saccharomyces cerevisiae :

KBDiSB/Aix 09-07 GG LISBP INSA Toulouse

Saccharomyces cerevisiae :


Evoutions in Bio Sciences

• Ecology

• Quantitative ecology

• Physiology,

• Quantitative biology

• Systemic Biology

• Holistic Biology


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


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


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


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


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?


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


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


Si X products:j

X

Feeds,Substrat(s), air, regulations and controls

Take down, culture medium, gaz out, biomass, products,,,,,,,,,,


Industrial (White) Biotechnology

SugarsBiofuelsBiomaterialsBiochemicals

Cell factories


Measures

Régulations

Correction pH, Antifoam

Tank,mixing, température control

Gaz out:analyseGaz in

logging

control


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


Réalisation

Mixing

FLUIDIC Mixing

Jets


AERATION : TECHNOLOGIES d’AERATION

ICI, Ltd. factory, Billingham, UK, (Chem. Eng. News, 18-Sep-78)

FERMENTEUR type air-lift


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


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


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


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


Top Down strategy

• Fit the macroscopic environnment,bioreactor• Find reproducible conditions:signature recognition• Biokinetics• Quantitative physiologie• Metabolic pathways• Proteomic • transcriptomics


- 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


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


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


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


The Tool box

Bio: the “omics”

+

Traditional technologies

+

mathematical tools

“the rule of innovation”


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


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


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


CO2 production

Gas Balance


CO2 production

Volume Volume




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)


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


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 ...


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


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)


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


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-


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


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


CATEGORISATION des SIGNAUX

Identification de classes de comportement

Mesures pertinentes / Comportements physiologiques

CLASSES

Item : temps

Item

: t

em

ps

SIMIL

ITUDE

Temps


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


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"


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


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


Heterogeneities:gradients(flux,stocks,,,) :microbe population


Top Down strategy

• Fit the macroscopic environnment,bioreactor• Find reproducible conditions:signature recognition• Biokinetics• Quantitative physiologie• Metabolic pathways• Proteomic • transcriptomics


Synthesis

Engineers Top down strategies

Biologists,bottum up , bioinformatics!!!!!!!!!

Both strategies are necessary


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


Tackling complexity in industrial microbiology for

bioprocessing

hh


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


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

puce àADN

puce àADN

puce àADN

Modélisation cellulaire globale

Modèles de systèmes complexes adaptatifs

Analyse statistique du

transcriptomeData Data Data Data

saccharomyces cerevisiae :

Documents

basic tool

low tech

right tech

high tech

bio processingfind

novo technology

complete tool box

generic technology synthetics