Please use this identifier to cite or link to this item: doi:10.22028/D291-31043
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Title: Moment-Based Parameter Estimation for Stochastic Reaction Networks in Equilibrium
Author(s): Backenkohler, Michael
Bortolussi, Luca
Wolf, Verena
Language: English
Title: IEEE ACM transactions on computational biology and bioinformatics
Volume: 15
Issue: 4
Startpage: 1180
Endpage: 1192
Publisher/Platform: IEEE
Year of Publication: 2017
Publikation type: Journal Article
Abstract: Calibrating parameters is a crucial problem within quantitative modeling approaches to reaction networks. Existing methods for stochastic models rely either on statistical sampling or can only be applied to small systems. Here, we present an inference procedure for stochastic models in equilibrium that is based on a moment matching scheme with optimal weighting and that can be used with high-throughput data like the one collected by flow cytometry. Our method does not require an approximation of the underlying equilibrium probability distribution and, if reaction rate constants have to be learned, the optimal values can be computed by solving a linear system of equations. We discuss important practical issues such as the selection of the moments and evaluate the effectiveness of the proposed approach on three case studies.
DOI of the first publication: 10.1109/TCBB.2017.2775219
URL of the first publication: https://ieeexplore.ieee.org/document/8115212
Link to this record: hdl:20.500.11880/29194
http://dx.doi.org/10.22028/D291-31043
ISSN: 1545-5963
1557-9964
Date of registration: 28-May-2020
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Informatik
Professorship: MI - Prof. Dr. Verena Wolf
Collections:UniBib – Die Universitätsbibliographie

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