Jove
Visualize
Contact Us
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 Concept Videos

Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
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 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...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Factorial Design02:01

Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...

You might also read

Related Articles

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

Sort by
Same author

Excited States in Bilayer Graphene Quantum Dots.

Physical review letters·2019
Same author

Type and capacity of glucose transport influences succinate yield in two-stage cultivations.

Microbial cell factories·2018
Same author

Congregate Meals: Opportunities to Help Vulnerable Older Adults Achieve Diet and Physical Activity Recommendations.

The Journal of frailty & aging·2018
Same author

Application of theoretical methods to increase succinate production in engineered strains.

Bioprocess and biosystems engineering·2017
Same author

Optimized SESAMs for kilowatt-level ultrafast lasers.

Optics express·2016
Same author

10  GHz pulse repetition rate Er:Yb:glass laser modelocked with quantum dot semiconductor saturable absorber mirror.

Applied optics·2016
Same journal

Identification of MTFR1 as a Novel Prognostic Biomarker and Putative Oncogene for Breast Cancer: A Multi-Omics Analysis and in Vitro Experimental Validation.

IET systems biology·2026
Same journal

scGMB: A scRNA-seq Cell Classification Method Combining GCN and Mamba.

IET systems biology·2026
Same journal

Identification of Chemokine-Related Genes Derived From T and NK Cells in the Tumour Microenvironment of Ovarian Cancer Based on scRNA-Seq.

IET systems biology·2026
Same journal

Unravelling the Mechanism of Compound Kushen Injection in Treating Cervical Cancer Through Ferroptosis Regulation: An Integrated Network Pharmacology and Molecular Docking Study.

IET systems biology·2026
Same journal

Metabolic Reprogramming in Recurrent Spontaneous Abortion: Key Biomarkers Identification and Diagnostic Model Development.

IET systems biology·2026
Same journal

Network Pharmacology and Experimental Validation to Explore the Potential Mechanism of Salvianolic Acid B in Reversing Oxaliplatin Resistance of Colorectal Cancer Cells.

IET systems biology·2026
See all related articles

Related Experiment Video

Updated: Jun 26, 2026

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

Optimal experimental design with the sigma point method.

R Schenkendorf1, A Kremling, M Mangold

  • 1Max-Planck-Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany. rschenke@mpi-magdeburg.mpg.de

IET Systems Biology
|January 22, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces the sigma point (SP) method to improve optimal experimental design (OED) for nonlinear mathematical models. The SP method offers a more accurate estimation of parameter uncertainties, leading to more precise models.

More Related Videos

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
13:54

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

Published on: August 18, 2023

Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

Related Experiment Videos

Last Updated: Jun 26, 2026

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

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
13:54

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

Published on: August 18, 2023

Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

Area of Science:

  • Mathematical modeling
  • Systems biology
  • Computational science

Background:

  • Quantitative descriptions of dynamical systems rely on parameter identification, which is affected by measurement noise and leads to parameter uncertainty.
  • Precise parameters are crucial for developing highly predictive models.
  • Optimal experimental design (OED) iteratively minimizes parameter variances to reduce uncertainty.

Purpose of the Study:

  • To address limitations of traditional OED methods for nonlinear models, specifically the underestimation of parameter variances and neglected estimator bias.
  • To introduce and demonstrate the sigma point (SP) method as an improved approach for OED.
  • To show the utility of the SP method in assessing the impact of parameter uncertainties on simulation outcomes.

Main Methods:

  • The study applies the sigma point (SP) method, a numerical optimization technique.
  • The SP method is compared against traditional OED cost functions based on the inverse Fisher information matrix.
  • The methods are demonstrated using a widely adopted biological model.

Main Results:

  • The sigma point (SP) method provides a better approximation of parameter statistics compared to traditional methods.
  • This improved approximation directly benefits the optimal experimental design (OED) process by yielding more accurate parameter variance estimates.
  • The SP method effectively quantifies the influence of parameter uncertainties on model simulations.

Conclusions:

  • The sigma point (SP) method offers a more accurate and robust approach to optimal experimental design for nonlinear dynamical systems.
  • It overcomes key limitations of the Fisher information matrix-based method by providing better variance approximations and accounting for bias.
  • The SP method enhances model predictability and aids in understanding uncertainty propagation in biological models.