Advertisement

Acta Biotheoretica

, Volume 66, Issue 4, pp 345–365 | Cite as

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

  • Angélique StéphanouEmail author
  • Eric Fanchon
  • Pasquale F. Innominato
  • Annabelle Ballesta
Regular Article

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.

Keywords

Big data Precision medicine Personalized medicine Drug development Multi-scale approach 

References

  1. 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 35Google Scholar
  2. 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 6:239–253Google Scholar
  3. Anderson ARA, Quaranta V (2008) Integrative mathematical oncology. Nat Rev Cancer 8:227–234Google Scholar
  4. 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):28Google Scholar
  5. 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):e453Google Scholar
  6. Avicenna (2017) A strategy for in silico clinical trial. http://avicenna-isct.org/about/
  7. 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 315–328Google Scholar
  8. Ayers D, Day PJ (2015) Systems medicine: the applications of systems biology approaches for modern medical research and drug development. Mol. Biol. Int. ID698169Google Scholar
  9. Ballesta A, Clairambault J (2014) Physiologically based mathematical models to optimize therapies against metastatic colorectal cancer: a mini-review. Curr Pharm Des 20(1):37–48Google Scholar
  10. 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:e112Google Scholar
  11. Ballesta A, Innominato PF, Dallmann R, Rand DA, Levi FA (2017) Systems chronotherapeutics. Pharmacol Rev 69:161–199Google Scholar
  12. Basch E (2017) Patient-reported outcomes—harnessing patients’ voices to improve clinical care. N Engl J Med 376:105–108Google Scholar
  13. 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):197–198Google Scholar
  14. Benson M (2016) Clinical implications of omics and systems medicine: focus on predictive and individualized treatment. J Intern Med 279:229–240Google Scholar
  15. 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 96:66–94Google Scholar
  16. Blanchard OL, Smoliga JM (2015) Translating dosages from animal models to human clinical trials—revisiting body surface area scaling. FASEB J 29:1629–1634Google Scholar
  17. Boissel JP, Auffray C, Noble D, Hood L, Boissel FH (2015) Bridging Systems medicine and patient needs. CPT Pharmacomet Syst Pharmacol 4:e00026Google Scholar
  18. Borrell-Carrió F, Suchman AL, Epstein RM (2004) The biopsychosocial model 25 years later: principles, practice, and scientific inquiry. Ann Fam Med 2(6):576–582Google Scholar
  19. 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):1513–1525Google Scholar
  20. 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 2016:7851789Google Scholar
  21. 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):S11Google Scholar
  22. 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 12:863–866Google Scholar
  23. Collins FS, Varmus H (2015) A new initiative on precision medicine. N Engl J Med 372(9):793–795Google Scholar
  24. Davies B, Morris T (1993) Physiological parameters in laboratory animals and humans. Pharm Res 10:1093–1095Google Scholar
  25. El Cheikh R, Bernard S, El Khatib N (2014) Modeling circadian clock–cell cycle interaction effects on cell population growth rates. J Theor Biol 363:318–331Google Scholar
  26. Engel GL (1977) The need for a new medical model: a challenge for biomedicine. Science 196(4286):129–136Google Scholar
  27. Filipp FV (2017) Precision medicine driven by cancer systems biology. Cancer Metastasis Rev 36(1):91–108Google Scholar
  28. Geiger B, Bershadsky A, Pankov R, Yamada KM (2001) Transmembrane crosstalk between the extracellular matrix-cytoskeleton crosstalk. Nat Rev Mol Cell Biol 2(11):793–805Google Scholar
  29. 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 55:107–113Google Scholar
  30. 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 64:13–21Google Scholar
  31. 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
  32. Hasin Y, Seldin M, Lusis A (2017) Multi-omics approaches to disease. Genome Biol 18(1):83Google Scholar
  33. 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 29:613–624Google Scholar
  34. Hutter OF, Noble D (1960) Rectifying properties of heart muscle. Nature 188:495Google Scholar
  35. Immunetrics (2017) Immunetrics quantitative systems pharmacology (QSP) modeling services & technology: designed in collaboration with modelers, for modelers. http://www.immunetrics.com
  36. Ingber DE (2006) Cellular mechanotransduction: putting all the pieces together again. FASEB J 20(7):811–827Google Scholar
  37. Innominato PF, Roche VP, Palesh OG, Ulusakarya A, Spiegel D, Lévi FA (2014) The circadian timing system in clinical oncology. Ann Med 46(4):191–207Google Scholar
  38. 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):e305Google Scholar
  39. Iyengar R, Altman RB, Troyanskya O, FitzGerald GA (2015) MEDICINE. Personalization in practice. Science 350:282–283Google Scholar
  40. Joly M, Rondó PHC (2017) The future of computational biomedicine: complex systems thinking. Math Comput Simul 132:1–27Google Scholar
  41. Kang HE, Lee MG (2011) Approaches for predicting human pharmacokinetics using interspecies pharmacokinetic scaling. Arch Pharm Res 34:1779–1788Google Scholar
  42. 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 5:367–376Google Scholar
  43. 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):387–404Google Scholar
  44. Kirschner M (2016) Systems medicine: sketching the landscape. Methods Mol Biol 1386:3–15Google Scholar
  45. Kuepfer L, Schuppert A (2016) Systems medicine in pharmaceutical research and development. Methods Mol Biol 1386:87–104Google Scholar
  46. Lave T, Chapman K, Goldsmith P, Rowland M (2009) Human clearance prediction: shifting the paradigm. Exp Opin Drug Metab Toxicol 5:1039–1048Google Scholar
  47. 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):e2001402Google Scholar
  48. Liang Y, Kelemen A (2017) Computational dynamic approaches for temporal omics data with applications to systems medicine. BioData Min 10:20Google Scholar
  49. 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):e420Google Scholar
  50. 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
  51. 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 936:225–246Google Scholar
  52. Majumder S, Mondal T, Deen MJ (2017) Wearable sensors for remote health monitoring. Sensors 17:130Google Scholar
  53. 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):605–619Google Scholar
  54. 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):1–278Google Scholar
  55. 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):95–103Google Scholar
  56. 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 medicineGoogle Scholar
  57. Mayer EA, Labus JS, Tillisch K, Cole SW, Baldi P (2015) Towards a systems view of IBS. Nat Rev Gastroenterol Hepatol 12(10):592–605Google Scholar
  58. 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 60:63–70Google Scholar
  59. 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 1386:375–404Google Scholar
  60. Morere JF, Innominato P (2014) ESMO 2014: new trends in precision medicine. Target Oncol 9:293–294Google Scholar
  61. Naylor S, Chen JY (2010) Unraveling human complexity and disease with systems biology and personalized medicine. Pers Med 7:275–289Google Scholar
  62. Noble D (1960) Cardiac action and pacemaker potentials based on the Hodgkin–Huxley equations. Nature 188:495–497Google Scholar
  63. Noble D (2007) From the Hodgkin–Huxley axon to the virtual heart. J Physiol 580(1):15–22Google Scholar
  64. Noble D (2008) Claude Bernard, the first systems biologist, and the future of physiology. Exp Physiol 93(1):16–26Google Scholar
  65. 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 4:39–49Google Scholar
  66. Orr MG, Plaut DC (2014) Complex systems and health behavior change: insights from cognitive science. Am J Health Behav 38:404–413Google Scholar
  67. 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 6:46Google Scholar
  68. Ortiz-Tudela E, Mteyrek A, Ballesta A, Innominato PF, Levi F (2013) Cancer chronotherapeutics: experimental, theoretical, and clinical aspects. Handb Exp Pharmacol 217:261–288Google Scholar
  69. 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):2168Google Scholar
  70. Pollard TD (2003) The cytoskeleton, cellular motility and the reductionist agenda. Nature 422:741–745Google Scholar
  71. 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):e1003120Google Scholar
  72. 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 35:747–756Google Scholar
  73. 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:e124Google Scholar
  74. 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 1386:43–60Google Scholar
  75. 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):34–40Google Scholar
  76. 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):34844–34857Google Scholar
  77. Siegel RL, Miller KD, Jemal A (2018) Cancer statistics, 2018. CA Cancer J Clin 68(1):7–30Google Scholar
  78. 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):17141Google Scholar
  79. Smuts JC (1927) Holism and evolution. Macmillan And Company Limited, LondonGoogle Scholar
  80. Sobradillo P, Pozo F, Agusti A (2011) P4 medicine: the future around the corner. Archivos de bronconeumologia 47:35–40Google Scholar
  81. Soto AM, Sonnenschein C (2012) Is systems biology a promising approach to resolve controversies in cancer research? Cancer Cell Int 12:12Google Scholar
  82. 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):1–3Google Scholar
  83. Stéphanou A, Volpert V (2016) Hybrid modelling in biology: a classification review. Math Model Nat Phenom 11:37–48Google Scholar
  84. 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):1053–1061Google Scholar
  85. 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/
  86. van Kampen AH, Moerland PD (2016) Taking bioinformatics to systems medicine. Methods Mol Biol 1386:17–41Google Scholar
  87. 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):307–323Google Scholar
  88. 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):aad0189Google Scholar
  89. 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):21Google Scholar
  90. 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 29:30–35Google Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Université Grenoble AlpesCNRS, TIMC-IMAG/DyCTIM2GrenobleFrance
  2. 2.North Wales Cancer CentreBetsi Cadwaladr University Health BoardBangorUK
  3. 3.INSERM and Université Paris 11 Unit 935VillejuifFrance
  4. 4.University of WarwickCoventryUK

Personalised recommendations