Research article

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

Miguel Rocha¹ , Paulo Maia¹ , Rui Mendes¹ , José P Pinto¹ , Eugénio C Ferreira² , Jens Nielsen⁴ , Kiran Raosaheb Patil³ and Isabel Rocha²

¹  Department of Informatics/CCTC, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal

²  IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, Universidade do Minho, 4710-057 Campus de Gualtar, Braga, Portugal

³  Center for Microbial Biotechnology, Department of Systems Biology, Building 223, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark

⁴  Systems Biology, Dept. Chemical and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden

author email corresponding author email

BMC Bioinformatics 2008, 9:499doi:10.1186/1471-2105-9-499

Published:	27 November 2008

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.

Conclusion

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.