Abstract
With the integration of pharmacogenomics and systems biology, personalized medicine would be possible by switching the gear from the reductionism-based and disease-focused medical system toward a dynamical systems-based and human-centric health care. Comprehensive models are needed to represent the properties of complex adaptive systems (CASs) to elucidate the complexity in health and diseases, including the features of emergence, nonlinearity, self-organization, and adaptation. As all diseases have the dynamical elements, nonlinear time-series analyses are necessary to characterize the system dynamics at various levels to elucidate the physiological and pathological rhythms, oscillations, and feedback loops. Such analyses can help detect patterns across multiple scales in both the spatial (e.g., from molecules to cells, from organisms to psychosocial environments) and the temporal (e.g., from nanoseconds to hours, from years to decades) dimensions. Based on such understanding, systems and dynamical medicine can be developed with the emphasis on the whole systems that change over time to address the nonlinearity and interconnectivity toward a holistic and proactive care. Accurate and robust biomarkers with predictive values can be discovered to reflect the systemic conditions and disease stages. Network and dynamical models may support individualized risk analysis, presymptomatic diagnosis, precise prognosis, and integrative interventions. Systems and dynamical medicine may provide the root for the achievement of predictive, preventive, personalized, and participatory (P4) medicine.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yan Q (2010) Translational bioinformatics and systems biology approaches for personalized medicine. Methods Mol Biol 662:167–178
Yan Q (2005) Pharmacogenomics and systems biology of membrane transporters. Mol Biotechnol 29:75–88
Yan Q (2011) Translation of psychoneuroimmunology into personalized medicine: a systems biology perspective. Pers Med 8:641–649
Yan Q (2008) The integration of personalized and systems medicine: bioinformatics support for pharmacogenomics and drug discovery. Methods Mol Biol 448:1–19
Chaffee MW, McNeill MM (2007) A model of nursing as a complex adaptive system. Nurs Outlook 55:232–241
Iris F (2008) Biological modeling in the discovery and validation of cognitive dysfunctions biomarkers. In: Turck CW (ed) Biomarkers for psychiatric disorders. Springers Science + Business Media, New York
Dinicola S, D’Anselmi F, Pasqualato A et al (2011) A systems biology approach to cancer: fractals, attractors, and nonlinear dynamics. OMICS 15:93–104
Sturmberg JP, Martin CM (2013) Complexity in health: an introduction. In: Sturmberg JP, Martin CM (eds) Handbook of systems and complexity in health. Springer Science + Business Media, New York
Bleeker FE, Lamba S, Rodolfo M et al (2009) Mutational profiling of cancer candidate genes in glioblastoma, melanoma and pancreatic carcinoma reveals a snapshot of their genomic landscapes. Hum Mutat 30:E451–E459
Manabe I (2011) Chronic inflammation links cardiovascular, metabolic and renal diseases. Circ J 75:2739–2748
Dinarello CA (2011) Blocking interleukin-1β in acute and chronic autoinflammatory diseases. J Intern Med 269:16–28
Heng HHQ (2008) The conflict between complex systems and reductionism. JAMA 300:1580–1581
Avner BS, Fialho AM, Chakrabarty AM (2012) Overcoming drug resistance in multi-drug resistant cancers and microorganisms: a conceptual framework. Bioengineered 3:262–270
Kitano H (2007) The theory of biological robustness and its implication in cancer. Ernst Schering Res Found Workshop 61:69–88
Yan Q (2012) The role of psychoneuroimmunology in personalized and systems medicine. Methods Mol Biol 934:3–19
Qu Z, Garfinkel A, Weiss JN, Nivala M (2011) Multi-scale modeling in biology: how to bridge the gaps between scales? Prog Biophys Mol Biol 107:21–31
Leyvraz S, Pampallona S, Martinelli G et al (2008) A threefold dose intensity treatment with ifosfamide, carboplatin, and etoposide for patients with small cell lung cancer: a randomized trial. J Natl Cancer Inst 100:533–541
Mittra I (2007) The disconnection between tumor response and survival. Nat Clin Pract Oncol 4:203
Krogh-Madsen T, Christini DJ (2012) Nonlinear dynamics in cardiology. Annu Rev Biomed Eng 14:179–203
Buchman TG (2004) Nonlinear dynamics, complex systems, and the pathobiology of critical illness. Curr Opin Crit Care 10:378–382
Chay TR, Rinzel J (1985) Bursting, beating, and chaos in an excitable membrane model. Biophys J 47:357–366
Huang S, Wikswo J (2006) Dimensions of systems biology. Rev Physiol Biochem Pharmacol 157:81–104
Jones DP, Go Y-M (2010) Redox compartmentalization and cellular stress. Diabetes Obes Metab 12(Suppl 2):116–125
Wilders R, Jongsma HJ (1993) Beating irregularity of single pacemaker cells isolated from the rabbit sinoatrial node. Biophys J 65:2601–2613
Zhang Z, Chen D, Liu W et al (2011) Nonparametric evaluation of dynamic disease risk: a spatio-temporal kernel approach. PLoS One 6:e17381
Kopec AM, Carew TJ (2013) Growth factor signaling and memory formation: temporal and spatial integration of a molecular network. Learn Mem 20:531–539
Gulsuner S, Walsh T, Watts AC (2013) Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network. Cell 154:518–529
Manor B, Lipsitz LA (2013) Physiologic complexity and aging: implications for physical function and rehabilitation. Prog Neuropsychopharmacol Biol Psychiatry 45:287–293
Jonker MJ, Melis JPM, Kuiper RV et al (2013) Life spanning murine gene expression profiles in relation to chronological and pathological aging in multiple organs. Aging Cell 12:901–909
Zykovich A, Hubbard A, Flynn JM et al (2014) Genome-wide DNA methylation changes with age in disease free human skeletal muscle. Aging Cell 13(2):360–366
Halberg F, Cornélissen G, Wilson D et al (2009) Chronobiology and chronomics: detecting and applying the cycles of nature. Biologist (London) 56:209–214
Lopes RS, Resende NM, Honorio-França AC et al (2013) Application of bioinformatics in chronobiology research. ScientificWorldJournal 2013:153839
Klevecz RR, Li CM, Marcus I et al (2008) Collective behavior in gene regulation: the cell is an oscillator, the cell cycle a developmental process. FEBS J 275:2372–2384
Kurz FT, Aon MA, O’Rourke B et al (2010) Spatio-temporal oscillations of individual mitochondria in cardiac myocytes reveal modulation of synchronized mitochondrial clusters. Proc Natl Acad Sci U S A 107:14315–14320
Schultze-Kraft M, Becker R, Breakspear M et al (2011) Exploiting the potential of three dimensional spatial wavelet analysis to explore nesting of temporal oscillations and spatial variance in simultaneous EEG-fMRI data. Prog Biophys Mol Biol 105:67–79
Stephane M, Leuthold A, Kuskowski M et al (2012) The temporal, spatial, and frequency dimensions of neural oscillations associated with verbal working memory. Clin EEG Neurosci 43:145–153
Lenz P, Søgaard-Andersen L (2011) Temporal and spatial oscillations in bacteria. Nat Rev Microbiol 9:565–577
Vandeput S, Verheyden B, Aubert AE, Van Huffel S (2012) Nonlinear heart rate dynamics: circadian profile and influence of age and gender. Med Eng Phys 34:108–117
Ramanujan VK, Herman BA (2007) Aging process modulates nonlinear dynamics in liver cell metabolism. J Biol Chem 282:19217–19226
Milton J, Black D (1995) Dynamic diseases in neurology and psychiatry. Chaos 5:8–13
Pezard L, Nandrino JL, Renault B et al (1996) Depression as a dynamical disease. Biol Psychiatry 39:991–999
Schmid GB (1991) Chaos theory and schizophrenia: elementary aspects. Psychopathology 24:185–198
An der Heiden U (2006) Schizophrenia as a dynamical disease. Pharmacopsychiatry 39(Suppl 1):S36–S42
Lopes da Silva F, Blanes W, Kalitzin SN et al (2003) Epilepsies as dynamical diseases of brain systems: basic models of the transition between normal and epileptic activity. Epilepsia 44(Suppl 12):72–83
Warren K, Hawkins RC, Sprott JC (2003) Substance abuse as a dynamical disease: evidence and clinical implications of nonlinearity in a time series of daily alcohol consumption. Addict Behav 28:369–374
Schiff SJ (2010) Towards model-based control of Parkinson’s disease. Philos Trans A Math Phys Eng Sci 368:2269–2308
Edelstein-Keshet L, Israel A, Lansdorp P (2001) Modelling perspectives on aging: can mathematics help us stay young? J Theor Biol 213:509–525
Harms HM, Prank K, Brosa U et al (1992) Classification of dynamical diseases by new mathematical tools: application of multi-dimensional phase space analyses to the pulsatile secretion of parathyroid hormone. Eur J Clin Invest 22:371–377
Tretter F, Gebicke-Haerter PJ, An der Heiden U et al (2011) Affective disorders as complex dynamic diseases—a perspective from systems biology. Pharmacopsychiatry 44(Suppl 1):S2–S8
Kumari M, Chandola T, Brunner E et al (2010) A nonlinear relationship of generalized and central obesity with diurnal cortisol secretion in the Whitehall II study. J Clin Endocrinol Metab 95:4415–4423
Damle RN, Calissano C, Chiorazzi N (2010) Chronic lymphocytic leukaemia: a disease of activated monoclonal B cells. Best Pract Res Clin Haematol 23:33–45
Stahlhut Espinosa CE, Slack FJ (2006) The role of microRNAs in cancer. Yale J Biol Med 79:131–140
Belair J, Glass L, An Der Heiden U, Milton J (1995) Dynamical disease: identification, temporal aspects and treatment strategies of human illness. Chaos 5:1–7
Frey U, Maksym G, Suki B (2011) Temporal complexity in clinical manifestations of lung disease. J Appl Physiol 110:1723–1731
Odgers CL, Mulvey EP, Skeem JL et al (2009) Capturing the ebb and flow of psychiatric symptoms with dynamical systems models. Am J Psychiatry 166:575–582
Shaffer DR, Scher HI (2003) Prostate cancer: a dynamic illness with shifting targets. Lancet Oncol 4:407–414
Abu-Asab MS, Chaouchi M, Alesci S et al (2011) Biomarkers in the age of omics: time for a systems biology approach. OMICS 15:105–112
Filiou MD, Turck CW (2011) General overview: biomarkers in neuroscience research. Int Rev Neurobiol 101:1–17
Dunn DA, Apanovitch D, Follettie M et al (2010) Taking a systems approach to the identification of novel therapeutic targets and biomarkers. Curr Pharm Biotechnol 11:721–734
Jack CR Jr, Knopman DS, Jagust WJ et al (2013) Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol 12:207–216
Chen L, Liu R, Liu Z-P et al (2012) Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Sci Rep 2:342
Li M, Zeng T, Liu R, Chen L (2014) 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 15(2):229–243
Li X, Blount PL, Vaughan TL, Reid BJ (2011) Application of biomarkers in cancer risk management: evaluation from stochastic clonal evolutionary and dynamic system optimization points of view. PLoS Comput Biol 7:e1001087
Younesi E, Hofmann-Apitius M (2013) From integrative disease modeling to predictive, preventive, personalized and participatory (P4) medicine. EPMA J 4:23
Hood L, Flores M (2012) A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. N Biotechnol 29:613–624
Bengoechea JA (2012) Infection systems biology: from reactive to proactive (P4) medicine. Int Microbiol 15:55–60
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this protocol
Cite this protocol
Yan, Q. (2014). From Pharmacogenomics and Systems Biology to Personalized Care: A Framework of Systems and Dynamical Medicine. In: Yan, Q. (eds) Pharmacogenomics in Drug Discovery and Development. Methods in Molecular Biology, vol 1175. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0956-8_1
Download citation
DOI: https://doi.org/10.1007/978-1-4939-0956-8_1
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-0955-1
Online ISBN: 978-1-4939-0956-8
eBook Packages: Springer Protocols