OptGene

General concept

The space of possible genetic manipulations for a given organism is large, thereby making the global search for metabolic engineering strain design difficult. Genetic algorithms can be used to iteratively improve upon strain designs until a strain is found that is able to produce a product of interest. In this method, a vector of metabolic genes with presence and absence calls is provided for each member in an in silico population. The number of desired gene deletions is also predetermined. A fitness score for each individual is determined based on the desired objective. The gene vectors of the most fit members of the population are selected for crossover and subjected to random mutation. This is continued until genotype providing a desired phenotype is found. However, this design is not guaranteed to be the best solution.

Items to consider when implementing

Software packages with this method

Applications of interest

Description of study and link to study:

What was learned with this method:

Relevant references

Patil, et al.

Related methods

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