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A systems biology approach to the production of biotechnological products through systematic in silico studies

机译：通过系统的计算机模拟研究来生产生物技术产品的系统生物学方法

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摘要

Background: Currently, the development of microbial strains for biotechnological production of chemicals and materials can be improved by using a rational metabolicengineering that may involve genetic engineering and/or systems biology techniques. Elementary ux mode analysis (EFM) and Flux balance analysis (FBA) are the twomost commonly used methods for probing the microbial network system properties for metabolic engineering purposes. EFM can be used to identify all possible pathways.However, combinatorial explosion of the number of EFMs obtained during EFM analysis, especially for large reaction networks, hinders the use of EFM data fordeveloping gene knockout strategies. The objective of this project was to identify interesting target products and design `proof of principle' Saccharomyces cerevisiaestrains capable of overproducing a target product; in this case lysine was chosen.;Methods: EFMs were calculated for a reaction network from S. cerevisiae. In order to make sense of the large EFM solution space, a novel approach based on com-putational reduction and clustering of EFM datasets into subsets was developed,which aided the prediction of knockouts for lysine production. A Pattern analysismethod, based on regular expression matching, was also developed to interpret the EFM data. FBA frameworks, OptKnock and GDLS, were used to design in silcoproduction strains based on genome-scale models of yeast. Double and triple S. cerevisiae lysine producing strains were constructed using a PCR-based deletion method.Absolute and relative metabolome measurements for lysine and other metabolites in the single and double mutants were achieved using GC-TOF-MS.Results: The new computational and clustering methodology aided significantly the EFM-based in silico design of S. cerevisiae strains for enhanced yield of lysine andother value chemicals. Ethanol and lysine overproducing in silico strains were also developed by OptKnock and GDLS. Remarkably, the production strains with singledeletions, lsc2 and glt1, excreted into the medium five times the amount of lysine than the control strain. Five S. cerevisiae double mutant strains were successfullyconstructed. Two-fold increase in flux towards lysine production was demonstrated by S. cerevisiae double mutant M1, while both S. cerevisiae double mutants M4 andM5 showed about four-fold increase in lysine production.;Conclusion: The general modelling and data reduction approaches developed here contributed in obviating the enormous problems associated with trying to obtainthe EFMs from large reaction network models and interpreting the resulting of large number of EFMs. EFM analysis aided the development of single and double S.cerevisiae mutant strains, capable of increased yield of lysine. The computational method was validated by construction of strains that are able to produce several foldmore lysine than the original strain.
机译：背景：目前，可以通过使用可能涉及基因工程和/或系统生物学技术的合理代谢工程来改善用于生物技术生产化学品和材料的微生物菌株的开发。基本的ux模式分析（EFM）和通量平衡分析（FBA）是为代谢工程目的探测微生物网络系统特性的两种最常用方法。 EFM可以用于识别所有可能的途径。但是，在EFM分析过程中，尤其是对于大型反应网络，EFM数量的组合爆炸式增长阻碍了EFM数据用于开发基因敲除策略。该项目的目的是确定有趣的目标产品，并设计能够“过量生产”目标产品的酿酒酵母菌株。方法：计算啤酒酵母反应网络的EFMs。为了理解大型EFM解决方案空间，开发了一种基于计算机约简和EFM数据集聚类的新方法，有助于预测赖氨酸生产的敲除。还开发了一种基于正则表达式匹配的模式分析方法来解释EFM数据。 FBA框架OptKnock和GDLS用于基于酵母的基因组规模模型设计产硅菌株。使用基于PCR的缺失方法构建了产啤酒酵母的双倍和三倍赖氨酸生产菌株，并使用GC-TOF-MS对单突变和双突变中的赖氨酸和其他代谢产物进行了绝对和相对代谢组学测量。聚类方法极大地帮助了酿酒酵母菌株基于EFM的计算机模拟设计，从而提高了赖氨酸和其他有价值化学品的产量。 OptKnock和GDLS还开发了在计算机菌株中过量生产乙醇和赖氨酸的方法。值得注意的是，具有单缺失的生产菌株lsc2和glt1排入培养基的赖氨酸量是对照菌株的5倍。成功构建了5株啤酒酵母双突变株。啤酒酵母双突变体M1证明向赖氨酸生产的通量增加了两倍，而啤酒酵母双突变体M4和M5都显示赖氨酸生产增加了约四倍。结论：这里开发了一般建模和数据缩减方法有助于消除与尝试从大型反应网络模型获得EFM相关的巨大问题，并解释了大量EFM的结果。 EFM分析有助于开发单双酿酒酵母突变株，能够提高赖氨酸的产量。通过构建能够产生比原始菌株多几倍的赖氨酸的菌株，验证了该计算方法。

著录项

  • 作者

    Oshota, Olusegun James.;

  • 作者单位

    The University of Manchester (United Kingdom).;

  • 授予单位 The University of Manchester (United Kingdom).;
  • 学科 Bioengineering.;Systematic biology.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 268 p.
  • 总页数 268
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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