Genome-scale analysis of Saccharomyces cerevisiae metabolism and ethanol production in fed-batch culture.
A dynamic flux balance model based on a genome-scale metabolic network reconstruction is developed for in silico analysis of Saccharomyces cerevisiae metabolism and ethanol production in fed-batch culture. Metabolic engineering strategies previously identified for their enhanced steady-state biomass and/or ethanol yields are evaluated for fed-batch performance in glucose and glucose/xylose media. Dynamic analysis is shown to provide a single quantitative measure of fed-batch ethanol productivity that explicitly handles the possible tradeoff between the biomass and ethanol yields. Productivity optimization conducted to rank achievable fed-batch performance demonstrates that the genetic manipulation strategy and the fed-batch operating policy should be considered simultaneously. A library of candidate gene insertions is assembled and directly screened for their achievable ethanol productivity in fed-batch culture. A number of novel gene insertions with ethanol productivities identical to the best metabolic engineering strategies reported in previous studies are identified, thereby providing additional targets for experimental evaluation. The top performing gene insertions were substrate dependent, with the highest ranked insertions for glucose media yielding suboptimal performance in glucose/xylose media. The analysis results suggest that enhancements in biomass yield are most beneficial for the enhancement of fed-batch ethanol productivity by recombinant xylose utilizing yeast strains. We conclude that steady-state flux balance analysis is not sufficient to predict fed-batch performance and that the media, genetic manipulations, and fed-batch operating policy should be considered simultaneously to achieve optimal metabolite productivity.
Hjersted
Jared L
JL
Department of Chemical Engineering, University of Massachusetts, 159 Goessmann Laboratory, 686 North Pleasant Street, Amherst, MA 01003-3110, USA.
Henson
Michael A
MA
Mahadevan
Radhakrishnan
R
D019149:Bioreactors / Q000382:microbiology; D002874:Chromosome Mapping / Q000379:methods; D003198:Computer Simulation; D000431:Ethanol / Q000378:metabolism*; D005947:Glucose / Q000378:metabolism*; D008657:Metabolic Clearance Rate; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology; D014994:Xylose / Q000378:metabolism*
D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins; D000431:Ethanol; D014994:Xylose; D005947:Glucose