Programmed evolution for optimization of orthogonal metabolic output in bacteria.

Journal Article (Journal Article)

Current use of microbes for metabolic engineering suffers from loss of metabolic output due to natural selection. Rather than combat the evolution of bacterial populations, we chose to embrace what makes biological engineering unique among engineering fields - evolving materials. We harnessed bacteria to compute solutions to the biological problem of metabolic pathway optimization. Our approach is called Programmed Evolution to capture two concepts. First, a population of cells is programmed with DNA code to enable it to compute solutions to a chosen optimization problem. As analog computers, bacteria process known and unknown inputs and direct the output of their biochemical hardware. Second, the system employs the evolution of bacteria toward an optimal metabolic solution by imposing fitness defined by metabolic output. The current study is a proof-of-concept for Programmed Evolution applied to the optimization of a metabolic pathway for the conversion of caffeine to theophylline in E. coli. Introduced genotype variations included strength of the promoter and ribosome binding site, plasmid copy number, and chaperone proteins. We constructed 24 strains using all combinations of the genetic variables. We used a theophylline riboswitch and a tetracycline resistance gene to link theophylline production to fitness. After subjecting the mixed population to selection, we measured a change in the distribution of genotypes in the population and an increased conversion of caffeine to theophylline among the most fit strains, demonstrating Programmed Evolution. Programmed Evolution inverts the standard paradigm in metabolic engineering by harnessing evolution instead of fighting it. Our modular system enables researchers to program bacteria and use evolution to determine the combination of genetic control elements that optimizes catabolic or anabolic output and to maintain it in a population of cells. Programmed Evolution could be used for applications in energy, pharmaceuticals, chemical commodities, biomining, and bioremediation.

Full Text

Duke Authors

Cited Authors

  • Eckdahl, TT; Campbell, AM; Heyer, LJ; Poet, JL; Blauch, DN; Snyder, NL; Atchley, DT; Baker, EJ; Brown, M; Brunner, EC; Callen, SA; Campbell, JS; Carr, CJ; Carr, DR; Chadinha, SA; Chester, GI; Chester, J; Clarkson, BR; Cochran, KE; Doherty, SE; Doyle, C; Dwyer, S; Edlin, LM; Evans, RA; Fluharty, T; Frederick, J; Galeota-Sprung, J; Gammon, BL; Grieshaber, B; Gronniger, J; Gutteridge, K; Henningsen, J; Isom, B; Itell, HL; Keffeler, EC; Lantz, AJ; Lim, JN; McGuire, EP; Moore, AK; Morton, J; Nakano, M; Pearson, SA; Perkins, V; Parrish, P; Pierson, CE; Polpityaarachchige, S; Quaney, MJ; Slattery, A; Smith, KE; Spell, J; Spencer, M; Taye, T; Trueblood, K; Vrana, CJ; Whitesides, ET

Published Date

  • January 2015

Published In

Volume / Issue

  • 10 / 2

Start / End Page

  • e0118322 -

PubMed ID

  • 25714374

Pubmed Central ID

  • PMC4340930

Electronic International Standard Serial Number (EISSN)

  • 1932-6203

International Standard Serial Number (ISSN)

  • 1932-6203

Digital Object Identifier (DOI)

  • 10.1371/journal.pone.0118322


  • eng