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BMC Systems Biology

, 10:109

Methods, software and technology

Abstract

BackgroundDespite the increasing availability of high performance computing capabilities, analysis and characterization of stochastic biochemical systems remain a computational challenge. To address this challenge, the Stochastic Parameter Search for Events SParSE was developed to automatically identify reaction rates that yield a probabilistic user-specified event. SParSE consists of three main components: the multi-level cross-entropy method, which identifies biasing parameters to push the system toward the event of interest, the related inverse biasing method, and an optional interpolation of identified parameters. While effective for many examples, SParSE depends on the existence of a sufficient amount of intrinsic stochasticity in the system of interest. In the absence of this stochasticity, SParSE can either converge slowly or not at all.

ResultsWe have developed SParSE++, a substantially improved algorithm for characterizing target events in terms of system parameters. SParSE++ makes use of a series of novel parameter leaping methods that accelerate the convergence rate to the target event, particularly in low stochasticity cases. In addition, the interpolation stage is modified to compute multiple interpolants and to choose the optimal one in a statistically rigorous manner. We demonstrate the performance of SParSE++ on four example systems: a birth-death process, a reversible isomerization model, SIRS disease dynamics, and a yeast polarization model. In all four cases, SParSE++ shows significantly improved computational efficiency over SParSE, with the largest improvements resulting from analyses with the strictest error tolerances.

ConclusionsAs researchers continue to model realistic biochemical systems, the need for efficient methods to characterize target events will grow. The algorithmic advancements provided by SParSE++ fulfill this need, enabling characterization of computationally intensive biochemical events that are currently resistant to analysis.

KeywordsStochastic simulation Parameter estimation Rare event Optimization Stochastic event Stochastic mass action kinetics AbbreviationsCEcross entropy

OPover-perturbation

SParSEstochastic parameter search for events

SSAstochastic simulation algorithm

WAweighted average

Electronic supplementary materialThe online version of this article doi:10.1186-s12918-016-0367-z contains supplementary material, which is available to authorized users.

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Autor: Min K. Roh - Bernie J. DaigleJr.

Fuente: https://link.springer.com/







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