Abstract
In November 2014, Georgetown University Medical Center and Medstar Institute for Innovation convened a conference related to “Systems and Complexity Sciences for Healthcare: a Conference on the Imperative for Systems Science in the twenty-first Century”. The symposium brought more than 100 biomedical researchers, medical educators, leaders and clinicians to deliberate the importance of understanding and incorporating biological system complexities into patient care. Such an approach provides tangible methods for breaking the bonds of traditional treatment and clinical management methods, towards truly personalized medicine, or cura personalis. Such a systems approach towards health, illness, and disease considers the inter-dependencies within and outside of a biological system and offers significant benefits towards improving clinical outcomes and quality of life. Despite substantial scientific evidence, however, the systems and complexity paradigm and its application to healthcare remains in its infancy. Herein, we present a summary of discussions and opinions of conference participants who collectively highlighted the tremendous opportunities of using such approaches, while recognizing the socio-economic, ethical, and organizational road-blocks that have prevented the widespread implementation of such a model for clinical research and patient care.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hawe P. Lessons from complex interventions to improve health. Annu Rev Public Health. 2015;18(36):307–323.
Ahmad S, Tejuja A, Newman KD, Zarychanski R, Seely AJ. Clinical review: a review and analysis of heart rate variability and the diagnosis and prognosis of infection. Crit Care. 2009;13(6):232.
Leykum LK, Lanham HJ, Pugh JA, Parchman M, Anderson RA, Crabtree BF, et al. Manifestations and implications of uncertainty for improving healthcare systems: an analysis of observational and interventional studies grounded in complexity science. Implement Sci. 2014;9(1):165.
Gomes AP, Blenis J. A nexus for cellular homeostasis: the interplay between metabolic and signal transduction pathways. Curr Opin Biotechnol. 2015;34C:110–7.
Schluesener JK, Zhu X, Schluesener HJ, Wang GW, Ao P. Key network approach reveals new insight into Alzheimer’s disease. IET Syst Biol. 2014;8(4):169–75.
Fiandaca MS, Mapstone ME, Cheema AK, Federoff HJ. The critical need for defining preclinical biomarkers in Alzheimer’s disease. Alzheimers Dement. 2014;10(3 Suppl):S196–212.
Sleimen-Malkoun R, Temprado JJ, Hong SL. Aging induced loss of complexity and dedifferentiation: consequences for coordination dynamics within and between brain, muscular and behavioral levels. Front Aging Neurosci. 2014;6:140.
Hong SL. The dynamics of structural and functional complexity across the lifespan. Nonlinear Dyn Psychol Life Sci. 2007;11(2):219–34.
Guarinoni MG, Motta PC, Petrucci C, Lancia L. Complexity of care: a concept analysis. Ann Ig. 2014;26(3):226–36.
Sturmberg JP. Emergent properties define the subjective nature of health and disease. J Public Health Policy. 2014;35(3):414–9.
Topolski S, Sturmberg J. Validation of a non-linear model of health. J Eval Clin Pract. 2014;20(6):1026–35.
Hood L, Auffray C. Participatory medicine: a driving force for revolutionizing healthcare. Genome Med. 2013;5(12):110.
Flores M, Glusman G, Brogaard K, Price ND, Hood L. P4 medicine: how systems medicine will transform the healthcare sector and society. Pers Med. 2013;10(6):565–76.
Vogt H, Ulvestad E, Eriksen TE, Getz L. Getting personal: can systems medicine integrate scientific and humanistic conceptions of the patient? J Eval Clin Pract. 2014;20(6):942–52.
Tian Q, Price ND, Hood L. Systems cancer medicine: towards realization of predictive, preventive, personalized, and participatory (P4) medicine. J Intern Med. 2012;271(2):111–21.
Spiegel AM, Hawkins M. ‘Personalized medicine’ to identify genetic risks for type 2 diabetes and focus prevention: can it fulfill its promise? Health Aff (Millwood). 2012;31(1):43–9.
Bielohuby M, Popp S, Bidlingmaier M. A guide for measurement of circulating metabolic hormones in rodents: Pitfalls during the pre-analytical phase. Mol Metab. 2012;1(1):47–60.
Johnson CH, Gonzalez FJ. Challenges and opportunities of metabolomics. J Cell Physiol. 2012;227(8):2975–81.
Norman AW. Sunlight, season, skin pigmentation, vitamin D, and 25-hydroxyvitamin D: integral components of the vitamin D endocrine system. Am J Clin Nutr. 1998;67(6):1108–10.
Lee S, Lenton E, Sexton L, Cooke I. The effect of age on the cyclical patterns of plasma LH, FSH, oestradiol and progesterone in women with regular menstrual cycles. Hum Reprod. 1988;3(7):851–5.
Wallace M, Hashim Y-Y, Wingfield M, Culliton M, McAuliffe F, Gibney M, et al. Effects of menstrual cycle phase on metabolomic profiles in premenopausal women. Hum Reprod. 2010;25(4):949–56.
Katz FH, Romfh P, Smith JA. Diurnal variation of plasma aldosterone, cortisol and renin activity in supine man. J Clin Endocrinol Metab. 1975;40(1):125–34.
Sampson JN, Boca SM, Shu XO, Stolzenberg-Solomon RZ, Matthews CE, Hsing AW, et al. Metabolomics in epidemiology: sources of variability in metabolite measurements and implications. Cancer Epidemiol Biomarkers Prev. 2013;22(4):631–40.
Xiao Q, Moore SC, Boca SM, Matthews CE, Rothman N, Stolzenberg-Solomon RZ, et al. Sources of variability in metabolite measurements from urinary samples. PloS One. 2014;9(5):e95749.
Cheema AK, Pathak R, Zandkarimi F, Kaur P, Alkhalil L, Singh R, et al. Liver metabolomics reveals increased oxidative stress and fibrogenic potential in Gfrp transgenic mice in response to ionizing radiation. J Proteome Res. 2014;13(6):3065–74.
Fiandaca MS, Federoff HJ. Using viral-mediated gene delivery to model Parkinson’s disease: do nonhuman primate investigations expand our understanding? Exp Neurol. 2014;256:117–25.
Want EJ, Masson P, Michopoulos F, Wilson ID, Theodoridis G, Plumb RS, et al. Global metabolic profiling of animal and human tissues via UPLC-MS. Nat Protocols. 2013;8(1):17–32.
Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010;11(10):733–9.
Kreutz C, Timmer J. Systems biology: experimental design. FEBS J. 2009;276(4):923–42.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Cheema, A.K., Fiandaca, M.S., Mapstone, M., Federoff, H.J. (2016). Systems Biology: Unravelling Molecular Complexity in Health and Disease. In: Sturmberg, J. (eds) The Value of Systems and Complexity Sciences for Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-319-26221-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-26221-5_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26219-2
Online ISBN: 978-3-319-26221-5
eBook Packages: MedicineMedicine (R0)