The Enhanced Finite State Projection algorithm, using conditional moment closure and time-scale separation
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CDC_ver1-1.pdf
Description
Accepted version
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439.72 KB
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Adobe PDF
Checksum (MD5)
4d767ac8278b8e11d0bdea5516c4e3aa
Author(s)
Kwon, Ukjin
Naghnaeian, Mohammad
Del Vecchio, Domitilla
Date Issued
2020
Journal
Proceedings of the IEEE Conference on Decision and Control
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Kwon, Ukjin, Naghnaeian, Mohammad and Del Vecchio, Domitilla. 2020. "The Enhanced Finite State Projection algorithm, using conditional moment closure and time-scale separation." Proceedings of the IEEE Conference on Decision and Control, 2020-December.
Version
Author's final manuscript
Abstract
© 2020 IEEE. The Chemical Master Equation (CME) is commonly used to describe the stochastic behavior of biomolecular systems. However, in general, the CME's dimension is very large or infinite, so analytical or even numerical solutions may be difficult to achieve. The truncation methods such as the Finite State Projection (FSP) algorithm alleviate this issue to some extent but not completely. To further resolve the computational issue, we propose the Enhanced Finite State Projection (EFSP) algorithm, in which the ubiquitous time-scale separation is utilized to reduce the dimension of the CME. Our approach combines the original FSP algorithm and the model reduction technique that we developed, to approximate an infinite dimensional CME with a finite dimensional CME that contains the slow species only. Unlike other time-scale separation methods, which rely on the fast-species counts' stationary conditional probability distributions, our model reduction technique relies on only the first few conditional moments of the fast-species counts. In addition, each iteration of the EFSP algorithm relies on the solution of the approximated CME that contains the slow species only, unlike the original FSP algorithm relies on the solution of the full CME. These two properties provide a significant computation advantage. The benefit of our algorithm is illustrated through a protein binding reaction example.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/CDC42340.2020.9303796
