Evaluating Factors That Influence Microbial Synthesis Yields by Linear Regression with Numerical and Ordinal Variables

被引:25
作者
Colletti, Peter F. [1 ]
Goyal, Yogesh [1 ]
Varman, Arul M. [1 ]
Feng, Xueyang [1 ]
Wu, Bing [1 ]
Tang, Yinjie J. [1 ]
机构
[1] Washington Univ, Dept Energy Environm & Chem Engn, St Louis, MO 63130 USA
关键词
enzymatic steps; Escherichia coli; flux; nutrients; overexpression; P-value; GENE KNOCKOUT SIMULATION; METABOLIC FLUX ANALYSIS; ESCHERICHIA-COLI; SACCHAROMYCES-CEREVISIAE; PATHWAY; ACID; BIOSYNTHESIS; BIOFUELS; BIOLOGY; DESIGN;
D O I
10.1002/bit.22996
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
In the production of chemicals via microbial fermentation, achieving a high yield is one of the most important objectives. We developed a statistical model to analyze influential factors that determine product yield by compiling data obtained from engineered Escherichia coli developed within last 10 years. Using both numerical and ordinal variables (e.g., enzymatic steps, cultivation conditions, and genetic modifications) as input parameters, our model revealed that cultivation modes, nutrient supplementation, and oxygen conditions were the three significant factors for improving product yield. Generally, the model showed that product yield decreases as the number of enzymatic steps in the biosynthesis pathway increases (7-9% loss of yield per enzymatic step). Moreover, overexpression of enzymes or removal of competitive pathways (e. g., knockout) does not necessarily result in an amplification of product yield (P-value > 0.1), possibly because of limited capacity in the biosynthesis pathway to accommodate an increase in flux. The model not only provides general guidelines for metabolic engineering and fermentation processes, but also allows a priori estimation and comparison of product yields under designed cultivation conditions. Biotechnol. Bioeng. 2011;108: 893-901. (C) 2010 Wiley Periodicals, Inc.
引用
收藏
页码:893 / 901
页数:9
相关论文
共 46 条
[1]   Engineering for biofuels: exploiting innate microbial capacity or importing biosynthetic potential? [J].
Alper, Hal ;
Stephanopoulos, Gregory .
NATURE REVIEWS MICROBIOLOGY, 2009, 7 (10) :715-723
[2]  
Anderson D.R., 2009, Essentials of statistics for business and economics
[3]  
[Anonymous], 2012, INTRO LINEAR REGRESS
[4]  
[Anonymous], 1985, Applied Linear Regression, DOI DOI 10.1002/BIMJ.4710300746
[5]   Metabolic engineering of Escherichia coli for 1-butanol production [J].
Atsumi, Shota ;
Cann, Anthony F. ;
Connor, Michael R. ;
Shen, Claire R. ;
Smith, Kevin M. ;
Brynildsen, Mark P. ;
Chou, Katherine J. Y. ;
Hanai, Taizo ;
Liao, James C. .
METABOLIC ENGINEERING, 2008, 10 (06) :305-311
[6]   Non-fermentative pathways for synthesis of branched-chain higher alcohols as biofuels [J].
Atsumi, Shota ;
Hanai, Taizo ;
Liao, James C. .
NATURE, 2008, 451 (7174) :86-U13
[7]   TOWARD A SCIENCE OF METABOLIC ENGINEERING [J].
BAILEY, JE .
SCIENCE, 1991, 252 (5013) :1668-1675
[8]   Systems metabolic engineering: Genome-scale models and beyond [J].
Blazeck, John ;
Alper, Hal .
BIOTECHNOLOGY JOURNAL, 2010, 5 (07) :647-659
[9]   Metabolic flux analysis and pharmaceutical production [J].
Boghigian, Brett A. ;
Seth, Gargi ;
Kiss, Robert ;
Pfeifer, Blaine A. .
METABOLIC ENGINEERING, 2010, 12 (02) :81-95
[10]   Effects of recombinant precursor pathway variations on poly [(R)-3-hydroxybutyrate] synthesis in Saccharomyces cerevisiae [J].
Carlson, Ross ;
Srienc, Friedrich .
JOURNAL OF BIOTECHNOLOGY, 2006, 124 (03) :561-573