Inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data

Abstract Mathematical modeling and analysis of biochemical reaction networks are key routines in computational systems biology and biophysics; however, it remains difficult to choose the most valid model. Here, we propose a computational framework for data-driven and systematic inference of a nonlin...

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Autores principales:	Yuki Shindo, Yohei Kondo, Yasushi Sako
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/82ad5b244fc04aa09dfb7172228676c1
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spelling	oai:doaj.org-article:82ad5b244fc04aa09dfb7172228676c12021-12-02T12:31:57ZInferring a nonlinear biochemical network model from a heterogeneous single-cell time course data10.1038/s41598-018-25064-w2045-2322https://doaj.org/article/82ad5b244fc04aa09dfb7172228676c12018-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-25064-whttps://doaj.org/toc/2045-2322Abstract Mathematical modeling and analysis of biochemical reaction networks are key routines in computational systems biology and biophysics; however, it remains difficult to choose the most valid model. Here, we propose a computational framework for data-driven and systematic inference of a nonlinear biochemical network model. The framework is based on the expectation-maximization algorithm combined with particle smoother and sparse regularization techniques. In this method, a “redundant” model consisting of an excessive number of nodes and regulatory paths is iteratively updated by eliminating unnecessary paths, resulting in an inference of the most likely model. Using artificial single-cell time-course data showing heterogeneous oscillatory behaviors, we demonstrated that this algorithm successfully inferred the true network without any prior knowledge of network topology or parameter values. Furthermore, we showed that both the regulatory paths among nodes and the optimal number of nodes in the network could be systematically determined. The method presented in this study provides a general framework for inferring a nonlinear biochemical network model from heterogeneous single-cell time-course data.Yuki ShindoYohei KondoYasushi SakoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-10 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yuki Shindo Yohei Kondo Yasushi Sako Inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data
description	Abstract Mathematical modeling and analysis of biochemical reaction networks are key routines in computational systems biology and biophysics; however, it remains difficult to choose the most valid model. Here, we propose a computational framework for data-driven and systematic inference of a nonlinear biochemical network model. The framework is based on the expectation-maximization algorithm combined with particle smoother and sparse regularization techniques. In this method, a “redundant” model consisting of an excessive number of nodes and regulatory paths is iteratively updated by eliminating unnecessary paths, resulting in an inference of the most likely model. Using artificial single-cell time-course data showing heterogeneous oscillatory behaviors, we demonstrated that this algorithm successfully inferred the true network without any prior knowledge of network topology or parameter values. Furthermore, we showed that both the regulatory paths among nodes and the optimal number of nodes in the network could be systematically determined. The method presented in this study provides a general framework for inferring a nonlinear biochemical network model from heterogeneous single-cell time-course data.
format	article
author	Yuki Shindo Yohei Kondo Yasushi Sako
author_facet	Yuki Shindo Yohei Kondo Yasushi Sako
author_sort	Yuki Shindo
title	Inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data
title_short	Inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data
title_full	Inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data
title_fullStr	Inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data
title_full_unstemmed	Inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data
title_sort	inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/82ad5b244fc04aa09dfb7172228676c1
work_keys_str_mv	AT yukishindo inferringanonlinearbiochemicalnetworkmodelfromaheterogeneoussinglecelltimecoursedata AT yoheikondo inferringanonlinearbiochemicalnetworkmodelfromaheterogeneoussinglecelltimecoursedata AT yasushisako inferringanonlinearbiochemicalnetworkmodelfromaheterogeneoussinglecelltimecoursedata
_version_	1718394224810917888

Inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data

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