TY - JOUR
T1 - Metabolic kinetic modeling provides insight into complex biological questions, but hurdles remain
AU - Strutz, Jonathan
AU - Martin, Jacob
AU - Greene, Jennifer
AU - Broadbelt, Linda
AU - Tyo, Keith
N1 - Funding Information:
This work was supported by the National Science Foundation (J.S., L.B., K.T. MCB-1614953 , J.G. DGE-1324585 ), the National Institute of Health (J.S. T32-GM008449-23 ), and the Department of Energy (J.M., J.G., L.B., K.T. DEEE0007728 and DESC0018249 ).
Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2019/10
Y1 - 2019/10
N2 - Metabolic models containing kinetic information can answer unique questions about cellular metabolism that are useful to metabolic engineering. Several kinetic modeling frameworks have recently been developed or improved. In addition, techniques for systematic identification of model structure, including regulatory interactions, have been reported. Each framework has advantages and limitations, which can make it difficult to choose the most appropriate framework. Common limitations are data availability and computational time, especially in large-scale modeling efforts. However, recently developed experimental techniques, parameter identification algorithms, as well as model reduction techniques help alleviate these computational bottlenecks. Opportunities for additional improvements may come from the rich literature in catalysis and chemical networks. In all, kinetic models are positioned to make significant impact in cellular engineering.
AB - Metabolic models containing kinetic information can answer unique questions about cellular metabolism that are useful to metabolic engineering. Several kinetic modeling frameworks have recently been developed or improved. In addition, techniques for systematic identification of model structure, including regulatory interactions, have been reported. Each framework has advantages and limitations, which can make it difficult to choose the most appropriate framework. Common limitations are data availability and computational time, especially in large-scale modeling efforts. However, recently developed experimental techniques, parameter identification algorithms, as well as model reduction techniques help alleviate these computational bottlenecks. Opportunities for additional improvements may come from the rich literature in catalysis and chemical networks. In all, kinetic models are positioned to make significant impact in cellular engineering.
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U2 - 10.1016/j.copbio.2019.02.005
DO - 10.1016/j.copbio.2019.02.005
M3 - Review article
C2 - 30851632
AN - SCOPUS:85062383152
SN - 0958-1669
VL - 59
SP - 24
EP - 30
JO - Current Opinion in Biotechnology
JF - Current Opinion in Biotechnology
ER -