Abstract
We describe an approach to modeling biological networks by action languages via answer set programming. To this end, we propose an action language for modeling biological networks, building on previous work by Baral et al. We introduce its syntax and semantics along with a translation into answer set programming, an efficient Boolean Constraint Programming Paradigm. Finally, we describe one of its applications, namely, the sulfur starvation response-pathway of the model plant Arabidopsis thaliana and sketch the functionality of our system and its usage.
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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.
Baral, C. (2003). Knowledge representation, reasoning and declarative problem solving. Cambridge University Press.
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.
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.
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.
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.
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.
Clarke, E., Grumberg, O., & Peled, D. (1999). Model checking. MIT Press.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
Gelfond, M., & Lifschitz, V. (1991). Classical negation in logic programs and disjunctive databases. New Generation Computing, 9, 365–385.
Gelfond, M., & Lifschitz, V. (1993). Representing action and change by logic programs. Journal of Logic Programming, 17(2–4), 301–321.
Gelfond, M., & Lifschitz, V. (1998). Action languages. Electronic Transactions on Artifical Intelligence, 3(6), 193–210.
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.
Halkier, B. A., & Gershenzon, J. (2006). Biology and biochemistry of glucosinolates. Annual Review of Plant Biology, 57, 303–333.
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.
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.
Lifschitz, V., & Razborov, A. (2006). Why are there so many loop formulas? ACM Transactions on Computational Logic, 7(2), 261–268.
Lifschitz, V., & Turner, H. (1994). Splitting a logic program. In Proceedings of the eleventh international conference on logic programming (pp. 23–37). MIT Press.
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.
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.
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.
McCarthy, J. (1998). Elaboration tolerance. http://www.formal.stanford.edu/jmc/elaboration.html.
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.
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.
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.
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.
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.
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.
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.
Pinney, J. W., Westhead, D. R., & McConkey, G. A. (2003). Petri net representations in systems biology. Biochemical Society Transactions, 31(Pt 6), 1513–1515.
Reddy, V. N., Mavrovouniotis, M. L., & Liebman, M. N. (1993). Petri net representations in metabolic pathways. In Proc. First ISMB (pp. 328–336).
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.
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.
Simons, P., Niemelä, I., & Soininen, T. (2002). Extending and implementing the stable model semantics. Artificial Intelligence, 138(1–2), 181–234.
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.
Syrjänen, T. Lparse 1.0 user’s manual. http://www.tcs.hut.fi/Software/smodels/lparse.ps.gz.
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.
Tran, N. (2006). Reasoning and hypothesing about signaling networks. Ph.D. thesis, Arizona State University.
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.
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.
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Dworschak, S., Grell, S., Nikiforova, V.J. et al. Modeling Biological Networks by Action Languages via Answer Set Programming. Constraints 13, 21–65 (2008). https://doi.org/10.1007/s10601-007-9031-y
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DOI: https://doi.org/10.1007/s10601-007-9031-y