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

Protein Networks02:26

Protein Networks

3.9K
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,...
3.9K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
12.5K
Probability Laws01:49

Probability Laws

40.4K
Overview
40.4K
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

6.8K
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....
6.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

29
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...
29

You might also read

Related Articles

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

Sort by
Same author

Aggregation for Computing Multi-Modal Stationary Distributions in 1-D Gene Regulatory Networks.

IEEE/ACM transactions on computational biology and bioinformatics·2017
See all related articles

Related Experiment Video

Updated: Jun 9, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.1K

Efficient probabilistic inference in biochemical networks.

Adrien Le Coënt1, Benoît Barbot1, Nihal Pekergin1

  • 1Université Paris Est Créteil, LACL, F-94010 Creteil, France.

Computers in Biology and Medicine
|October 26, 2024
PubMed
Summary

This study introduces dynamic Bayesian networks to approximate biochemical networks, enabling efficient parameter estimation. This computational approach improves accuracy for complex biological systems like cellular signaling pathways.

Keywords:
Bayesian networksBiochemical networksMarkov chainsOrdinary differential equations based modelsParameter estimationTime homogeneous systems

More Related Videos

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

10.0K
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

2.1K

Related Experiment Videos

Last Updated: Jun 9, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.1K
Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

10.0K
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

2.1K

Area of Science:

  • Computational Biology
  • Systems Biology
  • Biochemistry

Background:

  • Biochemical networks are typically modeled using ordinary differential equations (ODEs).
  • Parameter estimation for ODE models is computationally intensive, often leading to inefficiency or inaccuracy.
  • These models involve numerous variables and parameters, complicating analysis.

Purpose of the Study:

  • To present an alternative modeling approach for biochemical networks.
  • To enhance the computational efficiency and accuracy of parameter estimation.
  • To apply the novel method to real-world biological systems.

Main Methods:

  • Approximating biochemical networks using dynamic Bayesian networks (DBNs), a class of discrete probabilistic models.
  • Utilizing Bayesian inference for parameter estimation within the DBN framework.
  • Developing strategies to optimize the accuracy and computational performance of the approximation and estimation process.

Main Results:

  • Demonstrated that DBNs can effectively approximate complex biochemical networks.
  • Showcased the efficiency and accuracy gains of Bayesian inference for parameter estimation compared to traditional ODE methods.
  • Successfully applied the DBN approach to the EGF-NGF cellular signaling pathway.

Conclusions:

  • Dynamic Bayesian networks offer a computationally efficient and accurate alternative for modeling biochemical networks.
  • Bayesian inference provides a powerful tool for parameter estimation in these approximated models.
  • The proposed method holds significant potential for advancing systems biology research, particularly in analyzing complex signaling pathways.