saccharomyces cerevisiae :
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KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Saccharomyces cerevisiae :
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Evoutions in Bio Sciences
• Ecology
• Quantitative ecology
• Physiology,
• Quantitative biology
• Systemic Biology
• Holistic Biology
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Yeast as cell factoryYeast
AerobiosisSemi Anaerobiosis
Anaerobiosis
Baker yeast
Yeast extract
Flavouring agents
Metabolites, ex food
additives
Waste water treatment
Yeast as co productanimal feed
Recombinant yeast
enzyme pharmacentica
l
Ethanol (ETBE)
Ethanol solvant
chemistry
alcoolic beverages
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
MICROBIAL/BIO REACTOR ENGINEERING:A BASIC TOOL FOR
KNOWLEDGE IN HOLISTIC BIOLOGY
G.Goma,S Guillouet,C Jouve,J L Uribellarea
Laboratoire d ingenierie des systémes biologique et des procédés
UMR CNRS,INRA,INSA
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Intersections on
technology and common
fields
White biotechs
Red biotechs
Agro-food biotechs
Green biotechs
Basic knowledges Focused on
life sciences … engineerig sciences biomathematics physics
Economy, sociology, ...
Generic technology
Synthetics, pathways
Biocatalysis engineering
Bioprocessing
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Microbial Engineering:a part of biotechnologies
Find and improve the microorganisms for bio processing
Find the conditions of bio processing where the microrganism is economicaly performant
A multidisciplinary approachA contraint : find the bottlenecks,eliminate themAn obligation:need of handling a complete tool
box:from genes to bioproducts and bioprocess
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
What kind of technological strategy?
• Low tech ?
• High tech ?
• Right tech for the goal
• What are the criteria of production ?– Production of « active agents »
– Cost ?
– Invisible technology
– Relatively safe technology
– Reproducible protocols simplest as possible
– Semi speciality
• « de novo » technology?
use of existing tools of production?
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
The IB Value Chain
BiofuelsH2
Ethanol
SugarsAgricultural(by)products
BiochemicalsFood IngredientsPharmaceuticalsFine Chemicals
BiomaterialsPolylactic acid
1,3 propane diolPHAs
Physical treatmentand/or enzymes
(Micro-)organismsbiocatalysis
Bulk
Fine
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
The steps
• Factory and his environment• The reactors ,biorector:biocatalist,srategy• Raw materials and biocatalist,bioreaction
engineering• The biocatalist • Global implementation ;find the differents
bottleneks and solve the problems• Need a tool box,and combining experimentals
datas(strategy?) and simulations
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Si X products:j
X
Feeds,Substrat(s), air, regulations and controls
Take down, culture medium, gaz out, biomass, products,,,,,,,,,,
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Industrial (White) Biotechnology
SugarsBiofuelsBiomaterialsBiochemicals
Cell factories
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Measures
Régulations
Correction pH, Antifoam
Tank,mixing, température control
Gaz out:analyseGaz in
logging
control
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Dual use of fermentors
RPM
Qair
Pressure
CO2, O2 ?
Gas balance
OD?
Ph (controlled)
Temperature
For this 2 controlled parameters, the analysis of the « work » of the control regulator gives informations
Starters milk, silage, …
Baker yeast bread
Alcoholic beverages
Lactic acid/organic acids (citric)
Antibiotics
Vaccines
Monoclonal antibodies
Recombinant proteins (or toxin ?)
Waste water treatment
Bioleaching
Instrumentation of a fermentor Use of fermentors
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Réalisation
Mixing
FLUIDIC Mixing
Jets
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
AERATION : TECHNOLOGIES d’AERATION
ICI, Ltd. factory, Billingham, UK, (Chem. Eng. News, 18-Sep-78)
FERMENTEUR type air-lift
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Metabolic descriptor
• Mass conservation• Elemental biologicals reactions• Macroscopic kinetics • Matrix of reactions combining kinetics and
stoechiometry of elemental reaction of metabolic pathways
• Combining kinetics observed by on line measurements by robusts sensors evaluation of metabolics fluxes « on line » and nutritionals needs
• Identification of some bottlenecks
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Phenomenogical models Behavioural models Structured models and stoechiometric/metabolic descriptors
Experimental strategies
SPXO2
CO2
O2
CO2
Qe
Qs
PEP
ATPNADH,H+
Glucose
ATPNADH,H+
ATP
IsoCitrate
Suc-CoA
Malate
HS-CoA
HS-CoA
GTP
Fumarate
Succinate
ATP
ATP
Citrate
aKglu
NADH,H+CO2CO2
CO2CO2
+NADPH,H
NADH,H+
+NADPH,HCO2FadH2
NADH,H+CO2
ANABOLISME
ATP
CO2 NADH,H+
+NADPH,H
ATP+NADPH,H2
CO2
Acétate
Pyruvate
Glucose6-P
Fructose-P
TrioseP
Glycerate3P
Pentose P
Sedoheptulose7 P
Erythrose4P
OAA
NAD2
2+H 0 + 4 H
21/2 O
2+
FAD
FADH2 H 0 + 2 H
3 H+
ATP
1/2 O
NADH,H+
GlycerolP glycerol
ATP
SH-CoA
SH-CoA
Acetyl CoA
Qresp
%pO2
pH
Temps
Predictive modelisation and implementation of microbial
processes
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Systèmemétabolique
Systèmeprotéique
Systèmegénomique
Système:d’adaptation et de
défense
Interface de la cellule et échanges
Systèmed’échanges
11
22
33
Plate-forme métabolomique, fluxomique
Vers une biologie des systèmes par la réconciliation des niveaux métaboliques, génétiques et
moléculaires
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Prerequisite to “Systemic Biology”
Analytical methods
Kinetics Flux, StocksTechnology
in situcontinueon line
in parallelmicro samples
Metabolome
Data base(x2 every 18 months)
Metabolic pathwayscoupled kinetics
relaxation time,regulations,
« OMICS »
Sequences
genes
Profiles
proteins
Definitionof
functions , networks
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Top Down strategy
• Fit the macroscopic environnment,bioreactor• Find reproducible conditions:signature recognition• Biokinetics• Quantitative physiologie• Metabolic pathways• Proteomic • transcriptomics
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
- How osmotic conditions affect response to ethanol?
- Genes and mechanisms involved ?
Analysis of first fermentation
Comparison with another fermentation with better performances
> sequencial feeding glucose> Titer 50 h = 147 g/L, viability = 30%> Viability = 80% at 120g/L
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
2
Normally, we have sensors only for the environmental variables.
Physiological states are tracked through offline measurements and analysis, with an implied delay.
The physiological state can be identified by the fusion of environmental measurements.
The physiological state recognition
The cell population expresses stable characteristics within every physiological state, thus an invariant control strategy can be effectively applied in each state.
Motivation
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Identification and Classification of Physiological States
• A bottlenek for « the omics »studies,for control strategies and « quality »
• Morphometry
• Kinetics and stoechiometrics « parameters »
• Differentiation of biologicals and environmental effects
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Yeast :Axenic culture gives a population production linked to some mechanims
G1, G2, G3,G4,… Cycle
The family growth by budding
S1 sugarS2 oxygene
yes/no
S3 ethanol My job is bioconversion
I have a limitation
I do nothing
I am stressed – I became a filament
My job is to produce cell
biomass
I work
I work
I am ill
I am injuriedFinish : End ; cryptic growth !!! I am a substrate
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
The Tool box
Bio: the “omics”
+
Traditional technologies
+
mathematical tools
“the rule of innovation”
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Biocatalysis strategy
Diversity NaturalEco-systems
NaturalDiversity of Eco-systems
Screening
Screening
Engineering metabolic*
Building strains
DNA shuffling Global analysis “Omics and engineering”
Genes* et functions
screening
Genes* and functions
screening
Production-formulation« Bioprocédés »
Production-formulation« Bioprocesses »premières
Rawmaterials
Bioprocess strategy
* e.biotechnology's and engineering
BiomoléculesBiomolecules
High added value
Needs in size of market :
animal feed
Strategy on co-products/bio-products
plus value
Co-productsBiomaterialon energy
Increase the value
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Microbial engineering is multidisciplinary : need of quantitative and “system” biology
+ system biology modelling
Microbial engineering
Molecular physiological engineering
Microbial process analysis
and controlengineering
Microbial processing
Physiological engineering
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Dual use of fermentorsWhat is a fermentor
?Elemental biokinetics
x Biomassp Product
s Substrats
x p
s
t Time
t
x p
s1
tou
x p
s1
ts2
Cultivation : INSTRUMENTATION :MESURE
« STANDARD» MEASURES
pH - pH regulation
Oxygen dissolve- pO2 regulation
Temperature - temperature regulation
Pressure
Agitation
Gas Balance
Consummation of oxygen and
CO2 production
Volume
Massive flux of carbon substratesFlux of feededliquid (volume)
Cultivation : INSTRUMENTATION :MESURE
« STANDARD» MEASURES
pH - pH regulationpH - pH regulation
Oxygen dissolve- pO2 regulationOxygen dissolve- pO2 regulation
Temperature - temperature regulationTemperature - temperature regulation
PressurePressure
AgitationAgitation
Gas Balance
Consummation of oxygen and
CO2 production
Gas Balance
Consummation of oxygen and
CO2 production
Volume Volume
Massive flux of carbon substratesFlux of feededliquid (volume)
Massive flux of carbon substratesFlux of feededliquid (volume)
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Cell and glucose ethanol concentration vs time (Fed batch
with nutritional strategy)
0
1000
2000
3000
4000
5000
6000
7000
0 5 10 15 20 25 30 35 40 45 50
Time (h)
0
50
100
150
200
250
300
350
400
GlucoseEthanol BiomassViable
Biomass
(g) (g/L)(g)
(g)
0
1000
2000
3000
4000
5000
6000
7000
0 5 10 15 20 25 30 35 40 45 50
Time (h)
0
50
100
150
200
250
300
350
400
GlucoseEthanol BiomassViable
Biomass
(g) (g/L)(g)
(g)
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
0
20
40
60
80
100
120
140
160
180
200
0,00 10,00 20,00 30,00 40,00 50,00 60,00 70,00 80,00
0,0
0,2
0,4
0,6
0,8
1,0
1,2
Ethanol glucose Biomass viability
I IIVI
Ethanol Glucose
(g/L)
Biomass
(g/L)
2 phenomena:
- Decoupling growth-production
- Loss of viability
Viability
2
4
6
8
10
12
14
16
18
20III
IVV
Study of a reference fermentation
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Measurement of intracellular metabolites Sample quenching in -60°C methanol
Measurement of extracellular metabolites- direct filtration through adaptated membrane
Fast sampling :
Sampling for extraction of RNAs and proteins
Gas balances(Mass spectr.)
Biomass sensor
Xestim
control
Q, qH+
Controlledenvironment
rpm
Qair
inQair
out
Monitoring
µ
T°pHpO2
qO2 , qCO2
, Qresp
Measurement& rates/ 20 sec
On-line acquisitions
and monitoringOff-line analyses
Studying the fast biological responses ...
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
The hyper yeast
PEP
ATPNADH,H+
Glucose
ATPNADH,H+
ATP
IsoCitrate
Suc-CoA
Malate
HS-CoA
HS-CoA
GTP
Fumarate
Succinate
ATP
ATP
Citrate
aKglu
NADH,H+CO2CO2
CO2CO2
+NADPH,H
NADH,H+
+NADPH,HCO2
FadH2
NADH,H+CO2
ANABOLISME
ATP
CO2 NADH,H+
+NADPH,H
ATP+NADPH,H2
CO2
Acétate
Pyruvate
Glucose6-P
Fructose-P
TrioseP
Glycerate3P
Pentose P
Sedoheptulose7 P
Erythrose4P
OAA
NAD2
2+H 0 + 4 H
21/2 O
2+
FAD
FADH2 H 0 + 2 H
3 H+
ATP
1/2 O
NADH,H+
GlycerolP glycerol
ATP
SH-CoA
SH-CoA
Acetyl CoA
Nutrition
Stress
Hydrolytics potencies
Contaminations
Genetic engineeringDNA shuffling
Metabolic engineering
Ethanol
CS Fermentation
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Data Acquisition : Measures
paramètres de fermentation
temps (h)
0 2 4 6 8 10 12
36.8
37.0
37.2
37.4
2D Graph 2
0 2 4 6 8 10 12
600
900
1200
1500
1800
2D Graph 3
0 2 4 6 8 10 12
6.6
6.7
6.8
6.9
7.0
Paramètres de fermentation
0 2 4 6 8 10 12
510
515
520
Fermentation Parameters
Time(h)
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Biocatalyse enzymatique
Biocatalyse microbienne
« impact socio-économique »
Le microorganis
me est un système
biocatalytique évoluant
dans un système
Connaissance de systèmes ;
interactions de systèmes
le micro-organism
e en tant que
système est constitué d’« infra »
systèmes
« interactions de systèmes et hiérarchies »
Système d’échange
sSystème métaboliqu
e système protéique
Système génomique
Système: d’adaptation et de défense
Le biotope du système
microbien crée un
environnement; « en soi ,
un système »
Le biotope du système
microbien crée un
environnement; « en soi ,
un système »
Interface de la cellule et échanges
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Allosteric controls
Mass action law RNA control
Modification of enzymatic pools
CellCell
EnvironmentEnvironmentGradients due to
mixing Continuous culture
Batch, Fed-batch
Phenomenological model
Metabolic model
Virtual cell
Behavioural models
10-6 10-5 10-4 10-3 10-2 10-1 100 10+1 10+2 10+3 10+4 10+5 10+6
s-
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Extracellular components
Intracellular components
Perspective : Use of behavioural modelling
Segregation (size, viability, …)
Analysis of population or « dynamic systems »
Descriptor of physiological
state A
Descriptor of physiological
state B
* Relaxation time
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Bacteria
Yeast
Fungi
Eucaryotic cells
In every case
The basic law of biokinetics and stoechiometry are the same
But, every case have rules of utilisation with typical profile
What kind of micro organisms What kind of profile
dt
VCOd
dt
VCOdCOQCOQVr
dt
VOd
dt
VOdOQOQVr
liqcarbodis
gazgaz
sortsot
ententliq
liqCO
liqdis
gazgaz
sortsot
ententliq
liqO
).().(...
).().(.
2222
2222
2
2
?
?????
?????
Equationsbilan
Temps (h)
rO2rCO2
moles/h
Coefficientrespiratoire
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
0.7
0.8
0.9
1
1.1
1.2
1.3
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
0
0.2
0.4
0.6
0.8
1
1.2
1.4
dt
VCOd
dt
VCOdCOQCOQVr
dt
VOd
dt
VOdOQOQVr
liqcarbodis
gazgaz
sortsot
ententliq
liqCO
liqdis
gazgaz
sortsot
ententliq
liqO
).().(...
).().(.
2222
2222
2
2
?
?????
?????
Equations bilan
Temps (h)
rO2rCO2
moles/h
Coefficientrespiratoire
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
0.7
0.8
0.9
1
1.1
1.2
1.3
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
0
0.2
0.4
0.6
0.8
1
1.2
1.4
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
CATEGORISATION des SIGNAUX
Identification de classes de comportement
Mesures pertinentes / Comportements physiologiques
CLASSES
Item : temps
Item
: t
em
ps
SIMIL
ITUDE
Temps
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Phenomenogical models Behavioural models Structured models and stoechiometric/metabolic descriptors
Experimental strategies
SPXO2
CO2
O2
CO2
Qe
Qs
PEP
ATPNADH,H+
Glucose
ATPNADH,H+
ATP
IsoCitrate
Suc-CoA
Malate
HS-CoA
HS-CoA
GTP
Fumarate
Succinate
ATP
ATP
Citrate
aKglu
NADH,H+CO2CO2
CO2CO2
+NADPH,H
NADH,H+
+NADPH,HCO2FadH2
NADH,H+CO2
ANABOLISME
ATP
CO2 NADH,H+
+NADPH,H
ATP+NADPH,H2
CO2
Acétate
Pyruvate
Glucose6-P
Fructose-P
TrioseP
Glycerate3P
Pentose P
Sedoheptulose7 P
Erythrose4P
OAA
NAD2
2+H 0 + 4 H
21/2 O
2+
FAD
FADH2 H 0 + 2 H
3 H+
ATP
1/2 O
NADH,H+
GlycerolP glycerol
ATP
SH-CoA
SH-CoA
Acetyl CoA
Qresp
%pO2
pH
Temps
Predictive modelisation and implementation of microbial
processes
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Study of a fermentation of referenceFirst results:genes over expressed
% des genes significatif dans la famille considerée
0,0
2,0
4,0
6,0
8,0
10,0
12,0
14,0
16,0
CELL CYCLEAND DNA
PROCESSING CELL FATE
CELL RESCUE,DEFENSE ANDVIRULENCE
CELLULARTRANSPORT
AND
CONTROL OFCELLULAR
ORGANIZATION
ENERGY
METABOLISM PROTEIN FATE
(folding,modification,
PROTEINSYNTHESIS
REGULATION
OF /
INTERACTIONTRANSCRIPTION
TRANSPORTFACILITATION
III ="60g/L ethanol"
IV="80g/L ethanol"
V="90g/L ethanol"
VI="100g/L ethanol"
X="120g/L ethanol"
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Study of a fermentation of referenceFirst results:genes under expressed
% des genes significatif dans la famille considerée
0,0
5,0
10,0
15,0
20,0
25,0
30,0
CELL CYCLEAND DNA
PROCESSING CELL FATE
CELL RESCUE,DEFENSE ANDVIRULENCE
CELLULARTRANSPORT
AND
CLASSIFICATIONNOT YET CLEAR-
CUT ENERGY
METABOLISM PROTEIN FATE
(folding,modification,
PROTEINSYNTHESIS
REGULATION OF/ INTERACTIONWITH CELLULARTRANSCRIPTION
TRANSPORTFACILITATION TRANSPOSABLE
ELEMENTS,VIRAL AND
60g/L ethanol
80g/L ethanol
90g/L ethanol
100g/L ethanol
120g/L ethanol
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Plate-forme métabolomique, fluxomiqueExploration fonctionnelle des
systèmes métaboliques microbiens
Analyse des réseaux métaboliqueso Reconstruction métaboliqueo Analyse topologiqueo Modélisation métabolique
Exploration fonctionnelle Analyse in situ: RMN in vivo
o Couplages bioréacteurs / RMNo Métabolisme énergétique, carboné, etc..
Métabolomiqueo Identification/quantification des métabolites
Fluxomiqueo Quantification des flux métaboliqueso Approches isotopiques (13C)
Biomathématique/ bioinformatiqueo Modélisatio métaboliqueo Calculs de fluxo Réconciliation de données
Systèmes métaboliquesMétabolisme central E. coli :
89 métabolites, 110 réactions
Génome
Environnement
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Heterogeneities:gradients(flux,stocks,,,) :microbe population
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Top Down strategy
• Fit the macroscopic environnment,bioreactor• Find reproducible conditions:signature recognition• Biokinetics• Quantitative physiologie• Metabolic pathways• Proteomic • transcriptomics
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Synthesis
Engineers Top down strategies
Biologists,bottum up , bioinformatics!!!!!!!!!
Both strategies are necessary
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Basic concepts
Fuzzy logic
Hierarchical classification
Programmed by inductive logic
Classification machineMeasures
Biological and engineering knowledge
Biological modelling Rules
Hypothesis or
« Class »
•3 levels of multiscale analysis•Single cell, “statistic”
Analysis of information quality
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Tackling complexity in industrial microbiology for
bioprocessing
hh
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Système:d’adaptation et de
défense
Le biotope du systèmemicrobien crée unenvironnement;
« en soi,un système »
Le biotope du systèmemicrobien crée unenvironnement;
« en soi,un système »
Systèmemétabolique
Systèmeprotéique
Systèmegénomique
Systèmed’échanges
11
22
33
Biologie intégrative descendante
Biologie intégrative ascendante
Biocatalyse/enzymologieConstruction/sélection
enzymeModélisation
moléculaire PB
• Fluxome, métabolome• Construction de souches • Transcriptome, régulations
PMM
• Biochimie/biologie systémique• Génie moléculaire enzymatique• Génie cellulaire des procaryotes et eucaryotes inférieurs• Aptitude expérimentation/plate-formes • Modélisations locales/globales• Stratégies d’expérimentation/modélisations• Psychologie d’application
PGM
• Environnement physico-mécanique, physico-chimique•Réponse microbienne•Modélisation•Classification•Corrélation environnement/mise en œuvre, réponse transcriptome•Dynamique systèmes•Programmation expériences
KBDiSB/Aix 09-07 GG LISBP INSA Toulouse
Analyse génomique
Année 1 Année 2 Année 3
Analyse macro cinétiquede métabolismes
stabilisés
Analyse macro cinétique dynamique
Analyse macro cinétiqueEffecteurs environnementaux
Validation expérimentale
Obtention du matériel biologique : Cultures en bioréacteurs
6 12 18 24 36
PARTENAIRES
puce à ADN
Analyse comportementale
Description métabolique Analyse morphologique
Transfert de données
Classification – Modélisation comportementale
Analyse moléculaire
Interprétations des données / mécanismes moléculaires
Développement, Test et validation de la Plateforme bioµ ω Plateforme logicielle
systèmes multi-agentsauto-organisateurs
puce àADN
puce àADN
puce àADN
Modélisation cellulaire globale
Modèles de systèmes complexes adaptatifs
Analyse statistique du
transcriptomeData Data Data Data
Analysegénomique
1Année 2Année 3Année
Analyse macrocinétique
de métabolismesstabilisés
Analyse macro cinétique dynamique
Analyse macrocinétique
Effecteursenvironnementaux
Validationexpérimentale
Obtention du matériel : biologique
Cultures enbioréacteurs
6 12 18 24 36
PARTENAIRES
puce àADN
Analysecomportementale
Description métabolique Analyse morphologique
Transfert de données
Classification – Modélisation comportementale
Analysemoléculaire
/ Interprétations des données mécanismes moléculaires
, Développement Test et validation de la Plateforme bioµω Plateforme logicielle
systèmes multi-agentsauto-organisateurs
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Modélisation cellulaire globale
Modèles de systèmes complexes adaptatifs
Analyse statistique du
transcriptomeData Data Data Data
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