Journal: Free radical research

Article Title: Mechanistic computational modeling of the kinetics and regulation of NADPH oxidase 2 assembly and activation facilitating superoxide production

doi: 10.1080/10715762.2020.1836368

Figure Lengend Snippet: Distribution of Model 2 estimated parameter values based on a hybrid Monte-Carlo-Fmincon simulations method. Plots show the distribution of the 9 estimated unknown parameter values for Model 2 fitted to experimental data of Uhlinger et al. [60, 61], Peveri et al. [59], and Nisimoto et al. [62] using a hybrid approach of Monte-Carlo simulations and “fmincon” optimization algorithm in MATLAB. The model is provided with 10,000 sets of 9 random initial values between 0 and 1 and the unknown parameters are estimated by “fmincon” optimizer for every set resulting in the values with the most probability of occurrence. For every parameter, the value with more than 55% probability of occurrence is indicated via the highest histogram bars that are also reported as the final estimated parameter values in Table 2. In this model, Kp67 is fixed at 0.05 μM and the rest of parameters are estimated.

Article Snippet: The resulting model (Model 3) was used to fit jointly to the experimental datasets by Uhlinger et al. [ 60 , 61 ], Peveri et al. [ 59 ], and Nisimoto et al. [ 62 ] to estimate the unknown parameters of the model. Model 3 was able to describe all the datasets on GTP and GDP regulatory effects as well as cytosolic subunits mutual binding enhancements with a very small sum of squared differences error of 3.01, obtained by the “fmincon” optimization algorithm in MATLAB 2018 b. fig ft0 fig @position="anchor" mode=article f4 fig mode="anchored" f5 Figure B3. fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window caption a7 Matrix of correlation coefficients between Model 2 parameters: The correlation coefficients between Model 2 parameters from that best fit the model to the data is obtained using the “lsqcurvefit” algorithm.

Techniques:

Journal: Free radical research

Article Title: Mechanistic computational modeling of the kinetics and regulation of NADPH oxidase 2 assembly and activation facilitating superoxide production

doi: 10.1080/10715762.2020.1836368

Figure Lengend Snippet: Distribution of Model 3 estimated parameter values based on a hybrid Monte-Carlo–Fmincon simulations method. Plots show the distribution of the 12 estimated unknown parameter values for Model 3 fitted to the experimental data of Uhlinger et al. [60,61], Peveri et al. [59], and Nisimoto et al. [62] using a hybrid approach of Monte-Carlo simulations and “fmincon” optimization algorithm in MATLAB. The model is provided with 10,000 sets of 12 random initial values between 0 and 1 and the unknown parameters are estimated by “fmincon” optimizer for every set resulting in the values with the most probability of occurrence. For every parameter, the value with more than 55% probability of occurrence is indicated via the highest histogram bars that are also reported as the final estimated parameter values in Table 2.

Article Snippet: The resulting model (Model 3) was used to fit jointly to the experimental datasets by Uhlinger et al. [ 60 , 61 ], Peveri et al. [ 59 ], and Nisimoto et al. [ 62 ] to estimate the unknown parameters of the model. Model 3 was able to describe all the datasets on GTP and GDP regulatory effects as well as cytosolic subunits mutual binding enhancements with a very small sum of squared differences error of 3.01, obtained by the “fmincon” optimization algorithm in MATLAB 2018 b. fig ft0 fig @position="anchor" mode=article f4 fig mode="anchored" f5 Figure B3. fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window caption a7 Matrix of correlation coefficients between Model 2 parameters: The correlation coefficients between Model 2 parameters from that best fit the model to the data is obtained using the “lsqcurvefit” algorithm.

Techniques: