MOMO - multi-objective metabolic mixed integer optimization: application to yeast strain engineering

Abstract

Abstract Background In this paper, we explore the concept of multi-objective optimization in the field of metabolic engineering when both continuous and integer decision variables are involved in the model. In particular, we propose a multi-objective model that may be used to suggest reaction deletions that maximize and/or minimize several functions simultaneously. The applications may include, among others, the concurrent maximization of a bioproduct and of biomass, or maximization of a bioproduct while minimizing the formation of a given by-product, two common requirements in microbial metabolic engineering. Results Production of ethanol by the widely used cell factory Saccharomyces cerevisiae was adopted as a case study to demonstrate the usefulness of the proposed approach in identifying genetic manipulations that improve productivity and yield of this economically highly relevant bioproduct. We did an in vivo validation and we could show that some of the predicted deletions exhibit increased ethanol levels in comparison with the wild-type strain. Conclusions The multi-objective programming framework we developed, called Momo, is open-source and uses PolySCIP (Available at http://polyscip.zib.de/). as underlying multi-objective solver. Momo is available at http://momo-sysbio.gforge.inria.fr

Keywords

• Optimization
• Metabolic Engineering
• Systems Biology