Revolutionizing healthcare and wellness management through systems medicine approaches like predictive, preventive, personalized and participatory (P4) medicine. The document discusses establishing networks and consortiums across Europe to advance systems medicine through data and knowledge sharing, standardized methods, and integrating multi-omics data with clinical information. The goal is to transition to more proactive, cost-efficient healthcare by better understanding disease at the systems level.

1. Revolutionizing Heathcare and
Wellness Management through
Systems P4 Medicine
Personalized Respiratory
Medicine – Exploring the Horizon
Barcelona – June 13, 2014
Charles AUFFRAY cauffray@eisbm.org
European Institute for Systems
Biology & Medicine - CNRS-ENS-UCBL
Université de Lyon - France
1

2. www.eisbm.org
2
Institut de Recherche
Technologique
www.bioaster.comwww.biovision.org

3. Systemoscope International Consortium
« Rethinking research, understanding life, improving health »

4. Integrative Systems Biology & Medicine
Systems medicine: the future of medical genomics and healthcare
Auffray C, Chen Z, Hood L
(2009) Genome Medicine 1:2
Bridging the gap between systems biology and medicine
Clermont G, Auffray C et al.
(2009) Genome Medicine 1:88
Computational disease modeling - Fact or fiction?
Tegnér J, Compte A, Auffray C, Ann G, Cedersund G, Clermont G, Gutkin B,
Oltvai ZN, Stephan KE, Thomas R, Villoslada P
(2009) BMC Syst Biol 3:56.

5. The Iterative Process of Integrative Systems Biology
1- Formulate and formalize a (general or particular) question
2- Define the components of an appropriate biological system
and collect targeted and global data sets
3- Integrate them into an initial model of the system
4- Perturb systematically the system components (experimentally and
through simulation) and study the results
5- Compare the responses oberved to those predicted by the model
6- Refine the model so that its predictions fit better
with the experimental observations
7- Design and test new perturbations allowing arbitration between
multiple competing hypotheses
8- Iterate the process until an answer to the initial question is obtained

6. Peter Hunter, University of Auckland - Denis Noble, Oxford University
The Grand Challenge of Integrative Systems Biology:
Multiscale Integration

7. Integrative Systems Biology & Medicine
Systems biology approaches are combining high-dimensional
functional genomics data with biological, clinical, environmental and
lifestyle assessments through iterative statistical analyses,
computational modelling and experimental validation. They are
transforming biomedical research and clinical practice, triggering the
transition from a reactive to a proactive practice of medicine.
The effective development of predictive, preventive, personalized and
participatory systems (P4) medicine requires harmonization of
experimental and computational methods for data, information and
knowledge collection, storage and sharing.

8. Integrative Systems Biology & Medicine
In order to address the associated ethical, legal and social issues, the active
participation of all stakeholders including researchers and clinicians in
academy and industry, regulatory and funding bodies, individuals and
patient organizations is essential.
The expectation is that this collective endeavour will help reversing the
escalating costs of drug and diagnostic development in industry, and of
patient management in hospital and community practice to provide the basis
for a more cost-efficient and sustainable integrated healthcare system.

9. Integrative Systems Biology & Medicine
Predictive, Preventive, Personalized, Participatory Medicine
Revolutionizing medicine in the 21st century through systems approaches
Hood L, Balling R, Auffray C (2012) Biotechnol J 7:992-1001
Systems biology and personalized medicine - the future is now
Auffray C, Hood L (2012) Biotechnol J 7:938-939
Quantifying your body: A how-to guide from a systems biology perspective
Smarr L (2012) Biotechnol J 7:980-991
Personal omics profiling reveals dynamic molecular and medical phenotypes
Chen et al. (2012) Cell 148:1293-1307
The road from systems biology to systems medicine
Wolkenhauer O, Auffray C, Jaster R, Steinhoff G , Dammann O
(2013) NPG Pediatric Research, Epub Jan 11

10. Integrative Systems Biology & Medicine
Predictive, Preventive, Personalized, Participatory Medicine
Participatory medicine: a driving force for revolutionizing healthcare
Hood L, Auffray C. (2013) Genome Med 5:110

11. Quantifying your body: A how-to guide from a systems biology perspective
Smarr L (2012) Biotechnol J 7:980-991

12. Quantifying your body: A how-to guide from a systems biology perspective
Smarr L (2012) Biotechnol J 7:980-991

13. Personal omics profiling reveals dynamic molecular and medical phenotypes
Chen et al. (2012) Cell 148:1293-1307 – Michael Snyder, Stanford University

14. Personal omics profiling reveals dynamic molecular and medical phenotypes
Chen et al. (2012) Cell 148:1293-1307 – Medical findings

15. Monitoring Wellness, Health and Disease
ISB-EISBM Pilot Studies
WELLNESS
DISEASEHEALTH
EXPOSOME
CLINICOME
INTEGROME

16. The 4P4 Landscape of Systems Medicine
The Wellness Industry The Pharma Diagnostic
Industry
Participatory Personalized Preventive Predictive
People
Profesionnals
Payers
Politicians

17. 17

18. Monitoring Wellness, Health and Disease
ISB-EISBM Pilot Studies
18
Population
Year
2014 2019 2024 2029 2034 2039
102
103
104
105
105
106
107
108
109
1010
103
2.103
5.103
104
Cost/i

19. EISBM Symposium – Lyon – June 25, 2014
www.symposium.eisbm.org
The Road from Systems Biology to Systems Medicine
Andres Metspalu (Tartu) - Ines Thiele (Luxembourg) - Vincent Lotteau (Lyon)
Pioneers of Health and Wellness
Leroy Hood (Seattle) - Jesper Tegner (Stockholm) - Alexis Normand (Issy les Mx)
Systems Medicine of Respiratory Diseases
Fan Chung (London) - Antoine Magnan (Nantes) - Irina Lehmann (Leipzig)
Systems Medicine for Personalized Healthcare and Healthy Aging
Martine Laville (Lyon) - Olaf Dammann (Hannover) - Manlio Vinciguerra (London)
19

20. Integrative Systems Biology & Medicine
of Chronic Inflammatory Diseases
Aging / Disease Susceptibility
EnergyMetabolismBalance
Exercise / Nutrition
Infection / Immunity
Individual health and
disease trajectories

22. The U-BIOPRED Project
Unbiased Biomarkers in the Prediction
of Respiratory Disease Outcome
Phenotype Handprint of Severe Asthma
Coordinator: Peter Sterk
University of Amsterdam
An integrative systems biology approach to understanding pulmonary diseases.
Auffray C, Adcock I, Chung FK, Djukanovic R, Pison C, Sterk PJ
(2010) Chest 137:1410-1416

23. WP 6 Epigenetics and targetedWP 6 Epigenetics and targeted
proteomicsproteomics

24. AirPROM: AirAirPROM: Airway Diseaseway Disease PRPRedictingedicting OOutcomes throughutcomes through
Patient Specific ComputationalPatient Specific Computational MModellingodelling
FP7-ICT-VPH – Coordinator Chris Brightling, University of LeicesterFP7-ICT-VPH – Coordinator Chris Brightling, University of Leicester

25. SysCLAD: Systems prediction of
Chronic Lung Allograft Dysfunction
• FP7-Health Systems Medicine: SME-driven research applying
systems biology approaches to address medical and clinical needs
• Coordinator: Laurent Nicod, Centre Hospitalier Universitaire,
Lausanne, Switzerland; CHU Nantes; CHU Grenoble; Four SMEs:
Finovatis, Novadiscovery, Lyon; Biomax, GATC, Germany.
• Based on COLT cohort in lung transplantation (11 centres in France)
with established eCRF, SOPs and biobank extended to Swiss
(Lausanne, Zurich) and Belgium (Brussels) centres

26. Medical Need: Burden of Respiratory DiseasesMedical Need: Burden of Respiratory Diseases

27. European Translational Research Information
and Knowledge Management Services
Coordinators: Ian Dix, AstraZeneca and Yi-Ke Guo, Imperial College
Deputies: Anthony Rowe, J&J and Charles Auffray, CNRS-EISBM
Project Management: Scott Wagers, BioSci Consulting

28. Translational Research
Information Flow
eCRF
software
Data Data
Data
Biobank
Samples
Analytical Labs
Databases
External Analytics
API
External
Visualisation API
Collaboration and
KM platform
Clinical Sites
LIMS /
Sample
Tracker
Ontology
Management
Service
Analytical
Workflows
Central Cloud-
based Platform

29. Objectives
• Objectives:
– Provision of a KM Service to support Private/Public Translational Research
(TR) in IMI
– Single access point to standardised curated , IMI TR study information along
with IMI project relevant historic translational studies
– Establishing a common, open source, interoperable TR platform, based on
open agree standards across the IMI TR projects.
– Development of an active TR analytics & informatics community across IMI
• Budget: €23.79m for 5 years (Oct 2012---Sept 2017)
• Members:
– 10 Pharma, 3 Academic, 1 standards, 2 Commercial Suppliers

30. The Consortium
10 Pharma 6 Partners
+

31. Core Technology
Clinical Cohort
Phenotypic Data
Demographics
Clinical Observations
Clinical Trial Outcomes
Adverse Events
High Content
Biomarker Data
Gene Expression
Genotyping
Metabolomic
PK/PD Markers
Reference Data
Literature
Pathway Data
Gene Metadata
tranSMART
ETL
Mining
GPLv3

32. eTRIKS Platform
Collaboration Platform
•Collaboration
•Research process
•IP capture and management
•Secure access
Analytics Environment
•Access to analytics tools
•Open API for public and commercial
software to plug-in
TR Knowledge Hub
•Cloud Infrastructure
•Load procedures
•‘Big Data’ storage
•Ontology management
Study Book /
Visual Research
Access
Manag
ement
Ontology
Management
Scientific Data
Architecture
Study
Management

33. Global non-profit organization devoted to realizing the promise of
translational biomedical research through development of the tranSMART
knowledge management platform.
Goals
1.Establish and sustain tranSMART as the preferred data sharing and analytics
platform for translational biomedical research.
2.Link academic, non-profit and corporate research communities for collaborative
research facilitated by tranSMART.
3.Align and grow a vibrant developer network around the scientific goals of the
tranSMART community.
4.Reduce barriers to entry through use of advanced technologies and an active
marketplace.
Community:
Large scale KM consortia, AMCs, NFPs, Pharma, Regulators, Biotech service suppliers
tranSMART Foundation
http://www.transmartfoundation.org

34. 1. Ensure the legacy of project data/results
2. Facilitate dataset integration
3. Increase operational efficiency
4. Establish a common set of standards
www.eTRIKS.org
Linked In Discussion Group: eTRIKS Twitter @etriks1

35. The CASyM Consortium: 22 partners from 11 European countries
And 50 associated partners
OBC Ltd
Full partners
GERMANY (6)
UNITED KINGDOM (3)
FRANCE (3)
SWEDEN (2)
LUXEMBOURG (2)
NETHERLANDS (1)
SLOVENIA (1)
IRELAND (1)
ICELAND (1)
ISRAEL (1)
ITALY (1)
Associated partners
UK - OBC
GERMANY - BAYER
GERMANY - BioM Biotech
ESTONIA - Estonian
Genome Center,
IRELAND - Science
Foundation Ireland
(under discussion)

36. Major Objectives of the Coordination Action CASyM
Roadmap for Implementation of Systems Medicine in Europe
Engagement of
key stakeholders
from all significant
areas
Engagement of
key stakeholders
from all significant
areas
Developing a clear
strategy and a
practical road map
for sustainable
implementation of
Systems Medicine
across Europe
Developing a clear
strategy and a
practical road map
for sustainable
implementation of
Systems Medicine
across Europe
Interaction with
key national and
European Systems
Medicine
initiatives
Interaction with
key national and
European Systems
Medicine
initiatives

37. CASyM stakeholder meeting
March 25-26, 2013 - Lyon
EISBM Symposium and CASyM Workshop
The Road from Systems Biology
to Systems Medicine
June 24-25, 2013 - Lyon

38. Toward an International Society of Systems BioMedicine

39. Toward a Network of Systems BioMedicine Centers

40. Toward a Network of Systems BioMedicine Centers

41. Toward a Network of Systems BioMedicine Centers

42. 42

43. Toward a Network of Systems BioMedicine Centers
H2020-2014 Call
HCO-9 ERA-Net Systems Medicine to address clinical needs:
Germany, France, Spain, Netherlands, Italy, Luxembourg
H2020-2015 Call
PHC 2 Understanding diseases: systems medicine