| Texto completo | |
| Autor(es): |
Andrade, Ricardo
[1, 2, 3]
;
Doostmohammadi, Mahdi
[4, 5]
;
Santos, Joao L.
[6]
;
Sagot, Marie-France
[2, 3]
;
Mira, Nuno P.
[6]
;
Vinga, Susana
[7, 5]
Número total de Autores: 6
|
| Afiliação do(s) autor(es): | [1] Univ Sao Paulo, Inst Math & Stat, Sao Paulo - Brazil
[2] Univ Lyon 1, Univ Lyon, CNRS, Lab Biometrie & Biol Evolut, UMR 5558, F-69622 Villeurbanne - France
[3] INRIA, ERABLE European Team, Rhone Alpes - France
[4] Univ Strathclyde, Dept Management Sci, Glasgow, Lanark - Scotland
[5] Univ Lisbon, Inst Super Tecn, IDMEC, Lisbon - Portugal
[6] Univ Lisbon, Inst Bioengn & Biosci, Inst Super Tecn, Dept Bioengn, Lisbon - Portugal
[7] Univ Lisbon, Inst Super Tecn, INESC ID, Lisbon - Portugal
Número total de Afiliações: 7
|
| Tipo de documento: | Artigo Científico |
| Fonte: | BMC Bioinformatics; v. 21, n. 1 FEB 24 2020. |
| Citações Web of Science: | 0 |
| Resumo | |
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 (AU) | |
| Processo FAPESP: | 17/05986-2 - Histórias metabólicas a partir de dados de transcriptômica |
| Beneficiário: | Ricardo Luiz de Andrade Abrantes |
| Modalidade de apoio: | Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado |