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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 143-156 |
| Number of pages | 14 |
| Journal | Biophysical Journal |
| Volume | 117 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jul 9 2019 |
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ASJC Scopus subject areas
- Biophysics
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A Jump-Distance-Based Parameter Inference Scheme for Particulate Trajectories. / Menssen, Rebecca; Mani, Madhav.
In: Biophysical Journal, Vol. 117, No. 1, 09.07.2019, p. 143-156.Research output: Contribution to journal › Article
TY - JOUR
T1 - A Jump-Distance-Based Parameter Inference Scheme for Particulate Trajectories
AU - Menssen, Rebecca
AU - Mani, Madhav
PY - 2019/7/9
Y1 - 2019/7/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85067468188&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067468188&partnerID=8YFLogxK
U2 - 10.1016/j.bpj.2019.06.004
DO - 10.1016/j.bpj.2019.06.004
M3 - Article
C2 - 31235182
AN - SCOPUS:85067468188
VL - 117
SP - 143
EP - 156
JO - Biophysical Journal
JF - Biophysical Journal
SN - 0006-3495
IS - 1
ER -