Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.
Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.
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DOI：
10.1371/journal.pcbi.1005413
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发表时间：
2017-03
影响因子：
4.3
通讯作者：
Papin JA
中科院分区：
文献类型：
--
作者：
Biggs MB;Papin JA
基因组规模代谢网络重建（GENRE）是有关生物体中发生的代谢过程的知识库。 GENRE 已被用来发现和解释代谢功能，并设计新颖的网络结构。阻碍 GENRE 更广泛使用的一个主要障碍，特别是研究非模式生物，是产生高质量 GENRE 需要大量时间。人们已经开发了许多自动化方法来减少这一时间要求，但自动重建的流派草案仍然需要在做出有用的预测之前进行管理。我们提出了一种新的流派分析方法，它通过表示许多替代网络结构（所有这些结构都与可用数据同样一致）并从该集合中生成预测来提高草稿流派的预测能力。这种集成方法与许多重建方法兼容。我们将这种新方法称为集成通量平衡分析 (EnsembleFBA)。我们通过预测模型生物铜绿假单胞菌 UCBPP-PA14 的生长和基因必要性来验证 EnsembleFBA。我们通过预测六种链球菌的必需基因并将必需基因映射到 DrugBank 中的小分子配体，展示了如何将 EnsembleFBA 纳入系统生物学工作流程。我们发现，一些代谢子系统以每种链球菌物种独特的方式对一组预测的基本反应做出了不成比例的贡献，从而导致小分子相互作用产生物种特异性结果。通过对铜绿假单胞菌和六种链球菌的分析，我们表明集合可以提高预测质量，而不会大幅增加重建时间，从而使 GENRE 方法对于需要预测许多非模型生物的应用更加实用。我们所有的函数和随附的示例代码都可以在开放的在线存储库中找到。新陈代谢是所有生物活动背后的驱动力。基因组规模代谢网络重建（GENRE）是代谢系统的表示，可以通过数学分析来预测系统的行为方式，以及设计具有新特性的系统。传统上，流派是手动重建的，这可能需要大量的时间和精力。最近的软件解决方案使该过程自动化（大大减少了所需的工作量），但与手动策划的版本相比，生成的流派质量较低，并且产生的预测不太可靠。我们提出了一种新颖的方法（“EnsembleFBA”），该方法通过将许多不同的草稿类型汇集到一个整体中来解决自动重建中涉及的不确定性。我们通过预测常见病原体铜绿假单胞菌的生长和必需基因来测试 EnsembleFBA。我们发现，在预测生长或必需基因时，GENRE 的集合比集合中的任何单个 GENRE 实现了更好的精度或捕获了更多的必需基因。通过改进自动生成的 GENRE 的预测，这种方法可以对生化系统进行建模，否则这是不可行的。
Genome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository. Metabolism is the driving force behind all biological activity. Genome-scale metabolic network reconstructions (GENREs) are representations of metabolic systems that can be analyzed mathematically to make predictions about how a system will behave, as well as to design systems with new properties. GENREs have traditionally been reconstructed manually, which can require extensive time and effort. Recent software solutions automate the process (drastically reducing the required effort) but the resulting GENREs are of lower quality and produce less reliable predictions than the manually-curated versions. We present a novel method (“EnsembleFBA”) which accounts for uncertainties involved in automated reconstruction by pooling many different draft GENREs together into an ensemble. We tested EnsembleFBA by predicting the growth and essential genes of the common pathogen Pseudomonas aeruginosa. We found that when predicting growth or essential genes, ensembles of GENREs achieved much better precision or captured many more essential genes than any of the individual GENREs within the ensemble. By improving the predictions that can be made with automatically-generated GENREs, this approach enables the modeling of biochemical systems which would otherwise be infeasible.