Research article

BMC Bioinformatics

, 9:499

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Natural computation meta-heuristics for the in silico optimization of microbial strains

  • Miguel RochaAffiliated withDepartment of Informatics/CCTC, University of Minho Email author 
  • , Paulo MaiaAffiliated withDepartment of Informatics/CCTC, University of Minho
  • , Rui MendesAffiliated withDepartment of Informatics/CCTC, University of Minho
  • , José P PintoAffiliated withDepartment of Informatics/CCTC, University of Minho
  • , Eugénio C FerreiraAffiliated withIBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, Universidade do Minho
  • , Jens NielsenAffiliated withSystems Biology, Dept. Chemical and Biological Engineering, Chalmers University of Technology
  • , Kiran Raosaheb PatilAffiliated withDepartment of Systems Biology, Center for Microbial Biotechnology,Building 223, Technical University of Denmark
  • , Isabel RochaAffiliated withIBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, Universidade do Minho Email author 



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.


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.


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.