Computational identification of significantly regulated metabolic reactions by integration of data on enzyme activity and gene expression.
The concentrations and catalytic activities of enzymes control metabolic rates. Previous studies have focused on enzyme concentrations because there are no genome-wide techniques used for the measurement of enzyme activity. We propose a method for evaluating the significance of enzyme activity by integrating metabolic network topologies and genome-wide microarray gene expression profiles. We quantified the enzymatic activity of reactions and report the 388 significant reactions in five perturbation datasets. For the 388 enzymatic reactions, we identified 70 that were significantly regulated (P-value < 0.001). Thirty-one of these reactions were part of anaerobic metabolism, 23 were part of low-pH aerobic metabolism, 8 were part of high-pH anaerobic metabolism, 3 were part of low-pH aerobic reactions, and 5 were part of high-pH anaerobic metabolism.
Nam
Hojung
H
Department of Bio and Brain Engineering, KAIST, Deajeon, Korea.
Ryu
Taewoo
T
Lee
Kiyoung
K
Kim
Sangwoo
S
Lee
Doheon
D