Semi-automated Curation of Metabolic Models via Flux Balance Analysis: A Case Study with Mycoplasma gallisepticum

Primarily used for metabolic engineering and synthetic biology, genome-scale metabolic modeling shows tremendous potential as a tool for fundamental research and curation of metabolism. Through a novel integration of flux balance analysis and genetic algorithms, a strategy to curate metabolic networks and facilitate identification of metabolic pathways that may not be directly inferable solely from genome annotation was developed. Specifically, metabolites involved in unknown reactions can be determined, and potentially erroneous pathways can be identified. The procedure developed allows for new fundamental insight into metabolism, as well as acting as a semi-automated curation methodology for genome-scale metabolic modeling. To validate the methodology, a genome-scale metabolic model for the bacterium Mycoplasma gallisepticum was created. Several reactions not predicted by the genome annotation were postulated and validated via the literature. The model predicted an average growth rate of 0.358±0.12, closely matching the experimentally determined growth rate of M. gallisepticum of 0.244±0.03. This work presents a powerful algorithm for facilitating the identification and curation of previously known and new metabolic pathways, as well as presenting the first genome-scale reconstruction of M. gallisepticum.基因组规模代谢建模（genome-scale metabolic modeling）主要应用于代谢工程与合成生物学领域，作为基础研究与代谢通路整理注释的工具，展现出巨大的应用潜力。本研究通过将通量平衡分析（flux balance analysis）与遗传算法进行创新性整合，开发出一种可用于整理代谢网络、助力识别仅通过基因组注释无法直接推导的代谢通路的策略。具体而言，该策略可确定参与未知反应的代谢物，并识别存在潜在错误的代谢通路。所开发的方法不仅可增进人们对代谢过程的全新基础认知，还可作为基因组规模代谢建模的半自动整理注释工具。为验证该方法的有效性，本研究构建了针对鸡毒支原体（Mycoplasma gallisepticum）的基因组规模代谢模型。研究中提出了若干基因组注释未预测到的反应，并通过文献验证了这些反应的合理性。该模型预测的平均生长率为0.358±0.12，与实验测得的鸡毒支原体生长率0.244±0.03高度吻合。本研究提出了一种可高效助力识别与整理已知及全新代谢通路的算法，同时首次完成了鸡毒支原体的基因组规模代谢重建。