NDLI: Efficient anticorrelated variance reduction for stochastic simulation of biochemical reactions

Content Provider	IET Digital Library
Author	Thanh, Vo Hong
Abstract	We investigate the computational challenge of improving the accuracy of the stochastic simulation estimation by inducing negative correlation through the anticorrelated variance reduction technique. A direct application of the technique to the stochastic simulation algorithm (SSA), employing the inverse transformation, is not efficient for simulating large networks because its computational cost is similar to the sum of independent simulation runs. We propose in this study a new algorithm that employs the propensity bounds of reactions, introduced recently in their rejection-based SSA, to correlate and synchronise the trajectories during the simulation. The selection of reaction firings by our approach is exact due to the rejection-based mechanism. In addition, by applying the anticorrelated variance technique to select reaction firings, our approach can induce substantial correlation between realisations, hence reducing the variance of the estimator. The computational advantage of our rejection-based approach in comparison with the traditional inverse transformation is that it only needs to maintain a single data structure storing propensity bounds of reactions, which is updated infrequently, hence achieving better performance.
Starting Page	16
Ending Page	23
Page Count	8
ISSN	17518849
Volume Number	13
e-ISSN	17518857
Issue Number	Issue 1, Feb (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-syb/13/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-syb.2018.5035
Journal	IET Systems Biology
Publisher Date	2018-09-25
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Anticorrelated Variance Reduction Anticorrelated Variance Reduction Technique Anticorrelated Variance Technique Biochemical Reactions Biochemistry Biology And Medical Computing Biology Computing Computational Cost Data Structures Estimator File Organisation Independent Simulation Runs Integral Transforms Inverse Transforms Large-scale System Reaction Firings Rejection-based Approach Rejection-based Mechanism Rejection-based SSA Single Data Structure Storing Propensity Statistics Stochastic Linearised SCUC Stochastic Simulation Algorithm Stochastic Simulation Estimation Substantial Correlation
Content Type	Text
Resource Type	Article
Subject	Cell Biology Genetics Molecular Biology Modeling and Simulation Biotechnology

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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