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Abduction Based Drug Target Discovery Using Boolean Control Network

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Computational Methods in Systems Biology (CMSB 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10545))

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Abstract

A major challenge in cancer research is to determine the genetic mutations causing the cancerous phenotype of cells and conversely, the actions of drugs initiating programmed cell death in cancer cells. However, such a challenge is compounded by the complexity of the genotype-phenotype relationship and therefore, requires to relate the molecular effects of mutations and drugs to their consequences on cellular phenotypes. Discovering these complex relationships is at the root of new molecular drug targets discovery and cancer etiology investigation. In their elucidation, computational methods play a major role for the inference of the molecular causal actions from molecular and biological networks data analysis. In this article, we propose a theoretical framework where mutations and drug actions are seen as topological perturbations/actions on molecular networks inducing cell phenotype reprogramming. The framework is based on Boolean control networks where the topological network actions are modelled by control parameters. We present a new algorithm using abductive reasoning principles inferring the minimal causal topological actions leading to an expected behavior at stable state. The framework is validated on a model of network regulating the proliferation/apoptosis switch in breast cancer by automatically discovering driver genes and finding drug targets.

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Notes

  1. 1.

    Corresponding to the number of parts of size 1 to m in a set with n elements.

  2. 2.

    Exactly 19 415 908 147 835 trials.

  3. 3.

    A mapping will be described \(x=v\) instead of \(x\mapsto v\) for the sake of simplicity.

  4. 4.

    The formulas resulting from the instantiation of the BCN by a control input are simplified.

  5. 5.

    By reduction of the minimum hitting set problem.

  6. 6.

    For the sake of simplicity, the names of genes (by convention written in upper case letters) can also denominate the proteins they encode.

References

  1. Baldin, V., Lukas, J., Marcote, M.J., Pagano, M., Draetta, G.: Cyclin D1 is a nuclear protein required for cell cycle progression in G1. Genes Dev. 7(5), 812–821 (1993)

    Article  Google Scholar 

  2. Biane, C., Delaplace, F.: Abduction based drug target discovery using boolean control network. In: HAL Archive (2017). https://hal.archives-ouvertes.fr/hal-01522072

  3. Biane, C., Delaplace, F., Melliti, T.: Abductive network action inference for targeted therapy. In: Static Analysis and Systems Biology (2016)

    Google Scholar 

  4. Botting, G.M., Rastogi, I., Chhabra, G., Nlend, M., Puri, N.: Mechanism of resistance and novel targets mediating resistance to EGFR and c-Met tyrosine kinase inhibitors in non-small cell lung cancer. PloS one 10(8), e0136155 (2015)

    Article  Google Scholar 

  5. Burga, L.N., Hai, H., Juvekar, A., Tung, N.M., Troyan, S.L., Hofstatter, E.W., Wulf, G.M.: Loss of BRCA1 leads to an increase in epidermal growth factor receptor expression in mammary epithelial cells, and epidermal growth factor receptor inhibition prevents estrogen receptor-negative cancers in BRCA1-mutant mice. Breast Cancer Res. 13(2), R30 (2011)

    Article  Google Scholar 

  6. Ciliberto, A., Novák, B., Tyson, J.J.: Steady states and oscillations in the p53/Mdm2 network. Cell Cycle 4(3), 488–493 (2005)

    Article  Google Scholar 

  7. Creixell, P., Schoof, E.M., Simpson, C.D., Longden, J., Miller, C.J., Lou, H.J., Perryman, L., Cox, T.R., Zivanovic, N., Palmeri, A., Wesolowska-Andersen, A., Helmer-Citterich, M., Ferkinghoff-Borg, J., Itamochi, H., Bodenmiller, B., Erler, J.T., Turk, B.E., Linding, R.: Kinome-wide decoding of network-attacking mutations rewiring cancer signaling. Cell 163(1), 202–217 (2015)

    Article  Google Scholar 

  8. Croce, C.M.: Oncogenes and cancer. New Engl. J. Med. 358(5), 502–511 (2008). PMID: 18234754

    Article  Google Scholar 

  9. Csermely, P., Korcsmàros, T., Kiss, H.J.M., London, G., Nussinov, R.: Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol. Therapeutics 138(3), 333–408 (2013)

    Article  Google Scholar 

  10. Farmer, H., McCabe, N., Lord, C.J., Tutt, A.N.J., Johnson, D.A., Richardson, T.B., Santarosa, M., Dillon, K.J., Hickson, I., Knights, C., et al.: Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434(7035), 917–921 (2005)

    Article  Google Scholar 

  11. Gupta, S.: Molecular signaling in death receptor and mitochondrial pathways of apoptosis (review). Int. J. Oncol. 22(1), 15–20 (2003)

    Google Scholar 

  12. Hanahan, D., Weinberg, R.A.: Hallmarks of cancer: the next generation. Cell 144(5), 646–674 (2011)

    Article  Google Scholar 

  13. Kaelin, W.G.: The concept of synthetic lethality in the context of anticancer therapy. Nat. Rev. Cancer 5(9), 689–98 (2005)

    Article  Google Scholar 

  14. Kandoth, C., McLellan, M.D., Vandin, F., Ye, K., Niu, B., Charles, L., Xie, M., Zhang, Q., McMichael, J.F., Wyczalkowski, M.A., et al.: Mutational landscape and significance across 12 major cancer types. Nature 502(7471), 333–339 (2013)

    Article  Google Scholar 

  15. Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y., Morishima, K.: KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucl. Acids Res. 45(D1), D353–D361 (2017)

    Article  Google Scholar 

  16. Kolch, W., Halasz, M., Granovskaya, M., Kholodenko, B.N.: The Dynamic Control of Signal Transduction Networks in Cancer Cells. Nature Publishing Group (2015)

    Google Scholar 

  17. Layek, R., Datta, A., Bittner, M.: ER Dougherty: cancer therapy design based on pathway logic. Bioinformatics 27(4), 548–555 (2011)

    Article  Google Scholar 

  18. Lee, J.Y., Hong, M., Kim, S.T., Park, S.H., Kang, W.K., Kim, K.-M., Lee, J.: The impact of concomitant genomic alterations on treatment outcome for trastuzumab therapy in HER2-positive gastric cancer. Sci. Rep. 5, 9289 (2015)

    Article  Google Scholar 

  19. Lin, P.-C.K., Khatri, S.P.: Application of Max-SAT-based ATPG to optimal cancer therapy design. BMC Genomics 13(Suppl 6), S5 (2012)

    Article  Google Scholar 

  20. Livraghi, L., Garber, J.E.: PARP inhibitors in the management of breast cancer: current data and future prospects. BMC Med. 13(1), 1 (2015)

    Article  Google Scholar 

  21. Lodish, H., Zipursky, S.L.: Molecular cell biology. Biochem. Mol. Biol. Educ. 29, 126–133 (2001)

    Google Scholar 

  22. Marquis, P.: Extending abduction from propositional to first-order logic. In: Jorrand, P., Kelemen, J. (eds.) FAIR 1991. LNCS, vol. 535, pp. 141–155. Springer, Heidelberg (1991). doi:10.1007/3-540-54507-7_12

    Chapter  Google Scholar 

  23. Murrugarra, D., Veliz-Cuba, A., Aguilar, B., Laubenbacher, R.: Identification of control targets in boolean molecular network models via computational algebra. BMC Syst. Biol. 10(1), 94 (2016)

    Article  Google Scholar 

  24. Narod, S.A., Foulkes, W.D.: BRCA1 and BRCA2: 1994 and beyond. Nat. Rev. — Cancer 4(9), 665–676 (2004)

    Article  Google Scholar 

  25. Peirce, C.S.: On the natural classification of arguments. Proc. Am. Acad. Arts Sci. 7, 261–287 (1867)

    Article  Google Scholar 

  26. Perfetto, L., Briganti, L., Calderone, A., Perpetuini, A.C., Iannuccelli, M., Langone, F., Licata, L., Marinkovic, M., Mattioni, A., Pavlidou, T., Peluso, D., Petrilli, L.L., Pirro, S., Posca, D., Santonico, E., Silvestri, A., Spada, F., Castagnoli, L., Cesareni, G.: SIGNOR: a database of causal relationships between biological entities. Nucl. Acids Res. 44(D1), D548–D554 (2016)

    Article  Google Scholar 

  27. Phillips, P.C.: Epistasis - the essential role of gene interactions in the structure and evolution of genetic systems. Nat. Rev. Genetics 9(11), 855–867 (2008)

    Article  Google Scholar 

  28. Pizzuti, C.: Computing prime implicants by integer programming. In: Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence, pp. 332–336. IEEE Computer Society Press (1996)

    Google Scholar 

  29. Quine, W.V.: On cores and prime implicants of truth functions. Am. Math. Mon. 66(9), 755–760 (1959)

    Article  MathSciNet  MATH  Google Scholar 

  30. Spiliotaki, M., Mavroudis, D., Kapranou, K., Markomanolaki, H., Kallergi, G., Koinis, F., Kalbakis, K., Georgoulias, V., Agelaki, S.: Evaluation of proliferation and apoptosis markers in circulating tumor cells of women with early breast cancer who are candidates for tumor dormancy. Breast Cancer Res. 16(6), 485 (2014)

    Article  Google Scholar 

  31. Strimbu, K., Tavel, J.A.: What are biomarkers? Curr. Opin. HIV AIDS 5(6), 463–466 (2011)

    Article  Google Scholar 

  32. Vidal, M.: A unifying view of 21st century systems biology. FEBS Lett. 583(24), 3891–3894 (2009)

    Article  Google Scholar 

  33. Vidal, M., Cusick, M.E., Barabási, A.-L.: Interactome networks and human disease. Cell 144(6), 986–998 (2011)

    Article  Google Scholar 

  34. Vogelstein, B., Papadopoulos, N., Velculescu, V.E., Zhou, S., Diaz, L.A., Kinzler, K.W.: Cancer genome landscapes. Science 339(6127), 1546–1558 (2013)

    Article  Google Scholar 

  35. Von der Heyde, S., Bender, C., Henjes, F., Sonntag, J., Korf, U., Beissbarth, T.: Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines. BMC Syst. Biol. 8(1), 75 (2014)

    Article  Google Scholar 

  36. Wang, X., Fu, A.Q., McNerney, M.E., White, K.P.: Widespread genetic epistasis among cancer genes. Nat. Commun. 5, 4828 (2014)

    Article  Google Scholar 

  37. Zanudo, J.G.T., Albert, R.: Cell fate reprogramming by control of intracellular network dynamics. PLoS Comput. Biol. 11(4), e1004193 (2015)

    Article  Google Scholar 

  38. Zhong, Q., Simonis, N., Li, Q.-R., Charloteaux, B., Heuze, F., Klitgord, N., Tam, S., Haiyuan, Y., Venkatesan, K., Mou, D., Swearingen, V., Yildirim, M.A., Yan, H., Dricot, A., Szeto, D., Lin, C., Hao, T., Fan, C., Milstein, S., Dupuy, D., Brasseur, R., Hill, D.E., Cusick, M.E., Vidal, M.: Edgetic perturbation models of human inherited disorders. Mol. Syst. Biol. 5(321), 321 (2009)

    Google Scholar 

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Authors and Affiliations

  1. IBISC, Univ Evry, Université Paris-Saclay, 91025, Evry, France

    Célia Biane & Franck Delaplace

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  1. Célia Biane
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  2. Franck Delaplace
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Correspondence to Franck Delaplace .

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Editors and Affiliations

  1. Inria & École normale supérieure, Paris, France

    Jérôme Feret

  2. Technische Universität Darmstadt, Darmstadt, Germany

    Heinz Koeppl

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Biane, C., Delaplace, F. (2017). Abduction Based Drug Target Discovery Using Boolean Control Network. In: Feret, J., Koeppl, H. (eds) Computational Methods in Systems Biology. CMSB 2017. Lecture Notes in Computer Science(), vol 10545. Springer, Cham. https://doi.org/10.1007/978-3-319-67471-1_4

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  • DOI: https://doi.org/10.1007/978-3-319-67471-1_4

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