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 |
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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 |
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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