# Joint estimation of motion model, model parameters, and particle trajectories in single particle tracking

> **NIH NIH R01** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2020 · $334,125

## Abstract

PROJECT SUMMARY:
 Single particle tracking (SPT) is a powerful class of techniques for understanding biomolecular motion at
the subcellular level in the crowded environments of the plasma membrane, cytoplasm, and nucleus. The basic
scheme is to acquire image sequences, typically through wide-field fluorescence imaging, produce trajectories
from these images, and finally to estimate motion parameters from the trajectories through the use of tools
such as curve-fitting to the mean-square displacement (MSD) curve. The method has been extremely effective
for the study of single particles moving in the plane under a fixed model. SPT will have a transformative
impact once it is capable of studying biological macromolecules moving in three dimensions and undergoing
complex modes of motion that switch between different models during a single run as particles undergo, for
example, internalization, recycling, and trafficking between cells. In the 3-D setting, the assumptions that make
the standard methods both simple and robust no longer hold and issues such as motion blur, ad hoc choices
of fitting parameters that have a large impact on the accuracy of results, an assumption of stationarity in the
data which precludes analysis of mode switching in a single trajectory, separation of the analysis of particle
trajectory from motion parameter estimation, and lack of modeling of effects of non-Gaussian noise must be
addressed and overcome to make SPT as effective in 3-D as it has been in studying planar motion.
 The proposed project consists of three specific aims. The first is focused on creating techniques for jointly
estimating particle trajectory and motion parameters from SPT data sets using a framework that allows for
complex motion and observation models, including camera-specific descriptions, depth-dependent point spread
functions, and dynamics that switch between different models. The resulting method will greatly improve the
accuracy and applicability of SPT in the 3-D setting. The second aim targets data acquisition, using a confocal-
based tracking scheme inspired by nonlinear, stochastic extremum-seeking control. The confocal modality
provides a better SNR, innate 3-D capability and, most significantly, an extremely fast sampling rate to miti-
gate effects of motion blur. The proposed method, implementable on standard confocal instruments, is tunable
for optimal performance at different experimental settings and complements wide-field techniques when high
temporal resolution of a single particle is needed. Finally, the third aim seeks to validate the proposed tech-
niques in two experimental systems. The first is a simple setting of tracking quantum dots inside hydrogels.
These polymer-based systems are extensively used in a number of biomedical applications, including tissue
engineering, drug delivery, and immunoisolation. The second setting is that of tracking individual, labeled
AMPA receptors in rat hippocampal neurons, providing a biological setting for ...

## Key facts

- **NIH application ID:** 10020990
- **Project number:** 5R01GM117039-04
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Sean B. Andersson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $334,125
- **Award type:** 5
- **Project period:** 2017-09-20 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10020990

## Citation

> US National Institutes of Health, RePORTER application 10020990, Joint estimation of motion model, model parameters, and particle trajectories in single particle tracking (5R01GM117039-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10020990. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*