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Matrix Network: A New Data Structure for Efficient Enumeration of Microstates of a Genetic Regulatory Network
https://ipsj.ixsq.nii.ac.jp/records/145942
https://ipsj.ixsq.nii.ac.jp/records/145942375eaef3-260f-4ba5-a70f-0b732a68e703
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-JNL5611003.pdf (2.0 MB)
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Copyright (c) 2015 by the Information Processing Society of Japan
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| オープンアクセス | ||
| Item type | Journal(1) | |||||||||
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| 公開日 | 2015-11-15 | |||||||||
| タイトル | ||||||||||
| タイトル | Matrix Network: A New Data Structure for Efficient Enumeration of Microstates of a Genetic Regulatory Network | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Matrix Network: A New Data Structure for Efficient Enumeration of Microstates of a Genetic Regulatory Network | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | [一般論文] Matrix Network, gene regulatory network, microstates, microstate enumeration, stochasticity | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||
| 資源タイプ | journal article | |||||||||
| 著者所属 | ||||||||||
| Bioinformatics Center, Institute for Chemical Research, Kyoto University | ||||||||||
| 著者所属 | ||||||||||
| Bioinformatics Center, Institute for Chemical Research, Kyoto University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Bioinformatics Center, Institute for Chemical Research, Kyoto University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Bioinformatics Center, Institute for Chemical Research, Kyoto University | ||||||||||
| 著者名 |
Xiao, Cong
× Xiao, Cong
× Tatsuya, Akutsu
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| 著者名(英) |
Xiao, Cong
× Xiao, Cong
× Tatsuya, Akutsu
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Stochastic processes play an important role in gene regulatory networks. For many years, methods and algorithms have been developed to solve the problems regarding stochastic mechanisms in the cellular reaction system. Discrete Chemical Master Equation (dCME) is a method developed to analyze biological networks by computing the exact probability distribution of the microstates. With this method, because all computations and analyses of probability distribution can be processed based on the enumerated microstates, network microstates enumeration has been considered as a significant and prerequisite step. However, there is no efficient enumeration method. Applications will perform poorly when enumeration must address a complex or large network. To improve these microstate computation and analysis methods, we propose an efficient algorithm to enumerate microstates using Matrix Network, a new data structure we designed. Unlike traditional methods that perform the enumeration using simulation to apply reactions, the proposed approach utilizes the correlation of the microstate values and the geometric structure of the microstate map to accelerate the enumeration computation. In this paper, the theoretical basis, features and algorithms of Matrix Network are discussed. Moreover, sample applications demonstrating computation and analysis using Matrix Network are provided. \n------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.23(2015) No.6 (online) DOI http://dx.doi.org/10.2197/ipsjjip.23.804 ------------------------------ |
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| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Stochastic processes play an important role in gene regulatory networks. For many years, methods and algorithms have been developed to solve the problems regarding stochastic mechanisms in the cellular reaction system. Discrete Chemical Master Equation (dCME) is a method developed to analyze biological networks by computing the exact probability distribution of the microstates. With this method, because all computations and analyses of probability distribution can be processed based on the enumerated microstates, network microstates enumeration has been considered as a significant and prerequisite step. However, there is no efficient enumeration method. Applications will perform poorly when enumeration must address a complex or large network. To improve these microstate computation and analysis methods, we propose an efficient algorithm to enumerate microstates using Matrix Network, a new data structure we designed. Unlike traditional methods that perform the enumeration using simulation to apply reactions, the proposed approach utilizes the correlation of the microstate values and the geometric structure of the microstate map to accelerate the enumeration computation. In this paper, the theoretical basis, features and algorithms of Matrix Network are discussed. Moreover, sample applications demonstrating computation and analysis using Matrix Network are provided. \n------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.23(2015) No.6 (online) DOI http://dx.doi.org/10.2197/ipsjjip.23.804 ------------------------------ |
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| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AN00116647 | |||||||||
| 書誌情報 |
情報処理学会論文誌 巻 56, 号 11, 発行日 2015-11-15 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 1882-7764 | |||||||||
