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Wasserstein distances for estimating parameters in stochastic reaction networks

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Original languageEnglish
Title of host publicationComputational Methods in Systems Biology - 17th International Conference, CMSB 2019, Proceedings
EditorsLuca Bortolussi, Guido Sanguinetti
PublisherSpringer-Verlag
Pages347-351
Number of pages5
ISBN (Electronic)978-3-030-31304-3
ISBN (Print)9783030313036
DOIs
Publication statusE-pub ahead of print - 15 Nov 2019
Event17th International Conference on Computational Methods in Systems Biology, CMSB 2019 - Trieste, Italy
Duration: 18 Sep 201920 Sep 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11773 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Computational Methods in Systems Biology, CMSB 2019
CountryItaly
CityTrieste
Period18/09/1920/09/19

Abstract

Modern experimental methods such as flow cytometry and fluorescence in-situ hybridization (FISH) allow the measurement of cell-by-cell molecule numbers for RNA, proteins and other substances for large numbers of cells at a time, opening up new possibilities for the quantitative analysis of biological systems. Of particular interest is the study of biological reaction systems describing processes such as gene expression, cellular signalling and metabolism on a molecular level. It is well established that many of these processes are inherently stochastic [1–3] and that deterministic approaches to their study can fail to capture properties essential for our understanding of these systems [4, 5]. Despite recent technological and conceptual advances, modelling and inference for stochastic models of reaction networks remains challenging due to additional complexities not present in the deterministic case. The Chemical Master Equation (CME) [6] in particular, while frequently used to model many types of reaction networks, is difficult to solve exactly, and parameter inference in practice often relies on a variety of approximation schemes whose accuracy can vary widely and unpredictably depending on the context [6–8].

    Research areas

  • bayesian optimization, chemical master equation, parameter estimation, wasserstein distance

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