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Modeling multi-valued biological interaction networks using fuzzy answer set programming. (English) Zbl 1397.68027

Summary: Fuzzy answer set programming (FASP) is an extension of the popular answer set programming (ASP) paradigm that allows for modeling and solving combinatorial search problems in continuous domains. The recent development of practical solvers for FASP has enabled its applicability to real-world problems. In this paper, we investigate the application of FASP in modeling the dynamics of gene regulatory networks (GRNs). A commonly used simplifying assumption to model the dynamics of GRNs is to assume only Boolean levels of activation of each node. Our work extends this Boolean network formalism by allowing multi-valued activation levels. We show how FASP can be used to model the dynamics of such networks. We experimentally assess the efficiency of our method using real biological networks found in the literature, as well as on randomly-generated synthetic networks. The experiments demonstrate the applicability and usefulness of our proposed method to find network attractors.

MSC:

68N17 Logic programming
68T37 Reasoning under uncertainty in the context of artificial intelligence
92C42 Systems biology, networks

Software:

ASP-G; Potassco
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References:

[1] Kauffman, S. A., The origins of order: self-organization and selection in evolution, (1993), Oxford University Press
[2] Mendoza, L., A network model for the control of the differentiation process in th cells, Biosystems, 84, 2, 101-114, (2006)
[3] Espinosa-Soto, C.; Padilla-Longoria, P.; Alvarez-Buylla, E. R., A gene regulatory network model for cell-fate determination during arabidopsis thaliana flower development that is robust and recovers experimental gene expression profiles, Plant Cell, 16, 11, 2923-2939, (2004)
[4] Kauffman, S. A., Metabolic stability and epigenesis in randomly constructed genetic nets, J. Theor. Biol., 22, 3, 437-467, (1969)
[5] Kaufman, M.; Urbain, J.; Thomas, R., Towards a logical analysis of the immune response, J. Theor. Biol., 114, 4, 527-561, (1985)
[6] De Jong, H., Modeling and simulation of genetic regulatory systems: a literature review, J. Comput. Biol., 9, 1, 67-103, (2002)
[7] Garg, A.; Xenarios, I.; Mendoza, L.; DeMicheli, G., An efficient method for dynamic analysis of gene regulatory networks and in silico gene perturbation experiments, (Research in Computational Molecular Biology, (2007), Springer), 62-76
[8] Arellano, G.; Argil, J.; Azpeitia, E.; Benitez, M.; Carrillo, M.; Gongora, P.; Rosenblueth, D.; Alvarez-Buylla, E., “antelope”: a hybrid-logic model checker for branching-time Boolean GRN analysis, BMC Bioinform., 12, 1, 490, (2011)
[9] Berntenis, N.; Ebeling, M., Detection of attractors of large Boolean networks via exhaustive enumeration of appropriate subspaces of the state space, BMC Bioinform., 14, 1, 1-10, (2013)
[10] Ay, F.; Xu, F.; Kahveci, T., Scalable steady state analysis of Boolean biological regulatory networks, PLoS ONE, 4, 12, (2009), e7992, 12
[11] Dubrova, E.; Teslenko, M., A SAT-based algorithm for finding attractors in synchronous Boolean networks, IEEE/ACM Trans. Comput. Biol. Bioinform., 8, 5, 1393-1399, (2011)
[12] Zheng, D.; Yang, G.; Li, X.; Wang, Z.; Liu, F.; He, L., An efficient algorithm for computing attractors of synchronous and asynchronous Boolean networks, PLoS ONE, 8, 4, (2013)
[13] Dworschak, S.; Grell, S.; Nikiforova, V. J.; Schaub, T.; Selbig, J., Modeling biological networks by action languages via answer set programming, Constraints, 13, 1-2, 21-65, (2008) · Zbl 1148.68564
[14] Mushthofa, M.; Torres, G.; Van de Peer, Y.; Marchal, K.; De Cock, M., ASP-G: an ASP-based method for finding attractors in genetic regulatory networks, Bioinformatics, 30, 21, 3086, (2014)
[15] Fayruzov, T.; De Cock, M.; Cornelis, C.; Vermeir, D., Modeling protein interaction networks with answer set programming, (Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2009, (2009)), 99-104
[16] Inoue, K., Logic programming for Boolean networks, (Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI 2011), (2011)), 924-930
[17] Lifschitz, V., What is answer set programming?, (Proceedings of the 23rd AAAI Conference in Artificial Intelligence, vol. 8, (2008)), 1594-1597
[18] Baral, C., Knowledge representation, reasoning and declarative problem solving, (2003), Cambridge University Press · Zbl 1056.68139
[19] Gebser, M.; Kaufmann, B.; Kaminski, R.; Ostrowski, M.; Schaub, T.; Schneider, M., Potassco: the Potsdam answer set solving collection, AI Commun., 24, 2, 107-124, (2011) · Zbl 1215.68214
[20] Leone, N.; Pfeifer, G.; Faber, W.; Eiter, T.; Gottlob, G.; Perri, S.; Scarcello, F., The DLV system for knowledge representation and reasoning, ACM Trans. Comput. Log., 7, 3, 499-562, (2006) · Zbl 1367.68308
[21] Eiter, T.; Ianni, G.; Krennwallner, T., Answer set programming: a primer, (Tessaris, S.; Franconi, E.; Eiter, T.; Gutierrez, C.; Handschuh, S.; Rousset, M.-C.; Schmidt, R., Reasoning Web. Semantic Technologies for Information Systems, Lecture Notes in Computer Science, vol. 5689, (2009), Springer Berlin Heidelberg), 40-110 · Zbl 1254.68248
[22] Erdem, E., Theory and applications of answer set programming, (2002), The University of Texas at Austin, Ph.D. dissertation
[23] Didier, G.; Remy, E.; Chaouiya, C., Mapping multivalued onto Boolean dynamics, J. Theor. Biol., 270, 1, 177-184, (2011) · Zbl 1331.92051
[24] Garg, A.; Mendoza, L.; Xenarios, I.; DeMicheli, G., Modeling of multiple valued gene regulatory networks, (Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2007), (2007), IEEE), 1398-1404
[25] Sanchez, L.; Thieffry, D., Segmenting the fly embryo: a logical analysis of the pair-rule cross-regulatory module, J. Theor. Biol., 224, 4, 517-537, (2003)
[26] Bockmayr, A.; Siebert, H., Bio-logics: logical analysis of bioregulatory networks, (Programming Logics: Essays in Memory of Harald Ganzinger, (2013), Springer Berlin Heidelberg), 19-34 · Zbl 1383.92031
[27] Lodish, H.; Berk, A.; Zipursky, S. L.; Matsudaira, P.; Baltimore, D.; Darnell, J., Molecular cell biology, vol. 5, (2000), WH Freeman New York
[28] Van Nieuwenborgh, D.; De Cock, M.; Vermeir, D., Fuzzy answer set programming, (Proceedings of the 10th European Conference on Logics in Artificial Intelligence, (2006)), 359-372 · Zbl 1152.68619
[29] Blondeel, M.; Schockaert, S.; Vermeir, D.; De Cock, M., Complexity of fuzzy answer set programming under łukasiewicz semantics, Int. J. Approx. Reason., 55, 9, 1971-2003, (2014) · Zbl 1433.68082
[30] Alviano, M.; Peñaloza, R., Fuzzy answer sets approximations, Theory Pract. Log. Program., 13, 4-5, 753-767, (2013) · Zbl 1298.68058
[31] Mushthofa, M.; Schockaert, S.; De Cock, M., A finite-valued solver for disjunctive fuzzy answer set programs, (Proceedings of European Conference in Artificial Intelligence 2014, (2014)), 645-650 · Zbl 1366.68018
[32] Mushthofa, M.; Schockaert, S.; De Cock, M., Solving disjunctive fuzzy answer set programs, (Proceedings of the 13th International Conference on Logic Programming and Non-monotonic Reasoning, (2015)), 453-466 · Zbl 1467.68028
[33] Alviano, M.; Peñaloza, R., Fuzzy answer set computation via satisfiability modulo theories, Theory Pract. Log. Program., 15, 588-603, (July 2015)
[34] Vojtáš, P., Fuzzy logic programming, Fuzzy Sets Syst., 124, 3, 361-370, (2001) · Zbl 1015.68036
[35] Lee, J.; Wang, Y., Stable models of fuzzy propositional formulas, (Proceedings of the 14th European Conference on Logics in Artificial Intelligence, JELIA 2014, (2014)), 326 · Zbl 1432.68447
[36] Madrid, N.; Ojeda-Aciego, M., Towards a fuzzy answer set semantics for residuated logic programs, (Web Intelligence/IAT Workshops, (2008)), 260-264
[37] Damásio, C. V.; Pereira, L. M., Antitonic logic programs, (Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning, (2001)), 379-392 · Zbl 1007.68022
[38] Straccia, U., Annotated answer set programming, (Proceedings of the 11th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU-06), (2006))
[39] Straccia, U., Managing uncertainty and vagueness in description logics, logic programs and description logic programs, (Reasoning Web, 4th International Summer School, Tutorial Lectures, Lecture Notes in Computer Science, vol. 5224, (2008), Springer Verlag), 54-103
[40] Mushthofa, M.; Schockaert, S.; De Cock, M., Computing attractors of multi-valued gene regulatory networks using fuzzy answer set programming, (Proceedings of the 2016 IEEE World Congress on Computational Intelligence, (2016))
[41] Thomas, R., Boolean formalization of genetic control circuits, J. Theor. Biol., 42, 3, 563-585, (1973)
[42] Harvey, I.; Bossomaier, T., Time out of joint: attractors in asynchronous random Boolean networks, (Proceedings of the Fourth European Conference on Artificial Life, (1997)), 67-75
[43] Thomas, R., Regulatory networks seen as asynchronous automata: a logical description, J. Theor. Biol., 153, 1, 1-23, (1991)
[44] Klarner, H.; Bockmayr, A.; Siebert, H., Computing maximal and minimal trap spaces of Boolean networks, Nat. Comput., 14, 4, 535-544, (2015) · Zbl 1416.92072
[45] Blondeel, M.; Schockaert, S.; Vermeir, D.; De Cock, M., Complexity of fuzzy answer set programming under łukasiewicz semantics, Int. J. Approx. Reason., 55, 9, 1971-2003, (2014) · Zbl 1433.68082
[46] Schockaert, S.; Janssen, J.; Vermeir, D., Fuzzy equilibrium logic: declarative problem solving in continuous domains, ACM Trans. Comput. Log., 13, 4, (2012) · Zbl 1351.68270
[47] Lukasiewicz, T.; Straccia, U., Tightly integrated fuzzy description logic programs under the answer set semantics for the semantic web, (Proceedings of the 1st International Conference on Web Reasoning and Rule Systems, (2007)), 289-298
[48] Aguzzoli, S.; Ciabattoni, A., Finiteness in infinite-valued łukasiewicz logic, J. Log. Lang. Inf., 9, 1, 5-29, (2000) · Zbl 0951.03024
[49] Guespin-Michel, J.; Kaufman, M., Positive feedback circuits and adaptive regulations in bacteria, Acta Biotheor., 49, 4, 207-218, (2001)
[50] Fayruzov, T.; De Cock, M.; Cornelis, C.; Vermeir, D., Modeling protein interaction networks with answer set programming, (IEEE Internat. Conf. on Bioinformatics and Biomedicine, (2009)), 99-104
[51] Peres, S.; Comet, J.-P., Contribution of computational tree logic to biological regulatory networks: example from pseudomonas aeruginosa, (Proceedings of the First International Workshop on Computational Methods in Systems Biology (CMSB 2003), (2003)), 47-56 · Zbl 1112.92327
[52] Mendoza, L.; Xenarios, I., A method for the generation of standardized qualitative dynamical systems of regulatory networks, Theor. Biol. Med. Model., 3, 1, 13, (2006)
[53] Albert, R.; Barabási, A.-L., Statistical mechanics of complex networks, Rev. Mod. Phys., 74, 1, 47, (2002) · Zbl 1205.82086
[54] Mendoza, L.; Alvarez-Buylla, E. R., Dynamics of the genetic regulatory network for arabidopsis thaliana flower morphogenesis, J. Theor. Biol., 193, 2, 307-319, (1998)
[55] Mendoza, L.; Thieffry, D.; Alvarez-Buylla, E. R., Genetic control of flower morphogenesis in arabidopsis thaliana: a logical analysis, Bioinformatics, 15, 7, 593-606, (1999)
[56] Sanchez-Corrales, Y.-E.; Alvarez-Buylla, E. R.; Mendoza, L., The arabidopsis thaliana flower organ specification gene regulatory network determines a robust differentiation process, J. Theor. Biol., 264, 3, 971-983, (2010) · Zbl 1406.92386
[57] Li, F.; Long, T.; Lu, Y.; Ouyang, Q.; Tang, C., The yeast cell-cycle network is robustly designed, Proc. Natl. Acad. Sci. USA, 101, 14, 4781-4786, (2004)
[58] Sánchez, L.; Thieffry, D., A logical analysis of the drosophila gap-gene system, J. Theor. Biol., 211, 2, 115-141, (2001)
[59] Albert, R.; Othmer, H. G., The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in drosophila melanogaster, J. Theor. Biol., 223, 1, 1-18, (2003)
[60] González, A.; Chaouiya, C.; Thieffry, D., Logical modelling of the role of the hh pathway in the patterning of the drosophila wing disc, Bioinformatics, 24, 16, i234-i240, (2008)
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