A nonmonotonic logical approach for modelling and revising metabolic networks

Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2009

Volumn , Issue , 2009, Pages 825-829

  Ray, Oliver ^a   Whelan, Ken ^b   King, Ross ^b  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b ABERYSTWYTH UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AROMATIC AMINO ACID; ART MODEL; BIOLOGICAL PATHWAYS; INHIBITORY FEEDBACK; LOGIC-BASED APPROACH; LOGICAL APPROACHES; METABOLIC NETWORK; NONMONOTONIC; NONMONOTONIC REASONING SYSTEMS; OBSERVATIONAL DATA; PRACTICAL METHOD;

AMINES; AMINO ACIDS; BIOCHEMISTRY; CELL MEMBRANES; COMPUTER SOFTWARE; GRAFTING (CHEMICAL); LOGIC PROGRAMMING; ORGANIC ACIDS; SYNTHESIS (CHEMICAL);

METABOLISM;

EID: 70349728370     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CISIS.2009.175     Document Type: Conference Paper

References (20)

1. C. Baral et al. A knowledge based approach for representing and reasoning about signaling networks. In Proc. 12th Int. Conf. on Intelligent Systems for Molecular Biology, pages 15-22, 2004.
2. M. Gelfond and V. Lifschitz. Classical negation in logic programs and disjunctive databases. New Generation Comp., 9(3/4):365-386, 1991.
3. A. Kakas, R. Kowalski, and F. Toni. Abductive Logic Programming. Journal of Logic and Computation, 2(6):719-770, 1992.
4. R. King, K. Whelan, F. Jones, P. Reiser, C. Bryant, S. Muggleton, D. Kell, and S. Oliver. Functional Genomic Hypothesis Generation and Experimentation by a Robot Scientist. Nature, 427:247-252, 2004.
5. A. Lehninger. Biochemistry: The Molecular Basis of Cell Structure and Function. Worth Publishers, 2 edition, 1979.
6. J. Lloyd. Foundations of Logic Programming. Springer, 1987.
7. S. Muggleton. Inverse Entailment and Progol. New Generation Comp., 13(3-4):245-286, 1995.
8. S. Muggleton and C. Bryant. Theory Completion Using Inverse Entailment. In Proc. of the 10th Int. Conf. on Inductive Logic Programming, volume 1866 of LNCS, pages 130-146. Springer, 2000.
9. S. Muggleton and L. De Raedt. Inductive Logic Programming: Theory and Methods. Journal of Logic Programming, 19, 20:629-679, 1994.
10. H. Ogata, S. Goto, K. Sato, W. Fujibuchi, H. Bono, and M. Kanehisa. Kegg: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res., 27(1):29-34, 1999.
11. P. Juvan and J. Demsar and G. Shaulsky and B. Zupan. GenePath: from mutations to genetic networks and back. Nucleic Acids Res., 33, 2005.
12. I. Papatheodorou, A. Kakas, and M. Sergot. Inference of Gene Relations from Microarray Data by Abduction. In Proc. 8th Int. Conf. on Logic Programming and Nonmonotonic Reasoning, volume 3662 of LNCS, pages 389-393. Springer, 2005.
13. O. Ray. Nonmonotonic Abductive Inductive Learning. to appear in Journal of Applied Logic, 2008.
14. O. Ray and C. Bryant. Inferring the function of genes from synthetic lethal mutations. In Proc. 2nd Int. Conf. on Complex, Intelligent and Software Intensive Systems, pages 667-671. IEEE, 2008.
15. P. Reiser, R. King, D. Kell, S. Muggleton, C. Bryant, and S. Oliver. Developing a Logical Model of Yeast Metabolism. Linkoping Electronic Articles in Computer and Information Science, 6(24), 2001.
16. S. Dworschak and S. Grell and V. Nikiforova and T. Schaub and J. Selbig. Modeling Biological Networks by Action Languages via Answer Set Programming. Constraints, 13(1-2):21-65, 2008.
17. P. Simons, I. Niemelä, and T. Soininen. Extending and implementing the stable model semantics. Artificial Intel., 138(1-2):181-234, 2002.
18. A. Tamaddoni-Nezhad, R. Chaleil, A. Kakas, and S. Muggleton. Application of abductive ILP to learning metabolic network inhibition from temporal data. Machine Learning, 64(1-3):209-230, 2006.
19. A. Varma and B. Palsson. Metabolic flux balancing: Basic concepts, scientific and practical use. Nature Biotechnology, 12:994 - 998, 1994.
20. K. Whelan and R. King. Using a logical model to predict the growth of yeast. BMC Bioinformatics, 9(97), 2008.