BACKGROUND In order to improve the accuracy of constraint-based metabolic models, several approaches have been developed which intend to integrate additional biological information. Two of these methods, MOMENT and GECKO, incorporate enzymatic (kcat) parameters and enzyme mass constraints to further constrain the space of feasible metabolic flux distributions. While both methods have been proven to deliver useful extensions of metabolic models, they may considerably increase size and complexity of the models and there is currently no tool available to fully automate generation and calibration of such enzyme-constrained models from given stoichiometric models. RESULTS In this work we present three major developments. We first conceived short MOMENT (sMOMENT), a simplified version of the MOMENT approach, which yields the same predictions as MOMENT but requires significantly fewer variables and enables direct inclusion of the relevant enzyme constraints in the standard representation of a constraint-based model. When measurements of enzyme concentrations are available, these can be included as well leading in the extreme case, where all enzyme concentrations are known, to a model representation that is analogous to the GECKO approach. Second, we developed the AutoPACMEN toolbox which allows an almost fully automated creation of sMOMENT-enhanced stoichiometric metabolic models. In particular, this includes the automatic read-out and processing of relevant enzymatic data from different databases and the reconfiguration of the stoichiometric model with embedded enzymatic constraints. Additionally, tools have been developed to adjust (kcat and enzyme pool) parameters of sMOMENT models based on given flux data. We finally applied the new sMOMENT approach and the AutoPACMEN toolbox to generate an enzyme-constrained version of the E. coli genome-scale model iJO1366 and analyze its key properties and differences with the standard model. In particular, we show that the enzyme constraints improve flux predictions (e.g., explaining overflow metabolism and other metabolic switches) and demonstrate, for the first time, that these constraints can markedly change the spectrum of metabolic engineering strategies for different target products. CONCLUSIONS The methodological and tool developments presented herein pave the way for a simplified and routine construction and analysis of enzyme-constrained metabolic models.

中文翻译：

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具有酶限制的代谢模型的自动构建。

背景技术为了提高基于约束的代谢模型的准确性，已经开发了几种旨在整合其他生物学信息的方法。这些方法中的两种，MOMENT和GECKO，结合了酶学（kcat）参数和酶质量限制，以进一步限制可行的代谢通量分布的空间。尽管两种方法均已证明可提供新陈代谢模型的有用扩展，但它们可能会大大增加模型的大小和复杂性，并且目前尚无工具可从给定的化学计量模型中完全自动化此类酶限制模型的生成和校准。结果在这项工作中，我们提出了三个主要方面。我们首先构思了简短的MOMENT（sMOMENT），这是MOMENT方法的简化版本，产生与MOMENT相同的预测，但所需变量明显减少，并且可以在基于约束的模型的标准表示中直接包含相关酶的约束。当可以测量酶的浓度时，在已知所有酶浓度的极端情况下，也可以将其包括在内，从而得出类似于GECKO方法的模型表示。其次，我们开发了AutoPACMEN工具箱，该工具箱几乎可以完全自动化地创建sMOMENT增强的化学计量代谢模型。特别是，这包括从不同数据库中自动读取和处理相关酶数据，以及重新配置具有嵌入式酶约束的化学计量模型。另外，根据给定的通量数据，已经开发出工具来调整sMOMENT模型的参数（kcat和酶库）。我们最终应用了新的sMOMENT方法和Aut​​oPACMEN工具箱，以生成酶限制型大肠杆菌基因组规模模型iJO1366，并分析其关键特性和与标准模型的差异。尤其是，我们表明酶限制因素改善了通量预测（例如，解释溢流代谢和其他代谢转换），并首次证明了这些限制因素可以显着改变针对不同目标产品的代谢工程策略的范围。结论本文介绍的方法和工具开发为简化和常规的酶约束代谢模型的构建和分析铺平了道路。