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On the robustness of complex heterogeneous gene expression networks
Jesús Gómez-Gardeñes1, Yamir Moreno, Luis M Floría
1Departamento de Teoría y Simulación de Sistemas Complejos, Instituto de Ciencia de Materiales de Aragón, C.S.I.C.-Universidad de Zaragoza, Zaragoza 50009, Spain.
Biophysical Chemistry
|March 9, 2005
Summary
This study explores gene expression dynamics in complex networks, finding a region that avoids chaotic attractors but cannot fully suppress chaos, impacting network robustness and modeling.
Area of Science:
- Computational Biology
- Network Science
- Dynamical Systems
Background:
- Complex heterogeneous networks are fundamental to biological systems.
- Understanding gene expression dynamics is crucial for modeling cellular processes.
- Previous models, like Random Boolean Networks, have explored chaotic behavior.
Purpose of the Study:
- To analyze a continuous gene expression model on complex network topology.
- To investigate the chaotic and periodic dynamics of this model.
- To determine if chaotic attractors can be suppressed in this system.
Main Methods:
- Developed a continuous gene expression model.
- Performed numerical simulations to study system dynamics.
- Analyzed the interplay between network topology and dynamical behavior.
Main Results:
- Identified a specific region where dynamical and structural complexity avoid chaotic attractors.
- Demonstrated that complete suppression of the chaotic phase is not possible.
- Observed that residual chaos has implications for network robustness.
Conclusions:
- The continuous gene expression model exhibits complex dynamics influenced by network topology.
- Unlike Random Boolean Networks, complete elimination of chaos is not achieved, suggesting inherent robustness.
- Findings have significant implications for gene expression modeling and understanding biological network stability.