Efficient Computational Methods for Drug Design using
Metabolic Pathways
The human body at its genetic core consists of enzymes that catalyze
reactions. These reactions generate compounds by consuming a set of
other compounds. Chemical molecules (drugs) that inhibit enzymes can
reduce or increase the generation of one or more compounds. The goal
of drug discovery is to derive chemical molecules that inhibit enzymes
to have a therapeutic effect by increasing or reducing the generation
of more or more target compounds while minimizing the negative effects
(side effects) on other important compounds. Currently this is done
mainly by testing a large number of molecules to inhibit enzymes using
high throughput screening. This is a very costly (more than US $800
million per drug) and inaccurate (90 to 95% of the drugs entering the
clinical development fail) process. New strategies to reduce the
attrition rates of drugs by considering toxicity as well as efficacy
prior to clinical study is sorely needed. Metabolic networks define
the relationships between enzymes and compounds. Although many models
for metabolic networks have been developed, they have not been used
for predicting the efficacy and the side effects of drugs.
We are developing effective computational models and tools that will
exploit the structure of the metabolic networks to better understand
the impact of molecules on different enzymes as well understanding
which enzymes should be inhibited so that the target compounds can be
appropriate affected. This project aims to:
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Develop biologically accurate computational cost models for
metabolic networks.
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Develop efficient algorithms for identifying target enzymes
that can have a desired effect on target compounds. Given a
set of target (unwanted) compounds, we will develop
computational tools that will identify a subset of enzymes
whose inhibition disrupts the production of the target
compounds with minimum or close to minimum side effect (i.e.,
disruption of non-target compounds) as defined by the
underlying cost model.
We will validate the accuracy of the proposed models and the search
methods using successful and failed drugs that have been screened in
existing or ongoing preclinical trials.
Software
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A software for aligning two metabolic pathways.
People
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Tamer Kahveci
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Sanjay Ranka
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Bin Song
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Ferhat Ay
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Nirmalya Bandyopadhyay
Publications
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Ferhat Ay, Tamer Kahveci, Valerie de Crecy-Lagard,
Consistent alignment of metabolic pathways without any
abstraction in modeling,
CSB, 2008. (Abstract)
(PDF) (Best paper)
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Bin Song, Padmavati Sridhar, Tamer Kahveci and Sanjay Ranka,
Double Iterative Optimization for Metabolic Network-Based
Drug Target Identification,
accepted to International
Journal of Data Mining and Bioinformatics, 2007. (Abstract)
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Padmavati Sridhar, Bin Song, Tamer Kahveci and Sanjay Ranka,
Mining metabolic networks for optimal drug targets,
PSB, 2008. (Abstract)
(PDF)
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Padmavati Sridhar, Tamer Kahveci, Sanjay Ranka
An
iterative algorithm for metabolic network-based drug target
identification
accepted to PSB 2007. (Abstract)
(PDF)
Funding
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NSF (Project #: 00073278)
EMT/BSSE: Biological networks as
a communication model for entities with complex
interactions. (09/01/08 - 08/31/11)
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ORAU Powe Junior Faculty Enhancement Award. (Project #: 00061580)
New Technologies in Drug Discovery using Metabolic
Networks. (05/16/2006 - 12/31/2006)
Tamer Kahveci
Last modified: Tue Sep 2 09:19:30 EDT 2008