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Quantitative Biology

, Volume 1, Issue 2, pp 105–114 | Cite as

Dynamical network biomarkers for identifying critical transitions and their driving networks of biologic processes

  • Rui Liu
  • Kazuyuki Aihara
  • Luonan Chen
Review

Abstract

Non-smooth or even abrupt state changes exist during many biological processes, e.g., cell differentiation processes, proliferation processes, or even disease deterioration processes. Such dynamics generally signals the emergence of critical transition phenomena, which result in drastic changes of system states or eventually qualitative changes of phenotypes. Hence, it is of great importance to detect such transitions and further reveal their molecular mechanisms at network level. Here, we review the recent advances on dynamical network biomarkers (DNBs) as well as the related theoretical foundation, which can identify not only early signals of the critical transitions but also their leading networks, which drive the whole system to initiate such transitions. In order to demonstrate the effectiveness of this novel approach, examples of complex diseases are also provided to detect pre-disease stage, for which traditional methods or biomarkers failed.

Keywords

Critical Transition Tipping Point Biologic Process Pituitary Apoplexy Center Manifold Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Venegas, J. G., Winkler, T., Musch, G., Vidal Melo, M. F., Layfield, D., Tgavalekos, N., Fischman, A. J., Callahan, R. J., Bellani, G. and Harris, R. S. (2005) Self-organized patchiness in asthma as a prelude to catastrophic shifts. Nature, 434, 777–782.PubMedCrossRefGoogle Scholar
  2. 2.
    McSharry, P. E., Smith, L. A. and Tarassenko, L. (2003) Prediction of epileptic seizures: are nonlinear methods relevant? Nat. Med., 9, 241–242.PubMedCrossRefGoogle Scholar
  3. 3.
    Pastor-Barriuso, R., Guallar, E. and Coresh, J. (2003) Transition models for change-point estimation in logistic regression. Stat. Med., 22, 1141–1162.PubMedCrossRefGoogle Scholar
  4. 4.
    Paek, S. H., Chung, H. T., Jeong, S. S., Park, C. K., Kim, C. Y., Kim, J. E., Kim, D. G. and Jung, H. W. (2005) Hearing preservation after gamma knife stereotactic radiosurgery of vestibular schwannoma. Cancer, 104, 580–590.PubMedCrossRefGoogle Scholar
  5. 5.
    Liu, J. K., Rovit, R. L. and Couldwell, W. T. (2001) Pituitary Apoplexy. Semin. Neurosurg., 12, 315–320.Google Scholar
  6. 6.
    Appasani, K. and Appasani, R. K. (2011) Stem Cells and Regenerative Medicine. New York: Humana Press.CrossRefGoogle Scholar
  7. 7.
    Chen, L., Liu, R., Liu, Z. P., Li, M. and Aihara, K. (2012) Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Sci. Rep., 2, 342.PubMedGoogle Scholar
  8. 8.
    Liu, R., Li, M., Liu, Z. P., Wu, J., Chen, L. and Aihara, K. (2012) Identifying critical transitions and their leading biomolecular networks in complex diseases. Sci. Rep., 2, 813.PubMedGoogle Scholar
  9. 9.
    Scheffer, M., Bascompte, J., Brock, W. A., Brovkin, V., Carpenter, S. R., Dakos, V., Held, H., van Nes, E. H., Rietkerk, M. and Sugihara, G. (2009) Early-warning signals for critical transitions. Nature, 461, 53–59.PubMedCrossRefGoogle Scholar
  10. 10.
    Ao, P. (2004) Potential in stochastic differential equations: novel construction. J. Phys. A, 37, L25C30.CrossRefGoogle Scholar
  11. 11.
    Rhodes, D. R., Sanda, M. G., Otte, A. P., Chinnaiyan, A. M. and Rubin, M. A. (2003) Multiplex biomarker approach for determining risk of prostate-specific antigen-defined recurrence of prostate cancer. J. Natl. Cancer Inst., 9, 661–668.CrossRefGoogle Scholar
  12. 12.
    Ren, X., Wang, Y., Chen, L., Zhang, X. and Jin, Q. (2012) Ellipsoidfn: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions. Nucleic. Acids. Res., doi: 10.1093/nar/gks1288.Google Scholar
  13. 13.
    Hernandez, J. and Thompson, I. M. (2004) Prostate-specific antigen: A review of the validation of the most commonly used cancer biomarker. American Cancer Society, 101, 894–904.Google Scholar
  14. 14.
    Allhoff, E. P., Proppe, K. H., Chapman, C. M., Lin, C.W. and Prout, G. R. Jr. (1983) Evaluation of prostate specific acid phosphatase and prostate specific antigen in identification of prostatic cancer. J. Urol., 129, 315–318.PubMedGoogle Scholar
  15. 15.
    Hirata, Y., Bruchovsky, N. and Aihara, K. (2010) Development of a mathematical model that predicts the outcome of hormone therapy for prostate cancer. J. Theor. Biol., 264, 517–527.PubMedCrossRefGoogle Scholar
  16. 16.
    Berchuck, A. (1995) Biomarkers in the ovary. J. Cell Biochem. Suppl., 23, 223–226.PubMedCrossRefGoogle Scholar
  17. 17.
    Soussi, T., Wiman, K. G., Otte, A. P., Chinnaiyan, A. M. and Rubin, M. A. (2007) Shaping genetic alterations in human cancer: the p53 mutation paradigm. Cancer Cell, 12, 303–312.PubMedCrossRefGoogle Scholar
  18. 18.
    Jin, G., Zhou, X., Wang, H., Zhao, H., Cui, K., Zhang, X. S., Chen, L., Hazen, S. L., Li, K. and Wong, S. T. (2008) The knowledge-integrated network biomarkers discovery for major adverse cardiac events. J. Proteome. Res., 7, 4013–4021.PubMedCrossRefGoogle Scholar
  19. 19.
    Ideker, T. and Sharan, R. (2008) Protein networks in disease. Genome Res., 18, 644–652.PubMedCrossRefGoogle Scholar
  20. 20.
    Chuang, H. Y., Lee, E., Liu, Y. T., Lee, D. and Ideker, T. (2007) Network-based classification of breast cancer metastasis. Mol. Syst. Biol., 3, 140.PubMedCrossRefGoogle Scholar
  21. 21.
    Liu, M., Liberzon, A., Kong, S.W., Lai, W. R., Park, P. J., Kohane, I. S. and Kasif, S. (2007) Network-based analysis of affected biological processes in type 2 diabetes models. PLoS Genet., 3, e96.PubMedCrossRefGoogle Scholar
  22. 22.
    Jiang, B. B., Wang, J. G., Xiao, J. F. and Wang, Y. (2009) Gene prioritization for type 2 diabetes in tissue-specific protein interaction networks. Lect. Notes Oper. Res., 11, 319–328.Google Scholar
  23. 23.
    Jin, G., Zhou, X., Cui, K., Zhang, X. S., Chen, L. and Wong, S. T. (2009) Cross-platform method for identifying candidate network biomarkers for prostate cancer. IET Syst. Biol., 3, 505–512.PubMedCrossRefGoogle Scholar
  24. 24.
    Krauthammer, M., Kaufmann, C. A., Gilliam, T. C. and Rzhetsky, A. (2004) Molecular triangulation: bridging linkage and molecularnetwork information for identifying candidate genes in Alzheimer’s disease. Proc. Natl. Acad. Sci. USA, 101, 15148–15153.PubMedCrossRefGoogle Scholar
  25. 25.
    Liu, Z. P., Wang, Y., Wen, T., Zhang, X. S., Xia, W. and Chen, L. (2009) Dynamically dysfunctional protein interactions in the development of Alzheimers disease’. Proc. IEEE Int. Conf. on Systems, Man and Cybernetics, San Antonio, USA, 4262-4267.Google Scholar
  26. 26.
    Liu, Z. P., Wang, Y., Zhang, X. S. and Chen, L. (2012) Network-based analysis of complex diseases. IET Syst. Biol., 6, 22.PubMedCrossRefGoogle Scholar
  27. 27.
    Wen, Z., Liu, Z. P., Liu, Z., Zhang, Y. and Chen, L. (2012) An integrated approach to identify causal network modules of complex diseases with application to colorectal cancer. J. Am. Med. Inform. Assoc., doi: 10.1136/amiajnl-2012-001168.Google Scholar
  28. 28.
    Liu, K. Q., Liu, Z. P., Hao, J. K., Chen, L. and Zhao, X. M. (2012) Identifying disregulated pathways in cancers from pathway interaction networks. BMC Bioinformatics, 13, 126.PubMedCrossRefGoogle Scholar
  29. 29.
    He, D., Liu, Z. P., Honda, M., Kaneko, S. and Chen, L. (2012) Coexpression network analysis in chronic hepatitis B and C hepatic lesions reveals distinct patterns of disease progression to hepatocellular carcinoma. J. Mol. Cell Biol., 4, 140–152.PubMedCrossRefGoogle Scholar
  30. 30.
    Zeng, T. and Chen, L. (2012) Tracing dynamic biological processes during phase transition. BMC Syst. Biol., 6, S23.PubMedCrossRefGoogle Scholar
  31. 31.
    He, D., Liu, Z. P. and Chen, L. (2011) Identification of dysfunctional modules and disease genes in congenital heart disease by a network-based approach. BMC Genomics, 12, 592.PubMedCrossRefGoogle Scholar
  32. 32.
    Liu, X., Liu, Z. P., Zhao, X. M. and Chen, L. (2012) Identifying disease genes and module biomarkers by differential interactions. J. Am. Med. Inform. Assoc., 19, 241–248.PubMedCrossRefGoogle Scholar
  33. 33.
    Liu, Z. P., Wang, Y., Zhang, X. S., Xia, W. and Chen, L. (2011) Detecting and analyzing differentially activated pathways in brain regions of Alzheimer’s disease patients. Mol. Biosyst., 7, 1441–1452.PubMedCrossRefGoogle Scholar
  34. 34.
    Liu, X., Wang, J. and Chen, L. (2012) Whole-exome sequencing reveals recurrent somatic mutation networks in cancer. Cancer Lett., doi: 10.1016/j.canlet.2012.11.002.Google Scholar
  35. 35.
    Wang, J., Sun, Y., Zheng, S., Zhang, X., Zhou, H. and Chen, L. (2013) APG: an active protein-gene network model to quantify regulatory signals in complex biological systems. Scientific Reports, 3, 1097.PubMedGoogle Scholar
  36. 36.
    Ao, P., Galas, D., Hood, L. and Zhu, X. (2008) Cancer as robust intrinsic state of endogenous molecular-cellular network shaped by evolution. Med. Hypotheses, 70, 678–684.PubMedCrossRefGoogle Scholar
  37. 37.
    Tanaka, G., Tsumoto, K., Tsuji, S. and Aihara, K. (2008) Bifurcation analysis on a hybrid systems model of intermittent hormonal therapy for prostate cancer. Physica D, 237, 2616–2627.CrossRefGoogle Scholar
  38. 38.
    Gilmore, R. (1981) Catastrophe Theory for Scientists and Engineers. New York: Wiley.Google Scholar
  39. 39.
    Murray, J. D. (1993) Mathematical Biology. New York: Springer.CrossRefGoogle Scholar
  40. 40.
    Liu, X., Liu, R., Zhao, X. and Chen, L. (2013) Detecting early-warning signals of type 1 diabetes and its leading biomolecular networks by dynamical network biomarkers. BMC Med. Genomics (in press).Google Scholar
  41. 41.
    Chen, L., Wang, R., Li, C. and Aihara, K. (2010) Modeling Biomolecular Networks in Cells: Structures and Dynamics’. New York: Springer.CrossRefGoogle Scholar
  42. 42.
    Chen, L., Wang, R. and Zhang, X. (2009) Biomolecular Networks: Methods and Applications in Systems Biology. New Jersey: John Wiley & Sons, Hoboken.CrossRefGoogle Scholar
  43. 43.
    Voit, E. O. (2009) A systems-theoretical framework for health and disease: inflammation and preconditioning from an abstract modeling point of view. Math. Biosci., 217, 11–18.PubMedCrossRefGoogle Scholar
  44. 44.
    Hovinen, E., Kekki, M. and Kuikka, S. (1976) A theory to the stochastic dynamic model building for chronic progressive disease processes with an application to chronic gastritis. J. Theor. Biol., 57, 131–152.PubMedCrossRefGoogle Scholar
  45. 45.
    Guckenheimer, J. and Holmes, P. (1983) Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields. New York: Springer.Google Scholar
  46. 46.
    Wiggins, S. (1988) Global Bifurcations and Chaos: Analytical Methods. New York: Springer.CrossRefGoogle Scholar
  47. 47.
    Arnol’d, V. I. (1994) Dynamical Systems V, Bifurcation Theory and Catastrophe Theory. New York: Springer.Google Scholar
  48. 48.
    Murdock, J. (2003) Normal Forms and Unfoldings for Local Dynamical Systems. New York: Springer.Google Scholar
  49. 49.
    Dakos, V., Van Nes, E. H., Donangelo, R., Fort, H. and Scheffer, M. (2010) Spatial correlation as leading indicator of catastrophic shifts. Theor. Ecol., 3, 163–174.CrossRefGoogle Scholar
  50. 50.
    Litt, B., Esteller, R., Echauz, J., D’Alessandro, M., Shor, R., Henry, T., Pennell, P., Epstein, C., Bakay, R., Dichter, M., et al. (2001) Epileptic seizures may begin hours in advance of clinical onset: a report of five patients. Neuron, 30, 51–64.PubMedCrossRefGoogle Scholar
  51. 51.
    Sciuto, A. M., Phillips, C. S., Orzolek, L. D., Hege, A. I., Moran, T. S. and Dillman, J. F. 3rd. (2005) Genomic analysis of murine pulmonary tissue following carbonyl chloride inhalation. Chem. Res. Toxicol., 18, 1654–1660.PubMedCrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH 2013

Authors and Affiliations

  1. 1.Department of MathematicsSouth China University of TechnologyGuangzhouChina
  2. 2.Collaborative Research Center for Innovative Mathematical Modeling, Institute of Industrial ScienceUniversity of TokyoTokyoJapan
  3. 3.Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina

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