A) increasing degree of compartmentalization; an increasing number of cellular compartments are modeled accounting for sectionalization of metabolism, B) advances in knowledge; as novel pathways and increased details in known pathways are uncovered, a maturing GEM reflects increasing organism specificity with network structure features driving improvements and discovery, C) increasingly informed modeling assumptions; as more data covering a specific organisms is generated, the biomass function of a maturing GEM increases in complexity, D) technology driven advances; with the emergence of ‘omics’ data, gene knockout screens, and an increasing number of Biolog phenotype microarray data sets available, the diversity of approaches to validate a model increases E) enriched object-associated information; with the emergence and expansion of diverse reference databases, objects in GEMs are increasingly associated with crossreferences, F) crowd-sourcing; a higher diversity of expertise is used through direct (GitHub repositories, Jamborees) and indirect crowd-sourcing (sequential maturing of GEMs).