TY - JOUR
T1 - Next-generation genome-scale models for metabolic engineering
AU - King, Zachary A.
AU - Lloyd, Colton J.
AU - Feist, Adam M.
AU - Palsson, Bernhard O.
PY - 2015
Y1 - 2015
N2 - Constraint-based reconstruction and analysis (COBRA) methods have become widely used tools for metabolic engineering in both academic and industrial laboratories. By employing a genome-scale in silico representation of the metabolic network of a host organism, COBRA methods can be used to predict optimal genetic modifications that improve the rate and yield of chemical production. A new generation of COBRA models and methods is now being developed. -. encompassing many biological processes and simulation strategies. -. and next-generation models enable new types of predictions. Here, three key examples of applying COBRA methods to strain optimization are presented and discussed. Then, an outlook is provided on the next generation of COBRA models and the new types of predictions they will enable for systems metabolic engineering.
AB - Constraint-based reconstruction and analysis (COBRA) methods have become widely used tools for metabolic engineering in both academic and industrial laboratories. By employing a genome-scale in silico representation of the metabolic network of a host organism, COBRA methods can be used to predict optimal genetic modifications that improve the rate and yield of chemical production. A new generation of COBRA models and methods is now being developed. -. encompassing many biological processes and simulation strategies. -. and next-generation models enable new types of predictions. Here, three key examples of applying COBRA methods to strain optimization are presented and discussed. Then, an outlook is provided on the next generation of COBRA models and the new types of predictions they will enable for systems metabolic engineering.
U2 - 10.1016/j.copbio.2014.12.016
DO - 10.1016/j.copbio.2014.12.016
M3 - Journal article
C2 - 25575024
SN - 0958-1669
VL - 35
SP - 23
EP - 29
JO - Current Opinion in Biotechnology
JF - Current Opinion in Biotechnology
ER -