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An iterative algorithm for metabolic network-based drug target identification

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##### An iterative algorithm for metabolic network-based drug target identification

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**An iterative algorithm for metabolic network-based drug**target identification Padmavati Sridhar, Tamer Kahveci, Sanjay Ranka Department of Computer and Information Science and Engineering www.cise.ufl.edu/~tamer**Disease**Disease Target enzyme Target compound(s) Metabolic Network Potential compounds (drugs) Lead compounds Data mining Preclinical testing Target enzyme(s) Phase I – III trials Drug Discovery Process**Why Drugs?**• Excessive production or lack (or a combination of the two) of certain compounds may lead to disease. • Example: Malfunction (Phenylalanine hydroxylase) => accumulation of phenylalanine => Phenylketonuria => mental retardation. • Drugs can manipulate enzymes to reduce or increase the production of compounds !**An Example: Targets for Affecting Central Nervous System**Drug: Phenylbutazone (Therapeutic category = 1144)**Goal**• Given a set of target compounds, find the set of enzymes whose inhibition stops the production of the target compounds with minimum side-effects.**Directed Graph Model**Edges Vertices Catalyzes Enzyme Reaction Produces Compound Consumes Target compound**C1**Simple Metabolic Network E2 R3 C5 E3 R4 R1 C2 C3 R2 E1 C4**Target compound**removed C1 Three Non-target compounds removed Damage (E1) = 3 Inhibit E1 E2 R3 C5 E3 R4 R1 C2 C3 R2 E1 C4**Target compound**removed C1 Inhibit E2 or/and E3 E2 R3 C5 E3 R4 R1 C2 C3 R2 E1 C4 Damage (E1) = 3 Damage (E2) = 0 Damage (E3) = 0 • What is the best enzyme combination? • Number of combinations is exponential ! Damage (E2, E3) = 1**How can we find the right enzyme set?**• Iterative method • Initialization: Remove each node (reaction or compound or enzyme) from graph directly. • Iteration: Improve (reduce damage) each node by considering its precursors until no node improves.**C1**Initialization: Enzymes E2 R3 C5 E3 R4 R1 C2 C3 R2 E1 C4 T, 3 E1 E2 E3 F, 0 F, 0 =**C1**Initialization: Reactions E2 R3 C5 E3 R4 R1 C2 C3 R2 E1 C4 {E1}, T, 3 R1 R2 R3 R4 T, 3 E1 E2 E3 {E1}, T, 3 = F, 0 F, 0 = {E2}, F, 0 {E3}, F, 0**C1**Initialization: Compounds E2 R3 C5 E3 R4 R1 C2 C3 R2 E1 C4 {E1}, T, 3 C1 C2 C3 C4 C5 {E1}, T, 3 R1 R2 R3 R4 T, 3 {E1}, T, 3 {E1}, T, 3 {E1}, T, 3 E1 E2 E3 {E1}, T, 3 = F, 0 F, 0 = = {E2}, F, 0 {E3}, F, 0 {E2, E3}, T, 1**C1**{E1}, T, 3 {E2}, F, 0 {E3}, F, 0 {E1}, T, 3 R1 R2 R3 R4 {E1}, T, 3 = {E2}, F, 0 {E3}, F, 0 Iterations: Reactions E2 R3 R1 = min{R1, C5} = min{3, 1} C5 E3 R4 R1 C2 {E2, E3}, T, 1 R1 R2 R3 R4 = C3 R2 E1 C4 {E1}, T, 3 C1 C2 C3 C4 C5 T, 3 {E1}, T, 3 {E1}, T, 3 {E1}, T, 3 E1 E2 E3 F, 0 F, 0 = = {E2, E3}, T, 1**C1**{E2, E3}, T, 1 {E2, E3}, T, 1 C1 C2 C3 C4 C5 R1 R2 R3 R4 {E1}, T, 3 {E1}, T, 3 {E1}, T, 3 {E1}, T, 3 = {E2}, F, 0 = {E3}, F, 0 {E2, E3}, T, 1 Iterations: Compounds E2 R3 C1 = min{C1, R1} = min{3, 1} C5 E3 R4 R1 C2 C3 R2 E1 C4 {E1}, T, 3 C1 C2 C3 C4 C5 T, 3 {E1}, T, 3 {E1}, T, 3 {E1}, T, 3 E1 E2 E3 F, 0 F, 0 = = {E2, E3}, T, 1**How many iterations?**Number of iterations is at most the number of reactions on the longest path that traverses each node at most one**Experiments: Accuracy**• Average damage for one, two, and four randomly selected target compounds • 10 + 10 + 10 runs for each network