Presentation on theme: "An iterative algorithm for metabolic network-based drug target identification Padmavati Sridhar, Tamer Kahveci, Sanjay Ranka Department of Computer and."— Presentation transcript:
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
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
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.
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 !
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.