Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Pharmacokinetic Models: Comparison and Selection Criterion

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.

Methods of Medium Optimization

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...

Drug Concentration Versus Time Correlation

Drug Concentration Versus Time Correlation

The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the lowest drug...

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...

Introduction to Enzyme Kinetics

Introduction to Enzyme Kinetics

Enzyme kinetics studies the rates of biochemical reactions. Scientists monitor the reaction rates for a particular enzymatic reaction at various substrate concentrations. Additional trials with inhibitors or other molecules that affect the reaction rate may also be performed.
The experimenter can then plot the initial reaction rate or velocity (Vo) of a given trial against the substrate concentration ([S]) to obtain a graph of the reaction properties. For many enzymatic reactions involving a...

You might also read

Related Articles

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

Sort by

Same author

Decoding Biothreats With FT-ICR-MS: Metabotyping of Bacillus cereus Spores Through Untargeted Metabolomics.

Journal of mass spectrometry : JMS·2026

Same author

The role of Hysteroscopy in the management of caesarean scar pregnancy: a systematic review and meta-analysis.

European journal of obstetrics, gynecology, and reproductive biology·2026

Same author

Data Analysis in Extreme Resolution Mass Spectrometry Untargeted Metabolomics.

Advances in experimental medicine and biology·2026

Same author

Advancing Untargeted Metabolomics Through FT-ICR-MS: Challenges and Applications.

Analytical science advances·2025

Same author

Assessment of phyto-compatibility after soil remediation: Insights from plant growth, physiological, and metabolomic analyses.

Journal of environmental management·2025

Same author

Editorial: Metabolomics in crop research - current and emerging methodologies, volume III.

Frontiers in plant science·2025

Search research articles

Related Experiment Video

Updated: May 24, 2026

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

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

Published on: January 31, 2014

Optimization of time-course experiments for kinetic model discrimination.

Nuno F Lages¹, Carlos Cordeiro, Marta Sousa Silva

¹Centro de Química e Bioquímica, Departamento de Química e Bioquímica, Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal.

|March 10, 2012

Summary

This study introduces a novel method for designing enzyme kinetic assays to distinguish between competing biochemical models. The approach successfully identified a two-substrate mechanism for yeast glyoxalase I kinetics.

More Related Videos

Bio-layer Interferometry for Measuring Kinetics of Protein-protein Interactions and Allosteric Ligand Effects

Bio-layer Interferometry for Measuring Kinetics of Protein-protein Interactions and Allosteric Ligand Effects

Published on: February 18, 2014

Related Experiment Videos

Last Updated: May 24, 2026

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

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

Published on: January 31, 2014

Bio-layer Interferometry for Measuring Kinetics of Protein-protein Interactions and Allosteric Ligand Effects

Bio-layer Interferometry for Measuring Kinetics of Protein-protein Interactions and Allosteric Ligand Effects

Published on: February 18, 2014

Area of Science:

Systems biology and biochemical network modeling.
Enzyme kinetics and mechanism elucidation.

Background:

Quantitative models in systems biology require experimental consistency and predictive power.
Model discrimination is crucial when multiple models fit data equally well but yield different predictions.

Purpose of the Study:

To develop a method for optimizing enzyme kinetic assay design to discriminate between competing biochemical models.
To apply this method to distinguish between single- and two-substrate models for yeast glyoxalase I.

Main Methods:

Utilized ordinary differential equations for biochemical network modeling.
Employed an extension of the Kullback-Leibler distance to maximize model discrimination.
Applied a generalized differential evolution algorithm for multi-objective optimization.

Main Results:

Successfully designed an experiment to differentiate between kinetic models for yeast glyoxalase I.
Experimental results confirmed a two-substrate mechanism for yeast glyoxalase I.
Optimized initial substrate concentrations for effective model discrimination.

Conclusions:

The proposed assay design method is effective for model discrimination in systems biology.
Yeast glyoxalase I exhibits a two-substrate kinetic mechanism.
This work advances the ability to select accurate predictive models for cellular physiology.