Descrição
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.
; Maia, Paulo 
