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- Title
Data-Driven Method for Efficient Characterization of Rare Event Probabilities in Biochemical Systems.
- Authors
Roh, Min K.
- Abstract
As mathematical models and computational tools become more sophisticated and powerful to accurately depict system dynamics, numerical methods that were previously considered computationally impractical started being utilized for large-scale simulations. Methods that characterize a rare event in biochemical systems are part of such phenomenon, as many of them are computationally expensive and require high-performance computing. In this paper, we introduce an enhanced version of the doubly weighted stochastic simulation algorithm (dwSSA) (Daigle et al. in J Chem Phys 134:044110, 2011), called dwSSA + + , that significantly improves the speed of convergence to the rare event of interest when the conventional multilevel cross-entropy method in dwSSA is either unable to converge or converges very slowly. This achievement is enabled by a novel polynomial leaping method that uses past data to detect slow convergence and attempts to push the system toward the rare event. We demonstrate the performance of dwSSA + + on two systems—a susceptible–infectious–recovered–susceptible disease dynamics model and a yeast polarization model—and compare its computational efficiency to that of dwSSA.
- Subjects
CROSS-entropy method; PROBABILITY theory; MATHEMATICAL models; SYSTEM dynamics; MAGNETIC entropy
- Publication
Bulletin of Mathematical Biology, 2019, Vol 81, Issue 8, p3097
- ISSN
0092-8240
- Publication type
Article
- DOI
10.1007/s11538-018-0509-0