the number of myosins bound. Therefore, each vertical slice is fitted to a sum of Gaussian distributions with unconstrained intensities (shown as the fit line). The non-zero baseline represents the background noise, which is removed as a consequence of the fitting, which was performed using a custom-written Matlab routine. b, peak intensity values from all of the fitted Gaussian distributions were then plotted as a histogram ( blue squares ), and in this case the conditions are 15 n m myosin at p Ca 5 with 0.5 μ m ATP. As a result of this treatment, the histogram now represents steady-state intensities and has no spatial and temporal information from the kymograph. At low myosin concentrations without regulatory proteins, only single myosins were seen to bind actin resulting in a single peak for the intensity histogram (data not shown). This peak corresponds to the profile of a single eGFP. The next stage of analysis was to determine the number of intensity subpopulations that constitute a multiple myosin intensity histogram. This was achieved by fitting the histogram to multiple Gaussians, each with the standard deviation of a single eGFP until the fit could no longer be improved.

    Journal: The Journal of Biological Chemistry

    Article Title: Using Fluorescent Myosin to Directly Visualize Cooperative Activation of Thin Filaments * ♦

    doi: 10.1074/jbc.M114.609743

    Figure Lengend Snippet: Analyzing the interactions between myosin and actin. a, vertical box is drawn one pixel wide and scanned along the time axis. Each box contains all of the fluorescence intensity information for a single time point in the movie along the length of a thin filament. In this example, one vertical slice is rotated 90° and plotted as a graph of intensity versus position along the thin filament ( lower graph in a ). This graph shows two peaks with a full width half-maximum of ∼300 nm (>100 actin monomers) as would be expected for a point source. The intensity of the peaks provides information on the number of myosins bound. Therefore, each vertical slice is fitted to a sum of Gaussian distributions with unconstrained intensities (shown as the fit line). The non-zero baseline represents the background noise, which is removed as a consequence of the fitting, which was performed using a custom-written Matlab routine. b, peak intensity values from all of the fitted Gaussian distributions were then plotted as a histogram ( blue squares ), and in this case the conditions are 15 n m myosin at p Ca 5 with 0.5 μ m ATP. As a result of this treatment, the histogram now represents steady-state intensities and has no spatial and temporal information from the kymograph. At low myosin concentrations without regulatory proteins, only single myosins were seen to bind actin resulting in a single peak for the intensity histogram (data not shown). This peak corresponds to the profile of a single eGFP. The next stage of analysis was to determine the number of intensity subpopulations that constitute a multiple myosin intensity histogram. This was achieved by fitting the histogram to multiple Gaussians, each with the standard deviation of a single eGFP until the fit could no longer be improved.

    Article Snippet: To calculate the number of myosins bound for all clusters on individual thin filaments, we analyzed kymographic data directly by taking consecutive vertical slices through the kymograph and fitting each one with a custom-written (Matlab) multiple Gaussian fitting routine ( a ).

    Techniques: Fluorescence, Standard Deviation