Clement Tom J, Baalhuis Erik B, Teusink Bas, Bruggeman Frank J, Planqué Robert, de Groot Daan H
Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1087, 1081 HV Amsterdam, the Netherlands.
Department of Mathematics, Vrije Universiteit Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, the Netherlands.
Patterns (N Y). 2020 Dec 29;2(1):100177. doi: 10.1016/j.patter.2020.100177. eCollection 2021 Jan 8.
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.
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。对它们进行全面的实验表征通常是不可行的,尤其是对于不可培养的生物。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出完整的代谢能力范围。然而,当前的计算方法无法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。实际上,基本转化模式(由乌尔班茨克和瓦格纳定义的ECM)捕获了网络的完整代谢能力,但直到现在ECM的使用还无法实现。我们解释并扩展了ECM理论,在ecmtool中实现了它们的列举,并说明了它们的适用性。这项工作有助于阐明任何细胞的完整代谢足迹。