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.
Article PDF
Similar content being viewed by others
References
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.
McSharry, P. E., Smith, L. A. and Tarassenko, L. (2003) Prediction of epileptic seizures: are nonlinear methods relevant? Nat. Med., 9, 241–242.
Pastor-Barriuso, R., Guallar, E. and Coresh, J. (2003) Transition models for change-point estimation in logistic regression. Stat. Med., 22, 1141–1162.
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.
Liu, J. K., Rovit, R. L. and Couldwell, W. T. (2001) Pituitary Apoplexy. Semin. Neurosurg., 12, 315–320.
Appasani, K. and Appasani, R. K. (2011) Stem Cells and Regenerative Medicine. New York: Humana Press.
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.
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.
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.
Ao, P. (2004) Potential in stochastic differential equations: novel construction. J. Phys. A, 37, L25C30.
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.
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.
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.
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.
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.
Berchuck, A. (1995) Biomarkers in the ovary. J. Cell Biochem. Suppl., 23, 223–226.
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.
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.
Ideker, T. and Sharan, R. (2008) Protein networks in disease. Genome Res., 18, 644–652.
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.
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.
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.
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.
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.
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.
Liu, Z. P., Wang, Y., Zhang, X. S. and Chen, L. (2012) Network-based analysis of complex diseases. IET Syst. Biol., 6, 22.
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.
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.
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.
Zeng, T. and Chen, L. (2012) Tracing dynamic biological processes during phase transition. BMC Syst. Biol., 6, S23.
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.
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.
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.
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.
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.
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.
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.
Gilmore, R. (1981) Catastrophe Theory for Scientists and Engineers. New York: Wiley.
Murray, J. D. (1993) Mathematical Biology. New York: Springer.
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).
Chen, L., Wang, R., Li, C. and Aihara, K. (2010) Modeling Biomolecular Networks in Cells: Structures and Dynamics’. New York: Springer.
Chen, L., Wang, R. and Zhang, X. (2009) Biomolecular Networks: Methods and Applications in Systems Biology. New Jersey: John Wiley & Sons, Hoboken.
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.
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.
Guckenheimer, J. and Holmes, P. (1983) Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields. New York: Springer.
Wiggins, S. (1988) Global Bifurcations and Chaos: Analytical Methods. New York: Springer.
Arnol’d, V. I. (1994) Dynamical Systems V, Bifurcation Theory and Catastrophe Theory. New York: Springer.
Murdock, J. (2003) Normal Forms and Unfoldings for Local Dynamical Systems. New York: Springer.
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.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, R., Aihara, K. & Chen, L. Dynamical network biomarkers for identifying critical transitions and their driving networks of biologic processes. Quant Biol 1, 105–114 (2013). https://doi.org/10.1007/s40484-013-0008-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40484-013-0008-0