Systems biology approach deciphering the biochemical signaling pathway and pharmacokinetic study of PI3K/mTOR/p53-Mdm2 module involved in neoplastic transformation

  • Devender Arora
  • Ajeet Singh
Original Article


Cancer is a serious health concern growing at a rapid speed where normal cells take neoplastic transformation. Different pathway is tightly regulated with each other to maintain the harmony and sudden changes in single protein leads to aberrant changes in the whole system. Development of drugs to target these proteins aimed to block the signaling route that leads to cell death. Here, in this study, we performed in silico expression analysis of these potential proteins using system biological approach by mimicking the cell and understanding the behavior of different proteins in drugging condition. We performed in silico biomolecular interaction analysis for exploring the potential plant-derived compounds that can be served as an anticancerous drug with least toxicity by comparing with reference drug approved by FDA. Our results suggest that PI3K, p53-Mdm2 proteins are ideal proteins for targeting cancer cells, while overexpression of mTOR protein was observed when drug targeted this receptor. We state that PI3K family protein plays important role in drug discovery, and compounds obtained from in silico analysis can be served as a potential anticancerous drug for treating different cancer types.


PI3K/mTOR/p53-Mdm2 Systems biology Anticancerous drug Biomolecular interaction 



This study was conducted in the Department of Biotechnology, G.B. Pant Engineering College (GBPEC), Pauri Garhwal (Uttarakhand). Devender Arora is thankful to TEQIP-II (Technical Education Quality Improvement Program) for financial assistance.

Compliance with ethical standards

Conflict of interest

Authors have no conflict of interest regarding the publication of paper.


  1. Anderson AR, Quaranta V (2008) Integrative mathematical oncology. Nat Rev Cancer 8(3):227–234CrossRefGoogle Scholar
  2. Arora R, Gupta D, Chawla R, Sagar R, Sharma A, Kumar R, Prasad J, Singh S, Samanta N, Sharma RK (2005) Radioprotection by plant products: present status and future prospects. Phytother Res 19(1):1–22CrossRefGoogle Scholar
  3. Arora D, Singh V, Singh A (2016) Modeling and simulation analysis of Salmonella typhimurium inside human epithelial cells: host-pathogen relationship analysis by system biology. In: Bioinformatics and systems biology (BSB), IEEE, pp 1–4Google Scholar
  4. Arora D, Chaudhary R, Singh A (2017a) System biology approach to identify potential receptor for targeting cancer and biomolecular interaction studies of indole [2, 1-a] isoquinoline derivative as anticancerous drug candidate against it. Interdiscip Sci Sciences 26:1Google Scholar
  5. Arora D, Jyoti K, Singh A (2017b) Understanding the role of Salmonella pathogenic island 1 (SPI-I) and host-pathogen interaction for typhoid using system biology approach. Int J Bioinform Res Appl 13(3):187–199CrossRefGoogle Scholar
  6. Bader AG, Kang S, Vogt PK (2006) Cancer-specific mutations in PIK3CA are oncogenic in vivo. Proc Natl Acad Sci 103(5):1475–1479CrossRefGoogle Scholar
  7. Beauchamp EM, Platanias LC (2013) The evolution of the TOR pathway and its role in cancer. Oncogene 32(34):3923–3932CrossRefGoogle Scholar
  8. Boyd MR, Paull KD (1995) Some practical considerations and applications of the National Cancer Institute in vitro anticancer drug discovery screen. Drug Dev Res 34(2):91–109CrossRefGoogle Scholar
  9. Brent R (2000) Genomic biology. Cell 100(1):169–183CrossRefGoogle Scholar
  10. Chène P (2003) Inhibiting the p53–MDM2 interaction: an important target for cancer therapy. Nat Rev Cancer 3(2):102–109CrossRefGoogle Scholar
  11. Chico LK, Van Eldik LJ, Watterson DM (2009) Targeting protein kinases in central nervous system disorders. Nat Rev Drug Discov 8(11):892–909CrossRefGoogle Scholar
  12. Clarke PA, te Poele R, Wooster R, Workman P (2001) Gene expression microarray analysis in cancer biology, pharmacology, and drug development: progress and potential. Biochem Pharmacol 62(10):1311–1336CrossRefGoogle Scholar
  13. Cohen P (2002) Protein kinases—the major drug targets of the twenty-first century? Nat Rev Drug Discov 1(4):309–315CrossRefGoogle Scholar
  14. Cohen P, Alessi DR (2012) Kinase drug discovery–what’s next in the field? ACS Chem Biol 8(1):96–104CrossRefGoogle Scholar
  15. Csizmadia P (1999) September. MarvinSketch and MarvinView: molecule applets for the World Wide Web. In: Proceedings of ECSOC-3, the third international electronic conference on synthetic organic chemistry, September 1ą30, pp 367–369Google Scholar
  16. Dräger A, Hassis N, Supper J, Schröder A, Zell A (2008) SBMLsqueezer: a cell designer plug-into generate kinetic rate equations for biochemical networks. BMC Syst Biol 2(1):39CrossRefGoogle Scholar
  17. Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ (2016) Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc 11(5):905–919CrossRefGoogle Scholar
  18. Fridman JS, Lowe SW (2003) Control of apoptosis by p53. Oncogene 22(56):9030–9040CrossRefGoogle Scholar
  19. Funahashi A, Morohashi M, Kitano H, Tanimura N (2003) Cell Designer: a process diagram editor for gene-regulatory and biochemical networks. Biosilico 1(5):159–162CrossRefGoogle Scholar
  20. Funahashi A, Morohashi M, Matsuoka Y, Jouraku A, Kitano H (2007) cell designer: a graphical biological network editor and workbench interfacing simulator. In: Choi S (ed) Introduction to systems biology. Humana Press, New York, pp 422–434CrossRefGoogle Scholar
  21. Funahashi A, Matsuoka Y, Jouraku A, Morohashi M, Kikuchi N, Kitano H (2008) Cell Designer 3.5: a versatile modeling tool for biochemical networks. Proc IEEE 96(8):1254–1265CrossRefGoogle Scholar
  22. Gressner AM, Weiskirchen R (2006) Modern pathogenetic concepts of liver fibrosis suggest stellate cells and TGF-β as major players and therapeutic targets. J Cell Mol Med 10(1):76–99CrossRefGoogle Scholar
  23. Hainaut P, Hollstein M (1999) p53 and human cancer: the first ten thousand mutations. Adv Cancer Res 77:81–137CrossRefGoogle Scholar
  24. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144(5):646–674CrossRefGoogle Scholar
  25. Hartman JL, Garvik B, Hartwell L (2001) Principles for the buffering of genetic variation. Science 291(5506):1001–1004CrossRefGoogle Scholar
  26. Hendriks BS, Hua F, Chabot JR (2008) Analysis of mechanistic pathway models in drug discovery: p38 pathway. Biotechnol Prog 24(1):96–109CrossRefGoogle Scholar
  27. Hoops S, Sahle S, Gauges R, Lee C, Pahle J, Simus N, Singhal M, Xu L, Mendes P, Kummer U (2006) COPASI—a complex pathway simulator. Bioinformatics 22(24):3067–3074CrossRefGoogle Scholar
  28. Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D (2003) Cornish-Bowden A, Cuellar AA. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19(4):524–531CrossRefGoogle Scholar
  29. Hutchinson TE, Zhang J, Xia SL, Kuchibhotla S, Block ER, Patel JM (2012) Enhanced phosphorylation of caveolar PKC-α limits peptide internalization in lung endothelial cells. Mol Cell Biochem 360(1–2):309–320CrossRefGoogle Scholar
  30. Insall RH, Weiner OD (2001) PIP3, PIP2, and cell movement—similar messages, different meanings? Dev Cell 1(6):743–747CrossRefGoogle Scholar
  31. Jain RK, Duda DG, Clark JW, Loeffler JS (2006) Lessons from phase III clinical trials on anti-VEGF therapy for cancer. Nat Clin Pract Oncol 3(1):24–40CrossRefGoogle Scholar
  32. Jänne PA, Gray N, Settleman J (2009) Factors underlying sensitivity of cancers to small-molecule kinase inhibitors. Nat Rev Drug Discov 8(9):709–723CrossRefGoogle Scholar
  33. Jayaraj P, Sen S, Sharma A, Chosdol K, Kashyap S, Rai A, Pushker N, Bajaj M (2015) Eyelid sebaceous carcinoma: a novel mutation in lymphoid enhancer-binding factor-1. Br J Dermatol 173(3):811–814CrossRefGoogle Scholar
  34. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30CrossRefGoogle Scholar
  35. Koutsogiannouli E, Papavassiliou AG, Papanikolaou NA (2013) Complexity in cancer biology: is systems biology the answer? Cancer Med 2(2):164–177CrossRefGoogle Scholar
  36. Kreeger PK, Lauffenburger DA (2010) Cancer systems biology: a network modeling perspective. Carcinogenesis 31(1):2–8CrossRefGoogle Scholar
  37. Lakin ND, Jackson SP (1999) Regulation of p53 in response to DNA damage. Oncogene 18(53):7644–7655CrossRefGoogle Scholar
  38. Laplante M, Sabatini DM (2009) mTOR signaling at a glance. J Cell Sci 122(20):3589–3594CrossRefGoogle Scholar
  39. Lewis NE, Abdel-Haleem AM (2013) The evolution of genome-scale models of cancer metabolism. Front Physiol 4:237Google Scholar
  40. Lin MC, Lin GZ, Hwang CI, Jian SY, Lin J, Shen YF, Lin G (2012) Synthesis and evaluation of a new series of tri-, di-, and mono-N-alkylcarbamylphloroglucinols as conformationally constrained inhibitors of cholesterol esterase. Protein Sci 21(9):1344–1357CrossRefGoogle Scholar
  41. Melnikova I, Golden J (2004) Targeting protein kinases. Nat Rev Drug Discov 3(12):993–994CrossRefGoogle Scholar
  42. Momand J, Wu HH, Dasgupta G (2000) MDM2—master regulator of the p53 tumor suppressor protein. Gene 242(1):15–29CrossRefGoogle Scholar
  43. Prives C (1998) Signaling to p53: breaking the MDM2–p53 circuit. Cell 95(1):5–8CrossRefGoogle Scholar
  44. Rozengurt E, Soares HP, Sinnet-Smith J (2014) Suppression of feedback loops mediated by PI3K/mTOR induces multiple over activation of compensatory pathways: an unintended consequence leading to drug resistance. Mol Cancer Ther 13(11):2477–2488CrossRefGoogle Scholar
  45. Sakaguchi K, Herrera JE, Saito SI, Miki T, Bustin M, Vassilev A, Anderson CW, Appella E (1998) DNA damage activates p53 through a phosphorylation–acetylation cascade. Genes Dev 12(18):2831–2841CrossRefGoogle Scholar
  46. Sawyers CL, Abate-Shen C, Anderson KC, Barker A, Baselga J, Berger NA, Foti M, Jemal A, Lawrence TS, Li CI, Mardis ER (2013) AACR cancer progress report 2013. Clin Cancer Res 19(20 Suppl):S1–S98CrossRefGoogle Scholar
  47. Shangary S, Wang S (2008) Targeting the MDM2-p53 interaction for cancer therapy. Clin Cancer Res 14(17):5318–5324CrossRefGoogle Scholar
  48. Shi D, Gu W (2012) Dual roles of MDM2 in the regulation of p53 ubiquitination dependent and ubiquitination independent mechanisms of MDM2 repression of p53 activity. Genes Cancer 3(3–4):240–248CrossRefGoogle Scholar
  49. Singh L, Pushker N, Sen S, Singh MK, Chauhan FA, Kashyap S (2015) Prognostic significance of polo-like kinases in retinoblastoma: correlation with patient outcome, clinical and histopathological parameters. Clin Exp Ophthalmol 43(6):550–557CrossRefGoogle Scholar
  50. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461Google Scholar
  51. Turkson J, Jove R (2000) STAT proteins: novel molecular targets for cancer drug discovery. Oncogene 19(56):6613CrossRefGoogle Scholar
  52. Vogelstein B, Lane D, Levine AJ (2000a) Surfing the p53 network. Nature 408(6810):307–310CrossRefGoogle Scholar
  53. Vogelstein B, Lane D, Levine AJ (2000b) Surfing the p53 network. Nature 408(6810):307–310CrossRefGoogle Scholar
  54. von Manstein V, Min Yang C, Richter D, Delis N, Vafaizadeh V, Groner B (2013) Resistance of cancer cells to targeted therapies through the activation of compensating signaling loops. Curr Signal Transduct Ther 8(3):193–202CrossRefGoogle Scholar
  55. Vousden KH, Lu X (2002) Live or let die: the cell’s response to p53. Nat Rev Cancer 2(8):594–604CrossRefGoogle Scholar
  56. Wade M, Li YC, Wahl GM (2013) MDM2, MDMX and p53 in oncogenesis and cancer therapy. Nat Rev Cancer 13(2):83–96CrossRefGoogle Scholar
  57. Yang Y, Li CC, Weissman AM (2004) Regulating the p53 system through ubiquitination. Oncogene 23(11):2096–2106CrossRefGoogle Scholar
  58. Yarden Y, Pines G (2012) The ERBB network: at last, cancer therapy meets systems biology. Nat Rev Cancer 12(8):553–563CrossRefGoogle Scholar
  59. Yıldırım MA, Goh KI, Cusick ME, Barabási AL, Vidal M (2007) Drug—target network. Nat Biotechnol 25(10):1119–1126CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of BiotechnologyG.B. Pant Engineering CollegePauriIndia

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