Particle Surrogates
Particle Surrogates - Related Videos
08:16
Preparation and Analysis of In Vitro Three Dimensional Breast Carcinoma Surrogates
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Cited by 6 •
2016
We demonstrate a method to generate 3D breast cancer surrogates, which can be cultured using a perfusion bioreactor system to deliver oxygen and nutrients. Following growth, surrogates are fixed and processed to paraffin for evaluation of parameters of interest. The evaluation of one such parameter, cell density, is explained.
08:37
Microglia as a Surrogate Biosensor to Determine Nanoparticle Neurotoxicity
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Cited by 16 •
2016
Microglia (immune cells of the brain), are used as a surrogate biosensor to determine how nanoparticles influence neurotoxicity. We describe a series of experiments designed to assay microglial response to nanoparticles and exposure of hypothalamic neurons to supernatant from activated microglia to determine neurotoxicity.
09:17
Surrogate Model Development for Digital Experiments in Welding
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Cited by 2 •
2025
This protocol outlines a general workflow for constructing an artificial neural network for welding simulation using datasets generated automatically through Python scripts with macro functions. The workflow is validated against a benchmark case involving single-pass, straight-line welding. The surrogate model's predictive accuracy shows a strong agreement with finite element simulations.
03:37
Subatomic Particles
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2020
Dalton was only partially correct about the particles that make up matter. All matter is composed of atoms, and atoms are composed of three smaller subatomic particles: protons, neutrons, and electrons. These three particles account for the mass and the charge of an atom. The Discovery of the Electron The first clue about the subatomic structure came at the end of the 19th century when J.J. Thomson discovered the electron using a cathode ray tube. This apparatus consisted of a sealed glass...
05:58
Measuring Relative Insulin Secretion using a Co-Secreted Luciferase Surrogate
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Cited by 7 •
2019
This protocol describes how to perform rapid low-cost luciferase assays at medium-throughput using an insulin-linked Gaussia luciferase as a proxy for insulin secretion from beta cells. The assay can be performed with most luminescence plate readers and multichannel pipettes.
FAQs
Researchers begin with the behavior they need to reproduce, then prioritize the particle attributes most likely to control that behavior. Size, shape, density, composition, and surface chemistry can be adjusted independently or together. Matching only one attribute may be insufficient when transport, interactions, or deposition depend on several properties at once.
Surface chemistry is especially important when a surrogate encounters a biological interface. It can affect how the particle interacts with tissues or cells, so a particle that matches size and density may still produce different penetration or uptake behavior. This makes surface properties a key consideration when interpreting bioengineering experiments.
Fidelity is established by validation, not by assuming that engineered particles behave like the targets. Researchers compare the surrogate's relevant transport, interaction, or deposition behavior under defined conditions and identify where biological interfaces or complex physiological environments alter the match. This validation determines whether conclusions can be extended to the target system.
A useful workflow begins by identifying the target process, selecting surrogate properties that represent it, and defining the study conditions before testing. Measurements can then be interpreted against the intended transport, penetration, uptake, or deposition outcome. Keeping conditions defined helps separate effects of particle design from effects of the experimental environment.
In bioengineering, these particles support studies of fluid transport, tissue penetration, cellular uptake, aerosol behavior, and drug-delivery performance. They also assist assay development and device testing, where controlled particles can help examine system behavior before working with a more complex target. The appropriate application depends on which target process requires representation.
A mismatch is informative because it reveals that the selected properties or study conditions did not capture an important part of the target process. Researchers can reassess size, shape, density, composition, surface chemistry, or environmental conditions, then refine the surrogate or limit the interpretation. This comparison strengthens confidence in later bioengineering tests.