Quantitative Biology

, Volume 5, Issue 2, pp 191–198 | Cite as

Global quantitative biology can illuminate ontological connections between diseases

  • Guanyu WangEmail author


Owing to its interdisciplinary nature, quantitative biology is playing ever-increasing roles in biological researches. To make quantitative biology even more powerful, it is important to develop a holistic perspective by integrating information from multiple biological levels and by considering related biocomplexity simultaneously. Using complex diseases as an example, I show in this paper how their ontological connections can be revealed by considering the diseases on a common ground. The obtained insights may be useful to the prediction and treatment of the diseases. Although the example involves only with cancer and diabetes, the approaches are applicable to the study of other diseases, or even to other biological problems.


quantitative biology disease modeling systems biology nonlinear dynamics 


  1. 1.
    Guan, L., Yang, Q., Gu, M., Chen, L. and Zhang, X. (2014) Exon expression qtl (eeqtl) analysis highlights distant genomic variations associated with splicing regulation. Quant. Biol., 2, 71–79CrossRefGoogle Scholar
  2. 2.
    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, 342CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Servedio, M. R., Brandvain, Y., Dhole, S., Fitzpatrick, C. L., Goldberg, E. E., Stern, C. A., Van Cleve, J. & Yeh, D. J. (2014) Not just a theory—the utility of mathematical models in evolutionary biology. PLoS. Biol., 12, e1002017CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Nagel, E. and Hawkins, D. (1961) The structure of science. Am. J. Phys., 29, 716CrossRefGoogle Scholar
  5. 5.
    Bruggeman, F. J., Westerhoff, H. V. and Boogerd, F. C. (2002) Biocomplexity: a pluralist research strategy is necessary for a mechanistic explanation of the “live” state. Philos. Psychol., 15, 411–440CrossRefGoogle Scholar
  6. 6.
    Bergman, M. (2013) Pathophysiology of prediabetes and treatment implications for the prevention of type 2 diabetes mellitus. Endocrine, 43, 504–513CrossRefPubMedGoogle Scholar
  7. 7.
    Smyth, S. and Heron, A. (2006) Diabetes and obesity: the twin epidemics. Nat. Med., 12, 75–80CrossRefPubMedGoogle Scholar
  8. 8.
    Mukherjee, S. (2010) The Emperor of All Maladies: a Biography of Cancer. New York: ScribnerGoogle Scholar
  9. 9.
    Giovannucci, E., Harlan, D. M., Archer, M. C., Bergenstal, R. M., Gapstur, S. M., Habel, L. A., Pollak, M., Regensteiner, J. G. and Yee, D. (2010) Diabetes and cancer: a consensus report. CA Cancer J. Clin., 60, 207–221CrossRefPubMedGoogle Scholar
  10. 10.
    Pischon, T., Nöthlings, U. and Boeing, H. (2008) Obesity and cancer. Proc. Nutr. Soc., 67, 128–145CrossRefPubMedGoogle Scholar
  11. 11.
    Hsu, I. R., Kim, S. P., Kabir, M. and Bergman, R. N. (2007) Metabolic syndrome, hyperinsulinemia, and cancer. Am. J. Clin. Nutr., 86, s867–s871CrossRefPubMedGoogle Scholar
  12. 12.
    Larsson, S. C., Mantzoros, C. S. and Wolk, A. (2007) Diabetes mellitus and risk of breast cancer: a meta-analysis. Int. J. Cancer, 121, 856–862CrossRefPubMedGoogle Scholar
  13. 13.
    Engelman, J. A., Luo, J. and Cantley, L. C. (2006) The evolution of phosphatidylinositol 3-kinases as regulators of growth and metabolism. Nat. Rev. Genet., 7, 606–619CrossRefPubMedGoogle Scholar
  14. 14.
    Liao, Y. and Hung, M.-C. (2010) Physiological regulation of Akt activity and stability. Am. J. Transl. Res., 2, 19–42PubMedPubMedCentralGoogle Scholar
  15. 15.
    Li, T. & Wang, G. (2014) Computer-aided targeting of the PI3K/Akt/ mTOR pathway: toxicity reduction and therapeutic opportunities. Int. J. Mol. Sci., 15, 18856–18891CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Zoncu, R., Efeyan, A. and Sabatini, D. M. (2011) mTOR: from growth signal integration to cancer, diabetes and ageing. Nat. Rev. Mol. Cell Biol., 12, 21–35CrossRefPubMedGoogle Scholar
  17. 17.
    Wang, G. (2010) Singularity analysis of the AKT signaling pathway reveals connections between cancer and metabolic diseases. Phys. Biol. 7, 046015CrossRefPubMedGoogle Scholar
  18. 18.
    Arnold, V. (1986) Catastrophe Theory. Berlin: Springer-VerlagCrossRefGoogle Scholar
  19. 19.
    Golubitsky, M. and Schaeffer, D. G. (1985) Singularities and Groups in Bifurcation Theory. New York: Springer-VerlagCrossRefGoogle Scholar
  20. 20.
    Liu, R., Aihara, K. and Chen, L. (2013) Dynamical network biomarkers for identifying critical transitions and their driving networks of biologic processes. Quant. Biol., 1, 105–114CrossRefGoogle Scholar
  21. 21.
    Liu, R., Chen, P., Aihara, K. & Chen, L. (2015) Identifying earlywarning signals of critical transitions with strong noise by dynamical network markers. Sci. Rep., 5, 17501CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Hong, S. Y., Yu, F.-X., Luo, Y. and Hagen, T. (2016) Oncogenic activation of the PI3K/Akt pathway promotes cellular glucose uptake by downregulating the expression of thioredoxin-interacting protein. Cell. Signal., 28, 377–383CrossRefPubMedGoogle Scholar
  23. 23.
    Zhu, X., Song, Y., Wu, C., Pan, C., Lu, P., Wang, M., Zheng, P., Huo, R., Zhang, C., Li, W. et al. (2016) Cyr61 participates in the pathogenesis of acute lymphoblastic leukemia by enhancing cellular survival via the Akt/NFB signaling pathway. Sci. Rep., 6, 34018CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Wang, H., Fan, L., Wei, J., Weng, Y., Zhou, L., Shi, Y., Zhou, W., Ma, D. & Wang, C. (2012) Akt mediates metastasis-associated gene 1 (MTA1) regulating the expression of E-cadherin and promoting the invasiveness of prostate cancer cells. PloS One 7, e46888CrossRefGoogle Scholar
  25. 25.
    Tyson, J. J., Albert, R., Goldbeter, A., Ruoff, P. and Sible, J. (2008) Biological switches and clocks. J. R. Soc. Interface, 5, S1–S8CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Xiong, W. and Ferrell, J. E. (2003) A positive-feedback-based bistable “memory module” that governs a cell fate decision. Nature, 426, 460–465CrossRefPubMedGoogle Scholar
  27. 27.
    Tsai, T. Y.-C., Choi, Y. S., Ma, W., Pomerening, J. R., Tang, C. and Ferrell, J. E. (2008) Robust, tunable biological oscillations from interlinked positive and negative feedback loops. Science, 321, 126–129CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Wang, G. (2012) Optimal homeostasis necessitates bistable control. J. R. Soc. Interface, 9, 2723–2734CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Tzu, L. (1972) Tao De Ching, translated by Feng, G. and English, J. New York: Vintage (Original work published CA.350–250 BC)Google Scholar
  30. 30.
    Meng, H. and Wang, Y. (2015) Cis-acting regulatory elements: from random screening to quantitative design. Quant. Biol., 3, 107–114CrossRefGoogle Scholar
  31. 31.
    Zhou, T. and Liu, T. (2015) Quantitative analysis of gene expression systems. Quant. Biol., 3, 168–181CrossRefGoogle Scholar
  32. 32.
    Cui, H., Li, Y. and Zhang, X. (2016) An overview of major metagenomic studies on human microbiomes in health and disease. Quant. Biol., 4, 192–206CrossRefGoogle Scholar
  33. 33.
    Li, R., Chen, T. and Li, S. (2015) Network-based method to infer the contributions of proteins to the etiology of drug side effects. Quant. Biol., 3, 124–134CrossRefGoogle Scholar
  34. 34.
    Li, S. (2016) Exploring traditional chinese medicine by a novel therapeutic concept of network target. Chin. J. Integr. Med., 22, 647–652CrossRefPubMedGoogle Scholar
  35. 35.
    Tengholm, A., Teruel, M. N. and Meyer, T. (2003) Single cell imaging of PI3K activity and glucose transporter insertion into the plasma membrane by dual color evanescent wave microscopy. Sci. STKE, 2003, pl4.Google Scholar
  36. 36.
    Sato, M. (2006) Imaging molecular events in single living cells. Anal. Bioanal. Chem., 386, 435–443CrossRefPubMedGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH 2017

Authors and Affiliations

  1. 1.Department of BiologySouthern University of Science and TechnologyShenzhenChina

Personalised recommendations