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 Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

64
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...
64
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

184
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...
184
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

96
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...
96
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

944
The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
944
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

58.6K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
58.6K
Nonlinear Pharmacokinetics: Michaelis-Menten Equation01:18

Nonlinear Pharmacokinetics: Michaelis-Menten Equation

407
The Michaelis–Menten equation is a fundamental model for describing capacity-limited kinetics in drug metabolism. It offers insights into the rate of decline of plasma drug concentration Cp over time, with Vmax and KM as pivotal parameters.
Vmax represents the maximum achievable process rate, while KM, known as the Michaelis constant, signifies the drug concentration at which the process rate reaches half its maximum. This relationship between Vmax, KM, and Cp gives rise to three distinct...
407

You might also read

Related Articles

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

Sort by
Same author

Integrated MINFLUX tracking reveals two distinct chromatin dynamics classes across cell types.

Nature structural & molecular biology·2026
Same author

iFLinkC-EZ: A scalable and automatable method for the assembly of complex fusion proteins and multi-gene expression constructs based on the iFLinkC framework.

Synthetic and systems biotechnology·2026
Same author

Iterative design of a NAND hybrid riboswitch by deep batch Bayesian optimization.

Nucleic acids research·2026
Same author

QuantiTrack: A unified software to study protein dynamics in living cells.

bioRxiv : the preprint server for biology·2026
Same author

The Coli Toolkit (CTK): An Extension of the Modular Yeast Toolkit for Use in <i>E. coli</i>.

ACS synthetic biology·2026
Same author

Stress controls heterochromatin inheritance via histone H3 ubiquitylation.

Nature·2026

Related Experiment Video

Updated: Jul 21, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.1K

Bayesian analysis dissects kinetic modulation during non-stationary gene expression.

Christian Wildner1, Gunjan D Mehta2, David A Ball3

  • 1Centre for Synthetic Biology, Technische Universität Darmstadt, Darmstadt, 64283, Germany.

Biorxiv : the Preprint Server for Biology
|July 28, 2023
PubMed
Summary
This summary is machine-generated.

Researchers visualized gene transcription in living cells and discovered fast, seconds-scale bursting within slow, minutes-scale cycles. This reveals how burst amplitude regulates transcriptional oscillations.

More Related Videos

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae
07:00

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae

Published on: June 14, 2013

34.9K
Sealable Femtoliter Chamber Arrays for Cell-free Biology
13:44

Sealable Femtoliter Chamber Arrays for Cell-free Biology

Published on: March 11, 2015

9.5K

Related Experiment Videos

Last Updated: Jul 21, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.1K
Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae
07:00

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae

Published on: June 14, 2013

34.9K
Sealable Femtoliter Chamber Arrays for Cell-free Biology
13:44

Sealable Femtoliter Chamber Arrays for Cell-free Biology

Published on: March 11, 2015

9.5K

Area of Science:

  • Molecular Biology
  • Systems Biology
  • Biophysics

Background:

  • Visualizing nascent stem loops with fluorescent proteins allows for the study of transcription dynamics in living cells.
  • Quantitative analysis of fluorescence traces can reveal kinetic transcription parameters and regulatory mechanisms.
  • Existing methods often focus on steady-state dynamics, limiting the understanding of dynamic processes.

Approach:

  • Developed a stochastic process transcription model combined with a hierarchical Bayesian method.
  • Inferred global and locally shared parameters for cell groups.
  • Recovered unobserved quantities like gene initiation times and polymerase loading.

Key Points:

  • Applied the approach to study the cyclic response of the yeast CUP1 locus to heavy metal stress.
  • Discovered fast, time-modulated bursting (seconds) within the known slow transcriptional activity cycle (minutes).
  • Identified that slow oscillations in transcriptional output are regulated by burst amplitude.

Conclusions:

  • Multiple polymerases can initiate transcription during a single burst.
  • The findings provide a new framework for analyzing dynamic transcription processes.
  • This method enhances the understanding of gene regulation under stress conditions.