1887
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
oa
Modeling TGF-β signaling pathway in epithelial-mesenchymal transition
Rent:
Rent this article for
Access full text Article
/content/aip/journal/adva/2/1/10.1063/1.3697962
1.
1. Jean Paul Thiery, Hervé Acloque, Rubi Y. J. Huang, and M. Angela Nieto, “Epithelial-mesenchymal transition in development and disease,” Cell 21, 166176 (2009).
2.
2. JCarl-Henrik Heldin, Maréne Landström, and Aristidis Moustakas, “Mechanism of TGF-β signaling to growth arrest, apoptosis, and epithelial mesenchymal transition,” Current Opinion in Cell Biology 21, 166176 (2009).
http://dx.doi.org/10.1016/j.ceb.2009.01.021
3.
3. Bernhard Schmierer, Alexander L. Tournier, Paul A. Bates, and Caroline S. Hill, “Mathematical modeling identifies Smad nucleocytoplasmic shuttling as a dynamic signal-interpreting system,” PNAS 105 no. 18, 66086613 (2008).
http://dx.doi.org/10.1073/pnas.0710134105
4.
4. D. C. Clarke, M. D. Betterton, and X. Liu, “Systems theory of Smad signalling,” IEE Proc.-Syst. Biol. 153 no. 6, 412424 (2006).
http://dx.doi.org/10.1049/ip-syb:20050055
5.
5. Zhike Zi and Edda Klipp, “Constraint-Based Modeling and Kinetic Analysis of the Smad Dependent TGF-β Signaling Pathway,” PLOS one 9, e936 (2007).
http://dx.doi.org/10.1371/journal.pone.0000936
6.
6. Jose M. G. Vilar, Ronald Jansen, and Chris Sander, “Signal Processing in the TGF-b Superfamily Ligand-Receptor Network,” PLOS Computational Biology 1, e3 (2006).
http://dx.doi.org/10.1371/journal.pcbi.0020003
7.
7. Linsey E. Lindley and Karoline J. Briegel, “Molecular characterization of TGFb-induced epithelial-mesenchymal transition in normal finite lifespan human mammary epithelial cells,” Biochemical and Biophysical Research Communications 399, 659664 (2010).
http://dx.doi.org/10.1016/j.bbrc.2010.07.138
8.
8. Mark Schena, Dari Shalon, Ronald W. Davis, and Patrick O. Brown, “Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray,” Science 270 no. 5235, 467470 (1995).
http://dx.doi.org/10.1126/science.270.5235.467
9.
9. Ting Liu and Xin-Hua Feng, “Regulation of TGF-β signalling by protein phosphatases,” Biochem. J. 430, 191198 (2010).
http://dx.doi.org/10.1042/BJ20100427
10.
10. Jin J. Y., Richard R. Almon, Debra C. Dubois, and William J. Jusko, “Modeling of Corticosteroid Pharmacogenomics in Rat Liver Using Gene Microarrays,” The journal of pharmacology and experimental therapeutics 307 no. 1, 93109 (2003).
http://dx.doi.org/10.1124/jpet.103.053256
11.
11. Zhenling Yao, Eric P. Hoffman, Svetlana Ghimbovschi, Debra C. DuBois, Richard R. Almon, and William J. Jusko, “Mathematical Modeling of Corticosteroid Pharmacogenomics in Rat Muscle following Acute and Chronic Methylprednisolone Dosing,” Molecular Pharmaceutics 5 no. 2, 328339 (2008).
http://dx.doi.org/10.1021/mp700094s
12.
12. Tianhai Tian, Songlin Xu, Junbin Gao, and Kevin Burrage, “Simulated maximum likelihood method for estimating kinetic rates in gene expression,” Bioinformatics 23, 8491 (2007).
http://dx.doi.org/10.1093/bioinformatics/btl552
13.
13. Venkateshwar G. Keshamouni, George Michailidis, Catherine S. Grasso, Shalini Anthwal, John R. Strahler, Angela Walker, Douglas A. Arenberg, Raju C. Reddy, Sudhakar Akulapalli, Victor J. Thannickal, Theodore J. Standiford, Philip C. Andrews, and Gilbert S. Omenn, “Differential Protein Expression Profiling by iTRAQ-2DLC-MS/MS of Lung Cancer Cells Undergoing Epithelial-Mesenchymal Transition Reveals a Migratory/Invasive Phenotype,” Journal of Proteome Research 5, 11431154 (2006).
http://dx.doi.org/10.1021/pr050455t
14.
14. Shi Y and Massague J , “Mechanisms of TGF-beta signaling from cell membrane to the nucleus,” Cell 113, 685700 (2003).
http://dx.doi.org/10.1016/S0092-8674(03)00432-X
15.
15. Feng XH and Derynck R , “Specificity and versatility in TGF-β signaling through Smads,” Annu Rev Cell Dev Bio 21, 659693 (2005).
http://dx.doi.org/10.1146/annurev.cellbio.21.022404.142018
16.
16. Incidentally, we are now in a position to clarify why the families of fixed points (i), (ii), (iii) are not compatible with the selected initial data. First, let us note that TGF-β and R get progressively consumed at exactly the same pace, see the first two equations of system (7), as they are both implicated in the creation of the R* species. However, at t = 0, the quota of free receptors R is significantly larger than the number of injected TGF-β molecules (R(t = 0) = 1nM vs. TGF-β(t = 0) = 0.113nM). As a consequence, and because R and TGF-β obey to an identical kinetic, it is the TGF-β that vanish first, reaching its asymptotic state TGF-β = 0 when a residual quota of R is still present. From here on, the receptors R cannot be mutated in R* any longer and are therefore indefinitely frozen to the asymptotic value R(t = 0) − TGF-β(t = 0), which is positive and different from zero. Both solutions (i) and (ii) are therefore to be rejected because they require R = 0. A similar reasoning can be invoked to exclude solution (iii). This latter would in fact imply Sc = 0. However, as it can be readily appreciated by inspection of system (7), the rate of loss of both R* and Sc is governed by the same term, namely −kp[R*][Sc]. The maximum amount of bound receptors R* is equal to the number of TGF-β(t = 0) molecules, while the Sc elements at time t = 0 are definitely many more (121.1nM). Moreover, the population of Sc gets also re-integrated, via a source term controlled by the reaction rate kexp, which acts as long as Sn is different from zero. Based on the above, we can therefore conclude that Sc molecules are still present when R* becomes zero. From here on the Sc cannot decrease any longer and stay frozen to the value that they have eventually attained when the condition R* = 0 is met. Hence, solution (iii) cannot apply to the scrutinized setting, as it assumes the asymptotic condition Sc = 0.
17.
17. Katharine H Wrighton, Xia Lin, and Xin-Hua Feng, “Phospho-control of TGF-β superfamily signaling,” Cell Research 19, 820 (2009).
http://dx.doi.org/10.1038/cr.2008.327
18.
18. Andreas Wagner, “Robustness and Evolvability in Living Systems,” Princeton University Press (2007).
19.
19. Karline Soetaert and Thomas Petzoldt, “Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME,” Journal of Statistical Software 33(3), 128 (2010).
20.
20. R Development Core Team, Vienna, Austria, “R: A Language and Environment for statistical Computing,” ISBN 3-900051-07-0, (http://www.R-project.org).
21.
21. Daniel Sorensen and Daniel Gianola, “Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics,” Springer, New York (2002).
22.
22. Kornelia Polyak and Robert A. Weinberg, “Transitions between epithelial and mesenchymal states: acquisition of malignant and stem cell traits,” Nature Reviews 9, 265273 (2009).
http://dx.doi.org/10.1038/nrc2620
http://aip.metastore.ingenta.com/content/aip/journal/adva/2/1/10.1063/1.3697962
Loading
/content/aip/journal/adva/2/1/10.1063/1.3697962
Loading

Data & Media loading...

Loading

Article metrics loading...

/content/aip/journal/adva/2/1/10.1063/1.3697962
2012-03-21
2014-08-22

Abstract

The epithelial-mesenchymal transition (EMT) consists in a morphological change in epithelial cells characterized by the loss of the cell adhesion and the acquisition of mesenchymal phenotype. This process plays a crucial role in the embryonic development and in regulating the tissue homeostasis in the adult, but it proves also fundamental for the development of cancermetastasis. Experimental evidences have shown that the EMT depends on the TGF-β signaling pathway, which in turn regulates the transcriptional cellular activity. In this work, a dynamical model of the TGF-β pathway is proposed and calibrated versus existing experimental data on lung cancer A549 cells. The analysis combines Bayesian Markov Chain Monte Carlo (MCMC) and standard Ordinary Differential Equations (ODEs) techniques to interpolate the gene expression data via an iterative adjustments of the parameters involved. The kinetic of the Smad proteins phosphorylation, as predicted within the model is found in excellent agreement with available experiments, an observation that confirms the adequacy of the proposed mathematical picture.

Loading

Full text loading...

/deliver/fulltext/aip/journal/adva/2/1/1.3697962.html;jsessionid=35wkvk70phhkk.x-aip-live-03?itemId=/content/aip/journal/adva/2/1/10.1063/1.3697962&mimeType=html&fmt=ahah&containerItemId=content/aip/journal/adva
true
true
This is a required field
Please enter a valid email address
This feature is disabled while Scitation upgrades its access control system.
This feature is disabled while Scitation upgrades its access control system.
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Modeling TGF-β signaling pathway in epithelial-mesenchymal transition
http://aip.metastore.ingenta.com/content/aip/journal/adva/2/1/10.1063/1.3697962
10.1063/1.3697962
SEARCH_EXPAND_ITEM