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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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...
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
Determination of Michaelis Constant and Maximum Elimination Rate01:20

Determination of Michaelis Constant and Maximum Elimination Rate

The Michaelis constant (KM) and the theoretical maximum process rate (Vmax) are vital parameters in the Michaelis-Menten equation, central to many biochemical reactions. They provide essential insights into enzyme kinetics and drug metabolism.
These parameters can be estimated by analyzing plasma concentration data post-drug administration. A notable example of this application is phenytoin, a drug with capacity-limited kinetics. It's recommended that phenytoin should be administered at two...

You might also read

Related Articles

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

Sort by
Same author

PAGD: the Persea americana Genome Database and a Docker-based transcriptome analysis workflow.

BMC genomics·2026
Same author

Ultraelastic adaptive Bletilla striata polysaccharide hydrogel bandage for rapid sealing, hemostasis and infection-resistant healing.

Colloids and surfaces. B, Biointerfaces·2026
Same author

Release of antimony from Sb(V) incorporated iron oxides during oxalate-promoted recrystallization under aerobic conditions.

Environmental research·2026
Same author

Homocysteine is a risk factor for reduced ejection fraction in children with myocarditis: a single-center study.

Frontiers in pediatrics·2026
Same author

The photo-release of dissolved organic matter from soil-derived sources: An insight into the impact of acid mine drainage.

Journal of environmental sciences (China)·2026
Same author

Association of plasma remnant cholesterol with cognitive function in the middle-aged and elderly Chinese adults with type 2 diabetes: a cross-sectional study.

Frontiers in nutrition·2026

Related Experiment Video

Updated: Jun 2, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Dynamic programming procedure for searching optimal models to estimate substitution rates based on the

Chengjun Zhang1, Jia Wang, Weibo Xie

  • 1National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research Wuhan, Huazhong Agricultural University, Wuhan 430070, China.

Proceedings of the National Academy of Sciences of the United States of America
|April 28, 2011
PubMed
Summary

This study introduces a dynamic programming method to efficiently search for optimal gene substitution models. The new approach significantly improves likelihoods compared to conventional methods, aiding evolutionary and functional gene analysis.

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

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Related Experiment Videos

Last Updated: Jun 2, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 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

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

  • * Evolutionary Biology
  • * Bioinformatics
  • * Computational Biology

Background:

  • * Gene substitution rates are crucial for understanding gene function and evolution.
  • * Maximum-likelihood methods (e.g., CODEML in PAML) are standard for estimating substitution rates.
  • * Current methods test limited branch models, neglecting a vast number of potential models.

Purpose of the Study:

  • * To develop a computationally feasible method for searching globally optimal gene substitution models.
  • * To explore probable model spaces for efficient identification of optimal branch-specific models.
  • * To compare the performance of the new method against conventional approaches.

Main Methods:

  • * Dynamic programming techniques were employed to develop a computational search method.
  • * Three novel search strategies were proposed, exploring O(n) to O(n^2) models for a phylogeny with n branches.
  • * A formula was derived to calculate the total number of possible models, highlighting search complexity.

Main Results:

  • * The developed method allows for searching optimal branch-specific models within practical computational timeframes.
  • * Reanalysis of over 50 published studies demonstrated that the new method identified significantly better models.
  • * The improved models yielded substantially higher likelihoods than those from conventional hypothesis-driven methods.

Conclusions:

  • * The proposed dynamic programming approach offers a more comprehensive and efficient way to identify optimal gene substitution models.
  • * This method enhances the accuracy of evolutionary and functional analyses by utilizing globally optimal models.
  • * The findings suggest a significant improvement over existing methods for estimating gene substitution rates.