OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains.
OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains.
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DOI：
10.1186/1752-0509-4-53
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发表时间：
2010-04-28
影响因子：
--
通讯作者：
Reed JL
中科院分区：
文献类型：
--
作者：
Kim J;Reed JL
代谢网络的计算建模和分析已经成功地用于微生物菌株的代谢工程以用于有价值的生化生产。目前可用的代谢工程计算方法的局限性在于，它们通常基于反应缺失而不是基因缺失，并且不考虑控制代谢的调控网络。由于多功能酶和同工酶的存在，基于反应缺失的计算设计有时会导致遗传上复杂或不可行的策略。此外，由于监管限制，菌株最初可能无法生长。为了克服这些局限性，我们开发了一种新的方法（OptORF），用于识别基于基因缺失和过表达的代谢工程策略。在这里，我们提出了一个有效的方法，系统地整合转录调控网络和代谢网络。这允许线性优化问题的制定，该线性优化问题搜索耦合生物质和生化生产的代谢和/或调节扰动，从而提出适应性进化菌株设计。使用大肠杆菌的基因组规模模型，我们已经实现了OptORF算法（考虑基因缺失和转录调控），并将其用于乙醇生产的代谢工程策略与使用OptKnock（考虑反应缺失）的代谢工程策略进行了比较。我们的研究结果发现，基于反应的策略通常需要更多的基因删除，以消除所确定的反应（2个基因比反应），并导致致命的生长表型时，转录调控被认为（162 200例）。最后，我们提出了在大肠杆菌中生产乙醇和高级醇（如异丁醇）的代谢工程策略。大肠杆菌中进行了比较。我们已经发现了一些常见的基因修饰，如pgi的缺失和edd的过度表达，以及生产不同酒精的化学特异性策略。通过考虑调控效应，OptORF可以提出改变，例如代谢基因的过表达或转录因子的缺失，以及代谢基因的缺失，这可能导致更快的进化轨迹。而E.大肠杆菌中进行了评估，开发的OptORF方法是通用的，可以应用于优化其他生物系统中不同化合物的生产。
Computational modeling and analysis of metabolic networks has been successful in metabolic engineering of microbial strains for valuable biochemical production. Limitations of currently available computational methods for metabolic engineering are that they are often based on reaction deletions rather than gene deletions and do not consider the regulatory networks that control metabolism. Due to the presence of multi-functional enzymes and isozymes, computational designs based on reaction deletions can sometimes result in strategies that are genetically complicated or infeasible. Additionally, strains might not be able to grow initially due to regulatory restrictions. To overcome these limitations, we have developed a new approach (OptORF) for identifying metabolic engineering strategies based on gene deletion and overexpression. Here we propose an effective method to systematically integrate transcriptional regulatory networks and metabolic networks. This allows for the formulation of linear optimization problems that search for metabolic and/or regulatory perturbations that couple biomass and biochemical production, thus proposing adaptive evolutionary strain designs. Using genome-scale models of Escherichia coli, we have implemented the OptORF algorithm (which considers gene deletions and transcriptional regulation) and compared its metabolic engineering strategies for ethanol production to those found using OptKnock (which considers reaction deletions). Our results found that the reaction-based strategies often require more gene deletions to remove the identified reactions (2 more genes than reactions), and result in lethal growth phenotypes when transcriptional regulation is considered (162 out of 200 cases). Finally, we present metabolic engineering strategies for producing ethanol and higher alcohols (e.g. isobutanol) in E. coli using our OptORF approach. We have found common genetic modifications such as deletion of pgi and overexpression of edd, as well as chemical specific strategies for producing different alcohols. By taking regulatory effects into account, OptORF can propose changes such as the overexpression of metabolic genes or deletion of transcriptional factors, in addition to the deletion of metabolic genes, that may lead to faster evolutionary trajectories. While biofuel production in E. coli is evaluated here, the developed OptORF approach is general and can be applied to optimize the production of different compounds in other biological systems.
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