A convex approach to steady state moment analysis for stochastic chemical reactions

Yuta Sakurai; Yutaka Hori

doi:10.1109/CDC.2017.8263820

A convex approach to steady state moment analysis for stochastic chemical reactions

Yuta Sakurai, Yutaka Hori

Department of Applied Physics and Physico-Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

Model-based prediction of stochastic noise in biomolecular reactions often resorts to approximation with unknown precision. As a result, unexpected stochastic fluctuation causes a headache for the designers of biomolecular circuits. This paper proposes a convex optimization approach to quantifying the steady state moments of molecular copy counts with theoretical rigor. We show that the stochastic moments lie in a convex semi-algebraic set specified by linear matrix inequalities. Thus, the upper and the lower bounds of some moments can be computed by a semidefinite program. Using a protein dimerization process as an example, we demonstrate that the proposed method can precisely predict the mean and the variance of the copy number of the monomer protein.

Original language	English
Title of host publication	2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1206-1211
Number of pages	6
Volume	2018-January
ISBN (Electronic)	9781509028733
DOIs	https://doi.org/10.1109/CDC.2017.8263820
Publication status	Published - 2018 Jan 18
Event	56th IEEE Annual Conference on Decision and Control, CDC 2017 - Melbourne, Australia Duration: 2017 Dec 12 → 2017 Dec 15

Other

Other	56th IEEE Annual Conference on Decision and Control, CDC 2017
Country	Australia
City	Melbourne
Period	17/12/12 → 17/12/15

ASJC Scopus subject areas

Decision Sciences (miscellaneous)
Industrial and Manufacturing Engineering
Control and Optimization

Access to Document

10.1109/CDC.2017.8263820

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Cite this

Sakurai, Y & Hori, Y 2018, A convex approach to steady state moment analysis for stochastic chemical reactions. in 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1206-1211, 56th IEEE Annual Conference on Decision and Control, CDC 2017, Melbourne, Australia, 17/12/12. https://doi.org/10.1109/CDC.2017.8263820

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abstract = "Model-based prediction of stochastic noise in biomolecular reactions often resorts to approximation with unknown precision. As a result, unexpected stochastic fluctuation causes a headache for the designers of biomolecular circuits. This paper proposes a convex optimization approach to quantifying the steady state moments of molecular copy counts with theoretical rigor. We show that the stochastic moments lie in a convex semi-algebraic set specified by linear matrix inequalities. Thus, the upper and the lower bounds of some moments can be computed by a semidefinite program. Using a protein dimerization process as an example, we demonstrate that the proposed method can precisely predict the mean and the variance of the copy number of the monomer protein.",

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AB - Model-based prediction of stochastic noise in biomolecular reactions often resorts to approximation with unknown precision. As a result, unexpected stochastic fluctuation causes a headache for the designers of biomolecular circuits. This paper proposes a convex optimization approach to quantifying the steady state moments of molecular copy counts with theoretical rigor. We show that the stochastic moments lie in a convex semi-algebraic set specified by linear matrix inequalities. Thus, the upper and the lower bounds of some moments can be computed by a semidefinite program. Using a protein dimerization process as an example, we demonstrate that the proposed method can precisely predict the mean and the variance of the copy number of the monomer protein.

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