Momo – Multi-Objective Metabolic mixed integer Optimization: application to yeast strain engineering

Abstract

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.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. The multi-objective programming framework we developed, called Momo, is open-source and uses PolySCIP ‡ as underlying multi-objective solver. Momo is available at

Genome‐Scale Metabolic Model's multi‐objective solving algorithm based on the inflexion point of Pareto front including maximum energy utilization and its application in Aspergillus niger DS03043

“…Patané et al (2019) used multi‐objective metabolic engineering to determine the relationship between biomass and ethanol production to optimize and maximize ethanol production. In most of the literature on metabolic models using multi‐objective algorithms, objective functions include maximizing biological products and biomass, or maximizing biological products while minimizing the formation of a given by‐product, which are two common requirements in microbial metabolic engineering (Andrade et al, 2020). These objective functions use multi‐objective optimization methods to improve a model's product yield or economic benefit.…”

gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs

“…This is known as a Pareto front of optimal designs. Multi-objective optimization has been applied in metabolic engineering, for instance to kinetic models to find Pareto optimal reaction kinetics that maximize synthesis (Sendín et al ., 2006; Vera et al ., 2003), and tools have been developed for use on GSMs to determine genetic manipulations to maximize growth and synthesis (Andrade et al ., 2020; Patané et al ., 2019). Other tools have been developed to find growth-coupled designs (Feist et al ., 2010; Ohno et al ., 2014; Alter and Ebert, 2019), yet there is no tool to determine optimal designs that maximise coupling strength, growth, and synthesis, in order to create evolutionarily robust strains with high productivity and robust synthesis – critical for industrial application.…”