Unlocking Elementary Conversion Modes: ecmtool Unveils All Capabilities of Metabolic Networks.
Unlocking Elementary Conversion Modes: ecmtool Unveils All Capabilities of Metabolic Networks.
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DOI：
10.1016/j.patter.2020.100177
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发表时间：
2021-01-08
期刊：
影响因子：
--
通讯作者：
de Groot DH
中科院分区：
文献类型：
--
作者：
Clement TJ;Baalhuis EB;Teusink B;Bruggeman FJ;Planqué R;de Groot DH
细胞的代谢能力决定了它们的生物技术潜力、在生态系统中的适应性、致病威胁水平以及在多细胞生物中的功能。它们的全面实验表征通常是不可行的，特别是对于不可培养的生物体。原则上，可以使用代谢网络重建从生物体的注释基因组计算代谢能力的全部范围。然而，目前的计算方法不能处理基因组规模的代谢网络。部分问题在于，这些方法旨在列举所有代谢途径，而计算营养素和产物之间的所有（元素平衡）转化就足够了。事实上，基本转换模式（由Urbanczik和瓦格纳定义的ECM）捕获了网络的全部代谢能力，但ECM的使用直到现在还没有实现。我们解释和扩展的ECM理论，实现其枚举ecmtool，并说明其适用性。这项工作有助于阐明任何细胞的完整代谢足迹。基本转换模式（ECM）指定任何生物体的所有代谢能力Ecmtool从重建的代谢网络中计算所有ECM ECM枚举实现了比以往更大网络的代谢表征关注相关代谢物之间的ECM甚至实现了基因组规模的枚举了解细胞的代谢能力具有深远的重要性。微生物的新陈代谢塑造了全球元素的循环，并清洁了被污染的土壤。人体和病原体的代谢影响我们的健康。最近的进展允许自动重建任何生物体的反应网络，这已经用于合成生物学，（食品）微生物学和农业，以计算从资源到产品的最佳产量。然而，计算工具仅限于最佳状态或子网络，使生物体的许多能力隐藏起来。我们的程序ecmtool创建了任何生物体代谢功能的蓝图，大大提高了从基因组序列中获得的见解。ECMTool可能在探索性研究中变得至关重要，特别是对于研究在实验室条件下无法培养的细胞。理想情况下，有一天，基本转换模式计数将成为代谢网络重建后的标准步骤，实现所有已知生物体的代谢表征。Clement等人提出了一个现成的工具，揭示了生物体的代谢蓝图，从他们重建的代谢网络，通过计算的基本转换模式（ECM）。对于任何细胞，ECM涵盖了从营养物质到新细胞和产物分泌的所有整体转化。因此，该工具描述了生物体可能对其环境产生的所有可能影响。
The metabolic capabilities of cells determine their biotechnological potential, fitness in ecosystems, pathogenic threat levels, and function in multicellular organisms. Their comprehensive experimental characterization is generally not feasible, particularly for unculturable organisms. In principle, the full range of metabolic capabilities can be computed from an organism's annotated genome using metabolic network reconstruction. However, current computational methods cannot deal with genome-scale metabolic networks. Part of the problem is that these methods aim to enumerate all metabolic pathways, while computation of all (elementally balanced) conversions between nutrients and products would suffice. Indeed, the elementary conversion modes (ECMs, defined by Urbanczik and Wagner) capture the full metabolic capabilities of a network, but the use of ECMs has not been accessible until now. We explain and extend the theory of ECMs, implement their enumeration in ecmtool, and illustrate their applicability. This work contributes to the elucidation of the full metabolic footprint of any cell. Elementary conversion modes (ECMs) specify all metabolic capabilities of any organism Ecmtool computes all ECMs from a reconstructed metabolic network ECM enumeration enables metabolic characterization of larger networks than ever Focusing on ECMs between relevant metabolites even enables genome-scale enumeration Understanding the metabolic capabilities of cells is of profound importance. Microbial metabolism shapes global cycles of elements and cleans polluted soils. Human and pathogen metabolism affects our health. Recent advances allow for automatic reconstruction of reaction networks for any organism, which is already used in synthetic biology, (food) microbiology, and agriculture to compute optimal yields from resources to products. However, computational tools are limited to optimal states or subnetworks, leaving many capabilities of organisms hidden. Our program, ecmtool, creates a blueprint of any organism's metabolic functionalities, drastically improving insights obtained from genome sequences. Ecmtool may become essential in exploratory research, especially for studying cells that are not culturable in laboratory conditions. Ideally, elementary conversion mode enumeration will someday be a standard step after metabolic network reconstruction, achieving the metabolic characterization of all known organisms. Clement et al. present a ready-to-use tool that unveils metabolic blueprints of organisms from their reconstructed metabolic networks, through the computation of elementary conversion modes (ECMs). For any cell, the ECMs span all overall conversions from nutrients to new cells and the secretion of products. Therefore, the tool describes all possible effects that an organism may exert on its environment.
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