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

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

3.1K
3.1K
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

6.9K
Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein....
6.9K
IP3/DAG Signaling Pathway01:11

IP3/DAG Signaling Pathway

12.3K
Membrane lipids such as phosphatidylinositol (PI) are precursors for several membrane-bound and soluble second messengers. Specific kinases phosphorylate PI and produce phosphorylated inositol phospholipids. One such inositol phospholipids are the  phosphatidylinositol-4,5 bisphosphate [PI(4,5)P2], present in the inner half of the lipid bilayer. Upon ligand binding, GPCR stimulates Gq proteins to turn on phospholipase Cꞵ. Activated phospholipase Cꞵ cleaves PI(4,5)P2 and...
12.3K
Protein Networks02:26

Protein Networks

4.1K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.1K
Combinatorial Gene Control02:33

Combinatorial Gene Control

8.4K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
8.4K
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

6.4K
Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
6.4K

You might also read

Related Articles

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

Sort by
Same author

Robust Model-Free Identification of the Causal Networks Underlying Complex Nonlinear Systems.

Entropy (Basel, Switzerland)·2025
Same author

Short-circuit fault-tolerant control for five-phase fault-tolerant permanent magnet motors with trapezoidal back-EMF.

Fundamental research·2024
Same author

DFSNet: A 3D Point Cloud Segmentation Network toward Trees Detection in an Orchard Scene.

Sensors (Basel, Switzerland)·2024
Same author

Nonlinear causal network learning via Granger causality based on extreme support vector regression.

Chaos (Woodbury, N.Y.)·2024
Same author

Reconstruction of Complex Directional Networks with Group Lasso Nonlinear Conditional Granger Causality.

Scientific reports·2017
Same author

A novel PM motor with hybrid PM excitation and asymmetric rotor structure for high torque performance.

AIP advances·2017

Related Experiment Video

Updated: Aug 22, 2025

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

2.2K

Inferring Gene Regulatory Networks via Ensemble Path Consistency Algorithm Based on Conditional Mutual Information.

Jie Xu, Guanxue Yang, Guohai Liu

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |November 8, 2022
    PubMed
    Summary

    This study introduces a new gene regulatory network inference algorithm (EPCACMI) that effectively handles large datasets and noise. The novel method improves accuracy and robustness in reconstructing complex biological networks.

    More Related Videos

    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

    8.2K
    A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
    09:35

    A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

    Published on: August 16, 2017

    17.9K

    Related Experiment Videos

    Last Updated: Aug 22, 2025

    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

    2.2K
    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

    8.2K
    A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
    09:35

    A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

    Published on: August 16, 2017

    17.9K

    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Systems Biology

    Background:

    • Gene regulatory networks (GRNs) are crucial for understanding biological processes.
    • Reconstructing large-scale GRNs is challenging due to high dimensionality and noise.
    • Existing methods often struggle with accuracy and scalability.

    Purpose of the Study:

    • To develop a novel algorithm for robust and accurate reconstruction of large-scale gene regulatory networks.
    • To address the limitations of existing methods in handling noise and dimensionality.
    • To improve the inference of gene interactions from gene expression data.

    Main Methods:

    • Introduced the Ensemble Path Consistency Algorithm based on Conditional Mutual Information (EPCACMI).
    • Utilized Principal Component Analysis (PCA) to decompose large networks into subnetworks.
    • Dynamically adjusted mutual information thresholds and removed unrelated nodes based on principal component coefficients.
    • Integrated inferred subnetworks to form the complete network structure.

    Main Results:

    • EPCACMI effectively weakens the influence of redundant noise by inferring subnetworks.
    • The algorithm demonstrated superior effectiveness and robustness compared to MRNET, ARACNE, PCAPMI, and PCACMI.
    • Achieved better performance in inferring gene regulatory networks with a larger number of nodes.

    Conclusions:

    • EPCACMI offers a more effective and robust approach for large-scale gene regulatory network inference.
    • The subnetwork decomposition strategy mitigates the impact of noise and dimensionality.
    • This method advances the field of computational biology for network reconstruction.