Abstract
The accumulation of high-throughput genomic and proteomic data and results of thousands of experiments allow modeling of increasingly large and complex metabolic networks. Analysis of accumulated data requires identification of network patterns and evolutionary relations between metabolic networks. This challenge can be addressed by network alignment approach which can be used for comparing biological networks between different species to reveal evolutionary conservation of most vital life processes. We investigate a general network alignment problem: a pathway forms a pattern, and a cellular network (usually from another species) forms the text, and we want to find the best correspondence between the text and the pattern.
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