Solving Application Oriented Graph Theoretical Problems with DNA Computing

  • Veronika Halász
  • László Hegedüs
  • István Hornyák
  • Benedek Nagy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 201)


Social networks are represented by graphs. Important features of the network, e.g., length of shortest paths, centrality, are given by graph theoretical way. Bipartite graphs are used to represent various problems, for example, in medicine or in economy. The relations between customers and goods can be represented by bipartite graphs. Genes and various diseases can also form a bipartite graph, where a disease is connected to those genes that could cause it. In this paper DNA computing approach is presented for solving some graph theoretical problems. Since DNA computing uses a massively parallel approach, hard graph theoretical problems can be solved (at least in theory). Our main contribution is to present Projection algorithms for bipartite graphs; the molecular tube obtained by them can be used as a base for further processes.


DNA computing Graph algorithms Bipartite graphs DNA algorithm Networks Social network Bioinformatics 


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The authors wish to thank to the reviewers for their valuable remarks. The work is supported by the TÁMOP 4.2.1/B-09/1/KONV-2010-0007 and TÁMOP 4.2.2/C-11/1/KONV-2012-0001 projects. The projects are implemented through the New Hungary Development Plan, co-financed by the European Social Fund and the European Regional Development Fund.


  1. Leonard M. Adleman. Molecular computation of solutions to combinatorial problems. Science 266, 1994, pp. 1021–1024.Google Scholar
  2. Armen S. Asratian, Tristan M. J. Denley, and Roland Häggkvist. Bipartite Graphs and their Applications. Cambridge University Press, New York, USA, 1998.Google Scholar
  3. Albert-László Barabási. Linked: The New Science of Networks, Perseus Publishing, 2002.Google Scholar
  4. Anna Bauer-Mehren, Michael Rautschka, Ferran Sanz, and Laura I. Furlong. DisGeNET: a Cytoscape plugin to visualize, integrate, search and analyze genedisease networks. Bioinformatics 26/22 (2010), 2924–2926.Google Scholar
  5. Hossein Eghdami and Majid Darehmiraki. Application of DNA computing in graph theory. Artificial Intelligence Review 38, 2012, 223–235Google Scholar
  6. Jean-Claude Fournier. Combinatorics of perfect matchings in plane bipartite graphs and application to tilings. Theoretical Computer Science 303, 2003, 333–351.Google Scholar
  7. L. C. Freeman. Centrality in social networks: Conceptual clarification. Social Networks, 1/3, (1979), 215–239.Google Scholar
  8. Richard J. Lipton. DNA solution of HARD computational problems. Science 268, 1995, pp. 542–545.Google Scholar
  9. R. Sathyapriya, M.S. Vijayabaskar, S. Vishveshwara. Insights into protein-DNA interactions through structure network analysis. PLoS Comput Biol 4, 2008Google Scholar
  10. Amos Tanay, Roded Sharan, Ron Shamir. Discovering statistically significant biclusters in gene expression data. Proceedings of the Tenth International Conference on Intelligent Systems for, Molecular Biology, 2002, pp. 136–144.Google Scholar
  11. Hiroki Uejima, Masami Hagiya. Analyzing secondary structure transition paths of DNA/RNA molecules. DNA Computing, 9th International Workshop on DNA Based Computers, Madison, USA Springer-Verlag, Berlin, Heidelberg, 2004, pp. 86–90.Google Scholar
  12. Katharina Anna Zweig, Michael Kaufmann. A systematic approach to the one-mode projection of bipartite graphs. Social Network Analysis and Mining 1 (2011) 187-218.Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  • Veronika Halász
    • 1
  • László Hegedüs
    • 1
  • István Hornyák
    • 1
  • Benedek Nagy
    • 2
  1. 1.Faculty of InformaticsUniversity of DebrecenDebrecenHungary
  2. 2.Department of Computer ScienceFaculty of Informatics, University of DebrecenDebrecenHungary

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