Double iterative optimisation for metabolic network-based drug target identification.
The goal of drug discovery is to find molecules that manipulate enzymes in order to increase or decrease the production of desired compounds while incurring minimum side-effects. An important part of this problem is the identification of the target enzymes, i.e., the enzymes that will be inhibited by the drug molecules. We present novel and scalable algorithms for finding a set of enzymes, whose inhibition stops the production of a given set of target compounds, while eliminating minimal number of non-target compounds. Experimental results are presented for the E. coli metabolic network to demonstrate the accuracy and efficiency of our iterative method.
Song
Bin
B
Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA. bsong@cise.ufl.edu
Sridhar
Padmavati
P
Kahveci
Tamer
T
Ranka
Sanjay
S
D000465:Algorithms; D001426:Bacterial Proteins / Q000037:antagonists & inhibitors / Q000378:metabolism; D055808:Drug Discovery / Q000379:methods*; D004789:Enzyme Activation / Q000187:drug effects; D004791:Enzyme Inhibitors / Q000494:pharmacology; D004926:Escherichia coli / Q000187:drug effects / Q000201:enzymology; D053858:Metabolic Networks and Pathways / Q000187:drug effects*
D001426:Bacterial Proteins; D004791:Enzyme Inhibitors