Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal.
Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal.
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DOI：
10.1371/journal.pcbi.1005494
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发表时间：
2017-04
影响因子：
4.3
通讯作者：
Lercher MJ
中科院分区：
文献类型：
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作者：
Fritzemeier CJ;Hartleb D;Szappanos B;Papp B;Lercher MJ
能量代谢是细胞生物学的核心。因此，异养单细胞物种的基因组规模模型必须适当考虑利用外部营养物质合成能量代谢产物，如ATP。然而，为通量平衡分析（FBA）设计的代谢模型可能包含在物理上不可能的能量产生循环：在没有营养消耗的情况下，这些模型仍然能够为能量代谢物（如ADP→ATP或NADP+→NADPH）充电。在这里，我们发现，超过85%的代谢模型在没有大量手动管理的情况下都会发生能量生成周期，例如ModelSEED和MetaNetX数据库中包含的模型;相比之下，BiGG数据库中手动管理的模型很少出现这种周期。能量产生周期可以表示模型误差，例如，对反应可逆性的错误假设。或者，循环的一部分可能在一种环境中是热力学可行的，而其余部分在另一种环境中是热力学可行的;由于标准FBA不考虑热力学，因此将这些组合到FBA模型中允许错误的能量生成。能量产生周期的存在通常会使最大生物质生产率提高25%，并可能导致进化模拟中的偏差。我们提出了有效的计算方法（i），以确定能源发电周期，使用FBA，和（ii），以确定最小集的模型变化，消除他们，使用的GlobalFit算法的变体。基因组尺度的代谢模型通常用于模拟单细胞生物的生长，并且可能成为医学科学中的重要工具。最流行的方法是通量平衡分析（FBA），一种简化的数学描述，能够描述数百个生化反应的同时活动。细胞功能通常依赖于足够能量的可用性，因此能量代谢的正确表示对代谢建模至关重要。然而，我们发现，大多数直接从基因组序列生成的FBA模型，以及少数精心策划的模型，都能够凭空产生能量。这些模型在没有任何营养摄取的情况下为能量代谢物如ATP充电。我们将相应的反应集命名为“错误的能量产生周期”（EGCs），并开发了一种高通量算法来识别它们。我们在来自三个不同数据库的350个代谢模型中的238个（68%）中发现了EGC。我们开发了第二种完全自动化的EGC去除方法。在校正模型上的模拟通常显示出比原始模型慢25%的生长速率，这证明了检查EGC代谢模型重建的重要性。
Energy metabolism is central to cellular biology. Thus, genome-scale models of heterotrophic unicellular species must account appropriately for the utilization of external nutrients to synthesize energy metabolites such as ATP. However, metabolic models designed for flux-balance analysis (FBA) may contain thermodynamically impossible energy-generating cycles: without nutrient consumption, these models are still capable of charging energy metabolites (such as ADP→ATP or NADP+→NADPH). Here, we show that energy-generating cycles occur in over 85% of metabolic models without extensive manual curation, such as those contained in the ModelSEED and MetaNetX databases; in contrast, such cycles are rare in the manually curated models of the BiGG database. Energy generating cycles may represent model errors, e.g., erroneous assumptions on reaction reversibilities. Alternatively, part of the cycle may be thermodynamically feasible in one environment, while the remainder is thermodynamically feasible in another environment; as standard FBA does not account for thermodynamics, combining these into an FBA model allows erroneous energy generation. The presence of energy-generating cycles typically inflates maximal biomass production rates by 25%, and may lead to biases in evolutionary simulations. We present efficient computational methods (i) to identify energy generating cycles, using FBA, and (ii) to identify minimal sets of model changes that eliminate them, using a variant of the GlobalFit algorithm. Genome-scale metabolic models are routinely used to simulate the growth of unicellular organisms, and are likely to become an important tool in the medical sciences. The most popular method employed for this task is flux balance analysis (FBA), a simplified mathematical description able to describe the simultaneous activity of hundreds of biochemical reactions. Cellular functions are often dependent on the availability of sufficient energy, and thus a correct representation of energy metabolism appears crucial to metabolic modeling. However, we found that the majority of FBA models generated directly from genome sequences, as well as a minority of carefully curated models, are capable of generating energy out of thin air. These models charge energy metabolites such as ATP without any nutrient uptake. We named the corresponding sets of reactions “erroneous energy generating cycles” (EGCs) and developed a high-throughput algorithm for their identification. We found EGCs in 238 (68%) of 350 metabolic models from three different databases. We developed a second, fully automated method for EGC removal. Simulations on the corrected models typically showed growth rates that were 25% slower than in the original models, demonstrating the importance of checking metabolic model reconstructions for EGCs.