biotechnology advanced -...

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Graduate School of Engineering, Osaka UniversityGraduate School of Information Science, Osaka UniversityInternational Center for Biotechnology, Osaka University


Biotechnology AdvancedNo. 6: Design and Operation

of Biological Reactors



Handai Cyber University

Suteaki SHIOYA

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No. 6: Design & Operationof Biological Reactors

I. IntroductionII. ModelingIII. Optimal DesignIV. Operation

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I. Introduction

1. Industrial Production

2. BPSE for Design and Operation

3. BPSE and Modeling

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cellsubstrate

Process networkProcess datapH, DO, CO2, O2Cell conc. Sub.conc

MetaboliteMeta

objectproductivityyield

Cell-cell network

Process operationoptimizationcontrolproduction

schedulePlant design

expressiontranscription

genome

transcriptome

proteome

metabolome

metabolite

metabolic control

Object-oriented BioProcess Systems Engineering

gene

protein

Intracellular network

Measurementgrowthsubstratemetaboliteproduct

Metabolic flux analysisflux control analysis Genome information

micro arrayDNA chip2D electrophoresisLC-Massin vivo NMR

BPSE based on genome information

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Industrial BioIndustrial Bio--ProductionProductionResearch Process DevelopmentUp-stream Middle-stream Down-streamStrain preparation Bioreactor Bioreactor SeparationImprovement medium Design OperationDesign Operation Purificationby strainmutation microorganisms centrifugationgenetic eng animal cell chromatographycell fusion tissue culture distillationDNA manipulation ……..…….

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BPSE

DESIGN OPERATION

Optimization Monitoring Control

MODELING

Fig.1 Decision by BioProcess Systems Engineering

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II. Modeling

1. Mass Balance Equation 2. Batch, Fed-batch, Continuous

Reactors3. Rate Equation Model

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Classification of the modelPure culture

• Lumped parameter system (uniform-system)• Cell environmental variable level• Metabolic reaction level• Molecular reaction level

(gene expression, protein, signal transduction, transportation …)

• Distributed parameter system (Cell population system)cell cycle, age-distribution

Co-culture• Lumped• Distributed

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Cell

Phosphoenolpyruvate

Pyruvate

r2

r3

Lysine

r4

r6

NAD +ATP

NADH2

r7

r10

r9 Glucose

r1

r5

Glucose-6-phosphate

Fructose-6-phosphate

Glyceraldehyde-3-phosphate

r11

ATP

ADP

r8

61.3107

17.419.3

30.590.0

67.4215

47.3160

20.0106

9.9262.1

11.431.7

41.50

78.0322

0.0210.2

Glucose

rg 100(extracellular)

(intracellular)

Fig. Metabolic pathway

Metabolic Reaction LevelMetabolic Engineering

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Metabolic Reaction Modelr1: Glucose + ATP -> Glucose-6-phosphate r7: 3Glucose-6-phosphate -> Fructose-6-phosphate + r2: Glucose-6-phosphate -> Fructose-6-phosphate Glyceraldehyde-3-phosphate + 3CO2 + 6NADPHr3: Glucose-6-phosphate + ATP -> 2Glyceraldehyde-3-phosphate r8: Phosphoenolpyruvate + Pyruvate + 2NH3 + NADH + 3NADPH + 2 ATP r4: Glyceraldehyde-3-phosphate -> -> Lysine

Phosphoenolpyruvate +ATP +NADH r9: a Glucose + b Pyruvate + NH3 + MW/YATP ATP + c NADPH -> biomassr5: Phosphoenolpyruvate -> Pyruvate +ATP r10: O2 + 2NADH -> 2(P/O) ATPr6: Pyruvate -> 3CO2 +ATP + 14/3NADH r11: ATP -> ADP

⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢

⎣

⎡

1-000000000000000c-3-6000000

01-00000000000002-01-014/301000

001-000000000001-2(P/O)MW/YATP-2-01111-01-

0001-000000000000b-1-01-10000

00001-0000000000001-001-1000

000001-0000000000001001-200

0000001-0000000000020001-10

00000001-00000000003-00001-1

000000001-0000000010000000

0000000001-0000001-2-0000000

00000000001-00000003300000

000000000001-0001-000000000

0000000000001-000a-00000001-

00000000000001-00100000000

=E

Balancing Equation

r=[r1,r2,r3,r4,r5,r6,r7,r8,r9,r10,r11,rX,rg,ro,rc,rn,rL,rG6P,rF6P,rGAP,rPEP, rpyr,rATP,rNADH,rNADPH]T

Arc - Brm =0where,

rc=[r1,r2,r3,r4,r5,r6,r7,r8,r9,r10,r11, rn,rL]T to be calculated

rm=[rX,rg,ro,rc,rG6P,rF6P,rGAP,rPEP, rpyr,rATP,rNADH,rNADPH]T measured

Determination of Metabolic Reaction Rates

rc =(ATA)-1ATBrm

Er=0

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Network of Metabolites

Network of Genes

Network of Proteins

Physiology

Hierarchical Network Control of BioSystem

Environmental Conditions

Molecular Reaction Level

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Lactococcus lactis subsp. Lactis

L-lactate

Maltose

<Growth inhibition>

Nisin

Organic acids

Kluyveromyces marxianus

O2

CO2

<cannot assimilate>

Abu

AbuAbuAla Ala

Dhb

Dha

Dha

Ala Ala

AlaAla

AlaAla

IleIle

Ile

Leu

Leu

Abu

Pro Gly GlyLys

Lys

LysM et

GlyAsn

M et

His

His

Ser

Val

S

COOH

NH 2

Dha: Dehydroalanine Dhb: Dehydrobutyrine Aba: Am ino butyric acidAla-S-Ala: Lanthionine Aba-S-A la: β-M ethyl lanthionine

S

S

S

S

Nisin: Antimicrobial Peptide:produced by Lactococcus lactis ATCC 11454

(1) A safe and natural food preservative (GRAS)

(2) Wide range of growth inhibition spectrum for gram positive bacteria.

Interaction of microorganisms

Nisin Production by Mixed Culture

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＜＜ Comparison of TGGE patternComparison of TGGE pattern＞＞FirstFirst

SecondSecond0 24 48 72 96 120 168 216144 (h)

0 5 24 48 72 96 120 168 216 (h)

In the second composting, the change of main microflora occurred faster than the first composting.

In the second composting, the change of main In the second composting, the change of main microfloramicroflora occurred faster than the first composting.occurred faster than the first composting.

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Type of reactors

Complete Stirred Tank Reactor (CSTR) Air-lift Reactor

Air

Air

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Type of operation

Batch

Fed-Batch

Continuous steady state

Vol

ume

Time

Vol

ume

Time

Vol

ume

Time

Time

cell

conc

.Time

cell

conc

.

Time

cell

conc

.

Depend on feeding type

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Mass balance equations-1

Example: CSTRPrinciple of mass balance

Accumulation = Input – Output + Generation Term

Cin

FinCoutFout

tΔ V

[m3 h-1] [m3 h-1]

[m3

]C

CSTR

• C = Cout : same concentration

• Volume change

Accumulation

ttVrtCFtCFtCtVttCttV Coutinin Δ+Δ−Δ=⋅−Δ+⋅Δ+ )()]()()()([Input Output Generation Term

[kg m-3][kg m-3]

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Mass balance equations-2

dtdVC

tVC

t=

ΔΔ

→Δlim

0)(tVrCFCF

dtdVC

Coutinin +−=

outin FFdtdV

−=

VrrCFCFFFCdtdCV

dtdVC

dtdCV

dtdVC

ccoutininoutin ++−=−+=+= )(

VrCCFdtdCV Cinin +−=∴ )(

or Cinin rCC

VF

dtdC

+−= )(

ttVrtCFtCFtCtVttCttV Coutinin Δ+Δ−Δ=⋅−Δ+⋅Δ+ )()]()()()([

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Example

X; cell concentration [kg m-3] S; substrate (glucose) concentration [kg m-3] P; product concentration [kg m-3] C: dissolved oxygen concentration [kg m-3]

SinFin S

FX, S, P

μ; growth rate[h-1]ν; specific substrate uptake rate [h-1]ρ; production rate[h-1]

t [h]

XVFXX

VF

dtdX inin )( −=+−= μμ

XSSVF

dtdS

inin ν−−= )(

PVFX

dtdP in−= ρ

)()( * CCakCCVF

dtdC

Linin −+−=

outin FFdtdV

−=

P

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XVFXX

VF

dtdX inin )( −=+−= μμ

XSSVF

dtdS

inin ν−−= )(

PVFX

dtdP in−= ρ

)()( * CCakCCVF

dtdC

Linin −+−=

outin FFdtdV

−=

0== outin FF 0,0 =≠ outin FF0≠= outin FF

if Batch Fed-Batch

Chemostat (continuous culture)

DVFin =

Δ

D1

(dilution rate), [h-1] : average retention time, [h]

Other example; tubular type reactor

CrxCu

xCD

tC

+∂∂

−∂∂

=∂∂

2

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Rate Term Model

XdtdX μ=

SKS

S

m

+=

μμ

IS

m

KSSK

S2

++=

μμ

XdtdS ν−=

μνsxY /

1=

ksx

mY

+= μ/

1

XdtdP ρ=

X)( βαμρ +=

μ

S

μ

S

Monod’s model

Luedeking-Piret modelor

Substrate inhibition

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III. Optimal Design1. Optimization

2. Static optimization

steady state

3. Dynamic optimization

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Design -Optimization-

1. Definition of optimization•Degree of freedom

Optimization Objective = economic, enviroment•cost•efficiecy

…

Steady state

J

uux Min),( ⇒J

0uxf =),(Subject todim: −nxdim: −rudim: −nf

wherestate variabledecision variable

degree of freedom = r

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2. Static optimization: Example

DVFXJ Max⇒=

0)( =−= XDdtdX μ

0)(1

/

=−+−= SSDXYdt

dSin

Sx

μ

DMaxDXJ ⇒=

)(/ SSY inSx −= μμμ =−⇒=

∂∂ )(0 SSSJ

FS

SKS

S

m

+=

μμ 2)( SKK

S

Sms +=

μμ

)/1

11(Sin

m KSD

+−= μ

• Chemostat

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Stability

XDdtdX )( −= μ

XY

SSDdtdS

Sxin

/

)( μ−−=

if MinS μμμ ≤≤)(

Equilibrium point

S

x

S 2S 1

S in wash outx = Yx/s(Sin-S)

11 : SSE =)( 1/ SSYX inSx −=

22 : SSE =)( 2/ SSYX inSx −=

inSSE =:30=X wash out (stable)

(stable)

(unstable)

D=μμ

SS1 S2 Sin

μM

μ(S)

E1

E3

E2

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3. Dynamic optimization: Example

μρ Max

0⇒= ∫

ftVXJ

• Batch or fed-batch culture (Maximum Production)

μ

ρ

μC μ1

Relationship between μ and ρ

Maximum principle

μtｔC ｔf

μC

μ１

=optμ 1μ

cμfor

for

Ctt ≤≤0

fC ttt ≤<

VXdt

dVP ρ=

Growth phase

Production phase

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IV. Operation & Control

1. Exponential Fed-batch Culture

2. Feedforward/Feedback Controller

3. Example: PHA production

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VXdt

dVX μ=

Expotential Fed-batch Culture

outSx

FSVXYdt

dVS+−= μ

/

1

FdtdV

=

0)(1

/

=−+−= SSFVXYdt

dSV outSx

μ

.const=S .const)( =Sμ )exp()( 0 tVXVX μ=

0=dtdS

SxoutSxout YStVX

YSSVXF

/

0

/

)exp()()(

μμμ=

−=

)exp(0* tVF μμ=

SxoutYSX /0 =)exp(0 tVV μ=

if

if

if

then

then

then

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Programed-controller/Feedback-compesatorsystem

Programedcontroller

Pre Compensator Bio-Plant H(x)

y Δε+

- +

+

F

ΔF yF

MRAC or PI

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Example : Maximu Production in Fed-batch culture

Co-polymerization Control of Polyhydroxyalkanoatesin Fed-batch Culture based on a Metabolic Reaction Model

Deleted based on copyright concern.

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Metabolic pathway of P(3HBMetabolic pathway of P(3HB--coco--3HV) synthesis.3HV) synthesis.

nn--PeOHPeOH MetabolismMetabolism EtOHEtOH MetabolismMetabolism

CHCH33CHCH22CHCH22CHCH22CHCH22OHOH

CHCH33COCHCOCH22COCO--SCoASCoA

CHCH33CHCH22CH(OH)CHCH(OH)CH22COCO--SCoASCoA

3HV unit3HV unit

CHCH33CHCH22OHOH

CHCH33COCO--SCoASCoACHCH33CHCH22COCHCOCH22COCO--SCoASCoA

CHCH33CH(OH)CHCH(OH)CH22COCO--SCoASCoA

3HB unit3HB unitTCA cycleTCA cycle

CHCH33CHCH22COCO--SCoASCoA

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ttff

∫∫ ρρ((VXVXRR)dt)dt MaxMaxJ =J =00

Optimal profile of Optimal profile of μμ

Growth stageGrowth stage Production stageProduction stage

μμ = = μμ maxmax

Carbon source : Carbon source : EtOHEtOH

Set point of Set point of EtOHEtOH = 0.5 g/l= 0.5 g/l

C/N = 10C/N = 10

μμ = = μμcc

Carbon sources : Carbon sources : EtOHEtOH and and PeOHPeOH

Set point of Set point of EtOHEtOH = 0.5 g/l with = 0.5 g/l with 3HV mole fraction control strategy3HV mole fraction control strategy

C/N = 50C/N = 50

Performance IndexPerformance Index

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0 10 200

20

40

60

80

100 0

5

10

0 10 200

20

40

60

80

100 0

5

10

P(3H

B-c

o-3H

V) (

g)P(

3HB

-co-

3HV

) (g)

3HV

in P

(3H

B-c

o-3H

V)

(mol

%)

3HV

in P

(3H

B-c

o-3H

V)

(mol

%)

Comparison of optimal P(3HB-co-3HV) production. Target of HV mole fraction : 35 mol %

(●▲：μmax→μc、●▲：μc)

Comparison of optimal P(3HB-co-3HV) production. Target of HV mole fraction : 35 mol %

(●▲：μmax→μc、●▲：μc)

Time (h)Time (h)

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