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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

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Drug Discovery Process Disease Target enzyme Potential compounds (drugs) Lead compounds Preclinical testing Phase I – III trials Disease Target enzyme(s) Target compound(s) Metabolic Network Data mining

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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 !

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An Example: Targets for Affecting Central Nervous System Drug: Phenylbutazone (Therapeutic category = 1144)

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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.

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Directed Graph Model Enzyme Reaction Compound Edges Vertices Catalyzes Produces Consumes Target compound

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Simple Metabolic Network E1 E3 E2 R2 R4 R3 R1 C5 C2 C3 C4 C1

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Inhibit E1 E1 E3 E2 R2 R4 R3 R1 C5 C2 C3 C4 C1 Target compound removed Three Non- target compounds removed Damage (E1) = 3

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Inhibit E2 or/and E3 E1 E3 E2 R2 R4 R3 R1 C5 C2 C3 C4 C1 Damage (E2) = 0 Damage (E3) = 0 Target compound removed Damage (E2, E3) = 1 Damage (E1) = 3 What is the best enzyme combination? Number of combinations is exponential !

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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.

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Initialization: Enzymes E1 E3 E2 R2 R4 R3 R1 C5 C2 C3 C4 C1 E1 E2 E3 T, 3 F, 0 =

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Initialization: Reactions E1 E3 E2 R2 R4 R3 R1 C5 C2 C3 C4 C1 E1 E2 E3 T, 3 F, 0 = R1 R2 R3 R4 = {E1}, T, 3 {E2}, F, 0 {E3}, F, 0

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Initialization: Compounds E1 E3 E2 R2 R4 R3 R1 C5 C2 C3 C4 C1 E1 E2 E3 T, 3 F, 0 = R1 R2 R3 R4 = {E1}, T, 3 {E2}, F, 0 {E3}, F, 0 C1 C2 C3 C4 C5 = {E1}, T, 3 {E2, E3}, T, 1

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Iterations: Reactions E1 E3 E2 R2 R4 R3 R1 C5 C2 C3 C4 C1 E1 E2 E3 T, 3 F, 0 = C1 C2 C3 C4 C5 = {E1}, T, 3 {E2, E3}, T, 1 R1 = min{R1, C5} = min{3, 1} R1 R2 R3 R4 = {E1}, T, 3 {E2}, F, 0 {E3}, F, 0 R1 R2 R3 R4 = {E2, E3}, T, 1 {E1}, T, 3 {E2}, F, 0 {E3}, F, 0

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Iterations: Compounds E1 E3 E2 R2 R4 R3 R1 C5 C2 C3 C4 C1 E1 E2 E3 T, 3 F, 0 = C1 C2 C3 C4 C5 = {E1}, T, 3 {E2, E3}, T, 1 C1 = min{C1, R1} = min{3, 1} R1 R2 R3 R4 = {E2, E3}, T, 1 {E1}, T, 3 {E2}, F, 0 {E3}, F, 0 C1 C2 C3 C4 C5 = {E2, E3}, T, 1 {E1}, T, 3 {E2, E3}, T, 1

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How many iterations? Number of iterations is at most the number of reactions on the longest path that traverses each node at most one

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Experiments: Datasets

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Experiments: Accuracy Average damage for one, two, and four randomly selected target compounds runs for each network

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Experiments: Running Time

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Experiments: Number of Iterations

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