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

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:

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

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