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迈向细胞代谢的基因组规模动力学模型。

Towards a genome-scale kinetic model of cellular metabolism.

作者信息

Smallbone Kieran, Simeonidis Evangelos, Swainston Neil, Mendes Pedro

机构信息

Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, 131 Princess Street, Manchester, M1 7DN, UK.

出版信息

BMC Syst Biol. 2010 Jan 28;4:6. doi: 10.1186/1752-0509-4-6.

Abstract

BACKGROUND

Advances in bioinformatic techniques and analyses have led to the availability of genome-scale metabolic reconstructions. The size and complexity of such networks often means that their potential behaviour can only be analysed with constraint-based methods. Whilst requiring minimal experimental data, such methods are unable to give insight into cellular substrate concentrations. Instead, the long-term goal of systems biology is to use kinetic modelling to characterize fully the mechanics of each enzymatic reaction, and to combine such knowledge to predict system behaviour.

RESULTS

We describe a method for building a parameterized genome-scale kinetic model of a metabolic network. Simplified linlog kinetics are used and the parameters are extracted from a kinetic model repository. We demonstrate our methodology by applying it to yeast metabolism. The resultant model has 956 metabolic reactions involving 820 metabolites, and, whilst approximative, has considerably broader remit than any existing models of its type. Control analysis is used to identify key steps within the system.

CONCLUSIONS

Our modelling framework may be considered a stepping-stone toward the long-term goal of a fully-parameterized model of yeast metabolism. The model is available in SBML format from the BioModels database (BioModels ID: MODEL1001200000) and at http://www.mcisb.org/resources/genomescale/.

摘要

背景

生物信息学技术与分析方法的进步促使基因组规模代谢重建成为可能。此类网络的规模与复杂性往往意味着,其潜在行为只能通过基于约束的方法进行分析。尽管这类方法所需的实验数据极少,但无法深入了解细胞内底物浓度。相反,系统生物学的长期目标是利用动力学建模全面描述每个酶促反应的机制,并整合这些知识以预测系统行为。

结果

我们描述了一种构建代谢网络参数化基因组规模动力学模型的方法。采用简化的线性对数动力学,并从动力学模型库中提取参数。我们将该方法应用于酵母代谢,以展示其有效性。所得模型包含956个代谢反应,涉及820种代谢物,虽然是近似模型,但比任何同类现有模型的涵盖范围都要广泛得多。通过控制分析来确定系统中的关键步骤。

结论

我们的建模框架可被视为朝着实现酵母代谢完全参数化模型这一长期目标迈出的一步。该模型以SBML格式存于生物模型数据库(生物模型ID:MODEL1001200000),也可在http://www.mcisb.org/resources/genomescale/获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae5a/2829494/afd244bfa84b/1752-0509-4-6-1.jpg

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