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. 2020 Feb 24;21(1):69.
doi: 10.1186/s12859-020-3377-1.

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

Ricardo Andrade  1   2   3 , Mahdi Doostmohammadi  4   5 , João L Santos  6 , Marie-France Sagot  1   2 , Nuno P Mira  7 , Susana Vinga  8   9
Affiliations

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

Ricardo Andrade et al. BMC Bioinformatics. .

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: Metabolic Engineering; Optimization; Systems Biology.

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Conflict of interest statement

SV and M-FS are members of the Editorial Board of BMC Bioinformatics. The remaining authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
An example of a Pareto-efficient frontier with two objectives, v1 and v2. The red dots A, B, C, D, and E are examples of optimal choices while the points F, G, and H represent non-optimal solutions since we can improve one of the objectives without worsening the other
Fig. 2
Fig. 2
a Pareto-optimal points obtained upon simulation of yeast alcoholic fermentation maximizing biomass and ethanol production. The fluxes obtained for ethanol and biomass production on the A-G points of the Pareto frontier are as follows: A(8.8;0.43); B(11.8;0.41);C(15.3;0.27);D(17.2;0.19);E(17.4;0.18); F(17.5;0.17); G(19.8;0.0). b Heatmap illustrating the difference of the fluxes between the wild-type and mutants corresponding to each Pareto point. Considering reactions related to glycerol production, glycolysis, TCA cycle, and ethanol production and utilization
See this image and copyright information in PMC

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