High-throughput Experimentation
High-throughput Experimentation - Related Videos
09:44
High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes
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2015
Here we describe a cell-based reporter gene assay as a valuable tool to screen chemical libraries for compounds modulating post-transcriptional control mechanisms exerted through 3’ UTR.
06:19
High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography
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Cited by 16 •
2023
This protocol details high-throughput crystallization screening, ranging from the 1,536 microassay plate preparation to the end of a 6 week experimental time window. Details are included about the sample setup, the imaging obtained, and how users can perform analyses using an artificial intelligence-enabled graphical user interface to quickly and efficiently identify macromolecular crystallization conditions.
04:49
Realism in Experimentation
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2023
Source: Laboratories of Gary Lewandowski, Dave Strohmetz, and Natalie Ciarocco—Monmouth University In an ideal world researchers would conduct their studies in real world settings where behaviors naturally happen. For example, if you want to see what influences individuals’ voting behavior, it would be best to watch them vote. However, research in these settings is not always ethical or even practical. Further, a researcher may want more control over the setting to better pinpoint the exact...
09:00
Precise, High-throughput Analysis of Bacterial Growth
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Cited by 56 •
2017
Quantitative evaluation of bacterial growth is essential to understanding microbial physiology as a systems-level phenomenon. A protocol for experimental manipulation and an analytical approach are introduced, allowing for precise, high-throughput analysis of bacterial growth, which is a key subject of interest in systems biology.
06:12
Experimentation using a Confederate
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2023
Source: Laboratories of Gary Lewandowski, Dave Strohmetz, and Natalie Ciarocco—Monmouth University When orchestrating an experiment, it is important that the experience elicits the most natural reactions from the participants as possible. Researchers accomplish much of this through their creation of the experimental settings. Many research projects focus on interactions between two or more people. In these situations the environment or setting must often be less natural; often only one person...
FAQs
Systematic control of reagent concentration, incubation time, and temperature makes comparisons across many assays more meaningful. When these variables are varied deliberately rather than changing unpredictably, researchers can associate observed biological effects with specific experimental conditions. This supports reliable pattern detection and helps distinguish promising results from changes caused by inconsistent handling or environmental conditions.
Miniaturized assay formats allow many conditions to be arranged and tested in parallel, while automated liquid handling and robotic sample processing apply those conditions consistently. Together, these components reduce dependence on repetitive manual operations and coordinate large experimental runs. The resulting workflow can examine diverse biological conditions while maintaining greater control over how samples and reagents are handled.
Computational analysis converts measurements from numerous parallel experiments into structured datasets that can be examined systematically. It helps researchers identify patterns across conditions, recognize results that warrant further investigation, and prioritize candidates for more detailed study. This analytical step is essential because the value of a large experiment depends not only on collecting data, but also on interpreting relationships within it.
A typical workflow begins by selecting the biological conditions or candidates to compare, followed by arranging assays in miniaturized formats. Automated liquid handling introduces reagents, and robotic processing manages the samples through the experiment. Researchers then analyze the resulting measurements computationally, compare outcomes across controlled variables, and select findings or candidates for follow-up investigation.
Researchers choose this approach when they need to examine many candidates or experimental conditions efficiently and systematically. Supported uses include screening drugs or enzymes, investigating genetic or protein function, and optimizing cell-based assays. The method is particularly useful when a broad initial search is needed before committing resources to detailed experiments on a smaller set of promising candidates.
The resulting datasets can reveal meaningful patterns among tested conditions, candidates, or biological functions. In drug and enzyme screening, those patterns may help identify candidates for detailed study; in genetic or protein-function research, they can support systematic comparison of effects. For cell-based assays, the data can guide optimization of conditions and improve the efficiency and reproducibility of later workflows.