PSExplorer: whole parameter space exploration for molecular signaling pathway dynamics

Area of Science:

• Computational Biology
• Systems Biology
• Bioinformatics

Background:

• Biological models often contain numerous parameters, making exhaustive analysis of their combinatorial effects computationally intractable.
• Exploring the vast parameter space is crucial for understanding the dynamic behaviors of complex biological systems, such as molecular signaling pathways.

Purpose of the Study:

• To introduce PSExplorer, a novel computational tool designed for the systematic exploration of parameter spaces in biological models.
• To enable researchers to identify robust qualitative behaviors and key parameters within molecular signaling pathways.

Main Methods:

• Utilizes Latin hypercube sampling for efficient parameter space coverage.
• Employs a recursive clustering technique to partition the parameter space based on behavioral differences.
• Generates a tree structure to visualize parameter effects and identify influential factors.

Main Results:

• PSExplorer effectively partitions the parameter space, revealing sub-regions with robust qualitative behaviors.
• The tool facilitates the identification of individual and combinational effects of parameters on model dynamics.
• Key parameters influencing pathway behavior are readily identified through the generated tree structure.

Conclusions:

• PSExplorer provides a systematic computational strategy for exploring complex biological model parameter spaces.
• The software aids in understanding the robustness and key drivers of molecular signaling pathways.
• Accessible software and resources are available for broader application in biological modeling research.