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

Mechanistic Models: Compartment Models in Individual and Population Analysis

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)...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

Physiological Pharmacokinetic Models: Assumption with Protein Binding

Physiological Pharmacokinetic Models: Assumption with Protein Binding

Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...

You might also read

Related Articles

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

Sort by

Same author

A framework to enhance the signal-to-noise ratio for quantitative fluorescence microscopy.

PloS one·2025

Same author

An inhibitor-free, versatile, fast, and cheap precipitation-based DNA purification method.

PloS one·2025

Same author

Managing Select Immune-Related Adverse Events in Patients Treated with Immune Checkpoint Inhibitors.

Current oncology (Toronto, Ont.)·2024

Same author

Malarial Antibody Detection with an Engineered Yeast Agglutination Assay.

ACS synthetic biology·2022

Same author

Point of Care Molecular Testing: Community-Based Rapid Next-Generation Sequencing to Support Cancer Care.

Current oncology (Toronto, Ont.)·2022

Same author

A Guide to Implementing Immune Checkpoint Inhibitors within a Cancer Program: Experience from a Large Canadian Community Centre.

Current oncology (Toronto, Ont.)·2022

Search research articles

Related Experiment Video

Updated: Jul 19, 2026

Finite Element Modelling of a Cellular Electric Microenvironment

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

Extracting biochemical parameters for cellular modeling: A mean-field approach.

Marco A J Iafolla¹, David R McMillen

¹Department of Chemical and Physical Sciences and Institute for Optical Sciences, University of Toronto at Mississauga, 3359 Mississauga Road North, Mississauga, Ontario L5L 1C6, Canada.

The Journal of Physical Chemistry. B

|October 27, 2006

Summary

This study introduces a novel mean-field method to estimate missing kinetic parameters in gene expression models. By averaging effects across the genome, it reveals how enzyme availability changes with cellular growth rate.

More Related Videos

Creating a Structurally Realistic Finite Element Geometric Model of a Cardiomyocyte to Study the Role of Cellular Architecture in Cardiomyocyte Systems Biology

Creating a Structurally Realistic Finite Element Geometric Model of a Cardiomyocyte to Study the Role of Cellular Architecture in Cardiomyocyte Systems Biology

Published on: April 18, 2018

Related Experiment Videos

Last Updated: Jul 19, 2026

Finite Element Modelling of a Cellular Electric Microenvironment

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

Creating a Structurally Realistic Finite Element Geometric Model of a Cardiomyocyte to Study the Role of Cellular Architecture in Cardiomyocyte Systems Biology

Creating a Structurally Realistic Finite Element Geometric Model of a Cardiomyocyte to Study the Role of Cellular Architecture in Cardiomyocyte Systems Biology

Published on: April 18, 2018

Area of Science:

Molecular Biology
Systems Biology
Biophysics

Background:

Mathematical modeling of biochemical processes is advancing, but incomplete kinetic data hinders accurate gene expression prediction.
Experimental validation of models requires comprehensive kinetic information, which is often lacking in literature.

Purpose of the Study:

To develop a method for estimating missing kinetic parameters in biochemical models.
To apply this method to understand the impact of the Escherichia coli genome on gene expression enzyme availability.

Main Methods:

A mean-field approach, analogous to statistical mechanics, was used to estimate parameters by considering averaged effects of other genes.
Genome-wide averages were derived and matched to bulk literature values for E. coli K-12 and B/r.
The method was tested on the induced lac operon to validate RNA polymerase binding rate constants.

Main Results:

The method successfully estimated the number of free RNA polymerases and ribosomes as a function of cellular growth rate.
Average rate constants for enzyme binding were determined, suggesting changes in global transcriptional/translational regulation.
Derived RNA polymerase promoter binding rates for the lac operon aligned well with experimental data.

Conclusions:

The mean-field method provides a viable approach to fill gaps in kinetic data for biochemical models.
Cellular growth rates influence not only enzyme production but also global regulatory mechanisms affecting enzyme binding rates.
This work contributes to building detailed, validated models of gene expression.