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 | |
| 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 |
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ASJC Scopus subject areas
- Decision Sciences (miscellaneous)
- Industrial and Manufacturing Engineering
- Control and Optimization
Cite this
A convex approach to steady state moment analysis for stochastic chemical reactions. / Sakurai, Yuta; Hori, Yutaka.
2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1206-1211.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A convex approach to steady state moment analysis for stochastic chemical reactions
AU - Sakurai, Yuta
AU - Hori, Yutaka
PY - 2018/1/18
Y1 - 2018/1/18
N2 - 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.
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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U2 - 10.1109/CDC.2017.8263820
DO - 10.1109/CDC.2017.8263820
M3 - Conference contribution
AN - SCOPUS:85046131276
VL - 2018-January
SP - 1206
EP - 1211
BT - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -