American Institute of Mathematical Sciences

Advanced
February  2004, 4(1): 99-114. doi: 10.3934/dcdsb.2004.4.99

A mathematical model of BCR-ABL autophosphorylation, signaling through the CRKL pathway, and Gleevec dynamics in chronic myeloid leukemia

Pep Charusanti 1, , Xiao Hu 2, , Luonan Chen 2, , Daniel Neuhauser 1, and  Joseph J. DiStefano III 2,

1. 

Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095-1569, United States, United States
2. 

Biocybernetics Laboratory, Departments of Computer Science and Medicine, University of California, Los Angeles, Los Angeles, California 90095-1596, United States, United States, United States

Received  November 2002 Revised  June 2003 Published  November 2003

A mathematical model is presented that describes several signaling events that occur in cells from patients with chronic myeloid leukemia, i.e. autophosphorylation of the Bcr-Abl oncoprotein and subsequent signaling through the Crkl pathway. Dynamical effects of the drug STI-571 (Gleevec) on these events are examined, and a minimal concentration for drug effectiveness is predicted by simulation. Most importantly, the model suggests that, for cells in blast crisis, cellular drug clearance mechanisms such as drug efflux pumps dramatically reduce the effectiveness of Gleevec. This is a new prediction regarding the efficacy of Gleevec. In addition, it is speculated that these resistance mechanisms might be present from the onset of disease.
Keywords: Chronic myeloid leukemia, clearance, autophosphorylation., Gleevec, Bcr-Abl.
Mathematics Subject Classification: 92C45, 92B0.
Citation: Pep Charusanti, Xiao Hu, Luonan Chen, Daniel Neuhauser, Joseph J. DiStefano III. A mathematical model of BCR-ABL autophosphorylation, signaling through the CRKL pathway, and Gleevec dynamics in chronic myeloid leukemia. Discrete and Continuous Dynamical Systems - B, 2004, 4 (1) : 99-114. doi: 10.3934/dcdsb.2004.4.99
[1]

Natalia L. Komarova. Mathematical modeling of cyclic treatments of chronic myeloid leukemia. Mathematical Biosciences & Engineering, 2011, 8 (2) : 289-306. doi: 10.3934/mbe.2011.8.289
[2]

R. A. Everett, Y. Zhao, K. B. Flores, Yang Kuang. Data and implication based comparison of two chronic myeloid leukemia models. Mathematical Biosciences & Engineering, 2013, 10 (5&6) : 1501-1518. doi: 10.3934/mbe.2013.10.1501
[3]

Seema Nanda, Lisette dePillis, Ami Radunskaya. B cell chronic lymphocytic leukemia - A model with immune response. Discrete and Continuous Dynamical Systems - B, 2013, 18 (4) : 1053-1076. doi: 10.3934/dcdsb.2013.18.1053
[4]

Avner Friedman, Chuan Xue. A mathematical model for chronic wounds. Mathematical Biosciences & Engineering, 2011, 8 (2) : 253-261. doi: 10.3934/mbe.2011.8.253
[5]

Elena Fimmel, Yury S. Semenov, Alexander S. Bratus. On optimal and suboptimal treatment strategies for a mathematical model of leukemia. Mathematical Biosciences & Engineering, 2013, 10 (1) : 151-165. doi: 10.3934/mbe.2013.10.151
[6]

Samantha Erwin, Stanca M. Ciupe. Germinal center dynamics during acute and chronic infection. Mathematical Biosciences & Engineering, 2017, 14 (3) : 655-671. doi: 10.3934/mbe.2017037
[7]

Olga Vasilyeva, Tamer Oraby, Frithjof Lutscher. Aggregation and environmental transmission in chronic wasting disease. Mathematical Biosciences & Engineering, 2015, 12 (1) : 209-231. doi: 10.3934/mbe.2015.12.209
[8]

Silviu-Iulian Niculescu, Peter S. Kim, Keqin Gu, Peter P. Lee, Doron Levy. Stability crossing boundaries of delay systems modeling immune dynamics in leukemia. Discrete and Continuous Dynamical Systems - B, 2010, 13 (1) : 129-156. doi: 10.3934/dcdsb.2010.13.129
[9]

Ling Xu, Yi Jiang. Cilium height difference between strokes is more effective in driving fluid transport in mucociliary clearance: A numerical study. Mathematical Biosciences & Engineering, 2015, 12 (5) : 1107-1126. doi: 10.3934/mbe.2015.12.1107
[10]

K. E. Starkov, Svetlana Bunimovich-Mendrazitsky. Dynamical properties and tumor clearance conditions for a nine-dimensional model of bladder cancer immunotherapy. Mathematical Biosciences & Engineering, 2016, 13 (5) : 1059-1075. doi: 10.3934/mbe.2016030

2020 Impact Factor: 1.327

Tools

Metrics

  • PDF downloads (66)
  • HTML views (0)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2022 American Institute of Mathematical Sciences | Privacy Policy