Particle Trajectory Analysis
Particle Trajectory Analysis - Related Videos
16:14
Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Cited by 15 •
2013
A suite of spatiotemporal processing methods are presented to analyze human trajectory data, such as that collected using a GPS device, for the purpose of modeling pedestrian space-time activities.
04:24
Studying Cell Rolling Trajectories on Asymmetric Receptor Patterns
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Cited by 3 •
2011
We describe a protocol to observe and analyze cell rolling trajectories on asymmetric receptor-patterned substrates. The resulting data are useful for engineering of receptor-patterned substrates for label-free cell separation and analysis.
01:26
Orthogonal Trajectories
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2026
Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
12:58
In Situ Mapping of the Mechanical Properties of Biofilms by Particle-tracking Microrheology
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Cited by 11 •
2015
Particle-tracking microrheology investigates the viscoelasticity of materials. Here, the technique is used to determine the viscoelasticity, creep compliance and effective crosslinking roles of different matrix components of a bacterial biofilm. The matrix consists of polymeric substances secreted by the bacteria and its components determine biofilm structure and mechanical properties.
07:38
Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
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Cited by 9 •
2017
This manuscript uses the Fiji-based open-source software package VirusMapper to apply single-particle analysis to super-resolution microscopy images in order to generate precise models of nanoscale structure.
FAQs
The linking step turns separate position measurements into a time-ordered record for each particle. That record allows movement to be evaluated across successive observations rather than as isolated locations. From the resulting path, researchers can examine displacement, velocity, directionality, and diffusion, then relate those measurements to transport or interactions in the system.
These measures describe different aspects of the same motion. Displacement captures how far a particle changes position, velocity adds the rate of that change, and directionality indicates whether motion follows a consistent orientation. Diffusion describes movement that can be analyzed as a transport behavior. Together, the measures distinguish features that one metric alone would miss.
Particle paths are interpreted in relation to the physical or chemical conditions under which they were recorded. Changes in those conditions can alter observed transport, interactions, or dynamic behavior, so measurements are not considered independently of the system. This context enables mechanism-based modeling and helps connect trajectory patterns with the processes producing them.
A typical workflow begins with sequential microscopy images or video frames, followed by particle detection in each observation. The detected positions are linked into trajectories, after which displacement, velocity, directionality, or diffusion are calculated. The resulting quantitative records can then be compared with the relevant biological or engineered system to evaluate its dynamic behavior.
Researchers can apply the method to molecular motors, intracellular cargo, drug-delivery particles, cell migration, and microfluidic transport. These applications use trajectory measurements to characterize movement at the level of individual particles. The resulting data can connect particle motion with transport, interactions, or dynamic behavior in biological and engineered systems.
In bioengineering, the measurements support both analysis and design. They can inform mechanism-based models, guide optimization of engineered devices such as microfluidic systems, and help evaluate therapeutic strategies involving drug-delivery particles. Because trajectories are measured under specified physical or chemical conditions, results can be tied to how a biological or engineered system performs.