Modeling biological networks by action languages via answer set programming

Constraints

Volumn 13, Issue 1-2, 2008, Pages 21-65

  Dworschak, Steve ^a   Grell, Susanne ^a,b   Nikiforova, Victoria J ^b,c   Schaub, Torsten ^a   Selbig, Joachim ^a,b  

^a UNIVERSITY OF POTSDAM   (Germany)

^b MAX PLANCK INSTITUTE OF MOLECULAR PLANT PHYSIOLOGY   (Germany)

^c TIMIRYAZEV INSTITUTE OF PLANT PHYSIOLOGY   (Russian Federation)

Author keywords

Action language; Answer set programming; Biological network model

Indexed keywords

MATHEMATICAL MODELS; MATHEMATICAL PROGRAMMING; SET THEORY; SYNTACTICS;

ACTION LANGUAGE; ANSWER SET PROGRAMMING; BIOLOGICAL NETWORK MODEL;

BIOLOGICAL SYSTEMS;

EID: 42749086920     PISSN: 13837133     EISSN: 15729354     Source Type: Journal    
DOI: 10.1007/s10601-007-9031-y     Document Type: Article

References (49)

1. Backofen, R., Will, S., & Bornberg-Bauer, E. (1999). Application of constraint programming techniques for structure prediction of lattice proteins with extended alphabets. Bioinformatics, 15(3), 234-242.
2. Baral, C. (2003). Knowledge representation, reasoning and declarative problem solving. Cambridge University Press.
3. Baral, C., Brewka, G., & Schlipf, J. (Eds.) (2007). Proceedings of the ninth international conference on logic programming and nonmonotonic reasoning (LPNMR'07). Lecture Notes in Artificial Intelligence, Vol. 4483. Springer-Verlag.
4. Baral, C., Chancellor, K., Tran, N., Tran, N., Joy, A., & Berens, M. (2004). A knowledge based approach for representing and reasoning about signaling networks. In Proceedings of the twelfth international conference on intelligent systems for molecular biology/third European conference on computational biology (ISMB'04/ECCB'04) (pp. 15-22). ISCB.
5. Bonarius, H. P. J., Schmid, G., & Tramper, J. (1997). Flux analysis of underdetermined metabolic networks: The quest for the missing constraints. Trends in Biotechnology, 15, 308314.
6. Booth, E. J., Walker, K. C., & Griffiths, D. W. (1991). A time-course study of the effect of sulfur on glucosinolates in oilseed rape (brassica napus) from the vegetative stage to maturity. Journal of the Science of Food and Agriculture, 56, 479-493.
7. Chabrier-Rivier, N., Fages, F., & Soliman, S. (2004). The biochemical abstract machine biocham. In V. Danos & V. Schächter (Eds.) Proceedings of the second workshop on computational methods in systems biology (pp. 172-191). Springer.
8. Clarke, E., Grumberg, O., & Peled, D. (1999). Model checking. MIT Press.
9. Danos, V., & Schächter, V. (Eds.) (2005). Computational methods in systems biology, international conference (CMSB 2004), Paris, France, May 26-28, 2004, revised selected papers. Lecture Notes in Computer Science, Vol. 3082. Springer.
10. Eiter, T., Faber, W., Leone, N., Pfeifer, G., & Polleres, A. (2003). A logic programming approach to knowledge-state planning. Artificial Intelligence, 144(1-2), 157-211.
11. Eiter, T., Faber, W., Leone, N., Pfeifer, G., & Polleres, A. (2004). A logic programming approach to knowledge-state planning: Semantics and complexity. ACM Transactions On Computational Logic, 5(2), 206-263.
12. Eker, S., Knapp, M., Laderoute, K., Lincoln, P., & Talcott, C. L. (2002). Pathway logic: Executable models of biological networks. Electr. Notes Theor. Comp. Sci., 71.
13. Eveillard, D., Ropers, D., de Jong, H., Branlant, C., & Bockmayr, A. (2004). A multi-scale constraint programming model of alternative splicing regulation. Theoretical Computer Science, 325(1), 3-24.
14. Fanchon, E., Corblin, F., Trilling, L., Hermant, B., & Gulino, D. (2004). Modeling the molecular network controlling adhesion between human endothelial cells: Inference and simulation using constraint logic programming. In V. Danos & V. Schächter (Eds.), Lecture Notes in Bioinformatics-Computational Methods in Systems Biology (pp. 104-118).
15. Ferraris, P., & Lifschitz, V. (2005). Mathematical foundations of answer set programming. In S. Artëmov, H. Barringer, A. d'Avila Garcez, L. Lamb & J. Woods (Eds.), We will show them! Essays in honour of dov gabbay, (Vol. 1, pp. 615-664). College Publications.
16. Gebser, M., Kaufmann, B., Neumann, A., & Schaub, T. (2007). Clasp: A conflict-driven answer set solver. In C. Baral, G. Brewka & J. Schlipf (Eds.), Proceedings of the ninth international conference on logic programming and nonmonotonic reasoning (LPNMR'07). Lecture Notes in Artificial Intelligence, (Vol. 4483, pp. 260-265). Springer-Verlag.
17. Gebser, M., Kaufmann, B., Neumann, A., & Schaub, T. (2007). Conflict-driven answer set solving. In M. Veloso (Ed.), Proceedings of the twentieth international joint conference on artificial intelligence (IJCAI'07) (pp. 386-392). AAAI Press/The MIT Press. Available at http://www.ijcai.org/ papers07/contents.php.
18. Gebser, M., Schaub, T., & Thiele, S. (2007). GrinGo: A new grounder for answer set programming. In C. Baral, G. Brewka & J. Schlipf (Eds.), Proceedings of the ninth international conference on logic programming and nonmonotonic reasoning (LPNMR'07). Lecture Notes in Artificial Intelligence, (Vol. 4483, pp. 266-271). Springer-Verlag.
19. Gelfond, M., & Lifschitz, V. (1991). Classical negation in logic programs and disjunctive databases. New Generation Computing, 9, 365-385.
20. Gelfond, M., & Lifschitz, V. (1993). Representing action and change by logic programs. Journal of Logic Programming, 17(2-4), 301-321.
21. Gelfond, M., & Lifschitz, V. (1998). Action languages. Electronic Transactions on Artifical Intelligence, 3(6), 193-210.
22. Giunchiglia, E., & Lifschitz, V. (1998). An action language based on causal explanation: Preliminary report. In Proceedings of the national conference on artificial intelligence (AAAI) (pp. 623-630). AAAI.
23. Halkier, B. A., & Gershenzon, J. (2006). Biology and biochemistry of glucosinolates. Annual Review of Plant Biology, 57, 303-333.
24. Kutz, A., Müller, A., Hennig, P., Kaiser, W. M., Piotrowskiand, M., & Weiler, E. W. (2002). A role for nitrilase 3 in the regulation of root morphology in sulphur-starving arabidopsis thaliana. Plant Journal, 30, 95-106.
25. Levesque, H., Reiter, R., Lespérance, Y., Lin, F., & Scherl, R. (1997). Golog: A logic programming language for dynamic domains. Journal of Logic Programming, 31(1-3), 59-83.
26. Lifschitz, V., & Razborov, A. (2006). Why are there so many loop formulas? ACM Transactions on Computational Logic, 7(2), 261-268.
27. Lifschitz, V., & Turner, H. (1994). Splitting a logic program. In Proceedings of the eleventh international conference on logic programming (pp. 23-37). MIT Press.
28. Lifschitz, V., & Turner, H. (1999) Representing transition systems by logic programs. In M. Gelfond, N. Leone & G. Pfeifer (Eds.), Logic programming and non-monotonic reasoning. Lecture Notes in Artificial Intelligence, (Vol. 1730, pp. 92-106). Springer-Verlag.
29. Lynce, I., & Marques-Silva, J. (2006). Efficient haplotype inference with boolean satisfiability. In Y. Gil & R. Mooney (Eds.), Proceedings of the twenty-first national conference on artificial intelligence (AAAI'06). AAAI Press.
30. Lynce, I., & Marques-Silva, J. (2006). Sat in bioinformatics: Making the case with haplotype inference. In A. Biere & C. Gomes (Eds.) Proceedings of the ninth international conference on theory and applications of satisfiability testing (SAT'06). Lecture Notes in Computer Science, (Vol. 4121, pp. 136-141). Springer-Verlag.
31. McCarthy, J. (1998). Elaboration tolerance. http://www.formal.stanford. edu/jmc/elaboration.html.
32. Montiel, G., Gantet, P., Jay-Allemand, C., & Breton, C. (2004). Transcription factor networks. Pathways to the knowledge of root development. Plant Physiology, 136, 3478-3485.
33. Niemelä, I. (1999). Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence, 25(3-4), 241-273.
34. Nikiforova, V. J., Daub, C. O., Hesse, H., Willmitzer, L., & Hoefgen, R. (2005). Integrative gene-metabolite network with implemented causality deciphers informational fluxes of sulfur stress response. Journal of Experimental Botany, 56, 1887-1896.
35. Nikiforova, V. J., Freitag, J., Kempa, S., Adamik, M., Hesse, H., & Hoefgen, R. (2003). Transcriptome analysis of sulfur depletion in arabidopsis thaliana: Interlacing of biosynthetic pathways provides response specificity. Plant Journal, 33, 633-650.
36. Nikiforova, V. J., Kopka, J., Tolstikov, V., Fiehn, O., Hopkins, L., Hawkesford, M. J., et al. (2005). Systems re-balancing of metabolism in response to sulfur deprivation, as revealed by metabolome analysis of arabidopsis plants. Plant Physiology, 138, 304-318.
37. Pan, Y., Tu, P., Pontelli, E., & Son, T. (2004). Construction of an agent-based framework for evolutionary biology: A progress report. In J. Leite, A. Omicini, P. Torroni & P. Yolum (Eds.), Proceedings of the second international workshop on declarative agent languages and technologies (DALT'04). Lecture Notes in Computer Science, (Vol. 3476, pp. 92-111). Springer-Verlag.
38. Papatheodorou, I., Kakas, A., & Sergot, M. (2005). Inference of gene relations from microarray data by abduction. In C. Baral, G. Greco, N. Leone & G. Terracina (Eds.), Proceedings of the eighth international conference on logic programming and nonmonotonic reasoning (LPNMR'05). Lecture Notes in Artificial Intelligence, (Vol. 3662, pp. 389-393). Springer-Verlag.
39. Pinney, J. W., Westhead, D. R., & McConkey, G. A. (2003). Petri net representations in systems biology. Biochemical Society Transactions, 31(Pt 6), 1513-1515.
40. Reddy, V. N., Mavrovouniotis, M. L., & Liebman, M. N. (1993). Petri net representations in metabolic pathways. In Proc. First ISMB (pp. 328-336).
41. Regev, A., Panina, E. M., Silverman, W., Cardelli, L., & Shapiro, E. Y. (2004). Bioambients: An abstraction for biological compartments. Theoretical Computer Science, 325(1), 141-167.
42. Shmulevich, I., Dougherty, E. R., Kim, S., & Zhang, W. (2002). Probabilistic boolean networks: A rule-based uncertainty model for gene regulatory networks. Bioinformatics, 18(2), 261-274.
43. Simons, P., Niemelä, I., & Soininen, T. (2002). Extending and implementing the stable model semantics. Artificial Intelligence, 138(1-2), 181-234.
44. Son, T., & Pontelli, E. (2007). Planning for biochemical pathways: A case study of answer set planning in large planning problem instances. In M. De Vos & T. Schaub (Eds.), Proceedings of the workshop on software engineering for answer set programming (SEA'07), number CSBU-2007-05 in Department of Computer Science, University of Bath, Technical Report Series, (pp. 116-130). ISSN 1740-9497.
45. Syrjänen, T. Lparse 1.0 user's manual. http://www.tcs.hut.fi/ Software/smodels/lparse.ps.gz.
46. Tamaddoni-Nezhad, A., Chaleil, R., Kakas, A., & Muggleton, S. (2006). Application of abductive ilp to learning metabolic network inhibition from temporal data. Machine Learning, 64(1-3), 209-230.
47. Tran, N. (2006). Reasoning and hypothesing about signaling networks. Ph.D. thesis, Arizona State University.
48. Tran, N., & Baral, C. (2004). Reasoning about triggered actions in AnsProlog and its application to molecular interactions in cells. In D. Dubois, C. Welty & M. Williams (Eds.), Proceedings of the ninth international conference on principles of knowledge representation and reasoning (KR'04) (pp. 554-564). AAAI Press.
49. Tran, N., Baral, C., & Shankland, C. (2005). Issues in reasoning about interaction networks in cells: Necessity of event ordering knowledge. In M. Veloso & S. Kambhampati (Eds.), Proceedings of the twentieth national conference on artificial intelligence (AAAI'05) (pp. 676-681). AAAI Press.