Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli.
Integrated approaches utilizing in silico analyses will be necessary to successfully advance the field of metabolic engineering. Here, we present an integrated approach through a systematic model-driven evaluation of the production potential for the bacterial production organism Escherichia coli to produce multiple native products from different representative feedstocks through coupling metabolite production to growth rate. Designs were examined for 11 unique central metabolism and amino acid targets from three different substrates under aerobic and anaerobic conditions. Optimal strain designs were reported for designs which possess maximum yield, substrate-specific productivity, and strength of growth-coupling for up to 10 reaction eliminations (knockouts). In total, growth-coupled designs could be identified for 36 out of the total 54 conditions tested, corresponding to eight out of the 11 targets. There were 17 different substrate/target pairs for which over 80% of the theoretical maximum potential could be achieved. The developed method introduces a new concept of objective function tilting for strain design. This study provides specific metabolic interventions (strain designs) for production strains that can be experimentally implemented, characterizes the potential for E. coli to produce native compounds, and outlines a strain design pipeline that can be utilized to design production strains for additional organisms.
Feist
Adam M
AM
Department of Bioengineering, University of California, 9500 Gilman Drive #0412, San Diego, La Jolla, CA 92093-0412, USA.
Zielinski
Daniel C
DC
Orth
Jeffrey D
JD
Schellenberger
Jan
J
Herrgard
Markus J
MJ
Palsson
Bernhard Ø
BØ
D001419:Bacteria / Q000235:genetics / Q000378:metabolism*; D001686:Biological Phenomena; D004926:Escherichia coli / Q000235:genetics / Q000254:growth & development / Q000378:metabolism; D006128:Growth / Q000235:genetics; D008954:Models, Biological; D012307:Risk Factors