Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 05

Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 05 This model is described in the article: A model of yeast glycolysis based on a consistent kinetic characterization of all its enzymes Kieran Smallbone, Hanan L. Messiha, Kathleen M. Carroll, Catherine L. Winder, Naglis Malys, Warwick B. Dunn, Ettore Murabito, Neil Swainston, Joseph O. Dada, Farid Khan, Pınar Pir, Evangelos Simeonidis, Irena Spasić, Jill Wishart, Dieter Weichart, Neil W. Hayes, Daniel Jameson, David S. Broomhead, Stephen G. Oliver, Simon J. Gaskell, John E.G. McCarthy, Norman W. Paton, Hans V. Westerhoff, Douglas B. Kell, Pedro Mendes FEBS Letters (in press) Abstract: We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a “cycle of knowledge” strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. This model is hosted on BioModels Database and identified by: MODEL1303260005 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.Smallbone等2013——酿酒酵母糖酵解模型——第05次迭代 本模型源自以下研究论文： 《基于所有酶统一动力学表征的酵母糖酵解模型》 作者：基兰·斯莫尔伯恩、哈南·L·梅西哈、凯瑟琳·M·卡罗尔、凯瑟琳·L·温德、纳格利斯·马利斯、沃里克·B·邓恩、埃托雷·穆拉比托、尼尔·斯温斯顿、约瑟夫·O·达达、法里德·汗、皮纳尔·皮尔、伊万杰洛斯·西米奥尼迪斯、伊雷娜·斯帕西奇、吉尔·威沙特、迪特尔·魏夏特、尼尔·W·海斯、丹尼尔·詹姆森、戴维·S·布鲁姆黑德、斯蒂芬·G·奥利弗、西蒙·J·加斯克尔、约翰·E·G·麦卡锡、诺曼·W·佩顿、汉斯·V·韦斯特霍夫、道格拉斯·B·凯尔、佩德罗·门德斯 期刊：《FEBS通讯》（FEBS Letters），状态为已录用待刊。 摘要： 我们提出了一套用于代谢动力学模型构建的实验与计算一体化流程，并将其应用于酿酒酵母（Saccharomyces cerevisiae）的糖酵解研究。研究从近似数学模型起步，采用“知识循环”策略，筛选出对代谢流调控作用最强的反应步骤。在标准化实验条件下，实测得到这些步骤中各同工酶的动力学参数；同时通过实验设计确定了同工酶与代谢物的体内浓度集合。将上述数据整合至数学模型后，我们可预测新的代谢物浓度集合，并重新评估系统的调控特性。这项自下而上的建模研究表明：酿酒酵母糖酵解核心代谢网络的通量调控分布范围，较此前学界的认知更为广泛。 本模型托管于BioModels数据库（BioModels Database），其唯一标识符为MODEL1303260005。 如需引用BioModels数据库，请使用以下表述：《BioModels数据库：面向已发表定量动力学模型的增强型人工甄选注释资源》。 在法律允许的最大范围内，本编码模型的所有版权及相关邻接权利已在全球范围内奉献至公共领域。如需了解更多细节，请参阅CC0公共领域奉献协议（CC0 Public Domain Dedication）。