title: Improving Metabolic Pathway Efficiency by Statistical Model-Based Multivariate Regulatory Metabolic Engineering.
abstract: Metabolic engineering entails target modification of cell metabolism to maximize the production of a specific compound. For empowering combinatorial optimization in strain engineering, tools and algorithms are needed to efficiently sample the multidimensional gene expression space and locate the desirable overproduction phenotype. We addressed this challenge by employing design of experiment (DoE) models to quantitatively correlate gene expression with strain performance. By fractionally sampling the gene expression landscape, we statistically screened the dominant enzyme targets that determine metabolic pathway efficiency. An empirical quadratic regression model was subsequently used to identify the optimal gene expression patterns of the investigated pathway. As a proof of concept, our approach yielded the natural product violacein at 525.4 mg/L in shake flasks, a 3.2-fold increase from the baseline strain. Violacein production was further increased to 1.31 g/L in a controlled benchtop bioreactor. We found that formulating discretized gene expression levels into logarithmic variables (Linlog transformation) was essential for implementing this DoE-based optimization procedure. The reported methodology can aid multivariate combinatorial pathway engineering and may be generalized as a standard procedure for accelerating strain engineering and improving metabolic pathway efficiency.
authors.affiliation: Department of Chemical Engineering, Massachusetts Institute of Technology , 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
authors.forename: Peng
authors.initials: P
authors.lastname: Xu
authors.affiliation: Department of Chemistry, Wellesley College , 106 Central Street, Wellesley, Massachusetts 02481, United States.
authors.forename: Elizabeth Anne
authors.initials: EA
authors.lastname: Rizzoni
authors.affiliation: Department of Chemical Engineering, Massachusetts Institute of Technology , 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
authors.forename: Se-Yeong
authors.initials: SY
authors.lastname: Sul
authors.affiliation: Department of Chemical Engineering, Massachusetts Institute of Technology , 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
authors.forename: Gregory
authors.initials: G
authors.lastname: Stephanopoulos
mesh_terms: D017123:Bacteriophage T7 / Q000235:genetics; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D015870:Gene Expression; D007211:Indoles / Q000737:chemistry / Q000378:metabolism; D060847:Metabolic Engineering / Q000379:methods*; D053858:Metabolic Networks and Pathways; D015233:Models, Statistical; D015999:Multivariate Analysis; D011401:Promoter Regions, Genetic; D058615:Synthetic Biology; D013816:Thermodynamics
chemical_list: D007211:Indoles; C063155:violacein
keywords: combinatorial optimization; metabolic engineering; promoter library; statistical models and response surface methodology; synthetic biology