A Jump-Distance-Based Parameter Inference Scheme for Particulate Trajectories

Abstract

This study presents an improved quantitative tool for the analysis of particulate trajectories. Particulate trajectory data appears in several different biological contexts, from the trajectory of chemotaxing bacteria to the nuclear mobility inferred from the trajectory of MS2 spots. Presently, the majority of analyses performed on particulate trajectory data have been limited to mean-squared displacement (MSD) analysis. Although simple, MSD analysis has several pitfalls, including difficulty in selecting between competing methods of motion, handling systems with multiple distinct subpopulations, and parameter extraction from limited time-series data. Here, we provide an alternative to MSD analysis using the jump distance distribution (JDD), which addresses the aforementioned issues. In particular, the method outperforms MSD analysis in the data-poor limit, thereby giving access to a larger range of temporal dynamics. In this work, we construct and validate a derivation of the JDD for different transportation modes and dimensions and implement a parameter estimation and model selection scheme. This scheme is validated, and direct improvements over MSD analysis are shown. Through an analysis of bacterial chemotaxis data, we highlight the JDD's ability to extract parameters at a variety of timescales, as well as extract underlying biological features of interest.

Hop, Skip, and Jump: Hydrogen Molecular Transport through Amorphous Polyethylene Matrices Studied via Molecular Dynamics Simulations

“…The magnitude of the penetrant jump distance r p ( i )is quantified by , r p false( i false) 2 = false| false⟨ r i false⟩ τ − false⟨ r i − 1 false⟩ τ false| 2 In eq , r i is the position of the penetrant and τ is the time interval between jumps. JDD distributions are expected to show a positively skewed shape, suggesting short jumps and times of complete immobilization are the most frequent occurrences due to density fluctuations, obstructed motions, or complete constraint of the penetrant in the polymer matrix. , …”

Impact of neurite alignment on organelle motion

“…The diusive and super-diusive trajectory parts for each experimental condition were used to calculate the respective JDD PDF, for lag times of 0.2405 ms and 0.7585 ms respectively. The PDFs were tted to extract characteristic values of the motion, using the analytical expressions calculated in (Menssen and Mani 2019). Particularly, the diusive trajectory modes JDD PDF was tted for the x-direction, using…”

“…Over time, application of this analysis for Brownian motion in uids (Hopkins et al 2010) and in -actual or simulated -biological systems increased (Ghosh et al 2016;Menssen and Mani 2019;Grady et al 2017;Bhowmik, Tah, and Karmakar 2018;Witzel et al 2019), establishing it as a powerful tool for characterization of complex biological trajectories.…”

Effect of G4C2 Repeat Expansions on the Motion of Lysosomes Inside Neurites

“…The diffusive and super-diffusive trajectory parts for each experimental condition were used to calculate the respective JDD PDF, for lag times of 0.2405ms and 0.7585ms respectively. The PDFs were fitted to extract characteristic values of the motion, using the analytical expressions calculated in [77]. Particularly, the diffusive trajectory modes JDD PDF was fitted for the x-direction, using…”