Composition of biochemical networks using domain knowledge

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Graph composition has applications in a variety of practical applications. In drug development, for instance, in order to understand possible drug interactions, one has to merge known networks and examine topological variants arising from such composition. Similarly, the design of sensor nets may use existing network infrastructures, and the superposition of one network on another can help with network design and optimisation. The problem of network composition has not received much attention in algorithm and database research. Here, we work with biological networks encoded in Systems Biology Markup Language (SBML), based on XML syntax. We focus on XML merging and examine the algorithmic and performance challenges we encountered in our work and the possible solutions to the graph merge problem. We show that our XML graph merge solution performs well in practice and improves on the existing toolsets. This leads us into future work directions and the plan of research which will aim to implement graph merging primitives using domain knowledge to perform composition and decomposition on specific graphs in the biological domain.
Original languageEnglish
Title of host publicationCOmputational Modeling in BIology NEtwork (COMBINE) 2010
Number of pages1
Publication statusPublished - 2010
EventCOmputational Modeling in BIology NEtwork (COMBINE) 2010 - Edinburgh, United Kingdom
Duration: 6 Oct 20109 Oct 2010


ConferenceCOmputational Modeling in BIology NEtwork (COMBINE) 2010
Country/TerritoryUnited Kingdom


  • composition
  • biochemical networks
  • domain knowledge


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