title: Natural computation meta-heuristics for the in silico optimization of microbial strains.
abstract: BACKGROUND
  One of the greatest challenges in Metabolic Engineering is to develop quantitative models and algorithms to identify a set of genetic manipulations that will result in a microbial strain with a desirable metabolic phenotype which typically means having a high yield/productivity. This challenge is not only due to the inherent complexity of the metabolic and regulatory networks, but also to the lack of appropriate modelling and optimization tools. To this end, Evolutionary Algorithms (EAs) have been proposed for in silico metabolic engineering, for example, to identify sets of gene deletions towards maximization of a desired physiological objective function. In this approach, each mutant strain is evaluated by resorting to the simulation of its phenotype using the Flux-Balance Analysis (FBA) approach, together with the premise that microorganisms have maximized their growth along natural evolution.
  
  
  RESULTS
  This work reports on improved EAs, as well as novel Simulated Annealing (SA) algorithms to address the task of in silico metabolic engineering. Both approaches use a variable size set-based representation, thereby allowing the automatic finding of the best number of gene deletions necessary for achieving a given productivity goal. The work presents extensive computational experiments, involving four case studies that consider the production of succinic and lactic acid as the targets, by using S. cerevisiae and E. coli as model organisms. The proposed algorithms are able to reach optimal/near-optimal solutions regarding the production of the desired compounds and presenting low variability among the several runs.
  
  
  CONCLUSIONS
  The results show that the proposed SA and EA both perform well in the optimization task. A comparison between them is favourable to the SA in terms of consistency in obtaining optimal solutions and faster convergence. In both cases, the use of variable size representations allows the automatic discovery of the approximate number of gene deletions, without compromising the optimality of the solutions.
authors.affiliation: Department of Informatics/CCTC, University of Minho, Braga, Portugal. mrocha@di.uminho.pt
authors.forename: Miguel
authors.initials: M
authors.lastname: Rocha
authors.forename: Paulo
authors.initials: P
authors.lastname: Maia
authors.forename: Rui
authors.initials: R
authors.lastname: Mendes
authors.forename: José P
authors.initials: JP
authors.lastname: Pinto
authors.forename: Eugénio C
authors.initials: EC
authors.lastname: Ferreira
authors.forename: Jens
authors.initials: J
authors.lastname: Nielsen
authors.forename: Kiran Raosaheb
authors.initials: KR
authors.lastname: Patil
authors.forename: Isabel
authors.initials: I
authors.lastname: Rocha
mesh_terms: D000465:Algorithms; D001419:Bacteria / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D019143:Evolution, Molecular; D017353:Gene Deletion; D016680:Genome, Bacterial; D016681:Genome, Fungal; D007218:Industrial Microbiology / Q000379:methods; D019344:Lactic Acid / Q000378:metabolism; D008954:Models, Biological; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D019802:Succinic Acid / Q000378:metabolism
chemical_list: D019344:Lactic Acid; D019802:Succinic Acid