DOI QR코드

DOI QR Code

Parameters Involved in Autophosphorylation in Chronic Myeloid Leukemia: a Systems Biology Approach

  • Kumar, Himansu (Department of Bioinformatics, Indian Institute of Information Technology) ;
  • Tichkule, Swapnil (Department of Bioinformatics, Indian Institute of Information Technology) ;
  • Raj, Utkarsh (Department of Bioinformatics, Indian Institute of Information Technology) ;
  • Gupta, Saurabh (Department of Bioinformatics, Indian Institute of Information Technology) ;
  • Srivastava, Swati (Lovely Professional University) ;
  • Varadwaj, Pritish Kumar (Department of Bioinformatics, Indian Institute of Information Technology)
  • 발행 : 2015.08.03

초록

Background: Chronic myeloid leukemia (CML) is a stem cell disorder characterized by the fusion of two oncogenes namely BCR and ABL with their aberrant expression. Autophosphorylation of BCR-ABL oncogenes results in proliferation of CML. The study deals with estimation of rate constant involved in each step of the cellular autophosphorylation process, which are consequently playing important roles in the proliferation of cancerous cells. Materials and Methods: A mathematical model was proposed for autophosphorylation of BCR-ABL oncogenes utilizing ordinary differential equations to enumerate the rate of change of each responsible system component. The major difficulty to model this process is the lack of experimental data, which are needed to estimate unknown model parameters. Initial concentration data of each substrate and product for BCR-ABL systems were collected from the reported literature. All parameters were optimized through time interval simulation using the fminsearch algorithm. Results: The rate of change versus time was estimated to indicate the role of each state variable that are crucial for the systems. The time wise change in concentration of substrate shows the convergence of each parameter in autophosphorylation process. Conclusions: The role of each constituent parameter and their relative time dependent variations in autophosphorylation process could be inferred.

키워드

참고문헌

  1. Bayard C, Annabel S (1988). Biology of chronic myelogenous leukemia: Is discordant maturation the primary defect? Seminars in Hematology, 25, 1-19.
  2. Bogacki P, Shampine LF (1989). A 3(2) pair of Runge-Kutta formulas. Appl Math Letters, 2, 321-25. https://doi.org/10.1016/0893-9659(89)90079-7
  3. Clarkson BD, Fokas AS, Keller JB (1991). Mathematical model of granulocytopoiesis and chronic myelogenousleukaemia. Cancer Research, 51, 2084-91.
  4. Daley GQ, Van ERA, Baltimore D (1990). Induction of chronic myelogenous leukemia in mice by the P210 bcr/abl gene of the Philadelphia chromosome. Science, 247, 824-30. https://doi.org/10.1126/science.2406902
  5. Deininger MW, Goldman JM, Melo JV (2000). The molecular biology of chronic myeloid leukemia. Blood, 96, 3343-56.
  6. Deng Z, Tian T (2014). A continuous optimization approach for inferring parameters in mathematical models of regulatory networks. BMC bioinformatics, 15, 256. https://doi.org/10.1186/1471-2105-15-256
  7. Eaves C, Udomsakdi C, Cashman J, et al (1993). The biology of normal and neoplastic stem cells in CML. Leukemia and Lymphoma, 11, 245-53. https://doi.org/10.3109/10428199309047894
  8. Edda K, Wolfram L (2006). Mathematical modeling of intracellular signaling pathways. BMC Neuroscience, 7, 10. https://doi.org/10.1186/1471-2202-7-10
  9. Finley SD, Chu LH, Popel AS (2014). Computational systems biology approaches to anti-angiogenic cancer therapeutics. Drug discovery today, 20, 187-97.
  10. Heinrich R, Neel BG, Rapoport TA (2002). Mathematical models of protein kinase signal transduction. Mol Cell, 9, 957-70. https://doi.org/10.1016/S1097-2765(02)00528-2
  11. Howard S, Yiannis K (2005).Accurate hybrid stochastic simulation of a system of coupled chemical or biochemical reactions. AIP Journal of chemical physics, 122, 054103. https://doi.org/10.1063/1.1835951
  12. Lagarias JC, Reeds JA, Wright MH, et al (1998). Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM Journal on optimization, 9, 112-47. https://doi.org/10.1137/S1052623496303470
  13. Liepe J, Kirk P, Filippi S, et al (2014). A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. Nature protocols, 9, 439-56. https://doi.org/10.1038/nprot.2014.025
  14. MacLean AL, Harrington HA, Stumpf MP, et al (2015). Mathematical and statistical techniques for systems medicine: The Wnt signaling pathway as a case study. arXiv preprint arXiv, 1502, 1902.
  15. Marley SB, Deininger MW, Davidson R Jet al (2000). Thetyrosine kinase inhibitor STI571, like interferon-a, preferentially reduces the capacity for amplification of granulocyte-macrophage progenitors from patients with chronicmyeloid leukemia. Exp Hematol, 28, 551-7. https://doi.org/10.1016/S0301-472X(00)00142-9
  16. Marshall-Colon A, Sengupta N, Rhodes D, et al (2014). Simulating labeling to estimate kinetic parameters for flux control analysis. In Plant Metabolic Flux Analysis, 1090, 211-22. https://doi.org/10.1007/978-1-62703-688-7_13
  17. Mauro MJ, Druker BJ (2001). Chronicmyelogenous leukemia. Curr Opin Oncol, 13, 3-7. https://doi.org/10.1097/00001622-200101000-00002
  18. Michor F (2005). Dynamics of chronic myeloid leukemia. Nature, 435, 1267-70. https://doi.org/10.1038/nature03669
  19. Obel J, Souares Y, Hoy D, et al (2014). A systematic review of cervical cancer incidence and mortality in the pacific region. Asian Pac J Cancer Prev, 15, 9433. https://doi.org/10.7314/APJCP.2014.15.21.9433
  20. Papin JA, Hunter T, Palsson BO, et al (2005). Reconstruction of cellular signalling networks and analysis of their properties. Nat Rev Mol Cell Biol, 6, 99-111. https://doi.org/10.1038/nrm1570
  21. Pep C, Xiao Hu, Luonan, C, et al (2004). A mathematical model Ofbcr-ablautophosphorylation, Signaling through the crklpathway, and Gleevec dynamics in chronic myeloid leukemia. Discrete and Continuous, Dynamical Systems-Series B, 4.
  22. Pokhilko A, Bou Torrent J, Pulido P, et al (2015). Mathematical modelling of the diurnal regulation of the MEP pathway in Arabidopsis. New Phytologist.
  23. Powell Michael JD (1973). On search directions for minimization algorithms. Mathematical Programming, 4, 193-201. https://doi.org/10.1007/BF01584660
  24. Raue A, Karlsson J, Saccomani MP, et al (2014). Comparison of approaches for parameter identifiability analysis of biological systems. Bioinformatics, 30, 1440-8. https://doi.org/10.1093/bioinformatics/btu006
  25. Sailaja K, Rao DN, Rao DR, et al (2010). Analysis of CYP3A5* 3 and CYP3A5* 6 gene polymorphisms in Indian chronic myeloid leukemia patients. Asian Pac J Cancer Prev, 11, 781-4.
  26. Savage DG, & Antman KH (2002). Imatinib mesylate-a new oral targeted therapy. New England Journal of Medicine, 346, 683-93. https://doi.org/10.1056/NEJMra013339
  27. SmithJA, FrancisSH, Corbin JD (1993). Autophosphorylation: A salient feature of protein kinases. Molecular and cellular Biochemistry, 127-128, 51-70. https://doi.org/10.1007/BF01076757
  28. Summers KC, Shen F, Sierra PEA, et al (2011). Phosphorylation: the molecular switch of double-strand break repair. Int J Prot, 2011, 373816.
  29. Tianhai T, Jiangning S (2012). Mathematical Modeling of the MAP kinase pathway using proteomic datasets. PLoS ONE, 7.
  30. Tyson, JJ, Chen KC, Novak B (2003). Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. CurrOpin Cell Biol, 15, 221-31. https://doi.org/10.1016/S0955-0674(03)00017-6
  31. Vera J, Gupta SK, Wolkenhauer O, et al (2015). Envisioning the application of systems biology in cancer immunology. In Cancer Immunology, 429-49.
  32. Wu W, Feng S, Wang Y, et al (2014). Systems mapping of genes controlling chemotherapeutic drug efficiency for cancer stem cells. Drug discovery today, 19, 1125-30. https://doi.org/10.1016/j.drudis.2013.12.010
  33. Ye YX, Zhou J, Zhou YH, et al (2014) Clinical Significance of BCR-ABL Fusion Gene Subtypes in Chronic Myelogenous and Acute Lymphoblastic Leukemias. Asian Pac J Cancer Prev, 15, 9961-6. https://doi.org/10.7314/APJCP.2014.15.22.9961
  34. Yu-Qing Ge, Xiao-Feng Xu, Bo Yang, et al (2014). Saponins from Rubusparvifolius L. Induce Apoptosis in Human Chronic Myeloid Leukemia Cells through AMPK Activation and STAT3 Inhibition. Asian Pac J Cancer Prev, 15, 5455-61. https://doi.org/10.7314/APJCP.2014.15.13.5455
  35. Zhu XS, Lin ZY, Du J, Cao GX, Liu G (2014). BCR/ABL mRNA targeting small interfering RNA effects on proliferation and apoptosis in chronic myeloid leukemia. Asian Pac J Cancer Prev, 15, 4773-80. https://doi.org/10.7314/APJCP.2014.15.12.4773

피인용 문헌

  1. Systems biology: impressions from a newcomer graduate student in 2016 vol.40, pp.4, 2016, https://doi.org/10.1152/advan.00172.2015