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Related Concept Videos

Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

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...
Signal Flow Graphs01:18

Signal Flow Graphs

Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
IP3/DAG Signaling Pathway01:11

IP3/DAG Signaling Pathway

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 produces two-second...
Diversity in Cell Signaling Responses01:22

Diversity in Cell Signaling Responses

The physiological function of a cell and cellular communication are outcomes of a range of extrinsic signals, intracellular signaling pathways, and cellular responses. No two cell types express the same repertoire of signaling components. Receptors are highly selective for their cognate ligands, but once activated, they can alter multiple cellular processes such as DNA transcription, protein synthesis, and metabolic activity. 
Graded and Abrupt Responses
Some signaling systems generate...
Protein Networks02:26

Protein Networks

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,...
Protein Networks02:26

Protein Networks

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,...

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Related Experiment Video

Updated: Jun 24, 2026

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

Automatic modeling of signaling pathways by network flow model.

Xing-Ming Zhao1, Rui-Sheng Wang, Luonan Chen

  • 1Institute of Systems Biology, Shanghai University, Shanghai 200444, China. xm_zhao@shu.edu.cn

Journal of Bioinformatics and Computational Biology
|April 3, 2009
PubMed
Summary

This study introduces a new computational method to automatically model signaling pathways from protein-protein interaction networks. The approach uses a network flow model, formalized as a mixed integer linear programming model, for efficient pathway extraction.

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Area of Science:

  • Computational biology
  • Systems biology
  • Bioinformatics

Background:

  • Signal transduction regulates critical cellular processes like proliferation and metabolism.
  • Understanding signal transduction mechanisms requires effective computational models utilizing high-throughput omics data.
  • Protein-protein interactions (PPI) are central to signal transduction pathways.

Purpose of the Study:

  • To present a novel computational method for automatic modeling of signaling pathways from PPI networks.
  • To address the challenge of identifying optimal subnetworks representing signaling pathways.
  • To develop an efficient algorithm for extracting signaling pathways from complex biological networks.

Main Methods:

  • Developed a network flow model to identify signaling pathways within PPI networks.
  • Formalized the network flow model as a mixed integer linear programming (MILP) problem.
  • Applied the MILP model to extract signaling pathways from undirected weighted protein interaction networks.

Main Results:

  • Demonstrated the efficiency and effectiveness of the proposed network flow-based method.
  • Successfully extracted signaling pathways from known yeast MAPK signaling pathways.
  • The MILP approach proved computationally efficient and algorithmically simple.

Conclusions:

  • The proposed method provides an effective computational approach for modeling signaling pathways.
  • This technique aids in unraveling essential mechanisms underlying signal transduction using PPI data.
  • The MILP-based network flow model is a promising tool for bioinformatics research.