Skip Navigation

The Computer Journal 1993 36(8):690-701; doi:10.1093/comjnl/36.8.690
© 1993 by British Computer Society
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (1)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Clark, D. A.
Right arrow Articles by Véron, A.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Solving Large Combinatorial Problems in Molecular Biology Using the ElipSys Parallel Constraint Logic Programming System

D. A. Clark1 *, C. J. Rawlings1 *, J. Shirazi1 *, L.-L. Li2 §, K. Schuerman2 §, M. Reeve2 § and A. Véron2 §

1 Biomedical Informatics Unit, Imperial Cancer Research Fund Laboratories, PO Box 123, Lincoln's Inn Fields, London, WC2A 3PX, UK, 2 European Computer-Industry Research Centre, Arabellastrasse 17, D-81925, Munich, Germany

Many areas of scientific endeavour can be characterized as the attempt to provide a consistent interpretation of a broad range of heterogeneous data and theories. In the area of protein structure prediction, for example, there are many types of diverse mutually constraining data and theories of protein structural organization that need to be integrated in order to produce a single consistent prediction (or set of predictions) of the protein structure from the experimentally derived amino acid sequence data. Understanding the role and function of proteins in the control of cell growth is an important part of contemporary cancer research. Protein structure prediction is immensely (combinatorially) complex, and traditional computational approaches to problems such as this have been based on the ‘generate and test’ paradigm in which hypotheses are first generated and then tested against any relevant constraints. In this paper we demonstrate the benefits of a new approach to solving large constrained combinatorial problems which uses the ‘constrain-and-generate’ paradigm and the ElipSys parallel constraint logic programming system. In ElipSys, constraints are used for a priori pruning of the search tree while parallelism enhances the efficiency of the remaining search. Initial results show several orders of magnitude increase in performance over a sequential logic programming (Prolog) approach. The improved performance can be attributed to the complementary actions of the constraint handling and support for parallelism in the ElipSys runtime system. Taken together, the additional performance and new knowledge representation techniques made possible using ElipSys significantly extend the range and complexity of scientific problems that can be addressed using logic programming languages.

Received July 1993. revised September 1993.

* Biomedical Informatics Unit, Imperial Cancer Research Fund Laboratories, PO Box 123, Lincoln's Inn Fields, London, WC2A 3PX, UK

§ European Computer-Industry Research Centre, Arabellastrasse 17, D-81925, Munich, Germany


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.