Estimating Velocity for Processive Motor Proteins with Random Detachment

  • John Hughes
  • Shankar Shastry
  • William O. Hancock
  • John Fricks
Article

Abstract

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 Words

Bioengineering Infinite variance Maximum likelihood Nanotechnology Pearson type VII distribution Random sums Stopped Brownian motion 

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Copyright information

© International Biometric Society 2013

Authors and Affiliations

  • John Hughes
    • 1
  • Shankar Shastry
    • 2
  • William O. Hancock
    • 2
  • John Fricks
    • 3
  1. 1.Division of BiostatisticsUniversity of MinnesotaMinneapolisUSA
  2. 2.Department of BioengineeringThe Pennsylvania State UniversityUniversity ParkUSA
  3. 3.Department of StatisticsThe Pennsylvania State UniversityUniversity ParkUSA

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