Optimal strain design of saccharomyces cerevisiae for bioethanol production

Joshua C. Quarterman*, Yong Su Jin, Nathan D. Price, Pan Jun Kim

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

1 Citation (Scopus)


For several thousand years, yeast has been used as a biocatalyst for the fermentation of sugar into ethanol in a wide variety of food applications. More recently, yeast has been employed for large-scale ethanol production to replace petroleum fuels and meet a growing demand for homegrown energy, mainly using sugars from food products such as corn or sugarcane as a raw material. In the present study, we attempt to identify gene targets for improving ethanol production in the yeast Saccharomyces cerevisiae by using two mathematical modeling methods: flux balance analysis (FBA) and elementary flux mode (EFM) analysis. The results demonstrate the usefulness of an in silico analysis for identifying genetic perturbations in yeast for improved bioethanol production. Flux Balance Analysis (FBA) is a mathematical tool for calculating the flow of metabolites through a metabolic network with given constraints and an objective function. Using the concept of FBA, an in silico gene deletion study was conducted for the iND750 genome-scale yeast model and gene targets were identified for improving ethanol production with sufficient biomass for cell growth. Experimental validation confirmed the effectiveness of the in silico analysis and led to identification of novel gene targets for a 20-30% improvement in ethanol production during glucose fermentation. EFM analysis is another useful mathematical framework for defining and describing all metabolic routes that are both stoichiometrically and thermodynamically feasible for a group of enzymes. The analysis can decompose a complex metabolic network of many highly interconnected reactions into uniquely organized backbone pathways. In this study, a novel strategy was developed to identify knockout targets for improved phenotype using the concept of EFMs. Subsequently, the strategy was applied to a small-scale 58-reaction metabolic network for S. cerevisiae with the objective of high ethanol yield and sufficient biomass production for cell growth. METATOOL 5.1 was used to calculate the 1918 EFMs for the yeast metabolic network and the calculated EFMs were evaluated based on their ethanol-producing capabilities. Three sequential gene knockout targets have been identified for eliminating over 99% of the inefficient pathways for ethanol production through the network and thereby improving ethanol production.

Original languageEnglish
Title of host publication11AIChE - 2011 AIChE Annual Meeting, Conference Proceedings
PublisherAmerican Institute of Chemical Engineers
ISBN (Print)9780816910700
Publication statusPublished - Oct 2011
Event2011 AIChE Annual Meeting, 11AIChE - Minneapolis, MN, United States
Duration: 16 Oct 201121 Oct 2011

Publication series

NameAIChE Annual Meeting, Conference Proceedings


Conference2011 AIChE Annual Meeting, 11AIChE
Country/TerritoryUnited States
CityMinneapolis, MN

Scopus Subject Areas

  • Chemical Engineering(all)


Dive into the research topics of 'Optimal strain design of saccharomyces cerevisiae for bioethanol production'. Together they form a unique fingerprint.

Cite this