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Nucleoside Triphosphates - From Synthesis to Biochemical Characterization
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Data-Driven Method for Efficient Characterization of Rare Event Probabilities in Biochemical Systems.

Min K Roh1

  • 1Institute for Disease Modeling, 3150 139th Ave SE, Bellevue, WA, 98005, USA. mroh@idmod.org.

Bulletin of Mathematical Biology
|September 19, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces dwSSA[Formula: see text], a faster simulation method for rare events in biochemical systems. It improves convergence speed using a novel polynomial leaping technique, outperforming the standard doubly weighted stochastic simulation algorithm (dwSSA).

Keywords:
Gillespie algorithmImportance samplingRare event probability estimationSSAStochastic simulationdwSSA

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Area of Science:

  • Computational biology
  • Biochemical systems simulation
  • Mathematical modeling

Background:

  • Sophisticated computational tools enable large-scale simulations of complex system dynamics.
  • Characterizing rare events in biochemical systems is computationally intensive, often requiring high-performance computing.
  • Existing methods like the doubly weighted stochastic simulation algorithm (dwSSA) can face slow convergence or failure for rare event analysis.

Purpose of the Study:

  • To introduce an enhanced doubly weighted stochastic simulation algorithm (dwSSA[Formula: see text]) for faster rare event simulation.
  • To address the limitations of conventional multilevel cross-entropy methods in dwSSA for computationally expensive rare events.
  • To improve the speed of convergence towards rare events in biochemical systems.

Main Methods:

  • Development of an enhanced doubly weighted stochastic simulation algorithm (dwSSA[Formula: see text]).
  • Implementation of a novel polynomial leaping method to detect and mitigate slow convergence.
  • Utilizing past simulation data to guide the system towards the rare event of interest.

Main Results:

  • dwSSA[Formula: see text] significantly improves the speed of convergence to rare events compared to the conventional dwSSA.
  • The novel polynomial leaping method effectively pushes the system towards the rare event when standard methods falter.
  • Demonstrated performance on a susceptible-infectious-recovered-susceptible disease model and a yeast polarization model.

Conclusions:

  • dwSSA[Formula: see text] offers a computationally efficient alternative for simulating rare events in complex systems.
  • The polynomial leaping method enhances the applicability of dwSSA for challenging rare event simulations.
  • This advancement facilitates more accurate and rapid analysis of critical dynamics in biochemical and biological systems.