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    MathWorks Inc simulations matlab simbiology 2018
    Fig. 2. In vivo Near Infrared Imaging following Subcutaneous Administration. C57/black mice were administered SA + saline, SA-bNP or bNP + saline SC into the right footpad ( n = 3 each group). Injection volume was 40 μL in each group through a 28G needle. SA was loaded onto bNPs by incubation with an approximately 2x molar excess of SA: bNP surface biotin for 30 minutes. Unbound SA was washed from particles by centrifugation and decanting supernatant 2x. SA + saline was injected at the calculated maximum biotin binding on bNPs. T = 0 SA + saline dose was higher than SA-bNP dose due to incomplete binding of SA on bNP and washing steps to remove unbound SA. Observed fluorescence in the 700 nm channel for SA + saline (A) and SA in SA-bNP (B), and in the 800nm channel for bNP in SA-bNP (C) and unloaded bNPs (D). Normalized injection site elimination profiles vs time for SA + Saline (E ), SA in SA-bNP (F) bNP in SA-bNP (G ), and bNP + saline (H). I, Elimination rate constants from injection site. Data was fitted to a 1-compartment PK model utilizing <t>Matlab</t> symbiology. J, Area under the curve for free and bound SA in the injection site were calculated from concentration vs time data. ∗, ∗∗, ∗∗∗p < 0.05, < 0.01, and < 0.001 respectively, by 1-way ANOVA or two-tailed T-test.
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    Images

    1) Product Images from "Modeling the kinetics of lymph node retention and exposure of a cargo protein delivered by biotin-functionalized nanoparticles."

    Article Title: Modeling the kinetics of lymph node retention and exposure of a cargo protein delivered by biotin-functionalized nanoparticles.

    Journal: Acta biomaterialia

    doi: 10.1016/j.actbio.2023.08.048

    Fig. 2. In vivo Near Infrared Imaging following Subcutaneous Administration. C57/black mice were administered SA + saline, SA-bNP or bNP + saline SC into the right footpad ( n = 3 each group). Injection volume was 40 μL in each group through a 28G needle. SA was loaded onto bNPs by incubation with an approximately 2x molar excess of SA: bNP surface biotin for 30 minutes. Unbound SA was washed from particles by centrifugation and decanting supernatant 2x. SA + saline was injected at the calculated maximum biotin binding on bNPs. T = 0 SA + saline dose was higher than SA-bNP dose due to incomplete binding of SA on bNP and washing steps to remove unbound SA. Observed fluorescence in the 700 nm channel for SA + saline (A) and SA in SA-bNP (B), and in the 800nm channel for bNP in SA-bNP (C) and unloaded bNPs (D). Normalized injection site elimination profiles vs time for SA + Saline (E ), SA in SA-bNP (F) bNP in SA-bNP (G ), and bNP + saline (H). I, Elimination rate constants from injection site. Data was fitted to a 1-compartment PK model utilizing Matlab symbiology. J, Area under the curve for free and bound SA in the injection site were calculated from concentration vs time data. ∗, ∗∗, ∗∗∗p < 0.05, < 0.01, and < 0.001 respectively, by 1-way ANOVA or two-tailed T-test.
    Figure Legend Snippet: Fig. 2. In vivo Near Infrared Imaging following Subcutaneous Administration. C57/black mice were administered SA + saline, SA-bNP or bNP + saline SC into the right footpad ( n = 3 each group). Injection volume was 40 μL in each group through a 28G needle. SA was loaded onto bNPs by incubation with an approximately 2x molar excess of SA: bNP surface biotin for 30 minutes. Unbound SA was washed from particles by centrifugation and decanting supernatant 2x. SA + saline was injected at the calculated maximum biotin binding on bNPs. T = 0 SA + saline dose was higher than SA-bNP dose due to incomplete binding of SA on bNP and washing steps to remove unbound SA. Observed fluorescence in the 700 nm channel for SA + saline (A) and SA in SA-bNP (B), and in the 800nm channel for bNP in SA-bNP (C) and unloaded bNPs (D). Normalized injection site elimination profiles vs time for SA + Saline (E ), SA in SA-bNP (F) bNP in SA-bNP (G ), and bNP + saline (H). I, Elimination rate constants from injection site. Data was fitted to a 1-compartment PK model utilizing Matlab symbiology. J, Area under the curve for free and bound SA in the injection site were calculated from concentration vs time data. ∗, ∗∗, ∗∗∗p < 0.05, < 0.01, and < 0.001 respectively, by 1-way ANOVA or two-tailed T-test.

    Techniques Used: In Vivo, Imaging, Saline, Injection, Incubation, Centrifugation, Binding Assay, Concentration Assay, Two Tailed Test

    Fig. 4. Modeling Lymph Node Occupancy from In Vivo Data up to 21 Days. Observed versus predicted concentration vs time curves fitted to the developed 2-compartment model utilizing MatLab symbiology for SA + saline footpad kinetics (A ), SA in SA-bNP footpad and dLN kinetics (B&C ), bNP + saline footpad kinetics (D ), and bNP in SA-bNP footpad and dLN kinetics (E&F).
    Figure Legend Snippet: Fig. 4. Modeling Lymph Node Occupancy from In Vivo Data up to 21 Days. Observed versus predicted concentration vs time curves fitted to the developed 2-compartment model utilizing MatLab symbiology for SA + saline footpad kinetics (A ), SA in SA-bNP footpad and dLN kinetics (B&C ), bNP + saline footpad kinetics (D ), and bNP in SA-bNP footpad and dLN kinetics (E&F).

    Techniques Used: In Vivo, Concentration Assay, Saline

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    Image Search Results


    (A) Pulsing (blue) and constant (orange) 0A~P signal used as input to the biofilm matrix production stochastic model. Pulsing signal corresponds to the signal for growth rate equal to 0.4 h −1 predicted by the phosphorelay network model. The constant 0A~P signal corresponds to the mean of the pulsing signal. (B) Stochastic simulations starting from biofilm matrix inactive state for both constant and pulsing 0A~P signal. Matrix production is considered inactive if the number of TapA molecules < 200 (dashed line). (C) Stochastic simulations starting from matrix active state for both constant and pulsing 0A~P signal. Matrix production is considered active if the number of TapA molecules ≥ 200 (dashed line). (D) Fraction of active cells as a function of time for constant (dark orange) and pulsing 0A~P signal (dark blue) estimated from fitting and to the stochastic simulation data of ‘ Initially OFF cells ’ (light blue) and ‘ Initially ON’ (light orange) cells, respectively. All fits have R 2 >= 0.77 and MSE < 0.005. A total of 2000 simulations were performed. (inset) Estimated values of the biofilm activation rate ( k ON ) and of the matrix deactivation rate assuming pulsing and constant 0A~P input .

    Journal: bioRxiv

    Article Title: Changes in Spo0A~P pulsing frequency control biofilm matrix deactivation

    doi: 10.1101/2025.02.13.638117

    Figure Lengend Snippet: (A) Pulsing (blue) and constant (orange) 0A~P signal used as input to the biofilm matrix production stochastic model. Pulsing signal corresponds to the signal for growth rate equal to 0.4 h −1 predicted by the phosphorelay network model. The constant 0A~P signal corresponds to the mean of the pulsing signal. (B) Stochastic simulations starting from biofilm matrix inactive state for both constant and pulsing 0A~P signal. Matrix production is considered inactive if the number of TapA molecules < 200 (dashed line). (C) Stochastic simulations starting from matrix active state for both constant and pulsing 0A~P signal. Matrix production is considered active if the number of TapA molecules ≥ 200 (dashed line). (D) Fraction of active cells as a function of time for constant (dark orange) and pulsing 0A~P signal (dark blue) estimated from fitting and to the stochastic simulation data of ‘ Initially OFF cells ’ (light blue) and ‘ Initially ON’ (light orange) cells, respectively. All fits have R 2 >= 0.77 and MSE < 0.005. A total of 2000 simulations were performed. (inset) Estimated values of the biofilm activation rate ( k ON ) and of the matrix deactivation rate assuming pulsing and constant 0A~P input .

    Article Snippet: The stochastic model was implemented in MATLAB R2023b, and the stochastic simulations ( ) were done using the SimBiology tool of MATLAB R2023b.

    Techniques: Activation Assay

    Histogram of TapA molecules obtained from 3000 stochastic simulations (blue bars). Simulation time was set to 50 h. Solid lines represent Gaussian fits derived from a Gaussian Mixture Model with 2 components fitted to the data. The value of 200 molecules was set to be the threshold to distinguish between a biofilm matrix production active and inactive.

    Journal: bioRxiv

    Article Title: Changes in Spo0A~P pulsing frequency control biofilm matrix deactivation

    doi: 10.1101/2025.02.13.638117

    Figure Lengend Snippet: Histogram of TapA molecules obtained from 3000 stochastic simulations (blue bars). Simulation time was set to 50 h. Solid lines represent Gaussian fits derived from a Gaussian Mixture Model with 2 components fitted to the data. The value of 200 molecules was set to be the threshold to distinguish between a biofilm matrix production active and inactive.

    Article Snippet: The stochastic model was implemented in MATLAB R2023b, and the stochastic simulations ( ) were done using the SimBiology tool of MATLAB R2023b.

    Techniques: Derivative Assay

    Journal: bioRxiv

    Article Title: Changes in Spo0A~P pulsing frequency control biofilm matrix deactivation

    doi: 10.1101/2025.02.13.638117

    Figure Lengend Snippet:

    Article Snippet: The stochastic model was implemented in MATLAB R2023b, and the stochastic simulations ( ) were done using the SimBiology tool of MATLAB R2023b.

    Techniques:

    Fig. 2. In vivo Near Infrared Imaging following Subcutaneous Administration. C57/black mice were administered SA + saline, SA-bNP or bNP + saline SC into the right footpad ( n = 3 each group). Injection volume was 40 μL in each group through a 28G needle. SA was loaded onto bNPs by incubation with an approximately 2x molar excess of SA: bNP surface biotin for 30 minutes. Unbound SA was washed from particles by centrifugation and decanting supernatant 2x. SA + saline was injected at the calculated maximum biotin binding on bNPs. T = 0 SA + saline dose was higher than SA-bNP dose due to incomplete binding of SA on bNP and washing steps to remove unbound SA. Observed fluorescence in the 700 nm channel for SA + saline (A) and SA in SA-bNP (B), and in the 800nm channel for bNP in SA-bNP (C) and unloaded bNPs (D). Normalized injection site elimination profiles vs time for SA + Saline (E ), SA in SA-bNP (F) bNP in SA-bNP (G ), and bNP + saline (H). I, Elimination rate constants from injection site. Data was fitted to a 1-compartment PK model utilizing Matlab symbiology. J, Area under the curve for free and bound SA in the injection site were calculated from concentration vs time data. ∗, ∗∗, ∗∗∗p < 0.05, < 0.01, and < 0.001 respectively, by 1-way ANOVA or two-tailed T-test.

    Journal: Acta biomaterialia

    Article Title: Modeling the kinetics of lymph node retention and exposure of a cargo protein delivered by biotin-functionalized nanoparticles.

    doi: 10.1016/j.actbio.2023.08.048

    Figure Lengend Snippet: Fig. 2. In vivo Near Infrared Imaging following Subcutaneous Administration. C57/black mice were administered SA + saline, SA-bNP or bNP + saline SC into the right footpad ( n = 3 each group). Injection volume was 40 μL in each group through a 28G needle. SA was loaded onto bNPs by incubation with an approximately 2x molar excess of SA: bNP surface biotin for 30 minutes. Unbound SA was washed from particles by centrifugation and decanting supernatant 2x. SA + saline was injected at the calculated maximum biotin binding on bNPs. T = 0 SA + saline dose was higher than SA-bNP dose due to incomplete binding of SA on bNP and washing steps to remove unbound SA. Observed fluorescence in the 700 nm channel for SA + saline (A) and SA in SA-bNP (B), and in the 800nm channel for bNP in SA-bNP (C) and unloaded bNPs (D). Normalized injection site elimination profiles vs time for SA + Saline (E ), SA in SA-bNP (F) bNP in SA-bNP (G ), and bNP + saline (H). I, Elimination rate constants from injection site. Data was fitted to a 1-compartment PK model utilizing Matlab symbiology. J, Area under the curve for free and bound SA in the injection site were calculated from concentration vs time data. ∗, ∗∗, ∗∗∗p < 0.05, < 0.01, and < 0.001 respectively, by 1-way ANOVA or two-tailed T-test.

    Article Snippet: Modeling and simulations MATLAB Simbiology 2018 (Natick, MA) module was utilized to imulate the elimination of SA and bNP from the SC injections.

    Techniques: In Vivo, Imaging, Saline, Injection, Incubation, Centrifugation, Binding Assay, Concentration Assay, Two Tailed Test

    Fig. 4. Modeling Lymph Node Occupancy from In Vivo Data up to 21 Days. Observed versus predicted concentration vs time curves fitted to the developed 2-compartment model utilizing MatLab symbiology for SA + saline footpad kinetics (A ), SA in SA-bNP footpad and dLN kinetics (B&C ), bNP + saline footpad kinetics (D ), and bNP in SA-bNP footpad and dLN kinetics (E&F).

    Journal: Acta biomaterialia

    Article Title: Modeling the kinetics of lymph node retention and exposure of a cargo protein delivered by biotin-functionalized nanoparticles.

    doi: 10.1016/j.actbio.2023.08.048

    Figure Lengend Snippet: Fig. 4. Modeling Lymph Node Occupancy from In Vivo Data up to 21 Days. Observed versus predicted concentration vs time curves fitted to the developed 2-compartment model utilizing MatLab symbiology for SA + saline footpad kinetics (A ), SA in SA-bNP footpad and dLN kinetics (B&C ), bNP + saline footpad kinetics (D ), and bNP in SA-bNP footpad and dLN kinetics (E&F).

    Article Snippet: Modeling and simulations MATLAB Simbiology 2018 (Natick, MA) module was utilized to imulate the elimination of SA and bNP from the SC injections.

    Techniques: In Vivo, Concentration Assay, Saline