-
Bioreactors Modeling
and Control:
Dealing with Complexity
Dep. Of Chemical Engineering – Federal University of São Carlos – Brazil
Roberto C Giordano: [email protected]
Dep. Of Chemical Engineering – Federal University of São Carlos – Brazil
1
DEQDEQ
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First things first:
What is a bio−reactor, anyway?
The catalyst is...
One (or a great lot of) biomolecules
(usually enzymes, but not only)
2
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Nomenclature - Bioreactors:
• Enzymatic reactors• Cultivation of microorganisms/cells (“fermenters”)
ThyssenKrupp Stainless AG biogas
From µµµµL to 106L
http://www.usinasaofernando.com.br/
Micro bioreactors
Elisa Figallo et al , 2007 10.1039/B700063D
Disposable bioreactors
BioPharm International
Lab scale, bubble reactor
for P. chrysogenum 3
…
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�Enzymatic reactors: ex-vivo enzyme or enzymes
�“Fermenters” (not quite accurate: many times what we do
NOT want is that fermentation takes place…):
• Wild strains of microorganisms (bacteria, yeasts, fungi) ⇒ ex: ethanol, penicillin
Recombinant m.o., mammalian (or insect) cells ⇒• Recombinant m.o., mammalian (or insect) cells ⇒pharmaceuticals
Processes:
•Biofuels•Food industry•Pharmaceuticals… 4
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Bioprocess Engineering Principles. Doran PMAcademic Press ,1995 (5th re-impression, 2000)
5
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Phenomenon discovery Economic opportunity
1857: Pasteur effect , glucose + yeast � ethanol (anaerobiosis)
1973: low sugar prices, 1st oil crisis�Brazilian “PROÁLCOOL”
Bioproduct/bioprocess development: a long way
(but nowadays…)
1928: P. notatum → halo in S. aureusPetri plate
1941-43: II WW → high demand for antibiotics
1957: glucose isomerase → glucose-fructose isomerization
1965-80: high sucrose prices, over-production of corn in US�HFCS
1970: Restriciton endonucleasescleave DNA at restriction sites
1976: prevision of lack of swine pancreas; 1982�recombinant insulin on the shelf
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Desenvolvimento da tecnologia de produçãoTwo models:
1- Scientific discovery, bioprocess engineering scales up and reduces costs (“classic approach”)
Bioprocess development
costs (“classic approach”)2- Using rDNA: interactive and iterativeinteractive and iterativework!work!
Obs.: the bioreactor is at the core of the
process (upstream-bioreactor-downstream)
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MODEL 1 (“classic”)
Scaling up penicillin production
1928, Fleming – inhibitory halo 1939, Florey Chain isolates active penicillin1941-1943, surface cultivation (semi-solid reactors) does not 1941-1943, surface cultivation (semi-solid reactors) does not supply the demandDiscovery: P. chrysogenum growths in submerged cultures
Scale-up: Engineering solves several problems. Bioreactor design and operation: O2 (µ up to 200 cP), contamination, purification
0,001g/L(1941) 0,001g/L(1941) →→→→→→→→ 50g/L (1970)50g/L (1970)
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MODEL 2 (rDNA):
Human insulin: two polypeptides with a S-S bond
11-- Early 80’s: Separate expression of polypeptidesEarly 80’s: Separate expression of polypeptides-- fusion with a protein to avoid degradationfusion with a protein to avoid degradation-- complex bioprocess: cleavage, recomplex bioprocess: cleavage, re--linking…linking…-- complex bioprocess: cleavage, recomplex bioprocess: cleavage, re--linking…linking…-- huge engineering effort!huge engineering effort!
22-- Back to the lab: Better expression system: one stage, Back to the lab: Better expression system: one stage, proinsulinproinsulin linked to a linked to a guide peptide, enzymatic cleavage (process complex step…) guide peptide, enzymatic cleavage (process complex step…) –– pathway similar pathway similar to the cell’sto the cell’s
Interactive and iterative work between molecular biology and bioprocess Interactive and iterative work between molecular biology and bioprocess engineering! engineering!
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“Science and application of “Science and application of science are linked as fruit and science are linked as fruit and science are linked as fruit and science are linked as fruit and
tree”, Louis Pasteurtree”, Louis Pasteur
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Modern Industrial Biotechnology P&D
dilemma:
“The sooner the better” or “optimal/sub-
optimal processes”?
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Molecular
Biology
Systems Biology
Cultivation
(including scale-up)Downstream
Processing
444444444444 3444444444444 21 AGENCIESREGULATORY CONSTRAINT
⇒
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Process Analytical Technology (PAT)
Initiative (FDA, EUA) – for pharmaceuticals
Process Analytical Technology is:
A system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during
processing) of critical quality and performance attributes of raw and in-process materials and processes
with the goal of ensuring final product quality.
It is important to note that the term analytical in PAT is viewed broadly to include chemical, physical,
http://www.fda.gov/cder/OPS/PAT.htm
It is important to note that the term analytical in PAT is viewed broadly to include chemical, physical,
microbiological, mathematical, and risk analysis conducted in an integrated manner.
Process Analytical Technology tools:
There are many current and new tools available that enable scientific, risk-managed pharmaceutical
development, manufacture, and quality assurance…
In the PAT framework, these tools can be categorized as:
Multivariate data acquisition and analysis tools
Modern process analyzers or process analytical chemistry tools
Process and endpoint monitoring and control tools
Continuous improvement and knowledge management tools
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But using (classical and not so classical…) tools DURING the
Biotech. Process development may be an interesting approach:
• Process Systems Engineering:
Synthesis (mixed-integer optimization…)
Analysis (non-linear optimization…)
Rather complex task!
Analysis (non-linear optimization…)
Advanced control, etc
• Multivariate analysis:
Fault detection
Quality monitoring, etc
•Computational intelligence:
Tools for treating uncertain, non-linear systems
Adaptive learning
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Products:
• Macromolecules: monoclonal antibodies ⇒ recombinant mammalian cellshighly glycosylated enzymes (ex: cellulases) ⇒ (recombinant) fungi
Bioreactors for cultivation of m.o.’s and cells
highly glycosylated enzymes (ex: cellulases) ⇒ (recombinant) fungi
• “Mesomolecules”: simpler enzymes (ex: PGA), polypeptides (ex: vaccines), hormones (ex: insulin) ⇒(recombinant) bacteria or yeasts
• Micromolecules: pharmaceuticals
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And what’s special with bioreactors?
��Huge complexity of the “Huge complexity of the “reactionalreactional system”system”
��Reproducibility: Reproducibility:
�� Stability of the strain, number of Stability of the strain, number of �� Stability of the strain, number of Stability of the strain, number of
cells in the cells in the inoculuminoculum,…,…
�� Deactivation of enzymes, lot of the Deactivation of enzymes, lot of the
extract (impurities, activity…)extract (impurities, activity…)
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Bioreactors (for cultivation of cells/m.o.’s) are
our tools to connect the “cell factory” with the
environment (the process, and ultimately the
factory…)
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factory…)
Most common in industry: semi-continuous
(fed-batch)
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The “cell factory” , our industrial plant… translating:
Synthetic biology
Systems Biology
Open loop optimal
Process synthesis & design
Scheduling
Regulatory control
Open loop optimal policies
Adaptive policies
Data acquisition/pre-processing, reconciliation,fault detection 17
Plant optimization
Advanced control
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ftp://ftp.cordis.europa.eu/pub/nest/docs/syntheticbiol
ogy_b5_eur21796_en.pdf
“Cell factory synthesis/design”: SYNTHETIC BIOLOGY
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Omics
Systems Biology
90’s: Metabolic Engineering(ChE’s…); concept: Bailey, 1991
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http://fluorous.com/news/2010/10/technical-newsletters/981/
Metabolic Engineering, Principles & Methodologies
Stephanopoulos GN, Aristidou AA, Nielsen J
Academic Press, 1998
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�Stoichiometric models: S n×m.v m×1= 0 n×1
�Data-driven problems: labelled substrates (C13),
over-determined system (least squares)
Metabolic Flux Analysis
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�Optimization-driven problems: under-determined
system (more fluxes than balanced metabolites)
– What is the objective function? (Ex.: FBA,
max{biomass}; OK for E. coli, but not for
mammalian cells - secondary metabolites...)
-
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Boghigian et al., Metabolic Engineering 12 (2010) 81-95
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Signal preprocessing & fault detection
Regulatory control:
State-of-the-art bioreactor automation
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Regulatory control: pH, T, rpm, flow
rates
Model-based (?) control
Instrumentation
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Cell density and
viability…
•Flow cytometry•Turbidity (NIR)•Capacitance
Instrumentation
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Whitford W, Julien C:
Analytical Technology and PAT
BioProcess Int (Jan 2007), Suppl.
•Capacitance
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Sarco CR et al., submitted
BUT…
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Signal preprocessing (filters), fault detection
(PCA, PLS…), decision support (fuzzy,…)
Problem: inferring µ, specific growth rate
why?Filter: Committee of Cascade Correlation constructive NN. Giordano RC et al. Bioproc Biosys Eng 31:101-109, 2008Giordano RC et al. Bioproc Biosys Eng 31:101-109, 2008
0 5 10 15 20 25 30
0.0
0.5
1.0
1.5
2.0 Online signal Filtered signal
(a)
CO
2 (%
)
t (h)
CasCor1
CasCor2
COMMITTEE
:
:
M
E
D
I
A
T
O
R
Input Data Output Data
(b) Filtering Phase
CasCor algorithm
CasCorI NNTraining
Set
TSI
(a) Learning Phase
SMOOTHER
…………
CasCorN
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Softsensors… NNs (MLP, ANFIS, …)
0
1
2
3
4
5
6 Experimental Simulação "ON-LINE"
Con
cent
rção
Cel
ular
(g/
L)Y (p-k, p)O2
(p-k, p)xµµµµ
µµµµ (p-k, p-1)x
Y (p-k, p)O2
(p-k, p)YCO2µµµµ x (p)
. .
. . pµµµµ (p)
(p-k, p)YCO2
(p-k, p-1)µµµµ s
sµµµµ (p)
. .(p-k, p)xµµµµ
µµµµ (p-k, p-1)p
(A) (B)
(C)
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0 10 20 30 40 500
Tempo (horas) Validation of NN for state inference (cell mass). On-line data. Silva RG et al. J Chem Tech Biotech 83:739-749, 2008
0 5 10 15 200
1
2
3
4
5
6
7
8
0 5 10 15 200
1
2
3
4
5
6
7
8 Experimental Cell Concentration
Cel
l Con
cent
rati
on (
g.L
-1)
Time (h)
ANFIS
MLP
0 5 10 15 200
1
2
3
4
5
6
7
8 Experimental Cell Concentration
Cel
l Con
cent
ratio
n (g
.L-1)
Time (h)
ANFIS
MLP
ANFIS (cell mass). On-line data. Nucci ER et al. Bioproc Biosys Eng, 30:429-438,
2007
BUT…
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Multivariate calibration, MVDA
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Ribeiro MPA et al, Chemom Intel Lab Sys. 90:169-177, 2008
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Fuzzy inference of harvesting point
0
50
100
150
200
250
300
Warning: Stop the run!
Pro
duct
0 10 20 30 40 500.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
0 10 20 30 40 50
Time (h)
Y(C
O2)
, %
Nucci et al, Braz J Chem Eng 22:521-527, 2005
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Advanced Hybrid Control. Ex: GMC-Fuzzy for
cheese whey enzymatic tailor-made proteolysis
(with alcalase®)
Strong validation: artificial
structural model mismatch
Sousa Jr et al, Comp Chem Eng 28:1661-1672, 2004
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Regulatory control particularities... Ex.: tuning DO control in transient mode…Regulatory control particularities... Ex.: tuning DO control in transient mode…
Manipulated variables:
Q_Air
Q_O2Stirring
Q_O2
Q_Air
Hierarchically structured control:
Heuristic-PID
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Q_Air
Stirring
(PID)
Heuristic-PID
SUPERSYS_HCDC®
Horta AC et al., in preparation
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Model based strategies – but which models?
Structured,
segregated
Structured,
unsegregated
Unstructured,
segregated
Unstructured,
unsegregated
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segregated unsegregated
BUT ONE MUST ADAPT
(frequently…)
Gnoth S et al, Bioproc Biosyst Eng 31:21–39, 2008
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Example of Complexity… a “simple” system:
Kinetically-Controlled Enzymatic Synthesis
of Beta-lactam Antibiotics (green chemistry)
O
OH CH
NH 3
C OCH 3 O
+ S
CH 3 CH 3
O
NH 2
COOH N
O C
NH 3
CH OH N
COOH
NH
O
CH 3 CH 3
S
NH 2 CH 3 S
(νS) + CH 3 OH
(νh1) + H 2 O (νh2) H 2 O +
Giordano RC et al., Biotech
Adv 24: 27-41, 2006
32
O OH C
NH 3
CH OH + N
COOH
NH 2
O
CH 3 CH 3
S + CH 3 OH
Pen GH
HO
H
H
O
Phe A146 Arg A145
H
O
NH
H
H
NH
O
NH
N+
NH
Ser B1
H
O
N
CH3H O
O NH2
N
O
H
HH
HO
O
N
NH
O-
HH
O N+
O
NH
O
N
SCH3
CH3
O
O-
HH
OH
Ala B69Asn B241
Gln B23
Mechanism: elucidated by crystallography,
SDM, etc (almost…)
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A B + EH
N H
N H N H
N H
EH A B
N H N H + +
N H
A B + EH A B EH
N H B H
N H
EA EH + A N
N H
EH + A O H
N H +
N H
k 5 k -5 k -11 k 11
k 6
k -6
k 7 k 8
k -8
k 9
k -14 k 14 β k -14 β k 14
k 15
k -15
N H
k -10 k 10 STILL NOT “COMPLETE”: STILL NOT “COMPLETE”:
ACTION OF METHANOLACTION OF METHANOL
WAS NEGLECTEDWAS NEGLECTED!
33
A B + EH
+ + EH A B
B H N H N H
+ +
EH
N H
A B
N H
A B + EH
k -1
k 1 k 2 EA EH + A O H EH A O H
k 3 k 4
k -4
k 12 k -12 α k -12 α k 12
k 13
k -13
+
Enzymatic synthesis “complete mechanism”: EH = enzyme; BH = methanol; AOH= product of hydrolysis (PHPG); AB = activated acyl donator (PHPGME); E-A = acyl-enzyme complex; NH = nucleophile (6-APA) AN = amoxicillin.
-
Where:
num = (P1 CNH3+ P2 CNH
2 + P3 CAB3 + P4 CAB
2 + P5 CAB + P6 CNH3 + P7 CAB CNH + P8 CAB
CNH2 + P9 CAB
2 CNH +P10 CAB2 CNH
2 +P11 + P12 CAB CNH3 + P13 CAB
3 CNH ) CAB CNH
den = P14 CNH + P15 CNH5 + P16 CAB
2 CNH5 + P17 CNH
2 CAB4 + P18 CAB
4 CNH3 + P19 CAB
3
CNH4 + P20 CAB
4 CNH + P21 CAB CNH5 + P22 CAB
3 CNH3 + P23 CNH
2 + P24 CAB2 CNH
4 + P25CNH
3 + P26 CAB3 CNH
2 + P27 CAB CNH4 + P28 CAB
3 + P29 CAB2 CNH
3 + P30 CAB2 + P31 CNH
4 +
P32 CAB CNH + P33 CAB2 CNH + P34 CAB CNH
2 + P35 CAB CNH3 + P36 CAB
3 CNH + P37
EXAMPLE: the relatively simple expression for the initial rate of amoxicillin synthesis (νs,0), following Briggs-Haldane steady-sate approach:
νs,0 = num/den
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P32 CAB CNH + P33 CAB CNH + P34 CAB CNH + P35 CAB CNH + P36 CAB CNH + P37
Parameter P2, for instance, is:
P2=k8 k14 k12 (k6 k11 k-15 k-13 k7 k10 k5+(k6 k-15 k-12 k-13 k7 k10 k5+k-5 k11 k-15 k-12 k13 k7 k10+k6 k11 k-15 k12 k7 k10 k5) α+(k-14 k5 k-13k15 k11 k3 k7+k-14 k5 k-13 k-1 k15 k7 k10+k-14 k5 k-13 k15 k2 k10 k7 + k-14 k5 k-13 k15 k2 k10 k-11+k6 k11 k-13 k7 k14 k10 k5) β +(k-14 k5 k12k15 k11 k3 k7+k-14 k5 k12 k15 k2 k10 k7+k-14 k5 k12 k15 k2 k10 k-11+k-5 k11 k-12 k13 k7 k10 k14+k-14 k5 k-13 k-12 k15 k3 k7+k-14 k5 k12 k-1 k15k7 k10+k-5 k-15 k-12 k-14 k13 k2 k10+k6 k11 k12 k7 k14 k10 k5 + k6 k-12 k-13 k7 k14 k10 k5)αβ)
!!!...???!!!...???
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40
500 100 200 300 400
02468
10121416
PHPG PHPGME Hybrid-NN model Model 2
Con
cent
ratio
n (m
M)
6-APA Amoxicilline Hybrid-NN model Semi-empirical model
NN validation
35
HybridHybrid--NNNN vsvs simplifiedsimplified mechanisticmechanistic modelmodel..Synthesis of amoxicillin, pH 6.5, T = 25oC Goncalves LRB etal. Biotech Bioeng 80:622-631, 2002
0 100 200 300 4000
10
20
30
Hybrid-NN model Model 2
Con
cent
ratio
n (m
M)
Time (min)
-
Dynamic optimization
0))((0)),(),((
)(),,),(),((:..
),),((),(min
00
,),(
≤≤
==
=
f
fftptu
txTptutxS
xtxtptutxfxas
tptxpuJf
&
ψ
( ) ( ))(),()(),()(min += TT tutxStutxftH µλ
Necessary conditions
Direct methods
Indirect methods
( ) ( )
( )
( )
( ) ( ) 0)(0)(),(
,
)0(,)(),(
..
)(),()(),()(min
0
)(,
==
∂∂+
∂∂=
∂∂−=
==
+=
fTT
t
T
t
fTT
tutf
txTtutxS
x
T
xt
x
H
xxtutxfx
as
tutxStutxftH
ff
ξµ
ξψλλ
µλ
&
&
-
Variability… open loop feed policy may indicate
trends, but online control is another subject
37
-
Does the classical approach for
modeling ever work? ♦♦♦♦ Production of cephalosporin C by immobilized
Cephalosporium acremonium:
- cellular growth at radial position r:CkRdC ⋅
-mass balance for glucose and product in the bulk broth (species i):
( )
⋅−
∂⋅⋅−⋅=
∂= CQ
CDe
13t,RCdC iMSibedpibulki ε
Of course…
38
1x1
1xT1x1d
max
2O1x
Ck
CkCk
R
R
dt
dC
+⋅−⋅
−⋅= µ
1x1
1xT2x2d
max
2O2x
Ck
CkCk
R
R
dt
dC
+⋅+⋅
−⋅= µ
maz
2Ox
1S
1S
1
1S1S
21Sgel R
RC
Ck
Cm
Y
1
r
CDer
rr
1
t
C⋅⋅
+⋅+⋅−
∂∂
⋅∂∂=
∂∂
⋅ µε
xL2O
LmaxL2O
2Lgel CCk
CR
r
CDer
rr
1
t
C⋅
+⋅−
∂∂
⋅∂∂=
∂∂
⋅ε
- glucose and oxygen consumption:
( )
⋅−∂∂
⋅⋅−⋅=∂
∂=
=V
CQ
r
CDe
1
R
3
t
t,RC
dt
dC iMS
Rr
ii
bed
bed
p
pii
p
εε
- for oxygen:
( ) ( )L*LLRr
L2O
bed
bed
p
pLL CCakt
CDe
1
R
3
t
t,RC
dt
dC
p
−+∂
∂⋅⋅−⋅=
∂∂
==
εε
Cruz AJG et al., Chem Eng Sci 56:419-425, 2001
-
200
300
400
500
Time (hours) 21 40 46 119 142 158
Intr
apar
ticul
ar c
ell c
once
ntra
tion
g S
SV
/ L
gel
1.0
1.5
2.0
Time (hours) 21 40 46 119 142 158
Intr
apar
ticul
ar o
xyge
n co
ncen
trat
ion
CL
x 10
(m
mol
O2
/ L)
Simulated intra-particle radial profiles
39
0.0 0.2 0.4 0.6 0.8 1.00
100
Intr
apar
ticul
ar c
ell c
once
ntra
tion
r / Rp
0.0 0.2 0.4 0.6 0.8 1.00.0
0.5
Intr
apar
ticul
ar o
xyge
n co
ncen
trat
ion
CL
r / Rp
Cell mass Oxygen
-
Intra-particle cellular shell (SEM)
40
Model validated!Model validated!
SHELL WIDTH MATCHEDSHELL WIDTH MATCHED
-
( )tµ
SRS0
0X0
XS
SET SETeCC
VCm
Y
µF ⋅⋅
−⋅⋅
+=
BUT ONE MUST ADAPTBUT ONE MUST ADAPT
(usually)…(usually)…
Classical: µ depends only on S,Invariant, unstructured model
41
Ref Jens…
10
15
20
25
30
35
40
0 5 10 15 20
0.0
0.2
0.4
0.6
0.8
1.0
0
10
20
30
40
50
T(°
C)
t(h)
T(°C)
A B
µ(h-
1 )
µ
CS
Cs
-
Is it worth?
Time (h)Cx
(g (DCW) L-1)
PspA3
yield
(mg /g DCW-1)
PspA3
conc. (g L-1)
Protein
productivity (g L-1 h-1)
Where we 29.5 61.9 57 3.5 0.12
42
started29.5 61.9 57 3.5 0.12
What we got 20.0 120 232 ± 4 29 ± 1 1.2
-
THANK YOU
Team & Acknowledgements:
see the final version coming soon…
43
DEQDEQ