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芽孢杆菌属物种的基因组规模代谢网络模型表明，模型改进对于生物技术应用是必要的。

Genome-Scale Metabolic Network Models of Bacillus Species Suggest that Model Improvement is Necessary for Biotechnological Applications.

作者信息

Ghasemi-Kahrizsangi Tahereh, Marashi Sayed-Amir, Hosseini Zhaleh

机构信息

Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.

出版信息

Iran J Biotechnol. 2018 Aug 11;16(3):e1684. doi: 10.15171/ijb.1684. eCollection 2018 Aug.

DOI:10.15171/ijb.1684
PMID:31457023
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6697824/
Abstract

BACKGROUND

A genome-scale metabolic network model (GEM) is a mathematical representation of an organism's metabolism. Today, GEMs are popular tools for computationally simulating the biotechnological processes and for predicting biochemical properties of (engineered) strains.

OBJECTIVES

In the present study, we have evaluated the predictive power of two GEMs, namely Bsu1103 (for 168) and MZ1055 (for WSH002).

MATERIALS AND METHODS

For comparing the predictive power of and GEMs, experimental data were obtained from previous wet-lab studies included in PubMed. By using these data, we set the environmental, stoichiometric and thermodynamic constraints on the models, and FBA is performed to predict the biomass production rate, and the values of other fluxes. For simulating experimental conditions in this study, COBRA toolbox was used.

RESULTS

By using the wealth of data in the literature, we evaluated the accuracy of simulations of these GEMs. Our results suggest that there are some errors in these two models which make them unreliable for predicting the biochemical capabilities of these species. The inconsistencies between experimental and computational data are even greater where and do not have similar phenotypes.

CONCLUSIONS

Our analysis suggests that literature-based improvement of genome-scale metabolic network models of the two species is essential if these models are to be successfully applied in biotechnology and metabolic engineering.

摘要

背景

基因组规模代谢网络模型（GEM）是生物体新陈代谢的一种数学表示。如今，GEM是用于通过计算模拟生物技术过程以及预测（工程）菌株生化特性的常用工具。

目的

在本研究中，我们评估了两个GEM的预测能力，即Bsu1103（针对168）和MZ1055（针对WSH002）。

材料与方法

为了比较这两个GEM的预测能力，实验数据取自PubMed中包含的先前湿实验室研究。利用这些数据，我们对模型设置了环境、化学计量和热力学约束，并进行通量平衡分析（FBA）以预测生物量生产率和其他通量的值。在本研究中，使用COBRA工具箱来模拟实验条件。

结果

通过利用文献中的大量数据，我们评估了这些GEM模拟的准确性。我们的结果表明，这两个模型存在一些误差，这使得它们在预测这些物种的生化能力时不可靠。在168和WSH002没有相似表型的情况下，实验数据和计算数据之间的不一致性甚至更大。

结论

我们的分析表明，如果要将这两个物种的基因组规模代谢网络模型成功应用于生物技术和代谢工程，基于文献对其进行改进至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9337/6697824/7f95bbf712a8/ijb-2018-03-e1684-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9337/6697824/15de2d7753c8/ijb-2018-03-e1684-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9337/6697824/7f95bbf712a8/ijb-2018-03-e1684-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9337/6697824/15de2d7753c8/ijb-2018-03-e1684-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9337/6697824/7f95bbf712a8/ijb-2018-03-e1684-g002.jpg

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