Background: Currently, the development of microbial strains for biotechnological production of chemicals and materials can be improved by using a rational metabolicengineering that may involve genetic engineering and/or systems biology techniques. Elementary ux mode analysis (EFM) and Flux balance analysis (FBA) are the twomost commonly used methods for probing the microbial network system properties for metabolic engineering purposes. EFM can be used to identify all possible pathways.However, combinatorial explosion of the number of EFMs obtained during EFM analysis, especially for large reaction networks, hinders the use of EFM data fordeveloping gene knockout strategies. The objective of this project was to identify interesting target products and design `proof of principle' Saccharomyces cerevisiaestrains capable of overproducing a target product; in this case lysine was chosen.Methods: EFMs were calculated for a reaction network from S. cerevisiae. In order to make sense of the large EFM solution space, a novel approach based on com-putational reduction and clustering of EFM datasets into subsets was developed,which aided the prediction of knockouts for lysine production. A Pattern analysismethod, based on regular expression matching, was also developed to interpret the EFM data. FBA frameworks, OptKnock and GDLS, were used to design in silcoproduction strains based on genome-scale models of yeast. Double and triple S. cerevisiae lysine producing strains were constructed using a PCR-based deletion method.Absolute and relative metabolome measurements for lysine and other metabolites in the single and double mutants were achieved using GC-TOF-MS.Results: The new computational and clustering methodology aided significantly the EFM-based in silico design of S. cerevisiae strains for enhanced yield of lysine andother value chemicals. Ethanol and lysine overproducing in silico strains were also developed by OptKnock and GDLS. Remarkably, the production strains with singledeletions, lsc2 and glt1, excreted into the medium five times the amount of lysine than the control strain. Five S. cerevisiae double mutant strains were successfullyconstructed. Two-fold increase in flux towards lysine production was demonstrated by S. cerevisiae double mutant M1, while both S. cerevisiae double mutants M4 andM5 showed about four-fold increase in lysine production.Conclusion: The general modelling and data reduction approaches developed here contributed in obviating the enormous problems associated with trying to obtainthe EFMs from large reaction network models and interpreting the resulting of large number of EFMs. EFM analysis aided the development of single and double S.cerevisiae mutant strains, capable of increased yield of lysine. The computational method was validated by construction of strains that are able to produce several foldmore lysine than the original strain.