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


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.


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.


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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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