LA MÉTABOLOMIQUE 20 ANS DÉJÀ - Dominique Rolin Université de Bordeaux - INRA - Université de Lille
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LA MÉTABOLOMIQUE
20 ANS DÉJÀ….
Dominique Rolin
Université de Bordeaux - INRA
Lille 15 octobre 2019
ANR-INBS-0010
Menu du jour
➡ Métabolomique versus lipidomique et fluxomique
➡ Une approche analytique
• récente
• adaptée aux enjeux de la biologie du XXI° siècle
• à haut débit et à haute densité
• avec un workflow complexe
➡ Enjeux et défis de la métabolomique
• Quels étaient les enjeux au début des années 2000 ?
• Bilan de ces 20 années
• Que reste-t-il à faire ?
MetaboHUB
Metabolomics versus lipidomics and
fluxomics
Life sciences: Facing waves Synthetic
biology
of technological revolutions
System
biology
Integrative
biology
Life Molecular Sequencing Functionnal
Cell Biology
observation Biology genome genomics
XVII° XVIII° XIX° XX° XXI° Century
No science without progress in technology
No progress in technology without science
MetaboHUB 4
Metabolomics versus Fluxomics
Metabolomics & Fluxomics are 2 technological TOOLS to carry out Life Science
Metabolomics Fluxomics
Metabolic network
Metabolism
Metabolite identification Dynamic analysis of
& quantification metabolic fluxes
Flux quantification
Fingerprinting M
Profiling M.
Movie: at which speed
Pictures: which cars are Traffic on drive the cars
driving on the roads road network
MetaboHUB
Metabolomics & Fluxomics are more complicated than a simple biochemical analysis ?
The functional genomics and the
OMICS tool world ?
Molecule diversity
MetaboHUB
What do we mean by
metabolomics and metabolome ?
Metabolome (lipidome):
all small molecules named polar metabolites
(apolar metabolites) occurring in a biological
system.
Fluxome:
All quantified metabolite fluxes occurring
in a biological system.
Metabolomics (lipidomics):
Tools and strategy for determination of metabolite levels occurring in a biological
system and their changes over time as a consequence of stimuli
Fluxomics:
Tools and strategy for determination of metabolite fluxes occurring in a biological
system and their changes over time as a consequence of stimuli
MetaboHUB
Monarch butterfly : one genome but at
least 3 proteome and 3 metabolome
MetaboHUB
The multi-OMICS family and their biological significations
• ce qui peut arriver
• ce qui semble se
passer
• ce qui fait que ça
arrive
• ce qui s'est passé et
ce qui se passe
MetaboHUB
Flux maps provide more real informations
Same pools of metabolites but different metabolic flux
Metabolomics
100 100
40 20
40 20
60 80
10 5
60 75
60 40 75 30
20 45
20 45
20 45
Fluxomics Condition 1 Condition 2
MetaboHUBUne approche analytique récente
« Metabolome » terme first
appeared in 1998
Trends Biotechnol. 16, 373–378 ( 1998).
The Metabolome
Steven G. Oliver
MetaboHUB25471 publications depuis 2000
Web of Science DB du 29/09/19 interrogée sur la période 1956-2019
Web of science DB:
Metabolomics
MetaboHUBPlus de 185 domaines d’application de la métabolomique
Les 25 domaines qui publient le plus en métabolomique sur la période 2001-2019
MetaboHUBLes 10 publications les plus citées
entre 2001 et 2005
Total Year Title Authors Source Title
Citations
PLANT
Metabolomics - the link between
2079 2002
genotypes and phenotypes
Fiehn, O Strategy MOLECULAR
BIOLOGY
A functional genomics strategy that
NATURE
732 2001 uses metabolome data to reveal the Raamsdonk et al. Strategy BIOTECHNOLOGY
phenotype of silent mutations
Plant metabolomics: large-scale
632 2003 phytochemistry in the functional Sumner, LW et al. Strategy PHYTOCHEMISTRY
genomics era
Metabolomics by numbers: acquiring
TRENDS IN
753 2004 and understanding global metabolite Goodacre, R et al. Strategy BIOTECHNOLOGY
data
THERAPEUTIC
METLIN - A metabolite mass
1079 2005
spectral database
Smith, CA et al. Data base DRUG
MONITORING
GMD@CSB.DB: the Golm
750 2005
Metabolome Database
Kopka,J et al. Data base BIOINFORMATICS
The Orbitrap: a new mass JOURNAL OF MASS
734 2005
spectrometer
Hu, QZ; et al Equipment SPECTROMETRY
Quantitative metabolome analysis JOURNAL OF
588 2003 using capillary electrophoresis mass Soga, T; et al. Equipment PROTEOME
spectrometry RESEARCH
TRAC-TRENDS IN
Metabolomics: Current analytical
626 2005
platforms and methodologies
Dunn, WB; Ellis, DI Methodology ANALYTICAL
CHEMISTRY
Mitochondrial dysfunction in
schizophrenia: evidence for Prabakaran, S; et MOLECULAR
624 2004
compromised brain metabolism and al. Science PSYCHIATRY
oxidative stress MetaboHUBLes 10 publications les plus citées entre
2001 et 2019 (25471 publications)
Total
Year Title Authors Source Title
Citations
HMDB 3.0-The Human Wishart, David S. NUCLEIC ACIDS
1608 2013
Metabolome Database in 2013 et al. Data base RESEARCH
HMDB: the human metabolome Wishart, David S. NUCLEIC ACIDS
1394 2007
database et al. Data base RESEARCH
HMDB: a knowledgebase for the Wishart, David NUCLEIC ACIDS
1095 2009
human metabolome et al. Data base RESEARCH
THERAPEUTIC
METLIN - A metabolite mass
1079 2005
spectral database
Smith, CA et al. Data base DRUG
MONITORING
Xia, Jianguo; S
MetaboAnalyst 3.0-making NUCLEIC ACIDS
1368 2015
metabolomics more meaningful
et al. (Wishart, Methodology RESEARCH
David)
Proposed minimum reporting Sumner, Lloyd
1209 2007 METABOLOMICS
standards for chemical analysis W. et al. Methodology
PLANT
Metabolomics - the link between
2079 2002
genotypes and phenotypes
Fiehn, O Strategy MOLECULAR
BIOLOGY
Metabolite profiling for plant NATURE
1314 2000
functional genomics
Fiehn, O; et al. Strategy BIOTECHNOLOGY
Gut flora metabolism of
1811 2011 phosphatidylcholine promotes Wang, et al. Science NATURE
cardiovascular disease
Metabolite profiles and the risk Wang, Thomas
1333 2011
of developing diabetes J.; et al.
MetaboHUB
Science NATURE MEDICINEUne approche analytique adaptée
aux enjeux de la biologie du XXI°
siècleXXI century: new biology
The National Institutes of Health The National Research Council’s
The National Science Foundation Board on Life Science
The Department of Energy (2008-2009)
Report
A New Biology for the 21st Century
http://www.nap.edu/catalog/12764.html
1- to examine the current state of biological research in the United States
2- recommend how best to capitalize on recent technological and scientific advances
Human Health Food Science Energy Environment
MetaboHUBXXI century: new biology
Interconnected problems
need
Interconnected solutions
The challenge cannot be met
in isolation
Metabolomics needs all
these scientific domains
MetaboHUBUne approche analytique avec des
technologies diverses
à haut débit et à haute densitéWhere are the Challenges ?
MetaboHUBUne diversité de technologie pour
identifier et quantifier les molécules
Spectrométrie de masse: une technique de choix couplée ou non
à la chromatographie gazeuse ou liquide
RESOLUTION
Faible Moyenne Haute Ultra-haute
Quantité d’information, durée de traitement de l’information, moyens de stockage
MetaboHUBProfils Empreintes
X
métabolomiques métabolomiques
Analyse
-
ciblée
Moyens
d’obtenir
+
Quantité d’information, durée de traitement de l’information, moyens de stockage
l’information
Moyens pour
stocker
l’information
Moyens pour
traiter
l’information
MetaboHUBFaire la différence entre la
Métabolomique à haute densité
–
la Métabolomique à haut débit
Métabolomique à haute
densité
Nombre de
métabolites FT-ICR-MS
détectés Orbitrap
LC - MS non targeted
GC - MS
RMN
LC - MS targeted
Enzymatic M.
Nombre d’échantillons
MetaboHUB Métabolomique à haut débitUne approche analytique avec un
workflow complexeUne démarche commune avec de
très très nombreuses options
Information initiale Information analysable
Intensité des ions au sein des échantillons
MetaboHUBContext: Metabolomics & Fluxomics
Multidisciplinary approaches
Biological questions
Experimental
Design
Sampling acquisition
Numerous worflows for data analysis
Metadata
acquisition in DB Analytical
Sample preparation tools
Numerous analytical methods
27 Metabolomics fingerprints or
profilingsContext: Metabolomics & Fluxomics
Multidisciplinary approaches
Interpretation
Identification
Statistical analysis
ID RetTime Mass Significance sample 1 sample 2
70.1@1.2 1.2228 70.0695 0.0023 2.2385 1.8719
72.1@1.4 1.4379 72.0796 0.0106 9.4194 6.9449
80.9@0.9 0.9096 80.9474 0.0144 11.8372 8.0128
81@1 1.0163 80.9533 0.0006 0.3338 0.1262
83@0.9 0.9206 82.9588 0.0044 4.1611 3.3217
84.9@0.8 0.818 84.9451 0.0924 60.7749 32.0214
86.1@1.9
86.1@3.7
91@0.9
1.8814
3.7238
0.9038
86.0886
86.0925
90.9733
Data extraction &
0.0142 13.3605 13.2928
0.0363 29.7443 20.0236
0.007 6.3339 4.7593
normalisation
96.9@0.9 0.9312 96.9269 0.0027 2.0042 1.2272
100.1@4.3 4.3137 100.0674 0.0264 25.1952 20.3527
100.1@4.5 4.5163 100.069 0.0139 6.1492 1.4764
100.1@4.2 4.2209 100.0691 0.0387 12.1114 22.6781
100.1@19.5 19.4586
100.1@21.6 21.5638
100.0751
100.0759
0.011
0.0035
0.4926
1.3987
3.9759
2.2989 Need to visualise metabolic networks
RAW conversion T1_220909_101109AFAMM 375 (11.814) Cm (280:402)
100
289.2036
33362 465.2295
32595
1: TOF MS ES+
3.34e4
to solve biological questions
363.2011
23490
301.2049
21124
317.2080
17148
%
335.2192
13449
79.0247
Urgent needs for DB to store reference spectra
5850 393.2261
255.1689 271.1941 5939 466.2465
4556 5124 4619
Analytical methods
159.0724 181.1131 253.1821
111.0227 1498 2203 3009 394.2391 431.2523 467.2661
1052 807 493 773 492.3099
230.0992;787 9
0 m/z
80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500une démarche commune avec de
trés nombreuses options
Question
Acquisition
Question Question de l’ préparation
Profil ou
biologique échantillonnage des
empreintes
échantillons
Traitement
Sélection recherche
des Analyses des formules brutes
données statistiques signaux correspondantes
brutes
Integration des
Elucidation Validation et Interprétation
métabolites dans
structurale suivi des bio biologique
les réseaux
des signaux marqueurs
métaboliques
Traitement des échantillons, besoin d’équipements
Traitement l’information, besoin de serveurs, de soft, de base de
données, de stockage MetaboHUBMetabolite Metabolic
Lipidomics and
identification and fingerprinting high
the lipid world
quantification density
Sample
preparation Data
integration
Sampling
data mining
acquisition Infrared
technology Data
What is the reduction
Biology question
INFORMATICS
Analytical
Pipetting
BEFORE Robots Tools GC-MS
AFTER
Expertises
NMR
Experimental LC-MS
Statistic
Design analysis
Acquisition of
Metadata Network analysis
in database and modeling
Metabolomic flux Using stable isotopes for
quantification metabolomics
Metabolomics and fluxomics much more complicated than a simple metabolite analysisA real need to exchange and
Other
communicate
NMR
Which Where can I found
Analytical these needed analytical tools ?
GC-MS
tools ?
LC-MS What do you mean
by experimental design ?
Where can I found a Where and how can I
chemist or biochemist ? store my data (big) ?
Which statistical tools or
softwares can I use ? What do you mean
by metadata ?
Biologists face to metabolomics
How can I integrate and visualise my data in
the metabolic network ?
MetaboHUBA need to exchange and
Other
communicate between experts
NMR
LC-MS Where can I found
Which GC-MS
Specialists
GC-MS Analytical the needed analytical tools ?
Specialists
tools ?
NMR
LC-MSSpecialists What do you mean
Computer
By experimentalSpecialists
design ?
Biochemist Platform Concept
Where can I found a
Specialists Psychologist Where and how can I
chemist or biochemist ? to share store myBiology
data ?
Specialists
information
Which statistical tools or
Chemist
Specialists can I use ? Biologist face to metabolomics What do you mean
softwares
by metadata ?
Metabolism
How can I integrate Specialists
Biostatistician
Specialists my data in the meta-
Modelling
Specialists?
bolic network
MetaboHUBEnjeux et défis de la métabolomique
Quels étaient les besoins au début des années
2000
Bilan de l’actifLes besoins et les défis technologiques,
Question computationnels et humains sont
biologique presque infinis
Traitements Organiser les
Echantillons Equipements
des données échanges
• Questions Masse • Standardisation • développement
biologiques • + de résolution (workflow, format des des plateformes
• Dépendant du • + de sensibilité fichiers, protocoles, etc) • besoin de
domaine • + de répétabilité • Bases de données structurer les
• Cohorte de plus • + rapide • Interoperabilité échanges à
de 100 -10000 • + automatisation • Identifier les molécules l’échelle régionale,
échantillons • + robuste • Extraire l’information nationale et
• Automatisation • - cher + cher biologique (statistiques) européenne
• Procédures • etc • Développer des • développer
d'extraction ORBITRAP solutions softs l’enseignement
• Standardisation • La question du stockage universitaire
• MetaData RMN des données (brutes, • développer les
• etc. • + de sensibilité nettoyées, etc.) formations
• etc. • + automatisation spécialisées
•MetaboHUB
+ serveurs, icloudTwo initiatives to promote
metabolomics & fluxomics
MetaboHUB
a French governmental initiative
aimed to set up a French Infrastructure devoted to the M & F in France
Top down
initiative
2013
Bottom up 2005
initiative
French-speaking Network for Metabolomics and Fluxomics
This is a "bottom up" initiative aimed at facilitating and promoting
sustainable development of the M &F in France
MetaboHUBTwo initiatives to promote
metabolomics & fluxomics
French-speaking Metabolomics and Fluxomics network
Created in 2005, affiliated to metabolomics society since 2013
Currently ≈300 members
Aims:
∗ to make an inventory and promote French skills in the fields of metabolomics and
fluxomics
∗ to provide and support scientific meetings or workshops in metabolomics and
fluxomics
∗ to facilitate knowledge transfer to students and newcomers in the field and help
students to promote their work
■ MetaboHUBMetaboHUB impacts (2013-2017)
Why not a MétaNORD ??
French-speaking Network Networking and structuring
on Metabolomics &
Fluxomics French community
First contact
Les Lipidomystes: Identified PF
• French cluster working on lipidomics
In progress
MTH platforms in France
37
MetaboHUBFrench National Infrastructure
in Biology and Health (2013-2025)
Distributed & coordinated infrastructure for metabolomics &
fluxomics devoted to innovation, training and technology transfer
MetaboHUB v1: 4 platforms
HR in full-time permanents
Nantes-Rennes Saclay-Paris
Clermont-Ferrand
Bordeaux
Toulouse
MetaboHUB v2 (in 2020):
Corsaire PF
2 universities
+ 1 National Engineer School
5 National institutes
4 Universities
MetaboHUBMetaboHUB activities
1 Technology: Developing generic tools, analytical and computer solutions
2 Sciences: Proof of concept projects (Health, Plants, Biotechnology)
3 Training and teaching M & F
4 Web portal for national access to MetaboHUB services
5 Technology transfer to French community
6 Structuring French community through networking
7 Strengthening French position within European community
MetaboHUBA multilayered success
Development of 4 online reference platforms
Workflow4Metabolomics.org data analysis in Galaxy (Giacomoni et al. 2015; Guitton et al. 2017)
à in collaboration with IFB (French Institute of Bioinformatics)
NMRProcFlow.org interactive data processing (Jacob et al. 2017)
PeakForest.org database for metabolite identification (Damont et al. 2019)
MetExplore network analysis (Cottret et al. 2018)
World-wide use by academia and industry
RHU cohort projects (e.g. CHOPIN ANR-16-RHUS-0007, Bioart Lung ANR-15-RHUS-0002-07, QUID
ANR-17-RHUS-0009)
Collaboration with MedDay pharmaceuticals
« Bring Your Own Data » annual courses
Interoperability with the European Science Cloud (PHENOMENAL H2020 project)
Integrated offer at the national level
• Joint proteomics and metabolomics data analysis pipeline developed with ProFI, IFB, PHENOMIN
and France Génomique for systems phenotyping (ProMetIS project)
MetaboHUBMetaboHUB outputs
Science
•Proof of concept projects (Biotech., Health, Plants, etc.) (2013-2018)
Aim: Deep phenotyping tools for analyzing large epidemiological cohorts
Services for future collaborative projects with cohorts
➢ Identify specific signatures of diseases
➢ Characterize the phenotypic spectrum of pathologies
➢ New tools for patient stratification
• Proof-of-concept project on Metabolic Syndrome
Nutrition, Geriatrics, Aging, Epidemiology
• Project on the development of immune-related
disorder. Early life nutrition, allergy
• E.U. project on microbiome based treatments
for liver diseases (Personalized medicine)
• Implementation within a European COST Action.
Open Multiscale System Medicine CA15120
41
MetaboHUBMetaboHUB outputs:
National & international training (2013-2018)
•Training in-house: on the platforms
•Training out: workshops, webinar,
National & international teaching
•University in France & all around the world
•MOOC on metabolomics (in French)
•Open courses (in English)
Training Total
In-house Trainees
•3066 registered in 77 countries (63% in France, 7%
Masters 202 MOOC in Morocco)
PhD’s 254 Training Total •602 participated in evaluated activities (quizzes,
Trainees (National +
Post Doc 45 Out International trainings) weekly evaluations, peer reviews)
•214 participants were awarded the certificate of
Invited W4M 295 (8+5) success
51
scientists MetExplore 514 (12+ 18)
Others 40
Lipidomics 384 (8)
Total Two open courses: usemetabo.org
592 Total
trainees 1193 (28+ 23)
trainees
NMRProc 3726 web users
Flow 6882 web sessions
42
MetaboHUBMetaboHUB outputs:
Service
(2013-2018)
• a unique web portal for national access to MetaboHUB
services (MTH Analyses Manager: MAMA)
• For academic communities and private compagnies
MetaboHUB contribution to science :
Publications at high IF
1. Lenarčič T. et al. (2017) Eudicot plant-specific sphingolipids determine
host selecevity of microbial NLP cytolysins. Science
2. Asiliauskaite-Brooks, I. et al. (2018). Structure of a human
intramembrane ceramidase explains enzymaec dysfunceon found in
leukodystrophy. Nature Communicaoons
3. Perez-Berezo, T. et al. (2017). Ideneficaeon of an analgesic lipopepede
produced by the probioec Escherichia coli strain Nissle 1917. Nature
Communicaoons
Service 4. Clària J, Moreau R, Fenaille F et al. Orchestraeon of Tryptophan-
contracts Publications Kynurenine Pathway, Acute Decompensaeon, and Acute-on-Chronic Liver
Failure in Cirrhosis. Hepatology. 2019
2013 5. Despres, C. et al. (2017). Ideneficaeon of the Tau phosphorylaeon
267 99
pajern that drives its aggregaeon. Proceedings of the Naoonal Academy
of Sciences of the United States of America
2014 407 113 6. Tabet, R. et al. (2016). Fragile X Mental Retardaeon Protein (FMRP)
controls diacylglycerol kinase acevity in neurons. Proceedings of the
2015 330 98 Naoonal Academy of Sciences of the United States of America
7. Liu, R. et al. (2015). A DEMETER-like DNA demethylase governs tomato
2016 247 82 fruit ripening. Proceedings of the Naoonal Academy of Sciences of the
United States of America
2017 320 8. Cojret L. et al. (2018). MetExplore: collaboraeve edieon and
104
exploraeon of metabolic networks. Nucleic Acids Research
2018 390 129
TOT
AL
1961 625
43
MetaboHUBQuels sont les défis technologiques
et computationnels dans un futur
proche?Where are the M&F issues for the
next 10 years ?
t
pu
Int rtifi
n
gh
e r o c ia
s - nolo den h th tio
a
ftw es sity rou
pe l int
a
ing -
rn on
hig ific
rab ell
Da y, kn nce,
i
-le at
ilit ige
at nt
ta owl netw
- e om
on ide
a
t
sci edg or
ar - Au
e n e m k an
ati te
es
tic oli
ce ana aly
so gi
ba tech gh
an ab
s
ata al hi
qu Met
Metabolomics
- D lytic &
ge sis
se
& Fluxomics
me
Issues
nt
a
,
An
&
Standardization
Harmonisation – Regulatory compliance
Transfert to academic and industrial world
MetaboHUBSome areas of technological innovation
Sample
Creation of a FAIR
preparation:
from one cell to computational
whole organism e-infrastructure
Microfluidic Highly curated DB for
technology for enhanced metabolite
sample Infrared identification
technology
preparation (PeakForest DB)
Analytical
Pipetting
BEFORE Robots Tools GC-MS AFTER
Expertises
NMR
LC-MS Statistical,
Acquisition of Metadata computational and
in DB (domain mathematical tools
dependent) (moving to AI)
MS Imaging,
Advanced MS based Faster NMR with fast 2D-NMR
data acquisition Boosting the sensitivity of NMR
(identification & with Dynamic Nuclear
quantification) PolarizationData sciences Interoperability, knowledge management, artificial intelligence, network analysis better DB for molecule identification
analytical challenge: need robust spectral
reference data base
Typical processing flow of MS data
• Size and complexity of raw data
file (1 Go and more) in the field of metabolomics.
• Different file format and conversion
difficulties. need Open access
• Software heterogeneity and compatibility
• Mathematic complexity of the used methods
• High number of data treatment
• Difficulty to identify automatically
metabolites
Sugimoto et al. 2012 Current Boinformatics
MetaboHUBSearch databases for accurate mass
February 2018 Master Biology Agrosciences: Metabolomics course
MetaboHUBPeakForest: a community-based
spectral database
Online MetaboHUB resource
for identification of new compounds
in biological matrices
1D-2D NMR
GC-MS
LC-MS
LC-MSnPeakForest spectral DB will be share
MetaboHUB
Paris-Saclay
MetaboHUB
Nantes-Rennes
MetaboHUB Clermont-Ferrand
MetaboHUB Bordeaux
MetaboHUB Toulouse
In France, many laboratories have their own DB without sharing their resourcesComputational metabolomics
challenge
Jean-François Martin Mélanie Statistical
Mélanie Petera
Petera analysis
eMetaboHUB
The computational
solution
Raw files
LC-MS, GC-MS, NMR
Peak table
Etienne Thevenot
Identification
n samples
URINE30DIL4_CID20_endogènes #68 RT: 1.22 AV: 1 NL: 4.17E5
mz rt Db_015 ... Db_068 F: FTMS + p ESI Full ms2 173.09@cid20.00 [50.00-800.00]
p variables
116.07041
100
75.0322 41.28 22162 ... 48575 95
90
75.0441 174.83 1371 ... 820 85
80
75.0634 56.23 49111 ... 91769 75
70
... ... ... ... ... 65
time
-C2H3ON
Relative Abundance
60
999.6653 844.61 571 ... 636 55
50
999.6759 844.61 711 ... 665 45
[
40
999.6865 844.61 698 ... 612 35
30 127.08652 -HCOOH 173.09190
Fabien Jourdan
25
20
-
m/z
15
155.08136
M
10 80.49458
70.06497
169.67426
H2
5 86.06400
61.03990 102.71753 143.04167 184.86218
0
60 80 100 120 140 160 180
m/z
O
Signal processing Pathway analysis
LC-MS, GC-MS, NMR +
H]
+
MetaboHUBProgramme de la journée
• 10h35-11h20: Workflow4metabolomics (W4M), portail dédié à l’analyse métabolomique:
extraction de données, normalisation, analyses statistiques et annotations, Jean-François
Martin, MTH-Toulouse
• 11h20-11h40: Pause
• 11h40-12h25: Experimental design and data treatment in metabolomics applied to nutrition and
health, Mélanie Pétéra, MTH-Clermont,
• 12h25-12h45: Metabolomics in Alzheimer's disease, Vincent Damotte, Inserm U1167,
Institut Pasteur de Lille
• 12h45-14h30: Déjeuner (RU Pariselle)
• 14h30-15h15: Connecting the dots: metabolic networks for metabolome mining, Fabien
Jourdan, MTH-Toulouse
• 15h15-16h00: Data sciences for deep phenotyping and precision medicine, Etienne
Thevenot, MTH-Saclay, CEA, LIST
• 16h00-16h20: Métabolomique appliquée à l’identification de biomarqueurs en sélection variétale,
Philippe Hance, Institut Charles Viollette, Université de Lille
• 16h20-16h40: La bioinformatique des peptides nonribosomiques, des métabolites secondaires
microbiens remarquables, Valérie Leclère, Institut Charles Viollette, Université de
MetaboHUB
LilleThank you for your attention
Rennes
Saclay-Paris
Nantes Clermont
Bordeaux
Toulouse
54
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