Quantifying the underlying landscape and paths of cancer

Journal of the Royal Society Interface

Volumn 11, Issue 100, 2014, Pages

  Li, Chunhe ^a   Wang, Jin ^a,b  

^a STONY BROOK UNIVERSITY   (United States)

^b CHANGCHUN INSTITUTE OF APPLIED CHEMISTRY   (China)

Author keywords

Cancer; Gene network; Landscape; Path

Indexed keywords

CELL DEATH; DYNAMICAL SYSTEMS; GENES; SENSITIVITY ANALYSIS; TOPOGRAPHY;

CANCER; EXPERIMENTAL EVIDENCE; GENE NETWORKS; GLOBAL SENSITIVITY ANALYSIS; LANDSCAPE; LEAST ACTION PRINCIPLE; PATH; TRANSITION PROCESS;

DISEASES;

APOPTOSIS; ARTICLE; CARCINOGENESIS; GENE EXPRESSION; GENE REGULATORY NETWORK; HUMAN; MALIGNANT NEOPLASTIC DISEASE; MOLECULAR BIOLOGY; QUANTITATIVE ANALYSIS; SENSITIVITY ANALYSIS; TUMOR GENE; BIOLOGICAL MODEL; BIOSYNTHESIS; GENE EXPRESSION REGULATION; GENETICS; METABOLISM; NEOPLASM; PATHOLOGY;

TUMOR PROTEIN;

APOPTOSIS; GENE EXPRESSION REGULATION, NEOPLASTIC; GENE REGULATORY NETWORKS; MODELS, BIOLOGICAL; NEOPLASM PROTEINS; NEOPLASMS;

EID: 84908177103     PISSN: 17425689     EISSN: 17425662     Source Type: Journal    
DOI: 10.1098/rsif.2014.0774     Document Type: Article

References (51)

1. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. 2011 Global cancer statistics. CA Cancer J. Clin. 61, 69-90. (doi:10.3322/caac.20107)
2. Cowin P, Rowlands TM, Hatsell SJ. 2005 Cadherins and catenins in breast cancer. Curr. Opin. Cell Biol. 17, 499-508. (doi:10.1016/j.ceb.2005.08.014)
3. Kauffman S. 1971 Differentiation of malignant to benign cells. J. Theor. Biol. 31, 429-451. (doi:10. 1016/0022-5193(71)90020-8)
4. Hanahan D, Weinberg RA. 2000 The hallmarks of cancer. Cell 100, 57-70. (doi:10.1016/S0092-8674(00)81683-9)
5. Hanahan D, Weinberg RA. 2011 Hallmarks of cancer: The next generation. Cell 144, 646-674. (doi:10.1016/j.cell.2011.02.013)
6. Weinberg RA. 2007 The biology of cancer. New York, NY: Garland Science.
7. Hejmadi M. 2010 Introduction to cancer biology. Holstebro, Denmark: Ventus Publishing.
8. Vogelstein B, Kinzler KW. 2004 Cancer genes and the pathways they control. Nat. Med. 10, 789-799. (doi:10.1038/nm1087)
9. Ao P, Galas D, Hood L, Zhu X. 2008 Cancer as robust intrinsic state of endogenous molecular-cellular network shaped by evolution. Med. Hypotheses 70, 678-684. (doi:10.1016/j.mehy. 2007.03.043)
10. Waddington CH. 1957 The strategy of the genes: A discussion of some aspects of theoretical biology. London, UK: Allen and Unwin.
11. Wang J, Xu L, Wang EK. 2008 Potential landscape and flux framework of non-equilibrium networks: Robustness, dissipation and coherence of biochemical oscillations. Proc. Natl Acad. Sci. USA 105, 12 271-12 276. (doi:10.1073/pnas.0800579105)
12. Wang J, Li CH, Wang EK. 2010 Potential and flux landscapes quantify the stability and robustness of budding yeast cell cycle network. Proc. Natl Acad. Sci. USA 107, 8195-8200. (doi:10.1073/pnas. 0910331107)
13. Wang J, Zhang K, Xu L, Wang EK. 2011 Quantifying the Waddington landscape and biological paths for development and differentiation. Proc. Natl Acad. Sci. USA 108, 8257-8262. (doi:10.1073/pnas. 1017017108)
14. Li CH, Wang J. 2013 Quantifying cell fate decisions for differentiation and reprogramming of a human stem cell network: Landscape and biological paths. PLoS Comput. Biol. 9, e1003165. (doi:10.1371/journal.pcbi.1003165)
15. Li CH, Wang J. 2013 Quantifying Waddington landscapes and paths of non-adiabatic cell fate decisions for differentiation, reprogramming and transdifferentiation. J. R. Soc. Interface 10, 20130787. (doi:10.1098/rsif.2013.0787)
16. Liao C, Lu T. 2013 A minimal transcriptional controlling network of regulatory T cell development. J. Phys. Chem. B 117, 12 995-13 004. (doi:10.1021/jp402306g)
17. Qi H, Blanchard A, Lu T. 2013 Engineered genetic information processing circuits. WIREs Syst. Biol. Med. 5, 273-287. (doi:10.1002/wsbm.1216)
18. Huang S, Ernberg I, Kauffman S. 2009 Cancer attractors: A systems view of tumors from a gene network dynamics and developmental perspective. Semin. Cell Dev. Biol. 20, 869-876. (doi:10.1016/j. semcdb.2009.07.003)
19. Ao P. 2009 Global view of bionetwork dynamics: Adaptive landscape. J. Genet. Genom. 36, 63-73. (doi:10.1016/S1673-8527(08)60093-4)
20. Choi M, Shi J, Jung SH, Chen X, Cho KH. 2012 Attractor landscape analysis reveals feedback loops in the p53 network that control the cellular response to DNA damage. Sci. Signal. 5, ra83. (doi:10.1126/scisignal.2003363)
21. Creixell P, Schoof EM, Erler JT, Linding R. 2012 Navigating cancer network attractors for tumorspecific therapy. Nat. Biotechnol. 30, 842-848. (doi:10.1038/nbt.2345)
22. Davies P, Lineweaver C. 2011 Cancer tumors as metazoa 1.0: Tapping genes of ancient ancestors. Phys. Biol. 8, 015001. (doi:10.1088/1478-3975/8/1/015001)
23. Bar-Yam Y, Harmon D, de Bivort B. 2009 Attractors and democratic dynamics. Science 323, 1016C1017. (doi:10.1126/science.1163225)
24. Chu LH, Chen BS. 2008 Construction of a cancerperturbed protein-protein interaction network for discovery of apoptosis drug targets. BMC Syst. Biol. 2, 56. (doi:10.1186/1752-0509-2-56)
25. Wang X, Gotoh O. 2010 Inference of cancer-specific gene regulatory networks using soft computing rules. Gene Regul. Syst. Biol. 4, 19-34. (doi:10. 4137/GRSB.S4509)
26. Pujana MA et al. 2007 Network modeling links breast cancer susceptibility and centrosome dysfunction. Nat. Genet. 39, 1338-1349. (doi:10. 1038/ng.2007.2)
27. Wang P, Song C, Zhang H, Wu Z, Tian X-J, Xing J. 2014 Epigenetic state network approach for describing cell phenotypic transitions. Interface Focus 4, 20130068. (doi:10.1098/rsfs.2013.0068)
28. Sasai M, Kawabata Y, Makishi K, Itoh K, Terada T. 2013 Time scales in epigenetic dynamics and phenotypic heterogeneity of embryonic stem cells. PLoS Comput. Biol. 9, e1003380. (doi:10.1371/journal.pcbi.1003380)
29. Zhang B, Wolynes P. 2014 Stem cell differentiation as a many-body problem. Proc. Natl Acad. Sci. USA 111, 10 185-10 190. (doi:10.1073/pnas.1408561111)
30. Chang R, Shoemaker R, Wang W. 2011 Systematic search for recipes to generate induced pluripotent stem cells. PLoS Comput. Biol. 7, e1002300. (doi:10. 1371/journal.pcbi.1002300)
31. Wang E, Lenferink A, O'Connor-McCourt M. 2007 Cancer systems biology: Exploring cancer-associated genes on cellular networks. Cell Mol. Life Sci. 64, 1752-1762. (doi:10.1007/s00018-007-7054-6)
32. Van Landeghem S et al. 2013 Large-scale event extraction from literature with multi-level gene normalization. PLoS ONE 8, e55814. (doi:10.1371/journal.pone.0055814)
33. Huang S, Guo Y, May G, Enver T. 2007 Bifurcation dynamics of cell fate decision lineage-commitment in bipotent progenitor cells. Dev. Biol. 305, 695-713. (doi:10.1016/j.ydbio.2007.02.036)
34. Wang J, Zhang K, Wang EK. 2010 Kinetic paths, time scale, and underlying landscapes: A path integral framework to study global natures of nonequilibrium systems and networks. J. Chem. Phys. 133, 125103. (doi:10.1063/1.3478547)
35. Cline M, Smoot M, Cerami E, Kuchinsky A, Landys N. 2007 Integration of biological networks and gene expression data using Cytoscape. Nat. Prot. 2, 2366-2382. (doi:10.1038/nprot.2007.324)
36. Amson R, Karp J, Telerman A. 2013 Lessons from tumor reversion for cancer treatment. Curr. Opin. Oncol. 25, 59-65. (doi:10.1097/CCO. 0b013e32835b7d21)
37. Amson R et al. 2011 Reciprocal repression between p53 and TCTP. Nat. Med. 18, 91-99. (doi:10.1038/nm.2546)
38. Tuynder M, Susini L, Prieur S, Besse S, Fiucci G, Amson R, Telerman A. 2002 Biological models and genes of tumor reversion: Cellular reprogramming through tpt1/TCTP and SIAH-1. Proc. Natl Acad. Sci. USA 99, 14 976-14 981. (doi:10.1073/pnas. 222470799)
39. Lowe SW, Sherr CJ. 2003 Tumor suppression by Ink4a-Arf: Progress and puzzles. Curr. Opin. Genet. Dev. 13, 77-83. (doi:10.1016/S0959-437X(02) 00013-8)
40. Altomare D, Testa J. 2005 Perturbations of the AKT signaling pathway in human cancer. Oncogene 24, 7455-7464. (doi:10.1038/sj.onc.1209085)
41. Cardones A, Banez L. 2006 VEGF inhibitors in cancer therapy. Curr. Pharm. Des. 12, 387-394. (doi:10. 2174/138161206775201910)
42. Ellis L, Hicklin D. 2008 VEGF-targeted therapy: Mechanisms of anti-tumour activity. Nat. Rev. Cancer 8, 579-591. (doi:10.1038/nrc2403)
43. Heinlein CA, Chang C. 2004 Androgen receptor in prostate cancer. Endocr. Rev. 25, 276-308. (doi:10. 1210/er.2002-0032)
44. Sasai M, Wolynes P. 2003 Stochastic gene expression as a many-body problem. Proc. Natl Acad. Sci. USA 100, 2374-2379. (doi:10.1073/pnas. 2627987100)
45. Li CH, Wang J, Wang EK. 2011 Potential landscape and probabilistic flux of a predator prey network. PLoS ONE 6, e17888. (doi:10.1371/journal.pone. 0017888)
46. Van Kampen NG. 1992 Stochastic processes in chemistry and physics, 1st edn. Amsterdam, The Netherlands: North Holland.
47. Hu G. 1994 Stochastic forces and nonlinear systems. Shanghai, China: Shanghai Scientific and Technological Education Press.
48. Swain PS, Elowitz MB, Siggia ED. 2002 Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl Acad. Sci. USA 99, 12 795-12 800. (doi:10.1073/pnas.162041399)
49. Kaern M, Elston TC, Blake WJ, Collins JJ. 2005 Stochasticity in gene expression: From theories to phenotypes. Nat. Rev. Genet. 6, 451-464. (doi:10. 1038/nrg1615)
50. Thattai M, Van OA. 2001 Intrinsic noise in gene regulatory networks. Proc. Natl Acad. Sci. USA 98, 8614-8619. (doi:10.1073/pnas.151588598)
51. Hunt K, Ross J. 1981 Path integral solutions of stochastic equations for nonlinear irreversible processes: The uniqueness of the thermodynamic Lagrangian. J. Chem. Phys. 75, 976-984. (doi:10. 1063/1.442098)