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From Diagnosing Diseases to Predicting Diseases

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Curious2018

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Abstract

Chronic diseases can be considered as perturbations of complex adaptive systems. Transitions from healthy states to chronic diseases are often characterized by sudden and unexpected onset of diseases. These critical transitions or catastrophic shifts have been studied in theoretical and applied physics, ecology, social science, economics and recently also in biomedical applications. If we could understand the underlying mechanisms and the dynamics of critical transitions involved in the development of diseases, we would be better equipped to predict and eventually prevent them from arising. The current paper gives an overview of the potential application of the concept of critical transitions to biomedical applications.

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References

  1. Jones DS, Podolsky SH, Greene JA. The Burden of Disease and the Changing Task of Medicine. N Engl J Med. 2012. https://doi.org/10.1056/nejmp1113569.

  2. World Health Organization (WHO). Global Burden of Diseases. 2017.

    Google Scholar 

  3. Voskuil J. How difficult is the validation of clinical biomarkers? F1000Research. 2015;101(MAY):1–10. https://doi.org/10.12688/f1000research.6395.1.

  4. Engel J, Pitkanen A, Loeb JA, et al. Epilepsy biomarkers. Epilepsia. 2013;54(SUPPL.4):61–69. https://doi.org/10.1111/epi.12299.

  5. Hijazi Z, Oldgren J, Siegbahn A, Granger CB, Wallentin L. Biomarkers in atrial fibrillation: A clinical review. Eur Heart J. 2013;34(20):1475–80. https://doi.org/10.1093/eurheartj/eht024.

    Article  CAS  PubMed  Google Scholar 

  6. Tu Y, Rappel W-J. Adaptation in living systems. Annu Rev Condens Matter Phys. 2018. https://doi.org/10.1146/annurev-conmatphys-033117-054046.

  7. Bascompte J. Disentangling the web of life. Science (80-) 2009. https://doi.org/10.1126/science.1170749.

  8. Rinaldi S, Scheffer M. Geometric analysis of ecological models with slow and fast processes. Ecosystems. 2000. https://doi.org/10.1007/s100210000045.

  9. Ising E. Beitrag zur Theorie des Ferromagnetismus. Zeitschrift für Phys. 1925. https://doi.org/10.1007/bf02980577.

  10. Onsager L. Crystal statistics. I. A two-dimensional model with an order-disorder transition. Phys Rev. 1944. https://doi.org/10.1103/physrev.65.117.

  11. Smug D, Ashwin PA. Generalized 2D-dynamical mean-field ising model with a rich set of bifurcations (inspired and applied to financial crises). 2018;28(4). https://doi.org/10.1142/s0218127418300100.

  12. Karin O, Swisa A, Glaser B, Dor Y, Alon U. Dynamical compensation in physiological circuits LINEAR INTEGRAL FEEDBACK. 2018:1–7.

    Google Scholar 

  13. Zhou JX, Aliyu DSM, Aurell E, Huang S. Quasi-potential landscape in complex multi-stable systems. J R Soc Interface. 2012. https://doi.org/10.1098/rsif.2012.0434.

  14. Dai L, Vorselen D, Korolev KS, Gore J. Generic indicators for loss of resilience before a tipping point leading to population collapse. Science (80-). 2012. https://doi.org/10.1126/science.1219805.

  15. Drake JM, Griffen BD. Early warning signals of extinction in deteriorating environments. Nature. 2010. https://doi.org/10.1038/nature09389.

  16. Hirota M, Holmgren M, Van Nes EH, Scheffer M. Global resilience of tropical forest and savanna to critical transitions. Science. 2011;334(6053):232–5. https://doi.org/10.1126/science.1210657.

    Article  CAS  PubMed  Google Scholar 

  17. Kéfi S, Rietkerk M, Alados CL, et al. Spatial vegetation patterns and imminent desertification in Mediterranean arid ecosystems. Nature. 2007. https://doi.org/10.1038/nature06111.

  18. Rietkerk M, Dekker SC, De Ruiter PC, Van De Koppel J. Self-organized patchiness and catastrophic shifts in ecosystems. Science (80-). 2004. https://doi.org/10.1126/science.1101867.

  19. Scheffer M, Carpenter S, Foley JA, Folke C, Walker B. Catastrophic shifts in ecosystems. 2001;413(October).

    Google Scholar 

  20. Scheffer M, Carpenter SR. Catastrophic regime shifts in ecosystems: Linking theory to observation. Trends Ecol Evol. 2003. https://doi.org/10.1016/j.tree.2003.09.002.

  21. Veraart AJ, Faassen EJ, Dakos V, Van Nes EH, Lürling M, Scheffer M. Recovery rates reflect distance to a tipping point in a living system. Nature. 2012. https://doi.org/10.1038/nature10723.

  22. Ashwin P, Wieczorek S, Vitolo R, Cox P. Tipping points in open systems: bifurcation, noise-induced and rate-dependent examples in the climate system. Philos Trans R Soc A Math Phys Eng Sci. 2012. https://doi.org/10.1098/rsta.2011.0306.

  23. Strogatz SH, Shafer DS. Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering. Westview Press. 1994. https://doi.org/10.1137/1037077.

  24. Chang W-C, Kudlacek J, Hlinka J, et al. Loss of neuronal network resilience precedes seizures and determines the ictogenic nature of interictal synaptic perturbations. Nat Neurosci. 2018;21(12):1742–52. https://doi.org/10.1038/s41593-018-0278-y.

    Article  CAS  PubMed  Google Scholar 

  25. Jirsa VK, Stacey WC, Quilichini PP, Ivanov AI, Bernard C. On the nature of seizure dynamics. 2014:2210–30. https://doi.org/10.1093/brain/awu133.

  26. Scheffer M, Bascompte J, Brock WA, et al. Early-warning signals for critical transitions. Nature. 2009. https://doi.org/10.1038/nature08227.

  27. Scheffer M, Carpenter SR, Lenton TM, et al. Anticipating critical transitions. Science (80-). 2012. https://doi.org/10.1126/science.1225244.

  28. Lenton TM. Early warning of climate tipping points. Nat Clim Chang. 2011. https://doi.org/10.1038/nclimate1143.

  29. Dakos V, Carpenter SR, Brock WA, et al. Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS One. 2012. https://doi.org/10.1371/journal.pone.0041010.

  30. Meisel C, Klaus A, Kuehn C, Plenz D. Critical slowing down governs the transition to neuron spiking. 2015:1–20. https://doi.org/10.1371/journal.pcbi.1004097.

  31. Kramer MA, Truccolo W, Eden UT, et al. Human seizures self-terminate across spatial scales via a critical transition. Proc Natl Acad Sci. 2012;109(51):21116–21. https://doi.org/10.1073/pnas.1210047110.

    Article  PubMed  Google Scholar 

  32. Wang R, Dearing JA, Langdon PG, et al. Transition to a eutrophic lake state. Nature. 2012;492(7429):419–22. https://doi.org/10.1038/nature11655.

    Article  CAS  PubMed  Google Scholar 

  33. Trefois C, Antony PMA, Goncalves J, Skupin A, Balling R. Critical transitions in chronic disease: transferring concepts from ecology to systems medicine. Curr Opin Biotechnol. 2015. https://doi.org/10.1016/j.copbio.2014.11.020.

  34. Dahlem MA, Kurths J, Ferrari MD, Aihara K, Scheffer M, May A. Understanding migraine using dynamic network biomarkers. Cephalalgia. 2015. https://doi.org/10.1177/0333102414550108.

  35. Olde Rikkert MG, Dakos V, Buchman TG, et al. Slowing down of recovery as generic risk marker for acute severity transitions in chronic diseases. Crit Care Med. 2016.

    Google Scholar 

  36. Quail T, Shrier A, Glass L. Predicting the onset of period-doubling bifurcations in noisy cardiac systems. Proc Natl Acad Sci. 2015. https://doi.org/10.1073/pnas.1424320112.

  37. Huang S, Ernberg I, Kauffman S. Cancer attractors: A systems view of tumors from a gene network dynamics and developmental perspective. Semin Cell Dev Biol. 2009. https://doi.org/10.1016/j.semcdb.2009.07.003.

  38. Elowitz MB, Levine AJ, Siggia ED, Swain PS. Stochastic gene expression in a single cell. Science (80-). 2002. https://doi.org/10.1126/science.1070919.

  39. Remacle F, Kravchenko-balasha N, Levitzki A, Levine RD. Information-heoretic analysis of phenotype changes in early stages of carcinogenesis. 2010;107(22). https://doi.org/10.1073/pnas.1005283107.

  40. Zadran S, Remacle F, Levine R. Surprisal analysis of glioblastoma multiform (GBM) MicroRNA dynamics unveils tumor specific phenotype. PLoS One. 2014. https://doi.org/10.1371/journal.pone.0108171.

  41. Kravchenko-Balasha N, Johnson H, White FM, Heath JR, Levine RD. A thermodynamic-based interpretation of protein expression heterogeneity in different glioblastoma multiforme tumors identifies tumor-specific unbalanced processes. J Phys Chem B. 2016. https://doi.org/10.1021/acs.jpcb.6b01692.

  42. Chen L, Liu R, Liu ZP, Li M, Aihara K. Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Sci Rep. 2012. https://doi.org/10.1016/j.amjsurg.2003.12.037.

  43. Liu R, Li M, Liu ZP, Wu J, Chen L, Aihara K. Identifying critical transitions and their leading biomolecular networks in complex diseases. Sci Rep. 2012. https://doi.org/10.1038/srep00813.

  44. Yang B, Li M, Tang W, et al. Dynamic network biomarker indicates pulmonary metastasis at the tipping point of hepatocellular carcinoma. Nat Commun. 2018. https://doi.org/10.1038/s41467-018-03024-2.

  45. Li M, Zeng T, Liu R, Chen L. Detecting tissue-specific early warning signals for complex diseases based on dynamical network biomarkers: study of type 2 diabetes by cross-tissue analysis. Brief Bioinform. 2014. https://doi.org/10.1093/bib/bbt027.

  46. Liu X, Chang X, Liu R, Yu X, Chen L, Aihara K. Quantifying critical states of complex diseases using single-sample dynamic network biomarkers. PLoS Comput Biol. 2017. https://doi.org/10.1371/journal.pcbi.1005633.

  47. Mojtahedi M, Skupin A, Zhou J, et al. cell fate decision as high-dimensional critical state transition. PLoS Biol. 2016. https://doi.org/10.1371/journal.pbio.2000640.

  48. Jin B, Liu R, Hao S, et al. Defining and characterizing the critical transition state prior to the type 2 diabetes disease. PLoS One. 2017. https://doi.org/10.1371/journal.pone.0180937.

  49. Boettiger C, Hastings A. Quantifying limits to detection of early warning for critical transitions. J R Soc Interface. 2012. https://doi.org/10.1098/rsif.2012.0125.

  50. Rocha JC, Peterson G, Bodin Ö, Levin SA. Cascading regime shifts within and across scales. 2018;1383(December):1–20. https://doi.org/10.1101/364620.

    Article  Google Scholar 

  51. Brock WA, Carpenter SR. Early warnings of regime shift when the ecosystem structure is unknown. PLoS One. 2012. https://doi.org/10.1371/journal.pone.0045586.

  52. Drijfhout S, Bathiany S, Beaulieu C et al. Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models. Proc Natl Acad Sci. 2015. https://doi.org/10.1073/pnas.1511451112.

  53. Muñoz MA. Colloquium: criticality and dynamical scaling in living systems. Rev Mod Phys. 2018. https://doi.org/10.1093/ejcts/ezx068.

  54. Pearl J. Causality. Cambridge university press;2009.

    Google Scholar 

  55. Pearl J. The book of why: the new science of cause and effect. Basic Books;2018.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, Belvaux, Luxembourg

    Rudi Balling, Jorge Goncalves, Stefano Magni, Laurent Mombaerts, Alice Oldano & Alexander Skupin

  2. Scripps Translational Science Institute, 10550 North Torrey Pines Road, La Jolla, CA, 92037, USA

    Rudi Balling

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  1. Rudi Balling
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  2. Jorge Goncalves
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  3. Stefano Magni
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  4. Laurent Mombaerts
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  5. Alice Oldano
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  6. Alexander Skupin
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Correspondence to Rudi Balling .

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  1. Vice President Innovation, Merck KGaA, Darmstadt, Germany

    Ulrich A.K. Betz

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Balling, R., Goncalves, J., Magni, S., Mombaerts, L., Oldano, A., Skupin, A. (2019). From Diagnosing Diseases to Predicting Diseases. In: Betz, U. (eds) Curious2018. Springer, Cham. https://doi.org/10.1007/978-3-030-16061-6_11

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