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Related Concept Videos

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,...
Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...
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...
Network Function of a Circuit01:25

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
Block Diagram Reduction01:22

Block Diagram Reduction

The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...

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Related Experiment Video

Updated: May 7, 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

Detecting and removing inconsistencies between experimental data and signaling network topologies using integer

Ioannis N Melas1, Regina Samaga, Leonidas G Alexopoulos

  • 1National Technical University of Athens, Athens, Greece.

Plos Computational Biology
|September 17, 2013
PubMed
Summary
This summary is machine-generated.

We developed a novel data-driven method using integer linear programming to analyze cellular signaling networks. This approach helps identify inconsistencies and suggests improvements in network models based on experimental data.

Related Experiment Videos

Last Updated: May 7, 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

Area of Science:

  • Systems Biology
  • Computational Biology
  • Network Science

Background:

  • Mathematical modeling of cellular signal transduction networks is crucial for understanding biological systems.
  • Current methods for signaling network inference often rely on Bayesian, Boolean, or Ordinary Differential Equation (ODE) models.
  • Integrating experimental data with known network topologies remains a central challenge.

Purpose of the Study:

  • To present a new methodology for data-driven interrogation and training of signaling networks.
  • To enable prediction of qualitative changes in node activation levels based on network topology and experimental data.
  • To provide tools for detecting and resolving inconsistencies between signaling network models and experimental measurements.

Main Methods:

  • Utilizes integer linear programming (ILP) on interaction graphs to encode qualitative behavior constraints.
  • Develops four operations: finding consistent explanations, identifying minimal correction sets, determining optimal subgraphs, and detecting missing edges.
  • Applies the framework to a curated EGFR/ErbB signaling network model using high-throughput phosphoproteomic data.

Main Results:

  • The methodology successfully interrogates signaling network models against experimental data.
  • Identifies likely inactive interactions within the EGFR/ErbB signaling network in hepatocytes.
  • Suggests novel interactions that significantly improve the model's fit to the data.

Conclusions:

  • The proposed ILP-based framework offers a flexible and powerful approach for analyzing and refining cellular signaling networks.
  • The SigNetTrainer toolbox provides an accessible solution for researchers applying these methods.
  • This data-driven strategy enhances the accuracy of biological network models by integrating experimental evidence.