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

@article{citeulike:3718574, abstract = {Background:\&\#10;One of the greatest challenges in Metabolic Engineering is to identify a set of genetic manipulations that will result in a microbial strain that achieves a desired production goal. This challenge is due to, not only 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.\&\#10;Results:\&\#10;This work reports improved EAs, as well as novel Simulated Annealing (SA) algorithms to address the task of in silico metabolic engineering. Both approaches use a set based encoding scheme, together with appropriate operators to generate new solutions. The proposed algorithms 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.\&\#10;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 quickly reach good solutions regarding the production of the desired compounds and presenting low variability among the several runs. \&\#10;Conclusions:\&\#10;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 that seems to be more consistent in obtaining optimal solutions and displays a faster convergence to the solutions. In both cases, the use of variable sized representations allows the automatic discovery of the approximate number of gene deletions, without compromise in the optimality of the solutions.}, author = {Miguel and Rocha, Isabel}, citeulike-article-id = {3718574}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-9-499}, day = {27}, doi = {10.1186/1471-2105-9-499}, issn = {1471-2105}, journal = {BMC Bioinformatics}, keywords = {bioinformatics, fba, metabolic-engineering}, month = {November}, pages = {499+}, posted-at = {2008-11-30 01:59:35}, priority = {3}, title = {Natural computation meta-heuristics for the in silico optimization of microbial strains}, url = {http://dx.doi.org/10.1186/1471-2105-9-499}, volume = {9}, year = {2008} }

TY - JOUR ID - citeulike:3718574 L3 - citeulike-article-id:3718574 N2 - Background:
One of the greatest challenges in Metabolic Engineering is to identify a set of genetic manipulations that will result in a microbial strain that achieves a desired production goal. This challenge is due to, not only 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 improved EAs, as well as novel Simulated Annealing (SA) algorithms to address the task of in silico metabolic engineering. Both approaches use a set based encoding scheme, together with appropriate operators to generate new solutions. The proposed algorithms 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 quickly reach good 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 that seems to be more consistent in obtaining optimal solutions and displays a faster convergence to the solutions. In both cases, the use of variable sized representations allows the automatic discovery of the approximate number of gene deletions, without compromise in the optimality of the solutions. JF - BMC Bioinformatics SN - 1471-2105 TI - Natural computation meta-heuristics for the in silico optimization of microbial strains VL - 9 SP - 499 KW - bioinformatics KW - fba KW - metabolic-engineering AU - Miguel AU - Rocha, Isabel PY - 2008/11/27/ UR - http://dx.doi.org/10.1186/1471-2105-9-499 ER -