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Related Concept Videos

MAPK Signaling Cascades01:07

MAPK Signaling Cascades

Mitogen-activated protein kinase, or MAPK pathway, activates three sequential kinases to regulate cellular responses such as proliferation, differentiation, survival, and apoptosis. The canonical MAPK pathway starts with a mitogen or growth factor binding to an RTK. The activated RTKs stimulate Ras, which recruits Raf or MAP3 Kinase (MAPKKK), the first kinase of the MAPK signaling cascade. Raf further phosphorylates and activates MEK or MAP2 Kinases (MAPKK), which in turn phosphorylates MAP...
Protein Kinases and Phosphatases02:54

Protein Kinases and Phosphatases

Proteins undergo chemical modifications that trigger changes in the charge, structure, and conformation of the proteins. Phosphorylation, acetylation, glycosylation, nitrosylation, ubiquitination, lipidation, methylation, and proteolysis are various protein modifications that regulate protein activity. Such modifications are usually enzyme-driven.
Protein kinases
Many proteins in the cell are regulated by phosphorylation, the addition of a phosphate group. A family of enzymes called kinases...
Protein Kinases and Phosphatases02:54

Protein Kinases and Phosphatases

Proteins undergo chemical modifications that trigger changes in the charge, structure, and conformation of the proteins. Phosphorylation, acetylation, glycosylation, nitrosylation, ubiquitination, lipidation, methylation, and proteolysis are various protein modifications that regulate protein activity. Such modifications are usually enzyme-driven.
Protein kinases
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PI3K/mTOR/AKT Signaling Pathway01:22

PI3K/mTOR/AKT Signaling Pathway

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Protein Networks02:26

Protein Networks

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mTOR Signaling and Cancer Progression

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Related Experiment Video

Updated: Jun 4, 2026

Identification of Kinase-substrate Pairs Using High Throughput Screening
11:13

Identification of Kinase-substrate Pairs Using High Throughput Screening

Published on: August 29, 2015

Mining protein kinases regulation using graphical models.

Qingfeng Chen1, Yi-Ping Phoebe Chen

  • 1School of Computer, Electronic and Information, Guangxi University, Nanning, 530004, China. qingfeng@gxu.edu.cn

IEEE Transactions on Nanobioscience
|February 26, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian network framework using Markov Chain Monte Carlo methods to uncover novel kinase regulation patterns. The approach identifies key protein kinase dependencies, offering new biological insights and potential therapeutic targets for disease.

Related Experiment Videos

Last Updated: Jun 4, 2026

Identification of Kinase-substrate Pairs Using High Throughput Screening
11:13

Identification of Kinase-substrate Pairs Using High Throughput Screening

Published on: August 29, 2015

Area of Science:

  • Biochemistry
  • Computational Biology
  • Pharmacology

Background:

  • Abnormal kinase activity is implicated in numerous diseases, making kinases critical therapeutic targets.
  • Understanding protein kinase regulation is essential for developing targeted therapies.
  • Bayesian networks (BNs) are powerful tools for modeling dependencies but face challenges with high-dimensional data.

Purpose of the Study:

  • To develop a robust Bayesian network framework for discovering dependency correlations in protein kinase regulation.
  • To address the computational challenges of analyzing high-dimensional kinase data using probabilistic methods.

Main Methods:

  • Application of Bayesian networks (BNs) to model kinase regulatory pathways.
  • Utilizing the Markov Chain Monte Carlo (MCMC) method to generate samples and approximate probability distributions.
  • Identifying frequent connections (edges) in generated graphical models to infer regulatory relationships.

Main Results:

  • The proposed BN-MCMC framework successfully identified novel candidate kinase regulation patterns.
  • Inferred associations between kinase subunits provide new biological insights.
  • The method overcomes limitations of traditional BN approaches for high-dimensional biological data.

Conclusions:

  • The BN-MCMC framework is effective for uncovering complex kinase regulatory networks.
  • This approach facilitates the discovery of previously unknown biological associations.
  • The findings contribute to a deeper understanding of kinase function in health and disease, paving the way for new drug discovery efforts.