Identification of genome-scale metabolic network models using experimentally measured flux profiles.
Identification of genome-scale metabolic network models using experimentally measured flux profiles.
复制标题
使用实验测量的通量谱鉴定基因组规模的代谢网络模型。
DOI：
10.1371/journal.pcbi.0020072
复制
发表时间：
2006-07-07
影响因子：
4.3
通讯作者：
Palsson, Bernhard O.
中科院分区：
文献类型：
--
作者：
Herrgard, Markus J.;Fong, Stephen S.;Palsson, Bernhard O.
可以使用基因组注释和文献信息为特征明确的生物体重建基因组规模的代谢网络模型。然而，在很多情况下，模型预测的代谢通量与实验数据并不完全一致，这表明模型中的反应与体内系统中的活跃反应不匹配。我们引入了一种基于有限数量的实验测量通量来确定基因组规模代谢网络中的活性反应的方法。这种方法称为最佳代谢网络识别 (OMNI)，可以有效识别一组反应，从而使计算机预测的通量分布与实验测量的通量分布之间达到最佳一致性。我们将该方法应用于生长速率低于预测的进化大肠杆菌突变株的细胞内通量数据，以便识别在这些菌株中充当通量瓶颈的反应。与野生型菌株相比，在进化菌株中，与这些瓶颈反应相对应的基因的表达经常被发现下调。我们还证明了 OMNI 方法能够诊断为代谢物过量生产而设计的大肠杆菌菌株中的问题，这些菌株尚未达到其预测的生产潜力。应用于进化菌株通量数据的 OMNI 方法可用于深入了解限制微生物菌株向其预测的最佳生长表型进化的能力的机制。当应用于工业生产菌株时，OMNI 方法还可用于提出改善副产物分泌的代谢工程策略。除了这些应用之外，该方法应该被证明对于基于有限数量的实验数据重建特征不良的微生物有机体的代谢网络是有用的。计算机模型在生物学中的主要用途之一是识别模型预测与实验数据之间的差异，并利用这些差异来推动新生物机制的发现。然而，模型只能识别差异；它们不一定能帮助发现模型中哪些功能缺失或不正确，从而导致这些差异。赫尔加德等人。描述了一种新的计算机方法，即最佳代谢网络识别或 OMNI，该方法以有效且系统的方式针对基因组规模的代谢网络执行此发现过程。给定初步的代谢网络模型和实验确定的代谢通量数据，OMNI 找到需要对模型进行的更改，以便其预测尽可能与实验数据匹配。赫尔加德等人。应用该方法来识别实验进化的大肠杆菌菌株中的代谢瓶颈，并诊断通过代谢工程策略设计的菌株中的问题，以过度产生特定的所需副产物。 OMNI 方法还可以适应许多其他设置，包括基于有限的实验数据识别特征不良的生物体中的新生化途径。
Genome-scale metabolic network models can be reconstructed for well-characterized organisms using genomic annotation and literature information. However, there are many instances in which model predictions of metabolic fluxes are not entirely consistent with experimental data, indicating that the reactions in the model do not match the active reactions in the in vivo system. We introduce a method for determining the active reactions in a genome-scale metabolic network based on a limited number of experimentally measured fluxes. This method, called optimal metabolic network identification (OMNI), allows efficient identification of the set of reactions that results in the best agreement between in silico predicted and experimentally measured flux distributions. We applied the method to intracellular flux data for evolved Escherichia coli mutant strains with lower than predicted growth rates in order to identify reactions that act as flux bottlenecks in these strains. The expression of the genes corresponding to these bottleneck reactions was often found to be downregulated in the evolved strains relative to the wild-type strain. We also demonstrate the ability of the OMNI method to diagnose problems in E. coli strains engineered for metabolite overproduction that have not reached their predicted production potential. The OMNI method applied to flux data for evolved strains can be used to provide insights into mechanisms that limit the ability of microbial strains to evolve towards their predicted optimal growth phenotypes. When applied to industrial production strains, the OMNI method can also be used to suggest metabolic engineering strategies to improve byproduct secretion. In addition to these applications, the method should prove to be useful in general for reconstructing metabolic networks of ill-characterized microbial organisms based on limited amounts of experimental data. One of the major uses of in silico models in biology is to identify discrepancies between model predictions and experimental data and use these discrepancies to drive discovery of novel biological mechanisms. However, models only allow for identification of the discrepancies; they do not necessarily provide any assistance in discovering what are the missing or incorrect functionalities in the model that cause these discrepancies. Herrgård et al. describe a new in silico method, optimal metabolic network identification, or OMNI, that performs this discovery process in an efficient and systematic manner for genome-scale metabolic networks. Given a preliminary metabolic network model and experimentally determined metabolic flux data, OMNI finds the changes that need to be made to the model so that its predictions match the experimental data as well as possible. Herrgård et al. apply the method to identify metabolic bottlenecks in experimentally evolved Escherichia coli strains and to diagnose problems in strains designed through metabolic engineering strategies to overproduce specific desirable byproducts. The OMNI method can also be adapted to number of other settings, including identification of novel biochemical pathways in ill-characterized organisms based on limited amounts of experimental data.
登录
查看更多内容
影响因子：
12.3
作者：
Blank LM;Kuepfer L;Sauer U
通讯作者：
Sauer U
影响因子：
3.4
作者：
Feng, XJ;Rabitz, H
通讯作者：
Rabitz, H
影响因子：
4.8
作者：
Fong, SS;Nanchen, A;Sauer, U
通讯作者：
Sauer, U
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
2.1
作者：
Irizarry, RA;Hobbs, B;Speed, TP
通讯作者：
Speed, TP