Jove
Visualize
Contact Us

Related Concept Videos

Experimental Designs01:16

Experimental Designs

An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
Data Collection by Experiments01:13

Data Collection by Experiments

Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public clinical trial...
Crossover Experiments01:16

Crossover Experiments

Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
Experimental RNAi02:15

Experimental RNAi

RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Prioritizing chemicals for developmental neurotoxicity by integrating data from a new approach methods (NAMs) battery covering key cellular events in neurodevelopment.

Neurotoxicology·2026
Same author

Spatiotemporal dynamics of NF-κB/Dorsal inhibitor IκBα/Cactus in <i>Drosophila</i> blastoderm embryos.

iScience·2025
Same author

The GeoTox Package: open-source software for connecting spatiotemporal exposure to individual and population-level risk.

Human genomics·2025
Same author

The GeoTox Package: Open-source software for connecting spatiotemporal exposure to individual and population-level risk.

medRxiv : the preprint server for health sciences·2024
Same author

Dynamics of BMP signaling and stable gene expression in the early Drosophila embryo.

Biology open·2024
Same author

Cellular clarity: a logistic regression approach to identify root epidermal regulators of iron deficiency response.

BMC genomics·2023
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 23, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

Set membership experimental design for biological systems.

Skylar W Marvel1, Cranos M Williams

  • 1Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695, USA.

BMC Systems Biology
|March 23, 2012
PubMed
Summary

This study introduces a bounded-error experimental design framework for biological systems with limited data. It optimizes measurement strategies to reduce model uncertainty and conserve resources, enhancing predictive model accuracy.

More Related Videos

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage
06:46

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage

Published on: August 4, 2018

Related Experiment Videos

Last Updated: May 23, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage
06:46

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage

Published on: August 4, 2018

Area of Science:

  • Systems Biology
  • Computational Biology
  • Experimental Design

Background:

  • Limited experimental resources necessitate efficient design strategies for biological systems.
  • Traditional methods for characterizing uncertainty are inadequate with few data points, requiring a bounded-error approach.
  • Improved integration of modeling and experimental practices is crucial, linking design metrics to biological relevance.

Purpose of the Study:

  • Develop a bounded-error experimental design framework for nonlinear continuous-time biological systems with sparse data.
  • Enable better resource allocation by optimizing the number and timing of measurements.
  • Enhance the connection between modelers and experimentalists through biologically relevant metrics.

Main Methods:

  • Utilize interval analysis for parameter and state estimation within a bounded-error context.
  • Employ set-based uncertainty propagation to estimate measurement ranges at candidate time points.
  • Develop and apply biologically relevant metrics to guide measurement acquisition strategies.

Main Results:

  • Identified candidate measurement time points that maximize information gain for biologically relevant metrics.
  • Developed a method to assess the impact of combining multiple measurement time points.
  • Determined the point at which additional measurements yield diminishing returns in reducing model uncertainty.

Conclusions:

  • The proposed framework effectively balances resource availability with data acquisition for improved model predictability.
  • Demonstrated the ability to select optimal measurement time points and quantities for biological systems.
  • Provides a practical approach for optimizing experimental design under data constraints.