Probe Design Algorithm
Probe Design Algorithm - Related Videos
05:38
Custom-made Microdialysis Probe Design
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Cited by 9 •
2015
Microdialysis is a commonly used technique in neuroscience research. Therefore commercial probes are in great demand.In this work a probe assembly is explained in detail to build a reliable, concentric, custom-made microdialysis probe for less than $10.
15:43
Design and Fabrication of Ultralight Weight, Adjustable Multi-electrode Probes for Electrophysiological Recordings in Mice
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Cited by 29 •
2014
Understanding the neural substrates of behavior requires brain circuit ensemble recording. Because of its genetic tractability, the mouse offers a model for circuit dissection and disease mimicry. Here, a method of designing and fabricating miniaturized probes is described that is suitable for targeting deep brain structure in the mouse.
09:47
A Microfluidic Technique to Probe Cell Deformability
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Cited by 20 •
2014
We demonstrate a microfluidics-based assay to measure the timescale for cells to transit through a sequence of micron-scale constrictions.
09:51
Atom Probe Tomography Studies on the Cu(In,Ga)Se2 Grain Boundaries
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Cited by 29 •
2013
In this work, we describe the use of the atom-probe tomography technique for studying the grain boundaries of the absorber layer in a CIGS solar cell. A novel approach to prepare the atom probe tips containing the desired grain boundary with a known structure is also presented here.
02:01
Group Design
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2020
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
FAQs
It compares potential probes with several design constraints rather than relying on target complementarity alone. The analysis considers sequence length, nucleotide composition, melting temperature, secondary-structure potential, and the risk of nonspecific binding. Candidates are then ranked according to predicted performance, helping distinguish sequences that may bind the intended target effectively from those more likely to produce weak or ambiguous assay results.
Melting temperature helps predict the conditions under which a probe can bind its complementary DNA or RNA target. A design algorithm includes this value among its selection constraints so candidate sequences can be compared under the intended assay conditions. Accounting for melting temperature supports more consistent hybridization behavior and can improve the reliability of signal generation across experiments.
Secondary structures can make parts of a probe or target less available for hybridization, while nonspecific binding can produce signal from unintended sequences. The algorithm screens candidates for both risks during design. Excluding or lowering the rank of problematic sequences helps favor probes with better target accessibility and specificity, reducing the likelihood of misleading measurements or difficult laboratory optimization.
Ranking converts multiple sequence checks into a practical selection process. Rather than presenting candidate probes without guidance, the algorithm orders them according to predicted performance across factors such as complementarity, melting temperature, nucleotide composition, length, secondary structure, and nonspecific-binding potential. Researchers can therefore prioritize the strongest designs for testing and reduce time spent evaluating less suitable alternatives.
A typical workflow begins with the biological target and generates or evaluates possible short nucleic acid sequences. The algorithm tests those candidates against design constraints, including complementarity, length, nucleotide composition, melting temperature, secondary structure, and nonspecific binding. It then ranks the results, allowing researchers to select prioritized probes for subsequent laboratory assay development and optimization.
Algorithmically selected probes support several hybridization-based applications, including quantitative PCR, fluorescence in situ hybridization, and microarrays. The appropriate design must match the target and assay context, because each application depends on reliable probe-target binding and interpretable signal. Using computationally prioritized candidates can streamline probe development before experimental testing in these different biological formats.
Careful computational selection can improve target specificity, signal quality, and experimental reproducibility. It also reduces the time and cost associated with laboratory optimization by prioritizing candidates that satisfy multiple design constraints before testing. These benefits are especially relevant when researchers need dependable probes for measurements involving DNA or RNA targets in biological assays.