September 24, 2007 20:21 Proceedings Trim Size: 9in x 6in sridhar MINING METABOLIC NETWORKS FOR OPTIMAL DRUG TARGETS * PADMAVATI SRIDHAR, BIN SONG, TAMER KAHVECI † AND SANJAY RANKA Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611 E-mail: {psridhar, bsong, tamer, ranka}@cise.ufl.edu Recent advances in bioinformatics promote drug-design methods that aim to reduce side-effects. Efficient computational methods are required to identify the optimal enzyme-combination (i.e., drug targets) whose inhibition, will achieve the required effect of eliminating a given target set of compounds, while incurring minimal side-effects. We formulate the optimal enzyme- combination identification problem as an optimization problem on metabolic networks. We define a graph based computational damage model that encapsulates the impact of enzymes onto compounds in metabolic networks. We develop a branch-and-bound algorithm, named OPMET, to explore the search space dynamically. We also develop two filtering strategies to prune the search space while still guaranteeing an optimal solution. They compute an upper bound to the number of target compounds eliminated and a lower bound to the side-effect re- spectively. Our experiments on the human metabolic network demonstrate that the proposed algorithm can accurately identify the target enzymes for known successful drugs in the litera- ture. Our experiments also show that OPMET can reduce the total search time by several orders of magnitude as compared to the exhaustive search. 1. Introduction In pharmaceutics, the development of every drug mainly involves target identification, validation and lead inhibitor identification 9 . Traditional drug discovery approaches focus more on the efficacy of drugs than their toxicity (untoward side effects). Lack of predictive models that account for the complexity of the inter-relationships between the metabolic processes often leads to drug development failures. Toxicity and/or lack of efficacy can result if metabolic network components other than the intended target are affected. The current focus is on identification of biological targets (gene products, such as enzyme or protein) for drugs, which can be manipulated to produce the desired effect (of curing a disease) with minimum disruptive side-effects 23,27 . Enzymes catalyze reactions, which produce metabolites (compounds) in the metabolic networks of organisms. Enzyme malfunctions can result in the accumula- tion of certain compounds which may result in diseases. We term such compounds * this work is supported in part by the national science foundation under grant itr 0325459 and dbi - 0606607. any opinions, findings, and conclusions or recommendations expressed in this material are those of the au- thor(s) and do not necessarily reflect the views of the national science foundation. † to whom correspondence should be addressed 1 Pacific Symposium on Biocomputing 13:291-302(2008)