Figure 1
From: Evolutionary programming as a platform for in silico metabolic engineering
Schematic overview of the OptGene algorithm. A population of individuals is initiated by specifying a present/absent status for each gene in each of the individuals. Individuals are then scored for their fitness by using FBA/MOMA/other method of choice and the objective function (/s). Individuals are selected for mating based on their fitness score, and subsequently crossed to produce new offspring. Mutations are introduced in individuals randomly at specified mutation rate and thus a new population is obtained. This cycle of evolution is repeated until a mutant (or mutants) with a desired phenotypic characteristics is obtained. Please refer to the text for detailed description of each step in the algorithm. Grey shaded or red walled boxes are used to represent different individuals in the cross-over process. Ind.- Individual. FBA- Flux balance analysis [13,14]. MOMA- Minimization of the metabolic adjustment [19]. ROOM- Regulatory on/off minimization [20].