Bioreactors Modeling

and Control:

Dealing with Complexity

Dep. Of Chemical Engineering – Federal University of São Carlos – Brazil

Roberto C Giordano: roberto@ufscar.br

Dep. Of Chemical Engineering – Federal University of São Carlos – Brazil

1

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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

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

…

�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

Bioprocess Engineering Principles. Doran PMAcademic Press ,1995 (5th re-impression, 2000)

5

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

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)

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)

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!

“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

10

Modern Industrial Biotechnology P&D

dilemma:

“The sooner the better” or “optimal/sub-

optimal processes”?

11

Molecular

Biology

Systems Biology

Cultivation

(including scale-up)Downstream

Processing

444444444444 3444444444444 21 AGENCIESREGULATORY CONSTRAINT

⇒

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

13

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…)

15

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…)

16

factory…)

Most common in industry: semi-continuous

(fed-batch)

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

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

�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...)

21

Boghigian et al., Metabolic Engineering 12 (2010) 81-95

22

Signal preprocessing & fault detection

Regulatory control:

State-of-the-art bioreactor automation

23

Regulatory control: pH, T, rpm, flow

rates

Model-based (?) control

Instrumentation

Cell density and

viability…

•Flow cytometry•Turbidity (NIR)•Capacitance

Instrumentation

24

Whitford W, Julien C:

Analytical Technology and PAT

BioProcess Int (Jan 2007), Suppl.

•Capacitance

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

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)

27

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…

Multivariate calibration, MVDA

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Ribeiro MPA et al, Chemom Intel Lab Sys. 90:169-177, 2008

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

29

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

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

30

Q_Air

Stirring

(PID)

Heuristic-PID

SUPERSYS_HCDC®

Horta AC et al., in preparation

Model based strategies – but which models?

Structured,

segregated

Structured,

unsegregated

Unstructured,

segregated

Unstructured,

unsegregated

31

segregated unsegregated

BUT ONE MUST ADAPT

(frequently…)

Gnoth S et al, Bioproc Biosyst Eng 31:21–39, 2008

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…)

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

34

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)

!!!...???!!!...???

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

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