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タイトル: | Inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data |
著者: | Shindo, Yuki Kondo, Yohei https://orcid.org/0000-0002-2801-4522 (unconfirmed) Sako, Yasushi |
著者名の別形: | 近藤, 洋平 |
発行日: | 1-May-2018 |
出版者: | Springer Nature |
誌名: | Scientific Reports |
巻: | 8 |
論文番号: | 6790 |
抄録: | 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. |
著作権等: | © The Author(s) 2018. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
URI: | http://hdl.handle.net/2433/233965 |
DOI(出版社版): | 10.1038/s41598-018-25064-w |
PubMed ID: | 29717206 |
出現コレクション: | 学術雑誌掲載論文等 |
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