Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

[1]  Louis-Philippe Morency,et al.  Large-scale detection of drug off-targets: hypotheses for drug repurposing and understanding side-effects , 2017, BMC Pharmacology and Toxicology.

[2]  R. Gamelli,et al.  Genomic responses in mouse models poorly mimic human inflammatory diseases , 2013, Proceedings of the National Academy of Sciences.

[3]  Lennart Bodin,et al.  Clustering patients on the basis of their individual course of low back pain over a six month period , 2011, BMC musculoskeletal disorders.

[4]  N. Schork Personalized medicine: Time for one-person trials , 2015, Nature.

[5]  R. Goodacre,et al.  The role of metabolites and metabolomics in clinically applicable biomarkers of disease , 2010, Archives of Toxicology.

[6]  Thomas Kaiser,et al.  New drugs: where did we go wrong and what can we do better? , 2019, BMJ.

[7]  Ramouna Fouladi From Statistical to Biological Interactions towards an omics-integrated MB-MDR framework , 2018 .

[8]  Carl Boettiger,et al.  An introduction to Docker for reproducible research , 2014, OPSR.

[9]  Andreas Reif,et al.  NOS knockout or inhibition but not disrupting PSD-95-NOS interaction protect against ischemic brain damage , 2016, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[10]  José Luís Casteleiro-Roca,et al.  A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision-Making Process in Medicine , 2018, Complex..

[11]  Sigrun Alba Johannesdottir Schmidt,et al.  The Danish National Patient Registry: a review of content, data quality, and research potential , 2015, Clinical epidemiology.

[12]  E. Voest,et al.  Tumor Organoids as a Pre-clinical Cancer Model for Drug Discovery. , 2017, Cell chemical biology.

[13]  Martin Härter,et al.  Delivering patient decision aids on the Internet: definitions, theories, current evidence, and emerging research areas , 2013, BMC Medical Informatics and Decision Making.

[14]  Kathleen Marchal,et al.  Integration of omics data: how well does it work for bacteria? , 2006, Molecular microbiology.

[15]  M Benson,et al.  Clinical implications of omics and systems medicine: focus on predictive and individualized treatment , 2016, Journal of internal medicine.

[16]  John H Wasson,et al.  Patient reported outcome measures in practice , 2015, BMJ : British Medical Journal.

[17]  Victoria McGilligan,et al.  New models of atherosclerosis and multi-drug therapeutic interventions , 2018, Bioinform..

[18]  Sara Y. Brucker,et al.  A high-risk 70-gene signature is not associated with the detection of tumor cell dissemination to the bone marrow , 2018, Breast Cancer Research and Treatment.

[19]  E. Cuppen,et al.  Patient-derived organoids can predict response to chemotherapy in metastatic colorectal cancer patients , 2019, Science Translational Medicine.

[20]  Ronald Epstein,et al.  Time and the patient-physician relationship , 1999, Journal of General Internal Medicine.

[21]  Guoli Wang,et al.  PISCES: a protein sequence culling server , 2003, Bioinform..

[22]  Xingpeng Jiang,et al.  Multi-View Clustering of Microbiome Samples by Robust Similarity Network Fusion and Spectral Clustering , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[23]  Sigrun Alba Johannesdottir Schmidt,et al.  The Danish health care system and epidemiological research: from health care contacts to database records , 2019, Clinical epidemiology.

[24]  Kristel Van Steen,et al.  Practical aspects of genome-wide association interaction analysis , 2014, Human Genetics.

[25]  Kent A Weigel,et al.  Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data , 2013, Genetics Selection Evolution.

[26]  Sarala M. Wimalaratne,et al.  The Systems Biology Graphical Notation , 2009, Nature Biotechnology.

[27]  Jeffrey M. Perkel,et al.  Why Jupyter is data scientists’ computational notebook of choice , 2018, Nature.

[28]  G. Omenn,et al.  Evolution of Translational Omics: Lessons Learned and the Path Forward , 2013 .

[29]  Calum MacAulay,et al.  SIGMA2: A system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes , 2008, BMC Bioinformatics.

[30]  Merel Ritskes-Hoitinga,et al.  A combined pre-clinical meta-analysis and randomized confirmatory trial approach to improve data validity for therapeutic target validation , 2015, Scientific Reports.

[31]  O. Fiehn,et al.  Metabolite profiling for plant functional genomics , 2000, Nature Biotechnology.

[32]  Núria Malats,et al.  Challenges in the Integration of Omics and Non-Omics Data , 2019, Genes.

[33]  Jens Nielsen,et al.  Elucidating the interactions between the human gut microbiota and its host through metabolic modeling , 2014, Front. Genet..

[34]  Nicola Nosengo Can you teach old drugs new tricks? , 2016, Nature.

[35]  Jean-Baptiste Richard,et al.  Use of the Internet as a Health Information Resource Among French Young Adults: Results From a Nationally Representative Survey , 2014, Journal of medical Internet research.

[36]  Miha Mraz,et al.  LiverSex Computational Model: Sexual Aspects in Hepatic Metabolism and Abnormalities , 2018, Front. Physiol..

[37]  Alejandro Lucia,et al.  Chronic inflammation in the etiology of disease across the life span , 2019, Nature Medicine.

[38]  Sean Ferree,et al.  PAM50 Risk of Recurrence Score Predicts 10-Year Distant Recurrence in a Comprehensive Danish Cohort of Postmenopausal Women Allocated to 5 Years of Endocrine Therapy for Hormone Receptor-Positive Early Breast Cancer. , 2018, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[39]  N. Shanks,et al.  Are animal models predictive for humans? , 2009, Philosophy, ethics, and humanities in medicine : PEHM.

[40]  J. Ross,et al.  MammaPrint™ 70-gene signature: another milestone in personalized medical care for breast cancer patients , 2009, Expert review of molecular diagnostics.

[41]  L. Hood,et al.  A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. , 2012, New biotechnology.

[42]  Jon Emery,et al.  Comorbid chronic diseases and cancer diagnosis: disease-specific effects and underlying mechanisms , 2019, Nature Reviews Clinical Oncology.

[43]  Damjana Rozman,et al.  Genomic aspects of NAFLD pathogenesis. , 2013, Genomics.

[44]  F. Offner,et al.  MammaPrint versus EndoPredict: Poor correlation in disease recurrence risk classification of hormone receptor positive breast cancer , 2017, PloS one.

[45]  Georgios B. Giannakis,et al.  Inference of Gene Regulatory Networks with Sparse Structural Equation Models Exploiting Genetic Perturbations , 2013, PLoS Comput. Biol..

[46]  Jan Baumbach,et al.  From single drug targets to synergistic network pharmacology in ischemic stroke , 2019, Proceedings of the National Academy of Sciences.

[47]  John P A Ioannidis,et al.  Translation of highly promising basic science research into clinical applications. , 2003, The American journal of medicine.

[48]  Samo Ribaric,et al.  Monitoring the Depth of Anaesthesia , 2010, Sensors.

[49]  Benjamin J. Raphael,et al.  Visible Machine Learning for Biomedicine , 2018, Cell.

[50]  L. Hood,et al.  P4 medicine: how systems medicine will transform the healthcare sector and society. , 2013, Personalized medicine.

[51]  C. Byrne,et al.  Nonalcoholic fatty liver disease and chronic vascular complications of diabetes mellitus , 2018, Nature Reviews Endocrinology.

[52]  Geoffrey A. Donnan,et al.  Experimental Treatments in Acute Stroke , 2006 .

[53]  Ren-Hua Chung,et al.  A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification , 2018 .

[54]  Rudi Balling,et al.  A roadmap towards personalized immunology , 2018, npj Systems Biology and Applications.

[55]  Reza Mirnezami,et al.  Translational -omics: Future potential and current challenges in precision medicine. , 2018, Methods.

[56]  Guy A. Dumont,et al.  Extended habituating model predictive control of propofol and remifentanil anesthesia , 2020, Biomed. Signal Process. Control..

[57]  Arriel Benis,et al.  Communication Behavior Changes Between Patients With Diabetes and Healthcare Providers Over 9 Years: Retrospective Cohort Study , 2019, Journal of medical Internet research.

[58]  A. Lægreid,et al.  A high-throughput drug combination screen of targeted small molecule inhibitors in cancer cell lines , 2019, Scientific Data.

[59]  J. Lindon,et al.  'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. , 1999, Xenobiotica; the fate of foreign compounds in biological systems.

[60]  Kathleen Marchal,et al.  Integration of 'omics' data: does it lead to new insights into host-microbe interactions? , 2010, Future microbiology.

[61]  Livia Perfetto,et al.  SIGNOR 2.0, the SIGnaling Network Open Resource 2.0: 2019 update , 2019, Nucleic Acids Res..

[62]  Ana León,et al.  Closed loop administration of propofol based on a Smith predictor: a randomized controlled trial. , 2019, Minerva anestesiologica.

[63]  Zhuowen Tu,et al.  Similarity network fusion for aggregating data types on a genomic scale , 2014, Nature Methods.

[64]  Liam Smeeth,et al.  NNTs and NNHs: handle with care , 2017, The British journal of general practice : the journal of the Royal College of General Practitioners.

[65]  A. Doufas,et al.  Reinforcement Learning Versus Proportional–Integral–Derivative Control of Hypnosis in a Simulated Intraoperative Patient , 2011, Anesthesia and analgesia.

[66]  F. Prinz,et al.  Believe it or not: how much can we rely on published data on potential drug targets? , 2011, Nature Reviews Drug Discovery.

[67]  Waleska C Dornas,et al.  Animal models for the study of arterial hypertension , 2011, Journal of Biosciences.

[68]  Denis Thieffry,et al.  Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling , 2015, PLoS Comput. Biol..

[69]  R. Iyengar,et al.  Computation as the Mechanistic Bridge Between Precision Medicine and Systems Therapeutics , 2013, Clinical pharmacology and therapeutics.

[70]  M. Teshnehlab,et al.  Anesthesia Control Based on Intelligent Controllers , 2009, 2009 3rd International Conference on Bioinformatics and Biomedical Engineering.

[71]  Juan Albino Méndez Pérez,et al.  Adaptive pharmacokinetic and pharmacodynamic modelling to predict propofol effect using BIS-guided anesthesia , 2016, Comput. Biol. Medicine.

[72]  R. Cowen,et al.  Assessing pain objectively: the use of physiological markers , 2015, Anaesthesia.

[73]  David Wishart,et al.  Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community , 2019, Metabolites.

[74]  Jesse M. Ehrenfeld,et al.  An Evaluation of the State of Neuromuscular Blockade Monitoring Devices , 2016, Journal of Medical Systems.

[75]  J. Scannell,et al.  Diagnosing the decline in pharmaceutical R&D efficiency , 2012, Nature Reviews Drug Discovery.

[76]  Mary Dixon-Woods,et al.  Patient focused registries can improve health, care, and science , 2016, British Medical Journal.

[77]  T. Rosenthal,et al.  Animal Models in Obesity and Hypertension , 2013, Current Hypertension Reports.

[78]  D. Figeys Combining different 'omics' technologies to map and validate protein-protein interactions in humans. , 2004, Briefings in functional genomics & proteomics.

[79]  C. Kleinschnitz,et al.  Animal models of ischemic stroke and their application in clinical research , 2015, Drug design, development and therapy.

[80]  L. Hood,et al.  Systems medicine: the future of medical genomics and healthcare , 2009, Genome Medicine.

[81]  Hui Sun,et al.  Metabolomics in diabetes. , 2014, Clinica chimica acta; international journal of clinical chemistry.

[82]  M. Gerstein,et al.  What is bioinformatics ? An introduction and overview , 2001 .

[83]  T. Spector,et al.  Integration of ‘omics’ data in aging research: from biomarkers to systems biology , 2015, Aging cell.

[84]  K. Leffondré,et al.  Clustering patients according to health perceptions: relationships to psychosocial characteristics and medication nonadherence. , 2004, Journal of psychosomatic research.

[85]  Jure Acimovic,et al.  Training in Systems Approaches for the Next Generation of Life Scientists and Medical Doctors. , 2016, Methods in molecular biology.

[86]  Ritika Kundra,et al.  Abstract 3302: The molecular landscape of oncogenic signaling pathways in The Cancer Genome Atlas , 2018, Bioinformatics and Systems Biology.

[87]  Arriel Benis,et al.  Identification and Description of Healthcare Customer Communication Patterns Among Individuals with Diabetes in Clalit Health Services: A Retrospective Database Study. , 2017, Studies in health technology and informatics.

[88]  T. Krahn,et al.  Impact of Biomarkers on Personalized Medicine. , 2016, Handbook of experimental pharmacology.

[89]  Tim Beißbarth,et al.  pwOmics: an R package for pathway-based integration of time-series omics data using public database knowledge , 2015, Bioinform..

[90]  A. Hopkins Network pharmacology: the next paradigm in drug discovery. , 2008, Nature chemical biology.

[91]  Hiroaki Kitano,et al.  The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models , 2003, Bioinform..

[92]  Joshua M. Stuart,et al.  The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.

[93]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[94]  Risi Kondor,et al.  Diffusion kernels on graphs and other discrete structures , 2002, ICML 2002.

[95]  David Gomez-Cabrero,et al.  Data integration in the era of omics: current and future challenges , 2014, BMC Systems Biology.

[96]  K. Aihara,et al.  Early Diagnosis of Complex Diseases by Molecular Biomarkers, Network Biomarkers, and Dynamical Network Biomarkers , 2014, Medicinal research reviews.

[97]  R. Buckley,et al.  Personalized Cardiovascular Medicine and Drug Development - Time for a New Paradigm , 2011 .

[98]  A. Barabasi,et al.  Interactome Networks and Human Disease , 2011, Cell.

[99]  Arun Rai,et al.  Understanding Determinants of Consumer Mobile Health Usage Intentions, Assimilation, and Channel Preferences , 2013, Journal of medical Internet research.

[100]  Guillaume Noell,et al.  From systems biology to P4 medicine: applications in respiratory medicine , 2018, European Respiratory Review.

[101]  Diogo M. Camacho,et al.  Next-Generation Machine Learning for Biological Networks , 2018, Cell.

[102]  Edmund J. Crampin,et al.  Minimum Information About a Simulation Experiment (MIASE) , 2011, PLoS Comput. Biol..

[103]  G. Donnan,et al.  1,026 Experimental treatments in acute stroke , 2006, Annals of neurology.

[104]  Arriel Benis,et al.  Population-based cohort of 500 patients with Gaucher disease in Israel , 2019, BMJ Open.

[105]  Ralf Terlutter,et al.  Physicians' Motives for Professional Internet Use and Differences in Attitudes Toward the Internet-Informed Patient, Physician–Patient Communication, and Prescribing Behavior , 2012, Medicine 2.0.

[106]  Jure Leskovec,et al.  Interpretable Decision Sets: A Joint Framework for Description and Prediction , 2016, KDD.

[107]  Nichole Reisdorph,et al.  Microbiome and metabolome data integration provides insight into health and disease. , 2017, Translational research : the journal of laboratory and clinical medicine.

[108]  Michael E Phelps,et al.  Systems Biology and New Technologies Enable Predictive and Preventative Medicine , 2004, Science.

[109]  Annelien L Bredenoord,et al.  The FAIR guiding principles for data stewardship: fair enough? , 2018, European Journal of Human Genetics.

[110]  Burkhard Morgenstern,et al.  Meta-Analysis of Pathway Enrichment: Combining Independent and Dependent Omics Data Sets , 2014, PloS one.

[111]  C. Wild,et al.  The exposome: from concept to utility. , 2012, International journal of epidemiology.

[112]  Christina Kiel,et al.  Simple and complex retinal dystrophies are associated with profoundly different disease networks , 2017, Scientific Reports.

[113]  Lawrence Carin,et al.  Patient Clustering with Uncoded Text in Electronic Medical Records , 2013, AMIA.

[114]  J. Weiner,et al.  Doctor-patient communication in the e-health era , 2012, Israel Journal of Health Policy Research.

[115]  A. Barabasi,et al.  Uncovering disease-disease relationships through the incomplete interactome , 2015, Science.

[116]  John C Lindon,et al.  The emergent role of metabolic phenotyping in dynamic patient stratification , 2014, Expert opinion on drug metabolism & toxicology.

[117]  T. Ideker,et al.  Network-based classification of breast cancer metastasis , 2007, Molecular systems biology.

[118]  E. Génin,et al.  Integration of Omics Data in Genetic Epidemiology , 2015, Human Heredity.

[119]  A. Marrero,et al.  Adaptive fuzzy modeling of the hypnotic process in anesthesia , 2017, Journal of Clinical Monitoring and Computing.

[120]  Vineet Jain,et al.  3D Printing in Personalized Drug Delivery. , 2019, Current pharmaceutical design.

[121]  Avi Ma’ayan Introduction to Network Analysis in Systems Biology , 2011, Science Signaling.

[122]  Roland P. Bühlmann,et al.  From systems biology to systems medicine , 2017 .

[123]  Syed Haider,et al.  International Cancer Genome Consortium Data Portal—a one-stop shop for cancer genomics data , 2011, Database J. Biol. Databases Curation.

[124]  Weiwen Zhang,et al.  Integrating multiple 'omics' analysis for microbial biology: application and methodologies. , 2010, Microbiology.

[125]  A. Hanbury,et al.  Utilization and Perceived Problems of Online Medical Resources and Search Tools Among Different Groups of European Physicians , 2013, Journal of medical Internet research.

[126]  Henning Hermjakob,et al.  The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..

[127]  Elhanan Borenstein,et al.  Towards a predictive systems-level model of the human microbiome: progress, challenges, and opportunities. , 2013, Current opinion in biotechnology.

[128]  Richard B. Berlin,et al.  Systems Medicine Disease: Disease Classification and Scalability Beyond Networks and Boundary Conditions , 2018, Front. Bioeng. Biotechnol..

[129]  J H Moore,et al.  How to increase our belief in discovered statistical interactions via large-scale association studies? , 2019, Human Genetics.

[130]  Michel Tenenhaus,et al.  PLS path modeling , 2005, Comput. Stat. Data Anal..

[131]  Rolf Gebhardt,et al.  Lessons from Hepatocyte-Specific Cyp51 Knockout Mice: Impaired Cholesterol Synthesis Leads to Oval Cell-Driven Liver Injury , 2015, Scientific Reports.

[132]  Roni Peleg,et al.  Providing cell phone numbers and email addresses to Patients: the physician's perspective , 2011, BMC Research Notes.

[133]  Panagiota Galetsi,et al.  Values, challenges and future directions of big data analytics in healthcare: A systematic review. , 2019, Social science & medicine.

[134]  Farzana Rahman,et al.  Privacy in Healthcare , 2015 .

[135]  Christopher Southan,et al.  Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway , 2017, British journal of pharmacology.

[136]  Yukiko Matsuoka,et al.  Software support for SBGN maps: SBGN-ML and LibSBGN , 2012, Bioinform..

[137]  C. Greenwood,et al.  Data Integration in Genetics and Genomics: Methods and Challenges , 2009, Human genomics and proteomics : HGP.

[138]  Wolfgang Müller,et al.  Data management and data enrichment for systems biology projects. , 2017, Journal of biotechnology.

[139]  Roni Peleg,et al.  Providing cell phone numbers and e-mail addresses to patients: The patient’s perspective, a cross sectional study , 2012, Israel Journal of Health Policy Research.

[140]  H. Kitano Systems Biology: A Brief Overview , 2002, Science.

[141]  David A. Drubin,et al.  Early patient stratification and predictive biomarkers in drug discovery and development: a case study of ulcerative colitis anti-TNF therapy. , 2012, Advances in experimental medicine and biology.

[142]  B. Palsson,et al.  The model organism as a system: integrating 'omics' data sets , 2006, Nature Reviews Molecular Cell Biology.

[143]  Hyungwon Choi,et al.  iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery , 2019, npj Systems Biology and Applications.

[144]  Núria Malats,et al.  Toward the integration of Omics data in epidemiological studies: still a “long and winding road” , 2016, Genetic epidemiology.

[145]  Klaus-Robert Müller,et al.  Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data , 2019, IEEE Transactions on Neural Networks and Learning Systems.