Metabolic engineering challenges in the post-genomic era

被引:8
|
作者
Alper, H [1 ]
Stephanopoulos, G [1 ]
机构
[1] MIT, Dept Chem Engn, Cambridge, MA 02139 USA
关键词
metabolic engineering; metabolism; reaction engineering;
D O I
10.1016/j.ces.2004.09.027
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Metabolic engineering is a young field, just over ten-years old. During this period, it has developed a well-defined methodology and a focused research portfolio of rich intellectual content and particular relevance to biotechnology and biological engineering. New and diverse opportunities for metabolic engineering emerge quickly in this genomic era. Although the focus (e.g. improving cells) and central components (e.g. assessing cell physiology) of metabolic engineering remain the same, new tools are required to take advantage of the opportunities arising from the availability of whole-genome sequence information. Cellular phenotype is a manifestation of gene expression levels, metabolic demand, resource availability, and cellular stresses. Above all, metabolic function is constrained by the stoichiometry and individual reaction kinetics of the reaction network. To understand the behavior of these systems, the well-established framework of reaction engineering must be complemented with new experimental methods specifically designed for the elucidation of metabolic pathways and bioreaction networks. Most of all, a combination of rational and combinatorial approaches is required to effectively sample and map as much of the metabolic space as possible. The above framework along with important tools of metabolic engineering will be reviewed in this article. We will then show their application to case studies of industrial and medical interest while emphasizing the strong influence and links of metabolic engineering to chemical reaction engineering. (C) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码:5009 / 5017
页数:9
相关论文
共 50 条
  • [1] Signal processing challenges in the post-genomic era
    Handzel, AA
    [J]. 2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING, 2005, : 761 - 764
  • [2] Protein Engineering Approaches in the Post-Genomic Era
    Singh, Raushan K.
    Lee, Jung-Kul
    Selvaraj, Chandrabose
    Singh, Ranjitha
    Li, Jinglin
    Kim, Sang-Yong
    Kalia, Vipin C.
    [J]. CURRENT PROTEIN & PEPTIDE SCIENCE, 2018, 19 (01) : 5 - 15
  • [3] Proctolin in the post-genomic era:: new insights and challenges
    Isaac, R. Elwyn
    Taylor, Christine A.
    Hamasaka, Yasutaka
    Naessel, Dick R.
    Shirras, Alan D.
    [J]. INVERTEBRATE NEUROSCIENCE, 2004, 5 (02) : 51 - 64
  • [4] Systems metabolic engineering for citric acid production by Aspergillus niger in the post-genomic era
    Tong, Zhenyu
    Zheng, Xiaomei
    Tong, Yi
    Shi, Yong-Cheng
    Sun, Jibin
    [J]. MICROBIAL CELL FACTORIES, 2019, 18 (1)
  • [5] Systems metabolic engineering for citric acid production by Aspergillus niger in the post-genomic era
    Zhenyu Tong
    Xiaomei Zheng
    Yi Tong
    Yong-Cheng Shi
    Jibin Sun
    [J]. Microbial Cell Factories, 18
  • [6] Microbiology in the post-genomic era
    Duccio Medini
    Davide Serruto
    Julian Parkhill
    David A. Relman
    Claudio Donati
    Richard Moxon
    Stanley Falkow
    Rino Rappuoli
    [J]. Nature Reviews Microbiology, 2008, 6 : 419 - 430
  • [7] Tools for the post-genomic era
    不详
    [J]. CHIMICA OGGI-CHEMISTRY TODAY, 2000, 18 (7-8) : 68 - 68
  • [8] Vaccinology in the post-genomic Era
    Grandi, Guido
    [J]. JOURNAL OF BIOTECHNOLOGY, 2010, 150 : S98 - S98
  • [9] Genotype–phenotype databases: challenges and solutions for the post-genomic era
    Gudmundur A. Thorisson
    Juha Muilu
    Anthony J. Brookes
    [J]. Nature Reviews Genetics, 2009, 10 : 9 - 18
  • [10] Metabolic modeling: a tool of drug discovery in the post-genomic era
    Voit, EO
    [J]. DRUG DISCOVERY TODAY, 2002, 7 (11) : 621 - 628