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SParSE++: improved event-based stochastic parameter search.

Min K Roh1, Bernie J Daigle2

  • 1Applied Mathematics, Institute for Disease Modeling, 3150 139th Ave SE, Bellevue, 98005, WA, USA. mroh@intven.com.

BMC Systems Biology
|November 26, 2016
PubMed
Summary
This summary is machine-generated.

A new algorithm, SParSE++, improves the analysis of stochastic biochemical systems by accelerating event characterization, especially in low stochasticity scenarios. This computational tool enhances the study of complex biological processes.

Keywords:
OptimizationParameter estimationRare eventStochastic eventStochastic mass action kineticsStochastic simulation

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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.