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.

Metaheuristics for Strain Optimization Using Transcriptional Information Enriched Metabolic Models

“…can be implemented through gene knockouts). Comparing both meta-heuristics for optimization, we observe that the SA and EA shown very similar performances, but the SA confirms a slight advantage, already reported in [11].…”

“…In both cases, we show the results for our current approach using transcriptional information, compared to the results using the previous method based on reaction deletions [11]. It should be emphasized that all the setup is the same for both cases.…”

“…As before, the SA is also similar to the one proposed by the authors in [11]. The SA makes use of the same set-based representation used in Fig.…”

“…To address the previous task, we will use Evolutionary Algorithms (EAs) with a set-based representation, previously proposed in [11]. This EA uses four reproduction operators: one crossover and three mutation operators.…”

“…EAs are capable of providing near optimal solutions in a reasonable amount of time and also allow the optimization of nonlinear objective functions. Extending this work, the authors [11] proposed a new encoding scheme for the problem, consisting in variable-sized sets, allowing the automatic determination of the ideal number of reactions to eliminate, since solutions with distinct cardinalities compete within the search space. Also, a Simulated Annealing (SA) based algorithm was put forward for the same task.…”