Novel algorithm and MATLAB-based program for automated power law analysis of single particle, time-dependent mean-square displacement

Abstract
In many physical and biophysical studies, single-particle tracking is utilized to reveal interactions, diffusion coefficients, active modes of driving motion, dynamic local structure, micromechanics, and microrheology. The basic analysis applied to those data is to determine the time-dependent mean-square displacement (MSD) of particle trajectories and perform time- and ensemble-averaging of similar motions. The motion of particles typically exhibits time-dependent power-law scaling, and only...

Title of program: LINSA (acronym: linear segment analysis)
Catalogue Id: AEMD_v1_0

Nature of problem
In many physical and biophysical areas employing single-particle tracking, having the time-dependent power-laws governing the time-averaged meansquare displacement (MSD) of a single particle is crucial. Those power-laws determine the mode-of-motion and hint at the underlying mechanisms driving motion. Accurate determination of the power laws that describe each trajectory will allow categorization into groups for further analysis of single trajectories or ensemble analysis, e.g. ensemble and time ...

Versions of this program held in the CPC repository in Mendeley Data
AEMD_v1_0; LINSA (acronym: linear segment analysis); 10.1016/j.cpc.2012.03.001

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)