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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...
Mitogens and the Cell Cycle02:38

Mitogens and the Cell Cycle

Mitogens and their receptors play a crucial role in controlling the progression of the cell cycle. However, the loss of mitogenic control over cell division leads to tumor formation. Therefore, mitogens and mitogen receptors play an important role in cancer research. For instance, the epidermal growth factor (EGF) - a type of mitogen and its transmembrane receptor (EGFR), decides the fate of the cell's proliferation. When EGF binds to EGFR, a member of the ErbB family of tyrosine kinase...

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Related Experiment Video

Updated: Jul 9, 2026

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
13:34

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

Robustness analysis of EGFR signaling network with a multi-objective evolutionary algorithm.

Xiufen Zou1, Minzhong Liu, Zishu Pan

  • 1School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China.

Bio Systems
|December 7, 2007
PubMed
Summary

Biological systems require robustness. This study found that while the epidermal growth factor receptor (EGFR) signaling network is not inherently robust, a multi-objective evolutionary algorithm optimized its parameters for improved signal stability.

Related Experiment Videos

Last Updated: Jul 9, 2026

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
13:34

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

Area of Science:

  • Systems Biology
  • Biochemistry
  • Computational Biology

Background:

  • Robustness is crucial for biological systems to maintain function under perturbations.
  • The epidermal growth factor receptor (EGFR) signaling network plays a vital role in cellular processes.
  • Previous studies identified a reference parameter set for the EGFR network.

Purpose of the Study:

  • To analyze the robustness of the EGFR signaling network against parameter variations.
  • To quantify the robustness of EGFR signaling using statistical measures.
  • To develop and evaluate a multi-objective evolutionary algorithm (MOEA) for optimizing EGFR network robustness.

Main Methods:

  • Simulated EGFR signaling network dynamics using the reference parameter set and subjected it to parameter variations.
  • Quantified robustness using statistical metrics.
  • Employed a multi-objective evolutionary algorithm (MOEA) to find optimized reaction rate constants.
  • Compared MOEA performance against the NSGA-II algorithm.

Main Results:

  • The EGFR signaling network's signal time, duration, and amplitude showed limited robustness against simultaneous parameter variations.
  • The MOEA successfully identified optimized parameter sets that significantly enhanced the robustness of key downstream components (R-Sh-G-S, R-PLP, R-G-S, RP).
  • Optimized parameters resulted in superior robustness compared to the reference parameter set and NSGA-II.

Conclusions:

  • The EGFR signaling network's inherent robustness is limited.
  • Multi-objective evolutionary algorithms can effectively optimize signaling network parameters for enhanced robustness.
  • Findings offer insights for experimental design and understanding EGFR signaling dynamics.