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Published on: February 16, 2017
Ground state robustness as an evolutionary design principle in signaling networks
1Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany. kartal@mpimp-golm.mpg.de
Plos One
|December 4, 2009
Summary
Preventing self-activation is a key evolutionary principle shaping biological signaling networks. This study reveals how network topology, particularly positive feedback loops, ensures stable off-states for survival and adaptation.
Area of Science:
- Systems Biology
- Evolutionary Biology
- Network Science
Background:
- Organism survival relies on adaptation, requiring efficient signal transduction.
- Evolutionary pressures shape signaling network topologies, but the underlying principles remain unclear.
- Understanding these principles is crucial for deciphering cellular communication.
Purpose of the Study:
- To propose prevention of autoactivation as an evolutionary design principle for signaling networks.
- To investigate the topological properties that ensure a dynamically stable ground state (off-state) in signaling systems.
- To analyze how network structure influences robustness against perturbations.
Main Methods:
- Utilized a generic framework for continuous kinetic models.
- Applied graph theoretical methods to analyze digraph properties.
- Calculated kinetic and structural off-state robustness for real signaling networks.
Main Results:
- The index of the underlying digraph is a key determinant of kinetic ground state robustness.
- Strongly connected components (positive feedback loops) dictate off-state robustness, with the highest-index component dominating.
- Network features like low connectivity, high divergence, and low path sum enhance structural robustness against perturbations.
Conclusions:
- Ground state robustness, achieved by preventing autoactivation, is a plausible evolutionary driver for intracellular signaling network structures.
- Network topology plays a critical role in maintaining stability and adaptability.
- The findings provide insights into the design principles of biological signaling systems.
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