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

Authors
  • Andrade, Ricardo1, 2, 3
  • Doostmohammadi, Mahdi4, 5
  • Santos, João L.6
  • Sagot, Marie-France1, 2
  • Mira, Nuno P.6
  • Vinga, Susana4, 7
  • 1 ERABLE European Team, INRIA, Rhône-Alpes, France , Rhône-Alpes (France)
  • 2 Université de Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR 5558, Villeurbanne, F-69622, France , Villeurbanne (France)
  • 3 Institute of Mathematics and Statistics, Universidade de São Paulo, Sao Paulo, Brazil , Sao Paulo (Brazil)
  • 4 IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal , Lisbon (Portugal)
  • 5 Department of Management Science, University of Strathclyde, Glasgow, UK , Glasgow (United Kingdom)
  • 6 Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Department of Bioengineering, Universidade de Lisboa, Lisbon, Portugal , Lisbon (Portugal)
  • 7 INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal , Lisbon (Portugal)
Type
Published Article
Journal
BMC Bioinformatics
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Feb 24, 2020
Volume
21
Issue
1
Identifiers
DOI: 10.1186/s12859-020-3377-1
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundIn 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.ResultsProduction 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.ConclusionsThe 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

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