Utility of constraints reflecting system stability on analyses for biological models

Area of Science:

• Systems Biology
• Computational Biology
• Pharmacology

Background:

• Complex biological models often require accurate kinetic parameters for reliable prediction of pharmacological responses.
• Parameter estimation is a significant challenge in systems biology, limiting model utility.

Purpose of the Study:

• To develop a method for exploring the allowable parameter space of biological systems without determining optimal parameter sets.
• To identify parameter sets that satisfy biologically stable and resilient (BSR) properties.
• To apply the method to understand signaling pathways and predict drug effects.

Main Methods:

• Formulated objective functions for BSR using partial linear approximation around steady states.
• Developed a thorough exploration of the allowable parameter space for biological systems (TEAPS) algorithm.
• Applied TEAPS to the NF-κB signaling and arachidonic acid metabolic pathway models.

Main Results:

• TEAPS identified parameter space directions critical for BSR properties, including experimentally fitted values.
• Simulations on the arachidonic acid pathway revealed inter-drug differences in the prostacyclin to thromboxane A2 ratio, comparable to clinical observations.
• Analysis of parameter set distributions provided insights into system structural properties.

Conclusions:

• The TEAPS algorithm effectively generates biologically plausible parameter sets satisfying BSR conditions.
• This approach aids in understanding biological system properties and predicting pharmacological responses.
• The method offers a valuable tool for systems biology research and drug discovery.