评估E。 大肠杆菌基因组规模代谢模型的准确性与高通量突变适应度数据。

IF 8.5 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Molecular Systems Biology Pub Date : 2023-12-06 Epub Date: 2023-10-27 DOI:10.15252/msb.202311566
David B Bernstein, Batu Akkas, Morgan N Price, Adam P Arkin
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引用次数: 0

摘要

大肠杆菌基因组规模代谢模型(GEM)是模拟细胞代谢的典型系统生物学模型。模型预测的实验验证对于精确确定不确定性和确保准确模型的持续开发至关重要。在这里,我们量化了随后四个E。 使用已发表的数千个基因和25种不同碳源的突变适应度数据进行大肠杆菌GEMs。该评估证明了精确度-召回曲线下面积相对于替代精确度指标的效用。对最新(iML1515)模型错误的分析确定了几种维生素/辅因子,尽管实验生长培养基中没有这些因子,但它们很可能对突变体有效,并强调同工酶基因-蛋白质反应图谱是预测不准确的关键来源。机器学习方法进一步确定了通过氢离子交换和特定中枢代谢分支点的代谢通量是模型准确性的重要决定因素。这项工作概述了用高通量突变适应度数据评估GEM准确性的改进实践,并强调了未来在E。 大肠杆菌及其他。
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Evaluating E. coli genome-scale metabolic model accuracy with high-throughput mutant fitness data.

The Escherichia coli genome-scale metabolic model (GEM) is an exemplar systems biology model for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint uncertainty and ensure continued development of accurate models. Here, we quantified the accuracy of four subsequent E. coli GEMs using published mutant fitness data across thousands of genes and 25 different carbon sources. This evaluation demonstrated the utility of the area under a precision-recall curve relative to alternative accuracy metrics. An analysis of errors in the latest (iML1515) model identified several vitamins/cofactors that are likely available to mutants despite being absent from the experimental growth medium and highlighted isoenzyme gene-protein-reaction mapping as a key source of inaccurate predictions. A machine learning approach further identified metabolic fluxes through hydrogen ion exchange and specific central metabolism branch points as important determinants of model accuracy. This work outlines improved practices for the assessment of GEM accuracy with high-throughput mutant fitness data and highlights promising areas for future model refinement in E. coli and beyond.

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来源期刊
Molecular Systems Biology
Molecular Systems Biology 生物-生化与分子生物学
CiteScore
18.50
自引率
1.00%
发文量
62
审稿时长
6-12 weeks
期刊介绍: Systems biology is a field that aims to understand complex biological systems by studying their components and how they interact. It is an integrative discipline that seeks to explain the properties and behavior of these systems. Molecular Systems Biology is a scholarly journal that publishes top-notch research in the areas of systems biology, synthetic biology, and systems medicine. It is an open access journal, meaning that its content is freely available to readers, and it is peer-reviewed to ensure the quality of the published work.
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