Advertisement

Logic and Linear Programs to Understand Cancer Response

  • Misbah Razzaq
  • Lokmane Chebouba
  • Pierre Le Jeune
  • Hanen Mhamdi
  • Carito GuziolowskiEmail author
  • Jérémie BourdonEmail author
Chapter
Part of the Computational Biology book series (COBO, volume 30)

Abstract

Understanding which are the key components of a system that distinguish a normal from a cancerous cell has been approached widely in the recent years using machine learning and statistical approaches to detect gene signatures and predict cell growth. Recently, the idea of using gene regulatory and signaling networks, in the form of logic programs has been introduced in order to detect the mechanisms that control cells state change. Complementary to this, a large literature deals with constraint-based methods for analyzing genome-scale metabolic networks. One of the major outcome of these methods concern the quantitative prediction of growth rates under both given environmental conditions and the presence or absence of a given set of enzymes which catalyze biochemical reactions. It is of high importance to plug logic regulatory models into metabolic networks by using a gene-enzyme logical interaction rule. In this work, our aim is first to review previously proposed logic programs to discover key components in the graph-based causal models that distinguish different variants of cell types. These variants represent either cancerous versus healthy cell types, multiple cancer cell lines, or patients with different treatment response. With these approaches, we can handle experimental data coming from transcriptomic profiles, gene expression micro-arrays or RNAseq, and (multi-perturbation) phosphoproteomics measurements. In a second part, we deal with the problem of combining both, regulatory and signaling, logic models within metabolic networks. Such a combination allows us to obtain quantitative prediction of tumor cell growth. Our results point to logic program models built for three cancer types: Multiple Myeloma, Acute Myeloid Leukemia, and Breast Cancer. Experimental data for these studies was collected through DREAM challenges and in collaboration with biologists that produced them. The networks were built using several publicly available pathway databases, such as Pathways Interaction Database [39], KEGG [18], Reactome [10], and Trrust [13]. We show how these models allow us to identify the key mechanisms distinguishing a cancerous cell. In complement to this, we sketch a methodology, based on currently available frameworks and datasets, that relates both the linear component of the metabolic networks and the logical part of logic programing-based methods.

Notes

Acknowledgements

This work has been partly supported by the SyMeTRIC Pays de la Loire Connect Talent project and by the GRIOTE Pays de la Loire Regional project. We also would like to thank Bertrand Miannay for fruitful discussions.

References

  1. 1.
    Agren R, Bordel S, Mardinoglu A, Pornputtapong N, Nookaew I, Nielsen J (2012) Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput Biol 8(5):e1002,518CrossRefGoogle Scholar
  2. 2.
    Agren R, Mardinoglu A, Asplund A, Kampf C, Uhlen M, Nielsen J (2014) Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol Syst Biol 10:721CrossRefGoogle Scholar
  3. 3.
    Apic G, Ignjatovic T, Boyer S, Russell RB (2005) Illuminating drug discovery with biological pathways. FEBS Lett 579(8):1872–1877CrossRefGoogle Scholar
  4. 4.
    Ates O (2015) Systems biology of microbial exopolysaccharides production. Front Bioeng Biotechnol 3:200CrossRefGoogle Scholar
  5. 5.
    Baral C (2003) Knowledge representation, reasoning, and declarative problem solving. Cambridge University Press, New York, NY, USACrossRefGoogle Scholar
  6. 6.
    Bentele M, Lavrik I, Ulrich M, Stößer S, Heermann D, Kalthoff H, Krammer P, Eils R (2004) Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis. J Cell Biol 166(6)CrossRefGoogle Scholar
  7. 7.
    Bordbar A, Monk JM, King ZA, Palsson BO (2014) Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 15(2):107–120CrossRefGoogle Scholar
  8. 8.
    Brunk E, Sahoo S, Zielinski DC, Altunkaya A, Drager A, Mih N, Gatto F, Nilsson A, Preciat Gonzalez GA, Aurich MK, Prli A, Sastry A, Danielsdottir AD, Heinken A, Noronha A, Rose PW, Burley SK, Fleming RMT, Nielsen J, Thiele I, Palsson BO (2018) Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat Biotechnol 36(3):272–281CrossRefGoogle Scholar
  9. 9.
    Chebouba L, Miannay B, Boughaci D, Guziolowski C (2018) Discriminate the response of acute myeloid leukemia patients to treatment by using proteomics data and answer set programming. BMC Bioinform 19(Suppl 2):59CrossRefGoogle Scholar
  10. 10.
    Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, Haw R, Jassal B, Korninger F, May B, Milacic M, Roca CD, Rothfels K, Sevilla C, Shamovsky V, Shorser S, Varusai T, Viteri G, Weiser J, Wu G, Stein L, Hermjakob H, D’Eustachio P (2018) The reactome pathway knowledgebase. Nucleic Acids Res 46(D1):D649–D655CrossRefGoogle Scholar
  11. 11.
    Gatto F, Miess H, Schulze A, Nielsen J (2015) Flux balance analysis predicts essential genes in clear cell renal cell carcinoma metabolism. Sci Rep 5(10):738Google Scholar
  12. 12.
    Gawehn E, Hiss JA, Schneider G (2016) Deep learning in drug discovery. Mol Inform 35(1):3–14CrossRefGoogle Scholar
  13. 13.
    Han H, Cho JW, Lee S, Yun A, Kim H, Bae D, Yang S, Kim CY, Lee M, Kim E, Lee S, Kang B, Jeong D, Kim Y, Jeon HN, Jung H, Nam S, Chung M, Kim JH, Lee I (2018a) TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res 46(D1):D380–D386CrossRefGoogle Scholar
  14. 14.
    Han H, Cho JW, Lee S, Yun A, Kim H, Bae D, Yang S, Kim CY, Lee M, Kim E, Lee S, Kang B, Jeong D, Kim Y, Jeon HN, Jung H, Nam S, Chung M, Kim JH, Lee I (2018b) TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res 46(D1):D380–D386CrossRefGoogle Scholar
  15. 15.
    Hill SM, Heiser LM, Cokelaer T, Unger M, Nesser NK, Carlin DE, Zhang Y, Sokolov A, Paull EO, Wong CK et al (2016) Inferring causal molecular networks: empirical assessment through a community-based effort. Nat Methods 13(4):310–318CrossRefGoogle Scholar
  16. 16.
    Hill SM, Nesser NK, Johnson-Camacho K, Jeffress M, Johnson A, Boniface C, Spencer SE, Lu Y, Heiser LM, Lawrence Y et al (2017) Context specificity in causal signaling networks revealed by phosphoprotein profiling. Cell Syst 4(1):73–83CrossRefGoogle Scholar
  17. 17.
    Inoue K (2011) Logic programming for boolean networks. In: Proceedings of the twenty-second international joint conference on artificial intelligence - volume two. AAAI Press, vol 22, IJCAI’11, pp 924–930Google Scholar
  18. 18.
    Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res:D480–D484CrossRefGoogle Scholar
  19. 19.
    Kauffman SA (1969) Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol 22(3):437–467MathSciNetCrossRefGoogle Scholar
  20. 20.
    King ZA, Lu J, Drager A, Miller P, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, Lewis NE (2016) BiGG models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res 44(D1):D515–522CrossRefGoogle Scholar
  21. 21.
    Korkut A, Wang W, Demir E, Aksoy BA, Jing X, Molinelli EJ, Babur O, Bemis DL, Onur Sumer S, Solit DB, Pratilas CA, Sander C (2015) Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells. Elife 4Google Scholar
  22. 22.
    Kuhn M, Yates P, Hyde C (2016) Statistical methods for drug discovery. Springer International Publishing, Cham, pp 53–81Google Scholar
  23. 23.
    Le Jeune P, Paris J, Voinea J, Liu J, Boulkenafet K (2018) Iguana. https://github.com/ipeter50/Iguana
  24. 24.
    Lefebvre M, Bourdon J, Guziolowski C, Gaignard A (2017) Regulatory and signaling network assembly through linked open data. Demo paper, Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM2017)Google Scholar
  25. 25.
    Lima AN, Philot EA, Trossini GHG, Scott LPB, Maltarollo VG, Honorio KM (2016) Use of machine learning approaches for novel drug discovery. Expert Opin Drug Discov 11(3):225–239CrossRefGoogle Scholar
  26. 26.
    Liu W, Li C, Xu Y, Yang H, Yao Q, Han J, Shang D, Zhang C, Su F, Li X, Xiao Y, Zhang F, Dai M, Li X (2013) Topologically inferring risk-active pathways toward precise cancer classification by directed random walk. Bioinformatics (Oxford, England) 29(17):2169–2177.  https://doi.org/10.1093/bioinformatics/btt373CrossRefGoogle Scholar
  27. 27.
    Machado D, Herrgard M (2014) Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol 10(4):e1003,580CrossRefGoogle Scholar
  28. 28.
    Magnusdottir S, Heinken A, Kutt L, Ravcheev DA, Bauer E, Noronha A, Greenhalgh K, Jager C, Baginska J, Wilmes P, Fleming RM, Thiele I (2017) Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat Biotechnol 35(1):81–89CrossRefGoogle Scholar
  29. 29.
    Marx V (2013) Biology: the big challenges of big data. Nature 498(7453):255–260.  https://doi.org/10.1038/498255aCrossRefGoogle Scholar
  30. 30.
  31. 31.
    Miannay B, Minvielle S, Roux O, Drouin P, Avet-Loiseau H, Guérin-Charbonnel C, Gouraud W, Attal M, Facon T, Munshi NC, Moreau P, Campion L, Magrangeas F, Guziolowski C (2017) Logic programming reveals alteration of key transcription factors in multiple myeloma. Sci Rep 7(1):9257CrossRefGoogle Scholar
  32. 32.
    Miannay B, Minvielle S, Magrangeas F, Guziolowski C (2018) Constraints on signaling network logic reveal functional subgraphs on multiple myeloma OMIC data. BMC Syst Biol 12(Suppl 3):32CrossRefGoogle Scholar
  33. 33.
    Mitra K, Carvunis AR, Ramesh SK, Ideker T (2013) Integrative approaches for finding modular structure in biological networks. Nat Rev Genet 14(10):719–732CrossRefGoogle Scholar
  34. 34.
    Murphy RF (2011) An active role for machine learning in drug development. Nat Chem Biol 7:327–330CrossRefGoogle Scholar
  35. 35.
    Nevins JR (2001) The Rb/E2F pathway and cancer. Hum Mol Genet 10(7):699–703.  https://doi.org/10.1093/hmg/10.7.699CrossRefGoogle Scholar
  36. 36.
    Noren D, Long B, Norel R, Rrhissorrakrai K, Hess K, Hu C, Bisberg A, Schultz A, Engquist E, Liu L, Lin X, Chen G, Xie H, Hunter G, Boutros P, Stepanov O, Norman T, Friend S, Stolovitzky G, Kornblau S, Qutub A, DREAM 9 AML-OPC Consortium (2016) A crowdsourcing approach to developing and assessing prediction algorithms for aml prognosis. PLoS Comput Biol 12(6)Google Scholar
  37. 37.
    Ostrowski M, Paulevé L, Schaub T, Siegel A, Guziolowski C (2016) Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming. Biosystems 149:139–153CrossRefGoogle Scholar
  38. 38.
    Pornputtapong N, Nookaew I, Nielsen J (2015) Human metabolic atlas: an online resource for human metabolism. Database (Oxford) 2015:bav068Google Scholar
  39. 39.
    Pratt D, Chen J, Pillich R, Rynkov V, Gary A, Demchak B, Ideker T (2017) NDEx 2.0: a clearinghouse for research on cancer pathways. Cancer Res 77(21):e58–e61CrossRefGoogle Scholar
  40. 40.
    Rajkumar SV (2016) Multiple myeloma: 2016 update on diagnosis, risk-stratification, and management. Am J Hematol 91(7):719–734.  https://doi.org/10.1002/ajh.24402CrossRefGoogle Scholar
  41. 41.
    Razzaq M, Kaminski R, Romero J, Schaub T, Bourdon J, Guziolowski C (2018a) Computing diverse boolean networks from phosphoproteomic time series data. In: Ceska M, Safránek D (eds) Computational methods in systems biology - 16th international conference, CMSB 2018, Brno, Czech Republic, September 12-14, 2018, Proceedings, Lecture notes in computer science, vol 11095, Springer, Berlin, pp 59–74. https://doi.org/10.1007/978-3-319-99429-1_4CrossRefGoogle Scholar
  42. 42.
    Razzaq M, Paulevé L, Ostrowski M (2018b) Caspo-ts. https://github.com/misbahch6/caspo-ts
  43. 43.
    Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH (2009) PID: the Pathway Interaction Database. Nucleic acids research 37(Database issue):D674–D679. https://doi.org/10.1093/nar/gkn653CrossRefGoogle Scholar
  44. 44.
    Thiele S, Cerone L, Saez-Rodriguez J, Siegel A, Guziołowski C, Klamt S (2015) Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies. BMC Bioinform 16(1):345.  https://doi.org/10.1186/s12859-015-0733-7CrossRefGoogle Scholar
  45. 45.
    Thomas D, Powell JA, Vergez F, Segal DH, Nguyen NY, Baker A, Teh TC, Barry EF, Sarry JE, Lee EM, Nero TL, Jabbour AM, Pomilio G, Green BD, Manenti S, Glaser SP, Parker MW, Lopez AF, Ekert PG, Lock RB, Huang DC, Nilsson SK, Recher C, Wei AH, Guthridge MA (2013) Targeting acute myeloid leukemia by dual inhibition of PI3K signaling and Cdk9-mediated Mcl-1 transcription. Blood 122(5):738–748CrossRefGoogle Scholar
  46. 46.
    Thomas PD, Kejariwal A, Campbell MJ, Mi H, Diemer K, Guo N, Ladunga I, Ulitsky-Lazareva B, Muruganujan A, Rabkin S, Vandergriff JA, Doremieux O (2003) PANTHER: a browsable database of gene products organized by biological function, using curated protein family and subfamily classification. Nucleic Acids Res 31(1):334–341CrossRefGoogle Scholar
  47. 47.
    Videla S, Guziolowski C, Eduati F, Thiele S, Grabe N, Saez-Rodriguez J, Siegel A (2012) Revisiting the training of logic models of protein signaling networks with asp. Computational methods in systems biology. Springer, Berlin/Heidelberg, pp 342–361CrossRefGoogle Scholar
  48. 48.
    Videla S, Saez-Rodriguez J, Guziolowski C, Siegel A (2017) caspo: a toolbox for automated reasoning on the response of logical signaling networks families. Bioinformatics 33(6):947–950Google Scholar
  49. 49.
    Wang Y (Marcia) (2005) Statistical methods for high throughput screening drug discovery data. PhD thesis. http://hdl.handle.net/10012/1204
  50. 50.
    Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, Assempour N, Iynkkaran I, Liu Y, Maciejewski A, Gale N, Wilson A, Chin L, Cummings R, Le D, Pon A, Knox C, Wilson M (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46(D1):D1074–D1082CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Misbah Razzaq
    • 1
  • Lokmane Chebouba
    • 2
  • Pierre Le Jeune
    • 1
  • Hanen Mhamdi
    • 1
  • Carito Guziolowski
    • 1
    Email author
  • Jérémie Bourdon
    • 1
    Email author
  1. 1.Université de Nantes, Centrale Nantes, CNRS, LS2N-UMR 6004NantesFrance
  2. 2.Department of Computer Science, LRIA Laboratory, Electrical Engineering and Computer Science FacultyUniversity of Science and Technology Houari Boumediene (USTHB)AlgiersAlgeria

Personalised recommendations