Estimating Velocity for Processive Motor Proteins with Random Detachment
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We show that, for a wide range of models, the empirical velocity of processive motor proteins has a limiting Pearson type VII distribution with finite mean but infinite variance. We develop maximum likelihood inference for this Pearson type VII distribution. In two simulation studies, we compare the performance of our MLE with the performance of standard Student’s t-based inference. The studies show that incorrectly assuming normality (1) can lead to imprecise inference regarding motor velocity in the one-sample case, and (2) can significantly reduce power in the two-sample case. These results should be of interest to experimentalists who wish to engineer motors possessing specific functional characteristics.
Key WordsBioengineering Infinite variance Maximum likelihood Nanotechnology Pearson type VII distribution Random sums Stopped Brownian motion
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- Ihaka, R., and Gentleman, R. (1996), “R: A Language for Data Analysis and Graphics,” Journal of Computational and Graphical Statistics, 5, 299–314. Google Scholar
- Mogilner, A., Wang, H., Elston, T., and Oster, G. (2002), “Molecular Motors: Theory and Experiment,” in Computational Cell Biology, eds. C. Fall, E. Marland, J. Wagner and J. Tyson, New York: Springer. Google Scholar
- Spudich, J. A., Rice, S. E., Rock, R. S., Purcell, T. J., and Warrick, H. M. (2011), “Optical Traps to Study Properties of Molecular Motors,” in Cold Spring Harbor Protocols, p. 2011. Google Scholar