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本文引用的文献

1
Stepwise inference of likely dynamic flux distributions from metabolic time series data.从代谢时间序列数据中逐步推断可能的动态通量分布。
Bioinformatics. 2017 Jul 15;33(14):2165-2172. doi: 10.1093/bioinformatics/btx126.
2
Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy.大规模系统生物学模型中的参数估计:一种并行和自适应协作策略。
BMC Bioinformatics. 2017 Jan 21;18(1):52. doi: 10.1186/s12859-016-1452-4.
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On the relationship between sloppiness and identifiability.论草率与可识别性之间的关系。
Math Biosci. 2016 Dec;282:147-161. doi: 10.1016/j.mbs.2016.10.009. Epub 2016 Oct 24.
4
Time Hierarchies and Model Reduction in Canonical Non-linear Models.规范非线性模型中的时间层次结构与模型简化
Front Genet. 2016 Sep 21;7:166. doi: 10.3389/fgene.2016.00166. eCollection 2016.
5
A Comparison of Deterministic and Stochastic Modeling Approaches for Biochemical Reaction Systems: On Fixed Points, Means, and Modes.生化反应系统的确定性和随机建模方法比较:关于不动点、均值和模式
Front Genet. 2016 Aug 31;7:157. doi: 10.3389/fgene.2016.00157. eCollection 2016.
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Nonparametric dynamic modeling.非参数动态建模
Math Biosci. 2017 May;287:130-146. doi: 10.1016/j.mbs.2016.08.004. Epub 2016 Aug 30.
7
Design Space Toolbox V2: Automated Software Enabling a Novel Phenotype-Centric Modeling Strategy for Natural and Synthetic Biological Systems.设计空间工具箱V2:支持针对自然和合成生物系统采用新型以表型为中心建模策略的自动化软件
Front Genet. 2016 Jul 12;7:118. doi: 10.3389/fgene.2016.00118. eCollection 2016.
8
Exploring the Space of Viable Configurations in a Model of Metabolism-Boundary Co-construction.探索代谢-边界共同构建模型中可行配置的空间
Artif Life. 2016 Spring;22(2):153-71. doi: 10.1162/ARTL_a_00196. Epub 2016 Mar 2.
9
Spatiotemporal modeling of microbial metabolism.微生物代谢的时空建模
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10
Identification of Metabolic Pathway Systems.代谢途径系统的识别
Front Genet. 2016 Feb 10;7:6. doi: 10.3389/fgene.2016.00006. eCollection 2016.

最佳代谢模型。

The best models of metabolism.

作者信息

Voit Eberhard O

机构信息

Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.

出版信息

Wiley Interdiscip Rev Syst Biol Med. 2017 Nov;9(6). doi: 10.1002/wsbm.1391. Epub 2017 May 19.

DOI:10.1002/wsbm.1391
PMID:28544810
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5643013/
Abstract

Biochemical systems are among of the oldest application areas of mathematical modeling. Spanning a time period of over one hundred years, the repertoire of options for structuring a model and for formulating reactions has been constantly growing, and yet, it is still unclear whether or to what degree some models are better than others and how the modeler is to choose among them. In fact, the variety of options has become overwhelming and difficult to maneuver for novices and experts alike. This review outlines the metabolic model design process and discusses the numerous choices for modeling frameworks and mathematical representations. It tries to be inclusive, even though it cannot be complete, and introduces the various modeling options in a manner that is as unbiased as that is feasible. However, the review does end with personal recommendations for the choices of default models. WIREs Syst Biol Med 2017, 9:e1391. doi: 10.1002/wsbm.1391 For further resources related to this article, please visit the WIREs website.

摘要

生化系统是数学建模最古老的应用领域之一。在长达一百多年的时间里,构建模型和拟定反应的选项不断增加,然而,目前仍不清楚某些模型是否更好或在何种程度上更好,以及建模者应如何在它们之间进行选择。事实上,选项的多样性已变得势不可挡,无论是新手还是专家都难以应对。本综述概述了代谢模型设计过程,并讨论了建模框架和数学表示的众多选择。尽管无法做到全面,但它力求涵盖所有内容,并以尽可能公正的方式介绍各种建模选项。不过,该综述最后还是给出了关于默认模型选择的个人建议。《WIREs系统生物学与医学》2017年第9期,e1391。doi:10.1002/wsbm.1391 有关本文的更多资源,请访问WIREs网站。