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

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.

Prospecting Biochemical Pathways to Implement Microbe-Based Production of the New-to-Nature Platform Chemical Levulinic Acid

“…The glucose uptake was set at 3.008, and the oxygen was at 5.703 mmol/gDCW/h . In the Yeast 5.0 model, the “Ferrocytochrome-c/oxygen oxidoreductase” (to a 0.10 flux), “Triose-phosphate isomerase” (to a −1000.6 flux), and glucose exchange (to a −10 flux) reactions were constrained in order to simulate the production of ethanol and glycerol during fermentation, as performed before . Each of the six synthetic pathways selected for LA production were examined for their performance, leading to the creation of six heterologous models derived from iJO1366 and from Yeast 5.0.…”

“…39 In the Yeast 5.0 model, the "Ferrocytochrome-c/oxygen oxidoreductase" (to a 0.10 flux), "Triose-phosphate isomerase" (to a −1000.6 flux), and glucose exchange (to a −10 flux) reactions were constrained in order to simulate the production of ethanol and glycerol during fermentation, as performed before. 57 Each of the six synthetic pathways selected for LA production were examined for their performance, leading to the creation of six heterologous models derived from iJO1366 and from Yeast 5.0. The reactions added to these heterologous models (all considered in the cytoplasm) and the resulting xml models are summarized in the Supporting Information (in Table S1 and in the Supporting Information).…”

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 ( Alter and Ebert, 2019 ; Feist et al., 2010 ; Ohno et al., 2014 ), yet there is no tool to determine optimal designs that maximize coupling strength, growth and synthesis, in order to create evolutionarily robust strains with high productivity and robust synthesis—critical for industrial application.…”

Multi-Objective Optimization of Microalgae Metabolism: An Evolutive Algorithm Based on FBA

“…This situation becomes more complex in simultaneous bioproducts optimization. A recent trend that works in metabolic analysis involves optimizing several objectives to engage in the study of more than one bioproduct of interest [ 21 , 22 , 23 ]. In the past decade, this method can be traced back to the work of Zomorrodi and Maranas [ 24 ].…”

“…Budinich et al [ 21 ] extend FBA for microbial communities by defining a Multi-Objective FBA (MOFBA) in order to study multiple trade-offs between nutrients and growth rates. More recently, Andrade et al [ 22 ] and Pelt-KleinJan [ 23 ] proposes a multi-objective formulation of FBA that considers nutrient limitations for metabolic analysis.…”