The role of high-throughput transcriptome analysis in metabolic engineering

被引:0
|
作者
Michael C. Jewett
Ana Paula Oliveira
Kiran Raosaheb Patil
Jens Nielsen
机构
[1] Technical University of Denmark,Center for Microbial Biotechnology, BioCentrum
来源
Biotechnology and Bioprocess Engineering | 2005年 / 10卷
关键词
metabolic engineering; transcriptome; gene expression; bioinformatics; systems; biology; data integration; cell factory;
D O I
暂无
中图分类号
学科分类号
摘要
The phenotypic response of a cell results from a well orchestrated web of complex interactions which propagate from the genetic architecture through the metabolic flux network. To rationally design cell factories which carry out specific functional objectives by controlling this hierarchical system is a challenge. Transcriptome analysis, the most mature high-throughput measurement technology, has been readily applied in strain improvement programs in an attempt to identify genes involved in expressing a given phenotype. Unfortunately, while differentially expressed genes may provide targets for metabolic engineering, phenotypic responses are often not directly linked to transcriptional patterns. This limits the application of genome-wide transcriptional analysis for the design of cell factories. However, improved tools for integrating transcriptional data with other high-throughput measurements and known biological interactions are emerging. These tools hold significant promise for providing the framework to comprehensively dissect the regulatory mechanisms that identify the cellular control mechanisms and lead to more effective strategies to rewire the cellular control elements for metabolic engineering.
引用
收藏
页码:385 / 399
页数:14
相关论文
共 50 条
  • [1] The role of high-throughput transcriptome analysis in metabolic engineering
    Jewett, MC
    Oliveira, AP
    Patil, KR
    Nielsen, J
    BIOTECHNOLOGY AND BIOPROCESS ENGINEERING, 2005, 10 (05) : 385 - 399
  • [2] Transcriptome analysis of Cinnamomum longepaniculatum by high-throughput sequencing
    Yan, Kuan
    Wei, Qin
    Feng, Ruizhang
    Zhou, Wanhai
    Chen, Fang
    ELECTRONIC JOURNAL OF BIOTECHNOLOGY, 2017, 28 : 58 - 66
  • [3] High-throughput transcriptome and pathogenesis analysis of clinical psoriasis
    Yu, Zengyang
    Gong, Yu
    Cui, Lian
    Hu, Yifan
    Zhou, Qianqian
    Chen, Zeyu
    Yu, Yingyuan
    Chen, Youdong
    Xu, Peng
    Zhang, Xilin
    Guo, Chunyuan
    Shi, Yuling
    JOURNAL OF DERMATOLOGICAL SCIENCE, 2020, 98 (02) : 109 - 118
  • [4] High-Throughput Analysis of Ovarian Granulosa Cell Transcriptome
    Chronowska, Ewa
    BIOMED RESEARCH INTERNATIONAL, 2014, 2014
  • [5] Analysis of High-Throughput Transcriptome Sequencing of Orychophragmus violaceus Seedlings
    Hang, Hongtao
    POLISH JOURNAL OF ENVIRONMENTAL STUDIES, 2022, 31 (04): : 3561 - 3571
  • [6] Transcriptome analysis of tongue cancer based on high-throughput sequencing
    Tang, Mingming
    Dai, Wencheng
    Wu, Hao
    Xu, Xinjiang
    Jiang, Bin
    Wei, Yingze
    Qian, Hongyan
    Han, Liang
    ONCOLOGY REPORTS, 2020, 43 (06) : 2004 - 2016
  • [8] Transcriptome profile analysis of porcine adipose tissue by high-throughput sequencing
    Li, X. J.
    Yang, H.
    Li, G. X.
    Zhang, G. H.
    Cheng, J.
    Guan, H.
    Yang, G. S.
    ANIMAL GENETICS, 2012, 43 (02) : 144 - 152
  • [9] High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
    Aquime Goncalves, Andre Nicolau
    Maso, Vanessa Escolano
    Santos de Castro, Icaro Maia
    Vasconcelos, Amanda Pereira
    Tomio Ogava, Rodrigo Luiz
    Nakaya, Helder, I
    JOVE-JOURNAL OF VISUALIZED EXPERIMENTS, 2022, (181):
  • [10] Transcriptome Analysis of the Silkworm (Bombyx mori) by High-Throughput RNA Sequencing
    Li, Yinu
    Wang, Guozeng
    Tian, Jian
    Liu, Huifen
    Yang, Huipeng
    Yi, Yongzhu
    Wang, Jinhui
    Shi, Xiaofeng
    Jiang, Feng
    Yao, Bin
    Zhang, Zhifang
    PLOS ONE, 2012, 7 (08):