Utilization of the bootstrap method for determining confidence intervals of parameters for a model of MAP1B protein transport in axons

摘要

Abstract This paper develops a model of axonal transport of MAP1B protein. The problem of determining parameter values for the proposed model utilizing limited available experimental data is addressed. We used a global minimum search algorithm to find parameter values that minimize the discrepancy between model predictions and published experimental results. By analyzing the best fit parameter values it was established that some processes can be dropped from the model without losing accuracy, thus a simplified version of the model was formulated. In particular, our analysis suggests that reversals in MAP1B transport are infrequent and can be neglected. The detachment of anterogradely-biased MAP1B from microtubules (MTs) and the attachment of retrogradely-biased MAP1B to MTs can also be neglected. An analytical solution for the simplified model was obtained. Confidence intervals for the determined parameters were found by bootstrapping model residuals. The resultant analysis heavily constrained the values of some parameters while showing that some could vary without significantly impacting model error. For example, our analysis suggests that, above a certain threshold value, the kinetic constant determining the rate of MAP1B transition from the retrograde pausing state to the off-track state has little impact on model error. On the contrary, the kinetic constant describing MAP1B transition from a pausing to a running state has great impact on model error. Graphical abstract Display Omitted Highlights ? A model simulating MAP1B axonal transport was developed. ? Determining model parameters based on limited experimental data was discussed. ? A simplified model was obtained by eliminating parameters with negligible impact. ? An analytical solution for the simplified model was obtained. ? Confidence intervals for model parameters were determined utilizing bootstrapping. ]]>