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Detecting Conserved Interaction Patterns in Biological Networks
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KWY is a Computer Scientist, Algorithm Analysis and Design Expert, Bioinformatics Expert, Information Theory, Multimedia Compression Consultant, Computer Systems and Computer Communication Networks Consultant with world-class expertise in analysis and design of algorithms, bioinformatics, information theory, multimedia compression, random structures, performance evaluation, analytic combinatorics, discrete mathematics, pattern matching, stability problems in distributed systems, modeling of computer systems and computer communication networks, queueing theory, and operations research.
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Abstract
Molecular interaction data plays an important role in understanding biological processes at a modular level by providing a framework for understanding cellular organization, functional hierarchy, and evolutionary conservation. As the quality and quantity of network and interaction data increases rapidly, the problem of effectively analyzing this data becomes significant. Graph theoretic formalisms, commonly used for these analysis tasks, often lead to computationally hard problems due to their relation to subgraph isomorphism. This paper presents an innovative new algorithm, MULE, for detecting frequently occurring patterns and modules in biological networks. Using an innovative graph simplification technique based on ortholog contraction, which is ideally suited to biological networks, our algorithm renders these problems computationally tractable and scalable to large numbers of networks. We show, experimentally, that our algorithm can extract frequently occurring patterns in metabolic pathways and protein interaction networks from the KEGG, DIP, and BIND databases within seconds. When compared to existing approaches, our graph simplification technique can be viewed either as a pruning heuristic, or a closely related, but computationally simpler task. When used as a pruning heuristic, we show that our technique reduces effective graph sizes significantly, accelerating existing techniques by several orders of magnitude! Indeed, for most of the test cases, existing techniques could not even be applied without our pruning step. When used as a stand-alone analysis technique, MULE is shown to convey significant biological insights at near-interactive rates.
Key words: graph mining, frequent subgraph discovery, evolution, modular conservation
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Read other articles by this KKAI Associate:
A Framework for Self Manipulating Video Streams
Reliable Detection of Episodes in Event Sequences
2D-Pattern Matching Image and Video Compression: Theory, Algorithms, and Experiments
Error Resilient LZ'77 Data Compression: Algorithms, Analysis, and Experiments
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Kevin Kennedy & Associates, Inc.
Rapid Response Engineering® Solutions
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Indianapolis, Indiana 46268
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