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)...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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...
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal assumptions,...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

You might also read

Related Articles

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

Sort by
Same author

Distinguishable spreading dynamics in microbial communities.

Biophysical journal·2026
Same author

Effects of cell-cell communication on bacterial chemotaxis.

Physical review. E·2026
Same author

Inferring Resource Competition in Microbial Communities from Time Series.

PRX life·2026
Same author

Fast, long-range intercellular signal propagation through growth-assisted positive feedback.

Cell systems·2026
Same author

Colony morphogenesis regulates sporulation dynamics in bacterial biofilms.

bioRxiv : the preprint server for biology·2026
Same author

Paraplume: A fast and accurate antibody paratope prediction method provides insights into repertoire-scale binding dynamics.

PLoS computational biology·2026
Same journal

Mapping the 3D Chromosome Organization of a Biosynthetic Gene Cluster by Capture Hi-C (CHi-C).

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Mapping the 3D Chromosome Organization of Streptomyces by Hi-C.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

CUT&Tag Epigenomic Profiling of Biosynthetic Gene Clusters in Arabidopsis thaliana.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Rhizobium rhizogenes-Mediated Hairy Root Transformation Protocol for Lotus japonicus and Other Legumes.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Characterization of Bioactive Saponins from Sea Cucumbers.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Methods for Functional Validation of Terpenoid Metabolic Clusters in Nicotiana benthamiana and Aspergillus oryzae.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: May 14, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Analytic methods for modeling stochastic regulatory networks.

Aleksandra M Walczak1, Andrew Mugler, Chris H Wiggins

  • 1CNRS-Laboratoire de physique Theorique de l'Ecole Normale Superieure, Paris, France.

Methods in Molecular Biology (Clifton, N.J.)
|January 31, 2013
PubMed
Summary
This summary is machine-generated.

This study reviews mathematical models for intrinsic noise in gene networks. It highlights analytic methods for understanding molecular fluctuations in single cells, offering alternatives to computational simulations.

More Related Videos

Sealable Femtoliter Chamber Arrays for Cell-free Biology
13:44

Sealable Femtoliter Chamber Arrays for Cell-free Biology

Published on: March 11, 2015

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy
08:25

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Published on: April 27, 2021

Related Experiment Videos

Last Updated: May 14, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Sealable Femtoliter Chamber Arrays for Cell-free Biology
13:44

Sealable Femtoliter Chamber Arrays for Cell-free Biology

Published on: March 11, 2015

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy
08:25

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Published on: April 27, 2021

Area of Science:

  • Biochemistry
  • Systems Biology
  • Computational Biology

Background:

  • Single-cell experiments reveal intrinsic noise in biochemical and genetic networks due to low molecule counts.
  • Traditional models focus on deterministic concentration dynamics, neglecting molecular stochasticity.
  • Recent interest in stochastic modeling necessitates robust mathematical frameworks.

Purpose of the Study:

  • To review mathematical descriptions of intrinsic noise in biochemical and genetic networks.
  • To introduce probability distribution dynamics for modeling molecular copy numbers.
  • To explore analytic approaches as alternatives or precursors to numerical simulations.

Main Methods:

  • Review of mathematical frameworks for stochastic processes in biological networks.
  • Derivation of probability distribution dynamics for molecular copy numbers.
  • Illustration of analytic solutions for specific network models.

Main Results:

  • Stochastic simulation (Monte Carlo methods) has been a primary focus for modeling.
  • Analytic progress can be achieved using probability distributions, offering insights beyond numerical computation.
  • Analytic methods can guide the development of more efficient numerical techniques.

Conclusions:

  • Mathematical descriptions using probability distributions provide a powerful framework for understanding intrinsic noise.
  • Analytic progress in stochastic modeling can reduce computational costs and enhance understanding of parametric dependencies.
  • This review emphasizes the utility of analytic approaches in the field of stochastic gene expression and biochemical network analysis.