Frameworks for metabolic engineering have been successfully applied in combination with
pre- and post-processing algorithms on genome-wide metabolic models. However, genetic engineering
methods with a particular focus on understanding results from multiple perspectives and combining
automated and human design are still lacking. To this end, we adopt a multi-objective genetic design
technique to find the optimal gene expression levels in genome-scale metabolic reconstructions. Then,
we analyse the optimized network by introducing a new multi-omic, multi-level post-processing and
visualization procedure, Metabex, which uses Cytoscape for network visualization. These two
components are connected together to form a feedback loop that establishes a continual process of machine optimization
and human analysis and guidance. To benchmark our framework, we optimize two species of Geobacter for electricity
production and biomass synthesis; we achieve increases in electricity production for only a slight decrease in biomass.
Many regulatory strategies contributed to this value, locally and globally. For instance, a direct, local strategy was a
down-regulation of Cytochrome C Oxidase, while there was simultaneously a global reduction in cofactor and prosthetic
group biosynthesis. Finally, we discuss multiple applications of our tool, including model exploration, model engineering,
comparative modelling, meta-analysis and model refinement.