L?lactate production in engineered Saccharomyces cerevisiae using a multistage multiobjective automated design framework
In this research work, a new BioCAD framework was developed that combines three different design processes: genetic manipulation by gene knock?out and knock?in, gene expression optimization by fine tuning of the gene under? and over? expression, and medium design. Thanks to this Pareto optimal multiplexed strategy, it is possible to effectively design S. cerevisiae strains that can produce lactic acid and simultaneously allow the yeast to grow, maximizing the yield and productivity and concurrently minimizing the number of genetic manipulations.The design of alternative biodegradable polymers has the potential of severely reducing the environmental impact, cost and production time currently associated with the petrochemical industry. In fact, growing demand for renewable feedstock has recently brought to the fore synthetic biology and metabolic engineering. These two interdependent research areas focus on the study of microbial conversion of organic acids, with the aim of replacing their petrochemical?derived equivalents with more sustainable and efficient processes. The particular case of Lactic acid (LA) production has been the subject of extensive research because of its role as an essential component for developing an eco?friendly biodegradable plastic—widely used in industrial biotechnological applications. Because of its resistance to acidic environments, among the many LA?producing microbes, Saccharomyces cerevisiae has been the main focus of research into related biocatalysts. In this study, we present an extensive in silico investigation of S. cerevisiae cell metabolism (modeled with Flux Balance Analysis) with the overall aim of maximizing its LA production yield. We focus on the yeast 8.3 steady?state metabolic model and analyze it under the impact of different engineering strategies including: gene knock?in, gene knock?out, gene regulation and medium optimization; as well as a comparison between results in aerobic and anaerobic conditions. We designed ad?hoc constrained multiobjective evolutionary algorithms to automate the engineering process and developed a specific postprocessing methodology to analyze the genetic manipulation results obtained. The in silico results reported in this paper empirically show that our method is able to automatically select a small number of promising genetic and metabolic manipulations, deriving competitive strains that promise to impact microorganisms design in the production of sustainable chemicals.