Modelling concepts and database implementation techniques for complex biological data

  • Authors:

  • Ramez Elmasri;Feng Ji;Jack Fu;Yiming Zhang;Zoe Raja

  • Affiliations:

  • Department of Computer Science and Engineering, University of Texas at Arlington, P.O. Box 19015, Arlington, TX 76019, USA.;Department of Computer Science and Engineering, University of Texas at Arlington, P.O. Box 19015, Arlington, TX 76019, USA.;Department of Computer Science and Engineering, University of Texas at Arlington, P.O. Box 19015, Arlington, TX 76019, USA.;Department of Computer Science and Engineering, University of Texas at Arlington, P.O. Box 19015, Arlington, TX 76019, USA.;Department of Computer Science and Engineering, University of Texas at Arlington, P.O. Box 19015, Arlington, TX 76019, USA

  • Venue:
  • International Journal of Bioinformatics Research and Applications
  • Year:
  • 2007

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Abstract

Biological data such as protein structure and function, DNA sequences, and metabolic pathways require conceptual modelling characteristics that are not available in the widely used Entity-Relationship (ER) model and its variants, such as the Enhanced-Entity Relationship (EER) model. In particular, three constructs that occur frequently in bioinformatics data are ordered relationships, functional processes, and 3-dimensional (3D) structures. In this paper, we suggest a solution to this problem, requiring only minimal changes to the EER model by introducing specialised formal relationships for ordering, processes and molecular spatial structure. We show how these new concepts can be implemented in relational databases.