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

Chronopharmacokinetics: Time-Dependent Pharmacokinetics01:20

Chronopharmacokinetics: Time-Dependent Pharmacokinetics

222
Chronopharmacokinetics studies the temporal change in drug absorption and elimination. These changes can be cyclical or non-cyclical. Cyclical changes occur over a regular interval, while non-cyclical changes occur over a longer, irregular period.
Time-dependent pharmacokinetics refers to non-cyclical changes in drug rate processes over a period of time. It can lead to nonlinear pharmacokinetics, where the relationship between drug concentration and time is not proportional. Non-cyclical...
222
Linear time-invariant Systems01:23

Linear time-invariant Systems

407
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
407
Chronopharmacokinetics: Circadian Rhythms and Influence on Drug Response01:15

Chronopharmacokinetics: Circadian Rhythms and Influence on Drug Response

124
Circadian rhythms are cyclic changes that are crucial in plasma drug concentrations. Various standard circadian parameters, including core body temperature, heart rate, and other cardiovascular factors, directly impact disease states and the therapeutic response to drug therapy.
The time of drug administration is an important factor to consider, as it can influence the toxic dose of a drug. For example, a study conducted by Prins et al. in 1997 examined the effects of the timing of...
124
Biological Clocks and Seasonal Responses02:45

Biological Clocks and Seasonal Responses

38.1K
The circadian—or biological—clock is an intrinsic, timekeeping, molecular mechanism that allows plants to coordinate physiological activities over 24-hour cycles called circadian rhythms. Photoperiodism is a collective term for the biological responses of plants to variations in the relative lengths of dark and light periods. The period of light-exposure is called the photoperiod.
38.1K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

126
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...
126
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

1.2K
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...
1.2K

You might also read

Related Articles

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

Sort by
Same author

NoisyFlow: differentially private optimal transport using neural networks for secure biomedical data sharing across multiple institutions.

Bioinformatics (Oxford, England)·2026
Same author

Population-scale Y chromosome assemblies reveal recurrent remodeling within constrained architectures.

bioRxiv : the preprint server for biology·2026
Same author

Network and machine learning analysis of childhood trauma, mental health, and AI-based emotional support needs in adolescents from underdeveloped regions.

BMC psychology·2026
Same author

Disinfection of hospital sink drains enriches pseudomonadota and efflux pump-mediated antibiotic resistance in reestablished biofilms.

Nature communications·2026
Same author

Transcriptomic and phenotypic convergence of neurodevelopmental disorder risk genes in vitro and in vivo.

Nature neuroscience·2026
Same author

Epigenetic characterization of pseudogenes across human tissues.

Genome research·2026

Related Experiment Video

Updated: Sep 10, 2025

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.5K

The chronODE framework for modelling multi-omic time series with ordinary differential equations and machine

Beatrice Borsari1,2, Mor Frank1,2, Eve S Wattenberg1,2

  • 1Program in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT, USA.

Nature Communications
|August 19, 2025
PubMed
Summary

This study introduces chronODE, a new framework to model gene expression and chromatin kinetics over time. It reveals distinct gene kinetic patterns during development, highlighting biochemical limits and regulatory element roles.

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K
A Computational Method to Quantify Fly Circadian Activity
13:05

A Computational Method to Quantify Fly Circadian Activity

Published on: October 28, 2017

6.0K

Related Experiment Videos

Last Updated: Sep 10, 2025

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.5K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K
A Computational Method to Quantify Fly Circadian Activity
13:05

A Computational Method to Quantify Fly Circadian Activity

Published on: October 28, 2017

6.0K

Area of Science:

  • Developmental Biology
  • Systems Biology
  • Computational Biology

Background:

  • Longitudinal studies offer direct insights into kinetic processes like cell differentiation and organismal development, unlike static snapshots.
  • Modeling gene expression and chromatin dynamics over time is crucial for understanding regulatory mechanisms.

Purpose of the Study:

  • To present chronODE, an interpretable ordinary differential equation-based framework for modeling gene expression and chromatin kinetics.
  • To identify and categorize gene kinetic patterns during development.
  • To explore the relationship between chromatin changes and gene expression dynamics.

Main Methods:

  • Development of chronODE, a framework using ordinary differential equations with parameters for gene expression cooperativity and saturation.
  • Application of chronODE to bulk and single-cell time-series data from mouse brain development.
  • Extension of chronODE to model chromatin kinetics and integration with a bidirectional recurrent neural network for predicting gene expression from chromatin changes.

Main Results:

  • Most genes (~87%) exhibit simple logistic kinetics, with rare instances of simultaneous rapid acceleration and high saturation due to biochemical constraints.
  • Distinct kinetic patterns were observed in early- and late-emerging cell types, with essential genes showing faster upregulation.
  • Genes regulated by both enhancers and silencers are enriched in brain-specific functions.
  • A recurrent neural network successfully predicted gene expression changes from chromatin modifications, accounting for cumulative regulatory effects.

Conclusions:

  • The chronODE framework provides a powerful tool for investigating the kinetics of gene regulation in various biological systems.
  • Biophysical limitations impact the ability of genes to achieve both rapid expression changes and high saturation levels.
  • Chromatin regulation, involving both enhancers and silencers, plays a significant role in cell-type-specific gene expression, particularly in brain development.