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Novel two-stage processes for optimal chemical production in microbes
Institution:1. Faculty of Applied Sciences, UCSI University, UCSI Heights, 56000 Cheras, Kuala Lumpur, Malaysia;2. School of Engineering, Taylor''s University Lakeside Campus, 47500 Subang Jaya, Selangor, Malaysia;3. Bioprocess Technology, School of Industrial Technology, Universiti Sains Malaysia, 11800 Gelugor, Pulau Pinang, Malaysia;4. Biorefinery and Bioprocess Engineering Laboratory, Department of Chemical Engineering and Materials Science, Yuan Ze University, Chungli, Taoyuan 320, Taiwan;1. State Key Laboratory of Industry Control Technology, Control Department, Zhejiang University, Hangzhou 310027, PR China;2. Department of Chemical Engineering, University of Massachusetts, Amherst, MA 010003, USA;1. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;2. Synthetic Biology Engineering Research Center (SynBERC), Massachusetts Institute of Technology, Cambridge, MA 02139, USA;1. Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, Magdeburg D-39106, Germany;2. Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, ON, Canada M5S 3E5;3. Institute of Biomaterials and Biomedical Engineering, University of Toronto, 184 College Street, Toronto, ON, Canada, M5S3G9
Abstract:Microbial metabolism can be harnessed to produce a broad range of industrially important chemicals. Often, three key process variables: Titer, Rate and Yield (TRY) are the target of metabolic engineering efforts to improve microbial hosts toward industrial production. Previous research into improving the TRY metrics have examined the efficacy of having distinct growth and production stages to achieve enhanced productivity. However, these studies assumed a switch from a maximum growth to a maximum production phenotype. Hence, phenotypes with intermediate growth and chemical production in each of the growth and production stages of two-stage processes are yet to be explored. The impact of reduced growth rates on substrate uptake adds to the need for intelligent choice of operating points while designing two-stage processes. In this work, we develop a computational framework that scans the phenotypic space of microbial metabolism to identify ideal growth and production phenotypic targets, to achieve optimal TRY targets. Using this framework, with Escherichia coli as a model organism, we compare two-stage processes that use dynamic pathway regulation, with one-stage processes that use static intervention strategies, for different bioprocess objectives. Our results indicate that two-stage processes with intermediate growth during the production stage always result in optimal TRY values even in cases where substrate uptake is limited due to reduced growth during chemical production. By analyzing the flux distributions for the production enhancing strategies, we identify key reactions and reaction subsystems that require perturbation to achieve a production phenotype for a wide range of metabolites in E. coli. Interestingly, flux perturbations that increase phosphoenolpyruvate and NADPH availability are enriched among these production phenotypes. Furthermore, reactions in the pentose phosphate pathway emerge as key control nodes that function together to increase the availability of precursors to most products in E. coli. The inherently modular nature of microbial metabolism results in common reactions and reaction subsystems that need to be regulated to modify microbes from their target of growth to the production of a diverse range of metabolites. Due to the presence of these common patterns in the flux perturbations, we propose the possibility of a universal production strain.
Keywords:Dynamic pathway engineering  Two-stage process  Industrial bioprocess  Phenotypic choices  Production platforms  Substrate uptake
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