Figure 2. Stochastic force-balance model... | Rockefeller University Press

Figure 2.

$Figure 2. Stochastic force-balance model for antagonistic motility assays incorporating KLP61F and Ncd kinetics: model fit to assay data identifies motors’ biophysical parameters and suggests load-dependent kinetics and a specific superstall F–V relationship for motors. (A) Experimental (black) and computed (red) MT velocities in competitive gliding assays. The mole fraction is defined as (mole Ncd)/(mole Ncd + mole KLP61F), ignoring solvent/buffer concentrations. The stochastic force-balance model provides an excellent fit to the mean and the SD of the gliding data, even at the balance point. (B) Histogram of experimental (black) and computed (red) MT velocities at the balance point mole fraction with respective Gaussian fits. (C) Computed trajectories of four representative MTs at the balance point. (D) Histogram of the number of KLP61F and Ncd motors engaged with an MT (computed). Asterisks indicate peaks.$

Stochastic force-balance model for antagonistic motility assays incorporating KLP61F and Ncd kinetics: model fit to assay data identifies motors’ biophysical parameters and suggests load-dependent kinetics and a specific superstall F–V relationship for motors. (A) Experimental (black) and computed (red) MT velocities in competitive gliding assays. The mole fraction is defined as (mole Ncd)/(mole Ncd + mole KLP61F), ignoring solvent/buffer concentrations. The stochastic force-balance model provides an excellent fit to the mean and the SD of the gliding data, even at the balance point. (B) Histogram of experimental (black) and computed (red) MT velocities at the balance point mole fraction with respective Gaussian fits. (C) Computed trajectories of four representative MTs at the balance point. (D) Histogram of the number of KLP61F and Ncd motors engaged with an MT (computed). Asterisks indicate peaks.