SParSE++: improved event-based stochastic parameter search

Area of Science:

• Computational Biology
• Biochemical Systems Analysis
• Algorithm Development

Background:

• Stochastic biochemical systems analysis is computationally challenging despite advances in high-performance computing.
• The original Stochastic Parameter Search for Events (SParSE) algorithm identifies reaction rates for user-specified events but struggles with low intrinsic system stochasticity.
• Slow convergence or failure to converge can occur with SParSE when inherent system stochasticity is insufficient.

Purpose of the Study:

• To develop an improved algorithm, SParSE++, for efficient characterization of target events in biochemical systems.
• To enhance computational efficiency, particularly for systems with low stochasticity.
• To provide a more robust method for analyzing complex biochemical events.

Main Methods:

• Developed SParSE++ incorporating novel parameter leaping methods to accelerate convergence.
• Modified the interpolation stage to compute multiple interpolants and select the optimal one statistically.
• Tested SParSE++ on diverse models: birth-death process, reversible isomerization, SIRS disease dynamics, and yeast polarization.

Main Results:

• SParSE++ demonstrated significantly improved computational efficiency compared to SParSE across all tested systems.
• The most substantial improvements were observed in analyses with stringent error tolerances.
• The new parameter leaping and interpolation methods effectively address challenges in low stochasticity cases.

Conclusions:

• SParSE++ offers substantial algorithmic advancements for characterizing computationally intensive biochemical events.
• The improved efficiency and robustness of SParSE++ meet the growing need for analyzing complex biological models.
• This algorithm enables the analysis of biochemical events previously resistant to computational characterization.