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Systems Biology, Systems Medicine, Systems Pharmacology: The What and The Why

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Abstract

Systems biology is today such a widespread discipline that it becomes difficult to propose a clear definition of what it really is. For some, it remains restricted to the genomic field. For many, it designates the integrated approach or the corpus of computational methods employed to handle the vast amount of biological or medical data and investigate the complexity of the living. Although defining systems biology might be difficult, on the other hand its purpose is clear: systems biology, with its emerging subfields systems medicine and systems pharmacology, clearly aims at making sense of complex observations/experimental and clinical datasets to improve our understanding of diseases and their treatments without putting aside the context in which they appear and develop. In this short review, we aim to specifically focus on these new subfields with the new theoretical tools and approaches that were developed in the context of cancer. Systems pharmacology and medicine now give hope for major improvements in cancer therapy, making personalized medicine closer to reality. As we will see, the current challenge is to be able to improve the clinical practice according to the paradigm shift of systems sciences.

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References

  • Abdullah S, Murnane EL, Matthews M, Choudhury T (2017) Circadian computing: sensing, modeling, and maintaining biological rhythms. In: Rehg JM et al (eds) Mobile health. Springer, Cham, p 35

    Google Scholar 

  • Agur Z, Elishmereni M, Kheifetz Y (2014) Personalizing oncology treatments by predicting drug efficacy, side-effects, and improved therapy: mathematics, statistics, and their integration. Wiley Interdiscip Rev Syst Biol Med 6:239–253

    Google Scholar 

  • Anderson ARA, Quaranta V (2008) Integrative mathematical oncology. Nat Rev Cancer 8:227–234

    Google Scholar 

  • Appelboom G, Camacho E, Abraham ME, Bruce SS, Dumont EL, Zacharia BE, D’Amico R, Slomian J, Reginster JY, Bruyère O, Connolly ES Jr (2014) Smart wearable body sensors for patient self-assessment and monitoring. Arch Public Health 72(1):28

    Google Scholar 

  • Apweiler R, Beissbarth T, Berthold MR, Blüthgen N, Burmeister Y, Dammann O, Deutsch A, Feuerhake F, Franke A, Hasenauer J, Hoffmann S, Höfer T, Jansen PL, Kaderali L, Klingmüller U, Koch I, Kohlbacher O, Kuepfer L, Lammert F, Maier D, Pfeifer N, Radde N, Rehm M, Roeder I, Saez-Rodriguez J, Sax U, Schmeck B, Schuppert A, Seilheimer B, Theis FJ, Vera J, Wolkenhauer O (2018) Whither systems medicine? Exp Mol Med 50(3):e453

    Google Scholar 

  • Avicenna (2017) A strategy for in silico clinical trial. http://avicenna-isct.org/about/

  • Avila JL, Bonnet C, Clairambault J, Ozbay H, Niculescu SI, Merhi F, Ballesta A, Tang R, Marie JP (2014) Analysis of a new model of cell population dynamics in acute myeloid leukemia. Advances in delays and dynamics, delay systems. Springer, Berlin, pp 315–328

    Google Scholar 

  • Ayers D, Day PJ (2015) Systems medicine: the applications of systems biology approaches for modern medical research and drug development. Mol. Biol. Int. ID698169

  • Ballesta A, Clairambault J (2014) Physiologically based mathematical models to optimize therapies against metastatic colorectal cancer: a mini-review. Curr Pharm Des 20(1):37–48

    Google Scholar 

  • Ballesta A, Zhou Q, Zhang X, Lv H, Gallo JM (2014) Multiscale design of cell-type-specific pharmacokinetic/pharmacodynamic models for personalized medicine: application to temozolomide in brain tumors. CPT Pharmacomet Syst Pharmacol 3:e112

    Google Scholar 

  • Ballesta A, Innominato PF, Dallmann R, Rand DA, Levi FA (2017) Systems chronotherapeutics. Pharmacol Rev 69:161–199

    Google Scholar 

  • Basch E (2017) Patient-reported outcomes—harnessing patients’ voices to improve clinical care. N Engl J Med 376:105–108

    Google Scholar 

  • Basch E, Deal AM, Dueck AC, Scher HI, Kris MG, Hudis C, Schrag D (2017) Overall survival results of a trial assessing patient-reported outcomes for symptom monitoring during routine cancer treatment. JAMA 318(2):197–198

    Google Scholar 

  • Benson M (2016) Clinical implications of omics and systems medicine: focus on predictive and individualized treatment. J Intern Med 279:229–240

    Google Scholar 

  • Billy F, Clairambault J, Fercoq O, Gaubert S, Lepoutre T, Ouillon T, Saito S (2014) Synchronisation and control of proliferation in cycling cell population models with age structure. Math Comput Simul 96:66–94

    Google Scholar 

  • Blanchard OL, Smoliga JM (2015) Translating dosages from animal models to human clinical trials—revisiting body surface area scaling. FASEB J 29:1629–1634

    Google Scholar 

  • Boissel JP, Auffray C, Noble D, Hood L, Boissel FH (2015) Bridging Systems medicine and patient needs. CPT Pharmacomet Syst Pharmacol 4:e00026

    Google Scholar 

  • Borrell-Carrió F, Suchman AL, Epstein RM (2004) The biopsychosocial model 25 years later: principles, practice, and scientific inquiry. Ann Fam Med 2(6):576–582

    Google Scholar 

  • Bousquet J, Anto JM, Akdis M, Auffray C, Keil T, Momas I, Postma DS, Valenta R, Wickman M, Cambon-Thomsen A et al (2016) Paving the way of systems biology and precision medicine in allergic diseases: the MeDALL success story—mechanisms of the Development of ALLergy; EU FP7-CP-IP; Project No: 261357; 2010–2015. Allergy 71(11):1513–1525

    Google Scholar 

  • Caraguel F, Lesart AC, Estève F, van der Sanden B, Stéphanou A (2016) Towards the design of a patient-specific virtual tumour. Comput Math Methods Med 2016:7851789

    Google Scholar 

  • Cascante M, de Atauri P, Gomez-Cabrero D, Wagner P, Centelles JJ, Marin S, Cano I, Velickovski F, Marin de Mas I, Maier D, Roca J, Sabatier P (2014) Workforce preparation: the Biohealth computing model for Master and PhD students. J Transl Med 12(Suppl 2):S11

    Google Scholar 

  • Chung AE, Jensen RE, Basch EM (2016) Leveraging emerging technologies and the internet of things to improve the quality of cancer care. J Oncol Pract 12:863–866

    Google Scholar 

  • Collins FS, Varmus H (2015) A new initiative on precision medicine. N Engl J Med 372(9):793–795

    Google Scholar 

  • Davies B, Morris T (1993) Physiological parameters in laboratory animals and humans. Pharm Res 10:1093–1095

    Google Scholar 

  • El Cheikh R, Bernard S, El Khatib N (2014) Modeling circadian clock–cell cycle interaction effects on cell population growth rates. J Theor Biol 363:318–331

    Google Scholar 

  • Engel GL (1977) The need for a new medical model: a challenge for biomedicine. Science 196(4286):129–136

    Google Scholar 

  • Filipp FV (2017) Precision medicine driven by cancer systems biology. Cancer Metastasis Rev 36(1):91–108

    Google Scholar 

  • Geiger B, Bershadsky A, Pankov R, Yamada KM (2001) Transmembrane crosstalk between the extracellular matrix-cytoskeleton crosstalk. Nat Rev Mol Cell Biol 2(11):793–805

    Google Scholar 

  • Gietzelt M, Lopprich M, Karmen C, Knaup P, Ganzinger M (2016) Models and data sources used in systems medicine. A systematic literature review. Methods Inf Med 55:107–113

    Google Scholar 

  • Gresham G, Schrack J, Gresham LM, Shinde AM, Hendifar AE, Tuli R, Rimel BJ, Figlin R, Meinert CL, Piantadosi S (2018) Wearable activity monitors in oncology trials: current use of an emerging technology. Contemp Clin Trials 64:13–21

    Google Scholar 

  • Group QW (2011) Quantitative and systems pharmacology in the post-genomic era: new approaches to discovering drugs and understanding therapeutic mechanisms. An NIH White Paper. https://www.nigms.nih.gov/News/reports/Pages/201110-syspharma.aspx

  • Hasin Y, Seldin M, Lusis A (2017) Multi-omics approaches to disease. Genome Biol 18(1):83

    Google Scholar 

  • Hood L, Flores M (2012) A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. New Biotechnol 29:613–624

    Google Scholar 

  • Hutter OF, Noble D (1960) Rectifying properties of heart muscle. Nature 188:495

    Google Scholar 

  • Immunetrics (2017) Immunetrics quantitative systems pharmacology (QSP) modeling services & technology: designed in collaboration with modelers, for modelers. http://www.immunetrics.com

  • Ingber DE (2006) Cellular mechanotransduction: putting all the pieces together again. FASEB J 20(7):811–827

    Google Scholar 

  • Innominato PF, Roche VP, Palesh OG, Ulusakarya A, Spiegel D, Lévi FA (2014) The circadian timing system in clinical oncology. Ann Med 46(4):191–207

    Google Scholar 

  • Innominato PF, Komarzynski S, Mohammad-Djafari A, Arbaud A, Ulusakarya A, Bouchahda M, Haydar M, Bossevot-Desmaris R, Plessis V, Mocquery M, Bouchoucha D, Afshar M, Beau J, Karabou A, Morre JF, Fursse J, Rovira Simon J, Levi F (2016) Clinical relevance of the first domomedicine platform securing multidrug chronotherapy delivery in metastatic cancer patients at home: the inCASA European Project. J Med Internet Res 18(11):e305

    Google Scholar 

  • Iyengar R, Altman RB, Troyanskya O, FitzGerald GA (2015) MEDICINE. Personalization in practice. Science 350:282–283

    Google Scholar 

  • Joly M, Rondó PHC (2017) The future of computational biomedicine: complex systems thinking. Math Comput Simul 132:1–27

    Google Scholar 

  • Kang HE, Lee MG (2011) Approaches for predicting human pharmacokinetics using interspecies pharmacokinetic scaling. Arch Pharm Res 34:1779–1788

    Google Scholar 

  • Ke A, Barter Z, Rowland-Yeo K, Almond L (2016) Towards a best practice approach in PBPK modeling: case example of developing a unified Efavirenz model accounting for induction of CYPs 3A4 and 2B6. CPT Pharmacomet Syst Pharmacol 5:367–376

    Google Scholar 

  • Kirchmair J, Göller AH, Lang D, Kunze J, Testa B, Wilson ID, Glen RC, Schneider G (2015) Predicting drug metabolism: experiment and/or computation? Nat Rev Drug Discov 14(6):387–404

    Google Scholar 

  • Kirschner M (2016) Systems medicine: sketching the landscape. Methods Mol Biol 1386:3–15

    Google Scholar 

  • Kuepfer L, Schuppert A (2016) Systems medicine in pharmaceutical research and development. Methods Mol Biol 1386:87–104

    Google Scholar 

  • Lave T, Chapman K, Goldsmith P, Rowland M (2009) Human clearance prediction: shifting the paradigm. Exp Opin Drug Metab Toxicol 5:1039–1048

    Google Scholar 

  • Li X, Dunn J, Salins D, Zhou G, Zhou W, Schussler-Fiorenza Rose SM, Perelman D, Colbert E, Runge R, Rego S, Sonecha R, Datta S, McLaughlin T, Snyder MP (2017) Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol 15(1):e2001402

    Google Scholar 

  • Liang Y, Kelemen A (2017) Computational dynamic approaches for temporal omics data with applications to systems medicine. BioData Min 10:20

    Google Scholar 

  • Low CA, Dey AK, Ferreira D, Kamarck T, Sun W, Bae S, Doryab A (2017) Estimation of symptom severity during chemotherapy from passively sensed data: exploratory study. J Med Internet Res 19(12):e420

    Google Scholar 

  • Lowe D (2014) New look at clinical attrition. Science translational medicine. http://blogs.sciencemag.org/pipeline/archives/2014/01/10/a_new_look_at_clinical_attrition

  • Macklin P, Frieboes HB, Sparks JL, Ghaffarizadeh A, Friedman SH, Juarez EF, Jonckheere E, Mumenthaler SM (2016) Progress towards computational 3-D multicellular systems biology. Adv Exp Med Biol 936:225–246

    Google Scholar 

  • Majumder S, Mondal T, Deen MJ (2017) Wearable sensors for remote health monitoring. Sensors 17:130

    Google Scholar 

  • Maley CC, Aktipis A, Graham TA, Sottoriva A, Boddy AM, Janiszewska M, Silva AS et al (2017) Classifying the evolutionary and ecological features of neoplasms. Nat Rev Cancer 17(10):605–619

    Google Scholar 

  • Malottki K, Barton P, Tsourapas A, Uthman AO, Liu Z, Routh K, Connock M, Jobanputra P, Moore D, Fry-Smith A, Chen YF (2011) Adalimumab, etanercept, infliximab, rituximab and abatacept for the treatment of rheumatoid arthritis after the failure of a tumour necrosis factor inhibitor: a systematic review and economic evaluation. Health Technol Assess 15(14):1–278

    Google Scholar 

  • Marin de Mas I, Fanchon E, Papp B, Kalko S, Roca J, Cascante M (2017) Molecular mechanisms underlying COPD-muscle dysfunction unveiled through a systems medicine approach. Bioinformatics 33(1):95–103

    Google Scholar 

  • Maurice M, Lévi F, Breda G, Beaumatin N, Duclos A, Chkeir A, Hewson D, Duchêne J (2015) Innovative project for domomedicine deployment, The PiCADo Pilot Project. eTELEMED 2015: the seventh international conference on eHealth, telemedicine, and social medicine

  • Mayer EA, Labus JS, Tillisch K, Cole SW, Baldi P (2015) Towards a systems view of IBS. Nat Rev Gastroenterol Hepatol 12(10):592–605

    Google Scholar 

  • McConnell EL, Basit AW, Murdan S (2008) Measurements of rat and mouse gastrointestinal pH, fluid and lymphoid tissue, and implications for in-vivo experiments. J Pharm Pharmacol 60:63–70

    Google Scholar 

  • Mizeranschi A, Groen D, Borgdorff J, Hoekstra AG, Chopard B, Dubitzky W (2016) Anatomy and physiology of multiscale modeling and simulation in systems medicine. Methods Mol Biol 1386:375–404

    Google Scholar 

  • Morere JF, Innominato P (2014) ESMO 2014: new trends in precision medicine. Target Oncol 9:293–294

    Google Scholar 

  • Naylor S, Chen JY (2010) Unraveling human complexity and disease with systems biology and personalized medicine. Pers Med 7:275–289

    Google Scholar 

  • Noble D (1960) Cardiac action and pacemaker potentials based on the Hodgkin–Huxley equations. Nature 188:495–497

    Google Scholar 

  • Noble D (2007) From the Hodgkin–Huxley axon to the virtual heart. J Physiol 580(1):15–22

    Google Scholar 

  • Noble D (2008) Claude Bernard, the first systems biologist, and the future of physiology. Exp Physiol 93(1):16–26

    Google Scholar 

  • Noble D, Colatsky TJ (2000) A return to rational drug discovery: computer-based model of cells, organs and systems in drug target identification. Exp Opin Ther Targets 4:39–49

    Google Scholar 

  • Orr MG, Plaut DC (2014) Complex systems and health behavior change: insights from cognitive science. Am J Health Behav 38:404–413

    Google Scholar 

  • Ortega MA, Poirion O, Zhu X, Huang S, Wolfgruber TK, Sebra R, Garmire LX (2017) Using single-cell multiple omics approaches to resolve tumor heterogeneity. Clin Transl Med 6:46

    Google Scholar 

  • Ortiz-Tudela E, Mteyrek A, Ballesta A, Innominato PF, Levi F (2013) Cancer chronotherapeutics: experimental, theoretical, and clinical aspects. Handb Exp Pharmacol 217:261–288

    Google Scholar 

  • Ozturk N, Ozturk D, Kavakli IH, Okyar A (2017) Molecular aspects of circadian pharmacology and relevance for cancer chronotherapy. Int J Mol Sci 18(10):2168

    Google Scholar 

  • Pollard TD (2003) The cytoskeleton, cellular motility and the reductionist agenda. Nature 422:741–745

    Google Scholar 

  • Powathil GG, Adamson DJ, Chaplain MA (2013) Towards predicting the response of a solid tumour to chemotherapy and radiotherapy treatments: clinical insights from a computational model. PLoS Comput Biol 9(7):e1003120

    Google Scholar 

  • Price ND, Magis AT, Earls JC, Glusman G, Levy R, Lausted C, McDonald DT, Kusebauch U, Moss CL, Zhou Y, Qin S, Moritz RL, Brogaard K, Omenn GS, Lovejoy JC, Hood L (2017) A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat Biotechnol 35:747–756

    Google Scholar 

  • Rose RH, Neuhoff S, Abduljalil K, Chetty M, Rostami-Hodjegan A, Jamei M (2014) Application of a physiologically based pharmacokinetic model to predict OATP1B1-related variability in pharmacodynamics of rosuvastatin. CPT Pharmacomet Syst Pharmacol 3:e124

    Google Scholar 

  • Saqi M, Pellet J, Roznovat I, Mazein A, Ballereau S, De Meulder B, Auffray C (2016) Systems medicine: the future of medical genomics, healthcare, and wellness. Methods Mol Biol 1386:43–60

    Google Scholar 

  • Serrano KJ, Yu M, Riley WT, Patel V, Hughes P, Marchesini K, Atienza AA (2016) Willingness to exchange health information via mobile devices: findings from a population-based survey. Ann Fam Med 14(1):34–40

    Google Scholar 

  • Shi L, Zhang Y, Feng L, Wang L, Rong W, Wu F, Wu J, Zhang K, Cheng S (2017) Multi-omics study revealing the complexity and spatial heterogeneity of tumor-infiltrating lymphocytes in primary liver carcinoma. Oncotarget 8(21):34844–34857

    Google Scholar 

  • Siegel RL, Miller KD, Jemal A (2018) Cancer statistics, 2018. CA Cancer J Clin 68(1):7–30

    Google Scholar 

  • Skarke C, Lahens NF, Rhoades SD, Campbell A, Bittinger K, Bailey A, Hoffmann C, Olson RS, Chen L, Yang G, Price TS, Moore JH, Bushman FD, Greene CS, Grant GR, Weljie AM, FitzGerald GA (2017) A pilot characterization of the human chronobiome. Sci Rep 7(1):17141

    Google Scholar 

  • Smuts JC (1927) Holism and evolution. Macmillan And Company Limited, London

    Google Scholar 

  • Sobradillo P, Pozo F, Agusti A (2011) P4 medicine: the future around the corner. Archivos de bronconeumologia 47:35–40

    Google Scholar 

  • Soto AM, Sonnenschein C (2012) Is systems biology a promising approach to resolve controversies in cancer research? Cancer Cell Int 12:12

    Google Scholar 

  • Soto AM, Longo G, Noble D (2016) Preface to From the century of the genome to the century of the organism: new theoretical approaches. Prog Biophys Mol Biol 122(1):1–3

    Google Scholar 

  • Stéphanou A, Volpert V (2016) Hybrid modelling in biology: a classification review. Math Model Nat Phenom 11:37–48

    Google Scholar 

  • Thamrin C, Frey U, Kaminsky DA, Reddel HK, Seely AJ, Suki B, Sterk PJ (2016) Systems biology and clinical practice in respiratory medicine. The twain shall meet. Am J Respir Crit Care Med 194(9):1053–1061

    Google Scholar 

  • THE CASyM ROADMAP Implementation of Systems Medicine across Europe (2017). https://www.casym.eu/blog/publications/2017/the-casym-roadmap-updated-version-of-april-2017-released/

  • van Kampen AH, Moerland PD (2016) Taking bioinformatics to systems medicine. Methods Mol Biol 1386:17–41

    Google Scholar 

  • Vogt H, Hofmann B, Getz L (2016) The new holism: P4 systems medicine and the medicalization of health and life itself. Med Health Care Philos 19(2):307–323

    Google Scholar 

  • Williams EG, Wu Y, Jha P, Dubuis S, Blattmann P, Argmann CA, Houten SM, Amariuta T, Wolski W, Zamboni N, Aebersold R, Auwerx J (2016) Systems proteomics of liver mitochondria function. Science 352(6291):aad0189

    Google Scholar 

  • Wolkenhauer O, Auffray C, Brass O, Clairambault J, Deutsch A, Drasdo D, Gervasio F, Preziosi L, Maini P, Marciniak-Czochra A, Kossow C, Kuepfer L, Rateitschak K, Ramis-Conde I, Ribba B, Schuppert A, Smallwood R, Stamatakos G, Winter F, Byrne H (2014) Enabling multiscale modeling in systems medicine. Genome Med 6(3):21

    Google Scholar 

  • Yates LR, Seoane J, Le Tourneau C, Siu LL, Marais R, Michiels S, Soria JC, Campbell P, Normanno N, Scarpa A, Reis-Filho JS, Rodon J, Swanton C, Andre F (2018) The European Society for Medical Oncology (ESMO) precision medicine glossary. Ann Oncol 29:30–35

    Google Scholar 

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Authors and Affiliations

  1. Université Grenoble Alpes, CNRS, TIMC-IMAG/DyCTIM2, 38000, Grenoble, France

    Angélique Stéphanou & Eric Fanchon

  2. North Wales Cancer Centre, Betsi Cadwaladr University Health Board, Bangor, Denbighshire, UK

    Pasquale F. Innominato

  3. INSERM and Université Paris 11 Unit 935, Villejuif, France

    Pasquale F. Innominato & Annabelle Ballesta

  4. University of Warwick, Coventry, UK

    Pasquale F. Innominato & Annabelle Ballesta

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  1. Angélique Stéphanou
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  2. Eric Fanchon
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  3. Pasquale F. Innominato
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  4. Annabelle Ballesta
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Correspondence to Angélique Stéphanou.

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Stéphanou, A., Fanchon, E., Innominato, P.F. et al. Systems Biology, Systems Medicine, Systems Pharmacology: The What and The Why. Acta Biotheor 66, 345–365 (2018). https://doi.org/10.1007/s10441-018-9330-2

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