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

Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

1.4K
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...
1.4K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

53.2K
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).
53.2K
Nonlinear Pharmacokinetics: Causes of Nonlinearity01:22

Nonlinear Pharmacokinetics: Causes of Nonlinearity

938
Nonlinearity in drug pharmacokinetics is caused by various factors influencing how a drug is absorbed, distributed, metabolized, and excreted. Understanding these nonlinear processes is crucial for predicting drug behavior in the body and optimizing drug dosing regimens.
Nonlinear drug absorption can occur when the process is rate-limited by solubility, carrier-mediated transport systems, or saturation of the presystemic gut wall or hepatic metabolism. For instance, high doses of riboflavin...
938
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

3.3K
3.3K
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

20.2K
Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
20.2K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

335
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...
335

You might also read

Related Articles

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

Sort by
Same author

DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data.

BMC bioinformatics·2024
Same author

Returners and explorers dichotomy in the face of natural hazards.

Scientific reports·2024
Same author

Retraction: Tobacco-specific Carcinogens Induce Hypermethylation, DNA Adducts, and DNA Damage in Bladder Cancer.

Cancer prevention research (Philadelphia, Pa.)·2024
Same author

Role of adenosine deaminase in prostate cancer progression.

American journal of clinical and experimental urology·2023
Same author

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data.

Journal of visualized experiments : JoVE·2023
Same author

A generalized likelihood-based Bayesian approach for scalable joint regression and covariance selection in high dimensions.

Statistics and computing·2023

Related Experiment Video

Updated: May 6, 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

1.7K

Autoregressive models for gene regulatory network inference: sparsity, stability and causality issues.

George Michailidis1, Florence d'Alché-Buc

  • 1Department of Statistics, University of Michigan, Ann Arbor, MI 48109-1107, USA.

Mathematical Biosciences
|November 2, 2013
PubMed
Summary

This review explores autoregressive models for reconstructing gene regulatory networks using time-course data. It discusses sparsity, stability, and causality in inferring these networks from functional genomics data.

Keywords:
Autoregressive modelsCausalityDynamic Bayesian networksGene regulatory network inferenceSparsityStability

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

9.5K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.6K

Related Experiment Videos

Last Updated: May 6, 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

1.7K
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
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.6K

Area of Science:

  • Functional genomics
  • Bioinformatics
  • Statistical learning

Background:

  • Reconstructing gene regulatory networks (GRNs) is crucial in functional genomics.
  • Numerous computational and statistical methods exist, including clustering, dynamic Bayesian networks, and hybrid approaches.
  • Data types vary, including static and time-course data from wild-type or perturbed experiments.

Purpose of the Study:

  • This review focuses on autoregressive models for inferring GRNs.
  • It specifically examines the use of time-course data for this purpose.
  • Key themes of sparsity, stability, and causality are discussed.

Main Methods:

  • Focus on autoregressive models for GRN inference.
  • Utilizes time-course gene expression data.
  • Discusses integration of prior biological knowledge.

Main Results:

  • Autoregressive models offer a powerful framework for GRN reconstruction.
  • Time-course data enables dynamic network inference.
  • Consideration of sparsity, stability, and causality is essential for robust models.

Conclusions:

  • Autoregressive models are well-suited for inferring gene regulatory networks from time-course data.
  • Integrating prior biological knowledge enhances model accuracy.
  • Future work should continue to refine these models for complex biological systems.