Hostname: page-component-848d4c4894-sjtt6 Total loading time: 0 Render date: 2024-06-14T23:24:25.708Z Has data issue: false hasContentIssue false

Minimal intervention strategies in logical signaling networks with ASP

Published online by Cambridge University Press:  25 September 2013

ROLAND KAMINSKI
Affiliation:
University of Potsdam
TORSTEN SCHAUB
Affiliation:
University of Potsdam
ANNE SIEGEL
Affiliation:
CNRS - IRISA, Rennes INRIA - Dyliss, Rennes
SANTIAGO VIDELA
Affiliation:
CNRS - IRISA, Rennes INRIA - Dyliss, Rennes University of Potsdam

Abstract

Proposing relevant perturbations to biological signaling networks is central to many problems in biology and medicine because it allows for enabling or disabling certain biological outcomes. In contrast to quantitative methods that permit fine-grained (kinetic) analysis, qualitative approaches allow for addressing large-scale networks. This is accomplished by more abstract representations such as logical networks. We elaborate upon such a qualitative approach aiming at the computation of minimal interventions in logical signaling networks relying on Kleene's three-valued logic and fixpoint semantics. We address this problem within answer set programming and show that it greatly outperforms previous work using dedicated algorithms.

Type
Regular Papers
Copyright
Copyright © 2013 [ROLAND KAMINSKI, TORSTEN SCHAUB, ANNE SIEGEL and SANTIAGO VIDELA] 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdi, A., Tahoori, M. B. and Emamian, E. S. 2008. Fault diagnosis engineering of digital circuits can identify vulnerable molecules in complex cellular pathways. Science Signaling 1, 42, ra10.CrossRefGoogle ScholarPubMed
Acuña, V. V., Milreu, P. V. P., Cottret, L. L., Marchetti-Spaccamela, A. A., Stougie, L. L. and Sagot, M.-F. M. 2012. Algorithms and complexity of enumerating minimal precursor sets in genome-wide metabolic networks. Bioinformatics 28, 19 (September), 24742483.CrossRefGoogle ScholarPubMed
Baral, C. 2003. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge, UK.CrossRefGoogle Scholar
Baral, C., Chancellor, K., Tran, N., Tran, N., Joy, A. and 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), 15–22.Google Scholar
Batt, G., de Jong, H., Page, M. and Geiselmann, J. 2008. Symbolic reachability analysis of genetic regulatory networks using discrete abstractions. Automatica 44, 4 (March), 982989.CrossRefGoogle Scholar
Bouaynaya, N. N., Shterenberg, R. and Schonfeld, D. 2011. Methods for optimal intervention in gene regulatory networks. Signal Processing Magazine, IEEE 29, 1 (December), 158163.CrossRefGoogle Scholar
Calzone, L. L., Tournier, L. L., Fourquet, S. S., Thieffry, D. D., Zhivotovsky, B. B., Barillot, E. E. and Zinovyev, A. A. 2010. Mathematical modelling of cell-fate decision in response to death receptor engagement. PLoS Computational Biology 6, 3 (February), e10007021000702.CrossRefGoogle ScholarPubMed
Castell, T., Cayrol, C., Cayrol, M. and Le Berre, D. 1996. Using the Davis and Putnam procedure for an efficient computation of preferred models. In Proceedings of the Twelfth European Conference on Artificial Intelligence (ECAI'96), Wahlster, W., Ed. John Wiley & sons, 350354.Google Scholar
Di Rosa, E., Giunchiglia, E. and Maratea, M. 2010. Solving satisfiability problems with preferences. Constraints 15, 4, 485515.CrossRefGoogle Scholar
Erdem, E. and Türe, F. 2008. Efficient haplotype inference with answer set programming. In Proceedings of the Twenty-third National Conference on Artificial Intelligence (AAAI'08), Fox, D. and Gomes, C., Eds. AAAI Press, 436441.Google Scholar
Faryabi, B., Vahedi, G., Chamberland, J.-F., Datta, A. and Dougherty, E. R. 2008. Optimal constrained stationary intervention in gene regulatory networks. EURASIP Journal of Bioinformatics and Systems Biology 2008.CrossRefGoogle Scholar
Fitting, M. 1985. A Kripke-Kleene semantics for logic programs. Journal of Logic Programming 2, 4, 295312.CrossRefGoogle Scholar
Gebser, M., Guziolowski, C., Ivanchev, M., Schaub, T., Siegel, A., Thiele, S. and Veber, P. 2010. Repair and prediction (under inconsistency) in large biological networks with answer set programming. In Proceedings of the Twelfth International Conference on Principles of Knowledge Representation and Reasoning (KR'10), Lin, F. and Sattler, U., Eds. AAAI Press, 497507.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T. and Thiele, S. A user's guide to gringo, clasp, clingo, and iclingo.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B. and Schaub, T. 2012. Answer Set Solving in Practice. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan and Claypool Publishers.Google Scholar
Gebser, M., Kaminski, R. and Schaub, T. 2011. Complex optimization in answer set programming. Theory and Practice of Logic Programming 11, 4–5, 821839.CrossRefGoogle Scholar
Gebser, M., Kaufmann, B., Neumann, A. and Schaub, T. 2007. clasp: A conflict-driven answer set solver. In Proceedings of the Ninth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR'07), Baral, C., Brewka, G., and Schlipf, J., Eds. Lecture Notes in Artificial Intelligence, vol. 4483, Springer-Verlag, 260265.CrossRefGoogle Scholar
Gebser, M., Kaufmann, B., Otero, R., Romero, J., Schaub, T. and Wanko, P. 2013. Domain-specific heuristics in answer set programming. In Proceedings of the Twenty-Seventh National Conference on Artificial Intelligence (AAAI'13), desJardins, M. and Littman, M., Eds. AAAI Press. To appear.Google Scholar
Gebser, M., Kaufmann, B. and Schaub, T. 2013. Advanced conflict-driven disjunctive answer set solving. In Proceedings of the Twenty-third International Joint Conference on Artificial Intelligence (IJCAI'13), Rossi, F., Ed. IJCAI/AAAI. To appear.Google Scholar
Gebser, M., Schaub, T., Thiele, S. and Veber, P. 2011. Detecting inconsistencies in large biological networks with answer set programming. Theory and Practice of Logic Programming 11, 2–3, 323360.CrossRefGoogle Scholar
Inoue, K. 2011. Logic programming for boolean networks. In Proceedings of the Twenty-second International Joint Conference on Artificial Intelligence (IJCAI'11), Walsh, T., Ed. IJCAI/AAAI, 924930.Google Scholar
Inoue, K. and Sakama, C. 2012. Oscillating behavior of logic programs. In Correct Reasoning, Erdem, E., Lee, J., Lierler, Y. and Pearce, D., Eds. Lecture Notes in Computer Science, vol. 7265. Springer, 345362.CrossRefGoogle Scholar
Karlebach, G. G. and Shamir, R. R. 2009. Minimally perturbing a gene regulatory network to avoid a disease phenotype: the glioma network as a test case. BMC Systems Biology 4, 1515.CrossRefGoogle Scholar
Kauffman, S. 1969. Metabolic stability and epigenesis in randomly constructed genetic nets. Journal of Theoretical Biology 22, 3 (February), 437467.CrossRefGoogle ScholarPubMed
Kauffman, K. J., Prakash, P. and Edwards, J. S. 2003. Advances in flux balance analysis. Current opinion in biotechnology 14, 5 (October), 491496.CrossRefGoogle ScholarPubMed
Kitano, H. 2002. Systems biology: a brief overview. Science 295, 5560, 16621664.CrossRefGoogle ScholarPubMed
Klamt, S. S. 2006. Generalized concept of minimal cut sets in biochemical networks. Biosystems 83, 2–3 (January), 233247.CrossRefGoogle ScholarPubMed
Klamt, S., Haus, U.-U. and Theis, F. J. 2009. Hypergraphs and cellular networks. PLoS Computational Biology 5, 5 (May), e1000385.CrossRefGoogle ScholarPubMed
Kleene, S. 1950. Introduction to Metamathematics. Princeton, NJ, 1950.Google Scholar
Kreutz, C. and Timmer, J. 2009. Systems biology: experimental design. FEBS Journal 276, 4 (January), 923942.CrossRefGoogle ScholarPubMed
Mitsos, A., Melas, I., Siminelakis, P., Chairakaki, A., Saez-Rodriguez, J. and Alexopoulos, L. G. 2009. Identifying Drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data. PLoS Computational Biology 5, 12, e1000591.CrossRefGoogle ScholarPubMed
Morris, M., Saez-Rodriguez, J., Sorger, P. and Lauffenburger, D. A. 2010. Logic-based models for the analysis of cell signaling networks. Biochemistry 49, 15, 32163224.CrossRefGoogle ScholarPubMed
Naldi, A., Carneiro, J., Chaouiya, C. and Thieffry, D. 2009. Diversity and plasticity of th cell types predicted from regulatory network modelling. PLoS Computational Biology 6, 9 (December), e1000912.CrossRefGoogle Scholar
Ray, O., Whelan, K. and King, R. 2010. Logic-based steady-state analysis and revision of metabolic networks with inhibition. In Proceedings of the 2010 International Conference on Complex, Intelligent and Software Intensive Systems (CISIS '10) 0, 661666.Google Scholar
Saez-Rodriguez, J., Alexopoulos, L. G., Epperlein, J., Samaga, R., Lauffenburger, D. A., Klamt, S. and Sorger, P. 2009. Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Molecular Systems Biology 5, 331.CrossRefGoogle ScholarPubMed
Saez-Rodriguez, J., Alexopoulos, L. G., Zhang, M., Morris, M., Lauffenburger, D. A. and Sorger, P. 2011. Comparing signaling networks between normal and transformed hepatocytes using discrete logical models. Cancer Research 71, 16, 5400.CrossRefGoogle ScholarPubMed
Saez-Rodriguez, J., Simeoni, L., Lindquist, J., Hemenway, R., Bommhardt, U., Arndt, B., Haus, U.-U., Weismantel, R., Gilles, E., Klamt, S. and Schraven, B. 2007. A logical model provides insights into T cell receptor signaling. PLOS Computational Biology 3, 8 (August), e163.CrossRefGoogle ScholarPubMed
Samaga, R., Saez-Rodriguez, J., Alexopoulos, L. G., Sorger, P. and Klamt, S. 2009. The logic of EGFR/ErbB signaling: theoretical properties and analysis of high-throughput data. PLoS Computational Biology 5, 8 (August), e1000438.CrossRefGoogle ScholarPubMed
Samaga, R., Von Kamp, A. and Klamt, S. 2010. Computing combinatorial intervention strategies and failure modes in signaling networks. Journal of Computational Biology 17, 1 (Jan.), 3953.CrossRefGoogle ScholarPubMed
Sharan, R. and Karp, R. M. 2012. Reconstructing boolean models of signaling. In Research in Computational Molecular Biology. Springer Berlin Heidelberg, Berlin, Heidelberg, 261271.CrossRefGoogle Scholar
Sparkes, A., Aubrey, W., Byrne, E., Clare, A., Khan, M. N., Liakata, M., Markham, M., Rowland, J., Soldatova, L. N., Whelan, K. E., Young, M. and King, R. D. 2010. Towards robot scientists for autonomous scientific discovery. Automated Experimentation 2, 11.CrossRefGoogle ScholarPubMed
Stelling, J. J., Klamt, S. S., Bettenbrock, K. K., Schuster, S. S. and Gilles, E. D. E. 2002. Metabolic network structure determines key aspects of functionality and regulation. Nature 420, 6912 (November), 190193.CrossRefGoogle ScholarPubMed
Stelling, J., Sauer, U., Szallasi, Z., Doyle, F. and Doyle, J. 2004. Robustness of Cellular Functions. Cell 118, 6, 675685.CrossRefGoogle ScholarPubMed
Thomas, R. R. 1973. Boolean formalization of genetic control circuits. Journal of Theoretical Biology 42, 3 (November), 563585.CrossRefGoogle ScholarPubMed
Videla, S., Guziolowski, C., Eduati, F., Thiele, S., Grabe, N., Saez-Rodriguez, J. and Siegel, A. 2012. Revisiting the training of logic models of protein signaling networks with ASP. In Computational Methods in Systems Biology 2012, Gilbert, D. and Heiner, M., Eds. Springer Berlin/Heidelberg, 342361.CrossRefGoogle Scholar
Wang, R.-S. and Albert, R. 2011. Elementary signaling modes predict the essentiality of signal transduction network components. BMC Systems Biology 5, 44.CrossRefGoogle ScholarPubMed
Wang, B. and Buck, M. 2012. Customizing cell signaling using engineered genetic logic circuits. Trends in Microbiology 20, 8 (August), 376384.CrossRefGoogle ScholarPubMed
Wang, R.-S. R., Saadatpour, A. A. and Albert, R. R. 2012. Boolean modeling in systems biology: an overview of methodology and applications. Physical biology 9, 5 (September), 055001055001.CrossRefGoogle ScholarPubMed