Population Dynamics Tracking
Population Dynamics Tracking - Related Videos
02:14
Tracking Aggregation of Virus-Like Particles Using Dynamic Light Scattering
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2026
Source: Moore, M. D., et al. Alternative In Vitro Methods for the Determination of Viral Capsid Structural Integrity. J. Vis. Exp. (2017)The video demonstrates a protocol for monitoring the heat-induced aggregation of virus-like particles using dynamic light scattering (DLS). It begins with preparing a monodisperse suspension of purified particles. The sample is then loaded into a preheated DLS instrument. As the capsid proteins denature with heat, exposed hydrophobic regions cause particle...
04:52
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Cited by 1 •
2023
We developed a cost-effective method to follow non-single nucleotide polymorphism allele dynamics that can easily be adapted to experimental evolution frozen archives. A triplet PCR technique was coupled with automated parallel capillary electrophoresis to quantify the relative frequency of an insertion allele over the course of experimental evolution.
08:17
Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
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Cited by 9 •
2015
Stereotactic ablative body radiation therapy (SBRT) involves the precise delivery of high-dose radiation to cancer tumor targets. A novel SBRT platform offers a first-of-its-kind gimbaled radiation accelerator mounted within a pivoting O-ring gantry capable of dynamic image-guided tumor tracking. Here, we describe dynamic tumor tracking for lung targets.
04:35
Tracking Bacterial Infection Dynamics in a Mouse Using Bioluminescence
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2025
Source: Valentine, M. E., et al. Generation of In-Frame Gene Deletion Mutants in Pseudomonas aeruginosa and Testing for Virulence Attenuation in a Simple Mouse Model of Infection. J. Vis. Exp. (2020)This video demonstrates the use of bioluminescence to visualize and compare the in vivo behavior of virulent and attenuated bacterial strains in mice. It outlines the preparation of bioluminescent bacterial suspensions and their intraperitoneal injection into mice. The virulent strain exhibits...
13:30
Mass-Sensitive Particle Tracking to Characterize Membrane-Associated Macromolecule Dynamics
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Cited by 3 •
2022
This protocol describes an iSCAT-based image processing and single-particle tracking approach that enables the simultaneous investigation of the molecular mass and the diffusive behavior of macromolecules interacting with lipid membranes. Step-by-step instructions for sample preparation, mass-to-contrast conversion, movie acquisition, and post-processing are provided alongside directions to prevent potential pitfalls.
FAQs
Interpretation depends on separating changes in population size from changes in composition and location. A rising cell count may reflect proliferation, whereas altered composition can indicate differentiation or death, and redistribution can result from migration. Tracking these dimensions together prevents a single aggregate measurement from obscuring biological transitions that influence the behavior of an engineered system.
Growth-curve analysis converts measurements collected over time into a description of how a population changes, rather than treating each observation as isolated. From these trajectories, researchers can estimate parameters associated with population behavior and use them to build predictive models. In bioengineering, those models help anticipate changes in engineered tissues, microbial cultures, or other bioprocesses.
The measured outcome depends on which biological processes are occurring simultaneously. Proliferation can increase population size, while death can reduce it; differentiation changes composition, and migration changes spatial distribution. Consequently, two systems with similar total counts may have different states if their composition or location differs. Including these dimensions makes comparisons between time points more informative.
A practical workflow uses repeated measurements at successive time points, with imaging, sampling, or sensor-based readouts chosen according to the system being monitored. The resulting observations are compared across time to quantify changes in cell number or density, composition, and spatial distribution. Researchers can then analyze trajectories, estimate population-related parameters, and identify meaningful transitions.
When optimizing a culture, tracking reveals whether conditions are associated with changing population behavior over time. The same approach can be used to evaluate therapies or biomaterials by examining effects on cell number, density, composition, or distribution. Because the measurements are time-resolved, researchers can detect transitions rather than relying only on a final endpoint.
Within bioengineering, the method connects population-level measurements to system performance. In engineered tissues, it can follow changes in cell populations; in microbial cultures and bioprocesses, it can support growth-curve analysis and predictive modeling. This context is useful when population behavior changes during development or operation, because those transitions may affect how the engineered system performs.