Identifying robust hysteresis in networks.

Area of Science:

• Systems Biology
• Computational Biology
• Network Science

Background:

• Understanding complex biological networks requires analyzing their dynamics across diverse parameter values.
• Key cellular processes like the cell cycle exhibit critical dynamics such as bistability and hysteresis.

Purpose of the Study:

• To introduce a novel computational tool for rigorous summarization and analysis of network dynamics.
• To identify parameter regimes supporting specific dynamic behaviors like hysteresis.
• To link network structure to dynamic properties using cell cycle examples.

Main Methods:

• Development of a computational tool to generate and store summaries of network dynamics over large parameter spaces.
• Database searching for specific dynamics (e.g., hysteresis) within the computed summaries.
• Application and evaluation of the tool on human and yeast cell cycle restriction point networks.

Main Results:

• The tool successfully identifies parameter regimes supporting bistability and hysteresis.
• Networks were ranked based on the robustness with which they support hysteresis.
• A conserved topology and robustness of hysteresis were observed between a human cell cycle network and a yeast network, despite a lack of homology.

Conclusions:

• The developed tool effectively links biological network structure to its dynamic behaviors.
• Conserved network topologies can underlie similar functional dynamics (hysteresis) across species.
• This approach offers a powerful method for discovering and understanding biological network mechanisms.