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Comparative studies on cephalosporin C production process with free and immobilized cells of Cephalosporium acremonium ATCC 48272
ChemicalEngineeringScience,Vol.5|, No. 1I, pp.2835--2840,1996 CopyrightO 1996ElsevierScienceL~ PrintedinGreatBritain.Allrightsre~rved 0009-2509t96 $15.00+ 0.00
Pergamon
S001D-2509(96)00161-3
COMPARATIVE STUDIES ON CEPHALOSPORIN C PRODUCTION PROCESS WITH FREE AND IMMOBILIZED CELLS OF Cephalosporium acremonium ATCC 48272 M.L.G.C. ARAUJO, R P. OLIVEIRA, R. C. GIORDANO and C. O. HOKKA l)¢partamen~de EngenhariaQuimicada UniversidadeFederalde S~toCarlos.Cx.Posta1676 CEP 13565 - 905, S~toCarlos- SP - BRASIL AIBtract - Immobilizedcell utilizationin towertype bioreaetoris beingstudiedextensivelyas a promisingalternativefor industrialbioproc~x;~,s.In the cephalosporinC productionprocess,a strictlyaerobicfungus is utilizedand intrapartieleoxygen tranffercan limitthe production.To studyquantitativelythislimitation,resultsfromfreecellsbioprocessand immobilizedcells in Calciumalginategel beadsprocesswere comparedthrough a mathematicalmodel.The free cellsproce~ showedhigher productionrate whilethe immobilizedcellsprocessachievedhigherspccitieproductionrate in relationto sucroseconsumption. Furthannore,themodelof the heterogeneousreactionsystem,consideringintraparticlediffusionlimitation,explainedfairlywell the behaviorofthe process. INTRODUCTION
Cephalosporin C is a 13 - lactam antibiotic with some biological activity and is produced industrially through a bioprocess utilizing specific high yield strains of Cephalosporium acremonium, a filamentous strictly aerobic fungus. Its importance lies on the fact that Cephalosporin C is the raw material to obtain several semi-synthetic antibiotics widely used in the treatment of bacterial diseases (Smith, 1985). During the production process, lasting several days, the mircroorganism undergoes drastic metabolic changes and morphological differentiation which are in some extent associated to its production pattern. Matsumura et al., 1980 considered three morphological types of cells, thin hyphae, septated swollen hyphae and arthrospores, being the production capability associated with the two last forms, particularly the second one. These authors also studied the role of methionine, an amino-acid that stimulates cephalosporin C production as proved by Demain et al., 1963. They observed that at high glucose concentration in the medium, accumulation of intracellular methionine is hindered, resulting in a reduction of the enzimatic complex generically called "[~ - iactam sinthetase". Chu and Constantidines, 1988, incorporated these observations in their model aiming at optimization and computer control of this process. As generally occurs in any secondary metabolite production process, there are two distinct phases during the cephalosporin bioprocess. The first one is the growth phase, when an easily metabolized carbon source, usually glucose, is consumed and, as repported by Behmer and Demain, 1983, specific enzyme synthesis suffers from catabolite repression and low production rates are observed. In the production phase, slowly metabolized sugar, sucrose in this case, promotes synthesis of specific enzymes and consequently higher production rates take place. Typical diauxie is observed during the bioproccss, with the two sugars being metabolized distinctively. In this work, the cephalosporin C production by C.acremonium C-10, ATCC 48272 (Shen et al, 1986) immobilized in calcium alginate beads with alumina was examined and compared with the free cells process through a simulation study. A model describing quantitatively cell growth, cephalosporin C production, sugars, and oxygen consumption was proposed including the effects of intraparticular mass transfer resistance on this bioprocess kinetics. Simulation of a heterogeneous batch reactor and comparison with experimental results showed that the model depicted quite well the behavior of the whole process, enabling also the prediction of the components concentration profile inside the bioparticles. M A T E R I A L S AND METHODS
Microorganism: Cephalosporium acremonium ATCC 48272 was used throughout this work. Culture media: For inoculum preparation, a synthetic medium containing (% w/v) glucose, 2.0, ammonium acetate, 0.88, DL-methionine, 0.5, oleic acid, 0.15, supplemented with mineral salts, was utilized. The production medium contained (% w/v) glucose, 2.7, sucrose, 3.6, and the other components were the same as the inoculum medium. The media compositions were similar to that utilized by Demain et al, 1963. Experimental procedure: The experiments were carried out in shake flasks, at 26°C and agitation speed of 250 rpm. In the free cells process, the seed was 10 % in volume while for immobilized cells process, 20 ml of the inoculum was centrifuged for 5 minutes, 3000 rpm, and the cells were added to 95 ml of a sodium alginate (2.0 %) and alumina (1.0 %) suspension. The bioparticles were obtained by dropping the mixture to 0.1 M CaCI2 . 2 H20 through a syringe needle. The pellets so prepared were kept for 2 hours in the same solution, washed and added to the production medium at an approximate ratio of 15% (g bioparticle / m/ medium ), following Khang et al, 1988a. Bioparticle diameters of 2.3 + 0.1 mm were obtained in this way.The runs were carried out for 144 ~ 166 2835
2836
M.L.G.C. ARAUJOet aL
hours and samples were taken periodically for cell mass, sugars, and cephalosporin C concentrations, and pH medium measurements. The same procedure was followed when respiration rate measurement experiments were conducted. Intra-particle oxygen diffusivity: The oxygen diffusivity inside the bioparticle containing viable microorganism was measured according to experimental procedure proposed by Kurosawa et al, 1989. Analysis: The cell mass concentration was evaluated by determining the volatile suspension solids, VSS in g/l. Glucose (CSl)Was measured utilizing enzymatic GOD - PAP method and sucrose (Cs2) was measured through the same method following acid hydrolysis. Cephalosporin C titers (Cp) were determined by an agar diffusion bioassay utilizing Alcaligenesfaecalis ATCC 8750. The respiration rates (P'o2) at various conditions of sugars and cells concentrations of both free and immobilized cells were measured by using a biological oxygen monitor (model 5300 - Yellow Springs Instrument Co.). MATHEMATICAL MODEL AND COMPUTATIONAL PROCEDURES
Kinetic model: For the modelling, it was assumed that the total biomass was composed of cells, Xi, submitted to catabolite repression by glucose, $1, ' during the growth phase, and cells, X 2, derepressed after glucose depletion and capable to consume sucrose, S2. Repression and derepression refer to the regulatory mechanism of formation of the enzyme complex responsible for the cephalosporin synthesis. Under glucose concentration lower than a certain critical value, Cslc (1.5 g/0, cells X 1 are transformed into cells X 2. In practice cell differentiation also occurs, and one can consider also a form of higher productivity. During the growth phase Contois model for specific growth and glucose consumption rate is assumed, while for the production phase, specific growth and sucrose consumption rates follow the Monod model with substrate inhibition. Decay rates in both phases appears, and maintenance reaction is considered only during the sucrose utilization phase, when the increase of cells is very low. The effect of oxygen concentration (Co2) is taken into account in all rate cquations, cxcept for the decay of cells, as the microorganism is strictly aerobic and when analysing the behavior of the process with immobilized cells, an internal oxygen transfer limitation has to be considered. The respiration rate followed thc classical Monod type equation. Concerning production rate, it was considered a constitutive synthesis, that is, independent of derepression and growth associated during glucose consumption. During sucrose utilization phase, two terms, one of them a growth associated and the other considered to be part of the maintenance reaction, were taken into account. The rate equations describing the kinetic model are presented as follows:
l
Rx 1 = dCXl = kunaxlCSl . C°2 dt kxlCx 1 + Cs 1 k02 + C02
/
kd I Cx I
dCx2 = ]~max2Cs2 C°2 Rx2 = dt ks 2 +Cs2 +KiCs~ "k02 +C02
(1) k 1 + Cx I kd 2 / Cx 2 + kTCXl - k I +Cx 1
dCs I = 1 ~tmaxlCs 1 C°2 Cx I RSl = dt - Yxs---l"kxlCx I +Cs I "k02 +C02
R$2 -
dCs 2 d~---
( 1 ~max2Cs2 +m] CO2 ~Yxs 2 "ks 2 +Cs2 +KiCs~ ) ' k o 2 +Co2 Cx2
Rc p = dCp p l 'kxICXl ktmaxlCs I Cx I ~ Yxs2 "ks2 +Cs2 +KiCs~ "FYp3"m Cx 2 k02 +C02 dt = ILYYXSI +CSl R°2 = dCo2 dt = kka(C°2 * - C ° 2 )
(2)
(3)
(4)
(5)
Rmax'C°2 (Cxl +Cx2) (6) k02 + Co2 where kLa is the volumetric gas - liquid mass transfer coefficient e Co2" is the oxygen saturation concentration in the culture medium (0.22 mol O2/m3 ). Growth and decay of cells are represented by the first terms in Equations (1) and (2) while the second terms refer to the transformation of repressed cells into derepressed ones. Equation (3) is the glucose consumption rate, considering that this substrate is used only for growth, whereas in Equation (4), besides the first term corresponding to the growth on sucrose, it is considered also a maintenance term. Equation (5) describes the production rate, with three terms, related to the growth in glucose, growth and maintenance on sucrose. Each of these terms appears associated with yield factors (or pseudo stoichiometric coefficients) Ypl, Yo2, and Yo3, respectively.
Cephalosporin C production process
2837
Heterogeneous batch reactor modelling: Cephalosporin C bioprocess utilizing gel bioparticles with immobilized cells as depicted above was modelled assuming perfectly mixed bulk liquid, constant intraparticle effective diffusivities of all components, i.e., each one is near infinite dilution, and neglible film resistance due to adequate agitation of the reaction flask. Therefore, the differential mass balance of species "i" inside the bioparticle can be represented by the following general equation: 0C i 1 0 ( 2-- igCi') egel O~ = -~--~[ r l.Jei ----~J+K i (7) where %el is the gel porosity (98 %). The boundary conditions are:
Jr
=0,
t=0,
aCi(0't---~)=0,
Vt;
Or
Ci(r,0)=0,
r=Rp,Ci(0,t)=C~Utk=C~(t),
Vt
(8)
Vr
These equations apply for glucose, sucrose, oxygen and product. The differential mass balance for component 'T' (glucose, sucrose and product) in the liquid bulk yields:
dCibdt= °~Ci(RP' t)~
_
~ill r=R Rp'3s_-_p 1Deis ...~..
(9)
where s is the fluidized bed porosity (~ 85%) For oxygen, this equation is written as: dCbo2
t9Co2(Rp, t)
3
dt
&
Pp"
g-I
De c~C°2 +kLa. * • 02 t.3r r=Rp (Co2
-- CO2
)
(10)
Concerning cell mass inside the bioparticle, we have: 0Cxi c3t = Rxi (Ci) with initial condition:
(11)
Cx i = Cx i (0),
(12)
Vr
This system of non-linear differential equations was solved by a method of lines approach: discretization in space through orthogonal collocation, Villadsen and Michelsen, 1978, and integration in time with aid of a backward differentiation algorithm (differential algebraic system solver, DDASSL, Petzold, 1989). This method turns the set of partial differential equations into ordinary ones. The polynomial aproximation procedure used up to 20 collocation points and the DDASSL algorithm was able to overcome stiffness problems resulting from the resolution of the ordinary differential equations, particularly in the simulation of the begining of the process time course. Intrapellet cell mass was evaluated through Radau quadrature. Oxygen diffusivity evaluation: The experimental system was modelled assuming that: 1- Cell mass was constant during the experimental period (ca. 15 rain) allowing to consider a quasi-steady state. 2- Film resistance was negligible and liquid bulk perfectly mixed due to adequate agitation speed. 3- Respiration rate followed Monod's kinetics. 4- Intraparticle oxygen diffusivity, Deo2, was constant. 5- Cell concentration was constant from Rp to a critical radius, Rcr (1.05 × 10.3 m), being zero otherwise. The differential mass balance for intraparticle oxygen concentration in the dimentionless form yields:
[ ( R p - Rcr)~ + Rcr]2 . ( a p - Rcr) 'cl~ [ where: C = C°2 rinlet ~--o2
and
~-
r-Rcr Rp -Rcr
(Rp- Rcr)
"De°2"
=
RO2
(13)
M. L. G. C. ARAUJOet al.
2838 with the boundary conditions:
~=o,
d~=O
(14)
tL~=I, C(l) + =c b The mass balance for the dissolved oxygen in the reaction vessel liquid is: 4rtNpRp2Deo2 . d_~ ~=1
F(I-C
(Rp-Rcr)
(15)
where F is the medium flow rate (l/h) and Np is the bioparticles number. These equations were discretized througli orthogonal collocation and the resulting non-linear algebraic system was solved by Newton-Raphson method. To estimate oxygen effective diffusivity in the bioparticles containing viable cells, the Marquardt, 1963, method was applied. RESULTS AND DISCUSSION Initially, free cells experiments were carried out and a typical time course of the bioprocess is shown in Figure 1. As it can be observed, glucose is preferencially consumed while high growth rate takes place. After its depletion, sucrose starts to be metabolized in a slower rate showing the phenomenum of diauxie. During this phase higher production rate occurs while the growth is somewhat hindered. Specific experiments for determination of ttmax 1 , lamax2 and Rmax as well as the yield factors Yxs 1 and Yxs2 were also performed with samples taken in shorter periods. Linear and non-linear regression analysis allowed their calculation and it was observed that Rmax had the same value during both glucose and sucrose consumption phases. Based on these results, the kinetic model shown beforehand was elaborated. Oxygen effective diffusivity was determined through the results obtained in the experimental apparatus described earlier. Mention should be made here that this method allowed determination of the effective diffusion coefficient of oxygen inside the gel bead containing viable cells of the fungus. The calculated value compares quite well with that used by Khang e t al., 1988b, 2.11 xl0 -9 m2/s, although this value has been determined for a different bioprocess and microorganism. These values were applied in the heterogeneous batch reactor simulation, together with sugars diffusivities in calcium alginate from literature (Tanaka et al., 1984). The remaining kinetic constants were evaluated by sensitivity parametric analysis through resolution of the non-linear differencial balance equations (Equation 1 to 6) as described beforehand. Table 1 shows all estimated values of these parameters. This procedure allowed determination of all components concentration profiles inside the bioparticles at any time of the bioprocess, as well as the behavior of the process regarding liquid bulk concentrations.
36 _
-A
.A
$
\
''25°
\,
o/11ooo 8" L)
•
~' 18
+ [ r+ ,2
~o
~/~ - ~
\
\j.z
/
"--
\,
--,,oo
+ I
O
o+ J?___,,,+_-+--- ~>~-----. 24
48
__.__~_;
I
72 96 120 144 time(h) Figure 1, Experimental data of cephalosporin C fermentation by C . a c r e m o n i u m C-10 free cells Figure 2, (a) and Co), show the profiles of cell mass and oxygen respectively, at several process times. It can be seen that although most of the viable cell mass was confined in a small part of the pellet, similar to the dead core
model, cell mass concentration was much higher when compared with the free cells system, positively affecting the production rate. Figure 3 shows the general behavior of this reactor. As it can be observed, the model explains
2839
Cephalospodn C production process
quite well the complex features of this bioprocess, such as diauxic growth and higher production rate taking place mostly during the phase of sucrose consumption. Also the effectiveness of the process with immobilized ceils showed to be fairly high, even though the major part of bead contained no active cells. This fact has been compensated by the higher cell concentration in the outer ring of the beads, as predicted by the model. 200
-O--30h -A-~OOh
150
25
~
--o--30h --~--50h
~20 ~-- 15
--V--170h
~
100
10
'~
50
5
--
100h
o
120h --
o°
o
170h
o
/
o
o
,
2
4 6 r/Rpx10 (a)
8
10
[
.. ° . . - . . ~ : : z J
0 I
0
•
0
2
,
I
4 6 r/Rp×10 Cb)
~
1
8
,
I
10
Figure 2. Simulated total cell mass concentration (Cxl+Cx2) profiles, (a), and oxygen concentration profiles, (b), inside the pellet at various times for the heterogeneous batch reactor process. Table 1, Estimated parameters, with 95% confidence intervals, used for the heterogeneous batch bioreactor simulation of the cephalosporin C production process by C, acremonium C-10 immobilized in Ca-alginate beads maximum specific growth rates
tXmaxl
0.0645 + 0.0078 (h "1)
P~na~
0.0143 + 0.0028 (h "l)
Contois constant
kx I
0.33 5:0.4 (gS1/gXl)
saturation constant (substrate inhibition kinetics)
ks 2
10.0 -+ 8.0 (gS2//)
inhibition constant
Ki
0.15 (gS2//) "l
death rate constants
kd I
0.00612 (h q)
kd 2
0.0324 (hq)"
yield coefficients (sugar comsumption)
yield coefficients (product formation)
Yxsl
0.655 5:0.074 (gXl/gSl)
Yxs2
0.244 5:0.04 (gX2/gS2)
YPl x 103
6.0 (gP/gXl)
YP2 × 103
9.0 (gP/gX2)
YP3 × 101
1.0 (gP/gS2)
maintenance coefficient
m
maximum specific respiration rate
Rmax x 103
kinetic constant (respiration rate)
ko2 x 107
kinetic constants (morphological differentiation rate)
kT
3.6 (gXl/l.h)
k1
0.01 (gXl//)
-of glucose
Des I x 106
1.408 (m2/h)
-of sucrose
Des2 × 107
6.624 (m2/h)
-of oxygen in 2% Ca-alginate gel beads (42.8 g viable ceils X / I gel) volumetric gas-liquid mass transfer coefficient
Deo2 x 106
8.737 + 0.046 (m2/h)
0.036 5:0.008 (gS2/gX2.h) 1.310 + 0.011 (molO2/gX.h) 1.132 :t: 0.154 (molO2//)
effective diffussion coefficients in Ca-alginate gel:
kLa
162.0 (h -1)
The productivity with flee cells and with immobilized cells were evaluated as 0.55 and 0.44 mg CPC/g cell/h respectivelly. It has been observed that sucrose consumption, in the process utilizing bioparticles, was much lower
M.L.G.C. ArAuJo et al.
2840
than that with free cells, resulting in a higher productivity based on sucrose consumption for this process (0.33 mg CPC/g $2/h) as compared with that in free cells process (0.24 mg CPC/g $2/h). At present, in our laboratories, a tower bioreactor (1.7 t working volum) is being utilized for this process and the model presented here will be utilized for its simulation, together with correlations between mass transfer coefficient and operating variables.
75
J1,250
II
1,030
750
45
aSS
A
A
,
II •
A
250 O
t5i
05 0
jg______---y-50
,
3-..~ 103 time 0a)
A.
-.• ~ 150
~
,
o 203
Figure 3. Simulation results and experimental data of cephalosporin C batch production process by C.acremonium C-10 immobilized in Ca-alginate gel beads. References
Araujo, M.L.G.C.; Gomes, L.M.S.; Santana, M.H.A.; Oliveira, R.P., and Hokka, C.O., 1994, Estudo cin6tico do processo de produ~[to de cefalosporina C, Proceedings of the 10th Brazilian Congress of Chemical Engineering (10 COBEQ S. Paulo SP), 2 ,. 1240-1247. Behmer, C.J. and Demain, A.L., 1983, Further studies on carbon catabolite regulation of 1~- lactam antibiotic synthesis in Cephalosporium acremonium, Current Microbiology, 8, 107-114. Demain, A.L.; Newkirk, J.F., and Hendlin, D., 1963, Effect of methionine, norleucine, and lysine derivatives on cephalosporin C formation in chemically defined media, J.BacterioL, 85, 339-344. Khang, Y. -H.; Shankar, H. , and Senatore, F., 1988, Comparison of free and immobilized Cephalosporium acremonium for I3 - lactam antibiotic production, BiotechnoLLetters, 10,719-724. Khang, Y., Shankar, H. and Senatore, F., 1988b, Modeling the effect of oxygen mass transfer on fl - lactam antibiotic production by immobilized Cephalosporium acremonium, BiotechnoLLetters 10, 861-866. Kurosawa, H.; Matsumura, M., and Tanaka, H., 1989, Oxygen diffusivity in gel beads containing viable cells, BiotechnoLBioeng., 34, 926-932. Marquardt, D.W., 1963, An algorithm for least-squares estimation of nonlinear parameters, d. ,Sbc. lndust. AppL Math. 11 431-441. Petzold, L.R., 1989, Subroutine DDASSL, Computing and Mathematics Research Division, Lawrence Livermore National Laboratory, Livermore, California. Shen, Y. -Q; Wolfe, S., and Demain, A.L., 1986,Levels of isopeniciUin N synthetase and deacetoxycephalosporin C synthetase in Cephalosporium acremonium producing high and low levels of cephalosporin C, BiotechnoL, 4, 61-64 Tanaka, H., Matsumura, M. and Veliky, I.A., 1984, Diffusion characteristics of substrates in Ca-alginate beads BiotechnoLBioeng. 26, 53-58. Villadsen, J. and Michelsen, M.L., 1978 ,Solution of Differential Equation Models by Polynomial Approximation, Prentice Hall, Englewood Cliffs, New Jersey.
Pergamon
S001D-2509(96)00161-3
COMPARATIVE STUDIES ON CEPHALOSPORIN C PRODUCTION PROCESS WITH FREE AND IMMOBILIZED CELLS OF Cephalosporium acremonium ATCC 48272 M.L.G.C. ARAUJO, R P. OLIVEIRA, R. C. GIORDANO and C. O. HOKKA l)¢partamen~de EngenhariaQuimicada UniversidadeFederalde S~toCarlos.Cx.Posta1676 CEP 13565 - 905, S~toCarlos- SP - BRASIL AIBtract - Immobilizedcell utilizationin towertype bioreaetoris beingstudiedextensivelyas a promisingalternativefor industrialbioproc~x;~,s.In the cephalosporinC productionprocess,a strictlyaerobicfungus is utilizedand intrapartieleoxygen tranffercan limitthe production.To studyquantitativelythislimitation,resultsfromfreecellsbioprocessand immobilizedcells in Calciumalginategel beadsprocesswere comparedthrough a mathematicalmodel.The free cellsproce~ showedhigher productionrate whilethe immobilizedcellsprocessachievedhigherspccitieproductionrate in relationto sucroseconsumption. Furthannore,themodelof the heterogeneousreactionsystem,consideringintraparticlediffusionlimitation,explainedfairlywell the behaviorofthe process. INTRODUCTION
Cephalosporin C is a 13 - lactam antibiotic with some biological activity and is produced industrially through a bioprocess utilizing specific high yield strains of Cephalosporium acremonium, a filamentous strictly aerobic fungus. Its importance lies on the fact that Cephalosporin C is the raw material to obtain several semi-synthetic antibiotics widely used in the treatment of bacterial diseases (Smith, 1985). During the production process, lasting several days, the mircroorganism undergoes drastic metabolic changes and morphological differentiation which are in some extent associated to its production pattern. Matsumura et al., 1980 considered three morphological types of cells, thin hyphae, septated swollen hyphae and arthrospores, being the production capability associated with the two last forms, particularly the second one. These authors also studied the role of methionine, an amino-acid that stimulates cephalosporin C production as proved by Demain et al., 1963. They observed that at high glucose concentration in the medium, accumulation of intracellular methionine is hindered, resulting in a reduction of the enzimatic complex generically called "[~ - iactam sinthetase". Chu and Constantidines, 1988, incorporated these observations in their model aiming at optimization and computer control of this process. As generally occurs in any secondary metabolite production process, there are two distinct phases during the cephalosporin bioprocess. The first one is the growth phase, when an easily metabolized carbon source, usually glucose, is consumed and, as repported by Behmer and Demain, 1983, specific enzyme synthesis suffers from catabolite repression and low production rates are observed. In the production phase, slowly metabolized sugar, sucrose in this case, promotes synthesis of specific enzymes and consequently higher production rates take place. Typical diauxie is observed during the bioproccss, with the two sugars being metabolized distinctively. In this work, the cephalosporin C production by C.acremonium C-10, ATCC 48272 (Shen et al, 1986) immobilized in calcium alginate beads with alumina was examined and compared with the free cells process through a simulation study. A model describing quantitatively cell growth, cephalosporin C production, sugars, and oxygen consumption was proposed including the effects of intraparticular mass transfer resistance on this bioprocess kinetics. Simulation of a heterogeneous batch reactor and comparison with experimental results showed that the model depicted quite well the behavior of the whole process, enabling also the prediction of the components concentration profile inside the bioparticles. M A T E R I A L S AND METHODS
Microorganism: Cephalosporium acremonium ATCC 48272 was used throughout this work. Culture media: For inoculum preparation, a synthetic medium containing (% w/v) glucose, 2.0, ammonium acetate, 0.88, DL-methionine, 0.5, oleic acid, 0.15, supplemented with mineral salts, was utilized. The production medium contained (% w/v) glucose, 2.7, sucrose, 3.6, and the other components were the same as the inoculum medium. The media compositions were similar to that utilized by Demain et al, 1963. Experimental procedure: The experiments were carried out in shake flasks, at 26°C and agitation speed of 250 rpm. In the free cells process, the seed was 10 % in volume while for immobilized cells process, 20 ml of the inoculum was centrifuged for 5 minutes, 3000 rpm, and the cells were added to 95 ml of a sodium alginate (2.0 %) and alumina (1.0 %) suspension. The bioparticles were obtained by dropping the mixture to 0.1 M CaCI2 . 2 H20 through a syringe needle. The pellets so prepared were kept for 2 hours in the same solution, washed and added to the production medium at an approximate ratio of 15% (g bioparticle / m/ medium ), following Khang et al, 1988a. Bioparticle diameters of 2.3 + 0.1 mm were obtained in this way.The runs were carried out for 144 ~ 166 2835
2836
M.L.G.C. ARAUJOet aL
hours and samples were taken periodically for cell mass, sugars, and cephalosporin C concentrations, and pH medium measurements. The same procedure was followed when respiration rate measurement experiments were conducted. Intra-particle oxygen diffusivity: The oxygen diffusivity inside the bioparticle containing viable microorganism was measured according to experimental procedure proposed by Kurosawa et al, 1989. Analysis: The cell mass concentration was evaluated by determining the volatile suspension solids, VSS in g/l. Glucose (CSl)Was measured utilizing enzymatic GOD - PAP method and sucrose (Cs2) was measured through the same method following acid hydrolysis. Cephalosporin C titers (Cp) were determined by an agar diffusion bioassay utilizing Alcaligenesfaecalis ATCC 8750. The respiration rates (P'o2) at various conditions of sugars and cells concentrations of both free and immobilized cells were measured by using a biological oxygen monitor (model 5300 - Yellow Springs Instrument Co.). MATHEMATICAL MODEL AND COMPUTATIONAL PROCEDURES
Kinetic model: For the modelling, it was assumed that the total biomass was composed of cells, Xi, submitted to catabolite repression by glucose, $1, ' during the growth phase, and cells, X 2, derepressed after glucose depletion and capable to consume sucrose, S2. Repression and derepression refer to the regulatory mechanism of formation of the enzyme complex responsible for the cephalosporin synthesis. Under glucose concentration lower than a certain critical value, Cslc (1.5 g/0, cells X 1 are transformed into cells X 2. In practice cell differentiation also occurs, and one can consider also a form of higher productivity. During the growth phase Contois model for specific growth and glucose consumption rate is assumed, while for the production phase, specific growth and sucrose consumption rates follow the Monod model with substrate inhibition. Decay rates in both phases appears, and maintenance reaction is considered only during the sucrose utilization phase, when the increase of cells is very low. The effect of oxygen concentration (Co2) is taken into account in all rate cquations, cxcept for the decay of cells, as the microorganism is strictly aerobic and when analysing the behavior of the process with immobilized cells, an internal oxygen transfer limitation has to be considered. The respiration rate followed thc classical Monod type equation. Concerning production rate, it was considered a constitutive synthesis, that is, independent of derepression and growth associated during glucose consumption. During sucrose utilization phase, two terms, one of them a growth associated and the other considered to be part of the maintenance reaction, were taken into account. The rate equations describing the kinetic model are presented as follows:
l
Rx 1 = dCXl = kunaxlCSl . C°2 dt kxlCx 1 + Cs 1 k02 + C02
/
kd I Cx I
dCx2 = ]~max2Cs2 C°2 Rx2 = dt ks 2 +Cs2 +KiCs~ "k02 +C02
(1) k 1 + Cx I kd 2 / Cx 2 + kTCXl - k I +Cx 1
dCs I = 1 ~tmaxlCs 1 C°2 Cx I RSl = dt - Yxs---l"kxlCx I +Cs I "k02 +C02
R$2 -
dCs 2 d~---
( 1 ~max2Cs2 +m] CO2 ~Yxs 2 "ks 2 +Cs2 +KiCs~ ) ' k o 2 +Co2 Cx2
Rc p = dCp p l 'kxICXl ktmaxlCs I Cx I ~ Yxs2 "ks2 +Cs2 +KiCs~ "FYp3"m Cx 2 k02 +C02 dt = ILYYXSI +CSl R°2 = dCo2 dt = kka(C°2 * - C ° 2 )
(2)
(3)
(4)
(5)
Rmax'C°2 (Cxl +Cx2) (6) k02 + Co2 where kLa is the volumetric gas - liquid mass transfer coefficient e Co2" is the oxygen saturation concentration in the culture medium (0.22 mol O2/m3 ). Growth and decay of cells are represented by the first terms in Equations (1) and (2) while the second terms refer to the transformation of repressed cells into derepressed ones. Equation (3) is the glucose consumption rate, considering that this substrate is used only for growth, whereas in Equation (4), besides the first term corresponding to the growth on sucrose, it is considered also a maintenance term. Equation (5) describes the production rate, with three terms, related to the growth in glucose, growth and maintenance on sucrose. Each of these terms appears associated with yield factors (or pseudo stoichiometric coefficients) Ypl, Yo2, and Yo3, respectively.
Cephalosporin C production process
2837
Heterogeneous batch reactor modelling: Cephalosporin C bioprocess utilizing gel bioparticles with immobilized cells as depicted above was modelled assuming perfectly mixed bulk liquid, constant intraparticle effective diffusivities of all components, i.e., each one is near infinite dilution, and neglible film resistance due to adequate agitation of the reaction flask. Therefore, the differential mass balance of species "i" inside the bioparticle can be represented by the following general equation: 0C i 1 0 ( 2-- igCi') egel O~ = -~--~[ r l.Jei ----~J+K i (7) where %el is the gel porosity (98 %). The boundary conditions are:
Jr
=0,
t=0,
aCi(0't---~)=0,
Vt;
Or
Ci(r,0)=0,
r=Rp,Ci(0,t)=C~Utk=C~(t),
Vt
(8)
Vr
These equations apply for glucose, sucrose, oxygen and product. The differential mass balance for component 'T' (glucose, sucrose and product) in the liquid bulk yields:
dCibdt= °~Ci(RP' t)~
_
~ill r=R Rp'3s_-_p 1Deis ...~..
(9)
where s is the fluidized bed porosity (~ 85%) For oxygen, this equation is written as: dCbo2
t9Co2(Rp, t)
3
dt
&
Pp"
g-I
De c~C°2 +kLa. * • 02 t.3r r=Rp (Co2
-- CO2
)
(10)
Concerning cell mass inside the bioparticle, we have: 0Cxi c3t = Rxi (Ci) with initial condition:
(11)
Cx i = Cx i (0),
(12)
Vr
This system of non-linear differential equations was solved by a method of lines approach: discretization in space through orthogonal collocation, Villadsen and Michelsen, 1978, and integration in time with aid of a backward differentiation algorithm (differential algebraic system solver, DDASSL, Petzold, 1989). This method turns the set of partial differential equations into ordinary ones. The polynomial aproximation procedure used up to 20 collocation points and the DDASSL algorithm was able to overcome stiffness problems resulting from the resolution of the ordinary differential equations, particularly in the simulation of the begining of the process time course. Intrapellet cell mass was evaluated through Radau quadrature. Oxygen diffusivity evaluation: The experimental system was modelled assuming that: 1- Cell mass was constant during the experimental period (ca. 15 rain) allowing to consider a quasi-steady state. 2- Film resistance was negligible and liquid bulk perfectly mixed due to adequate agitation speed. 3- Respiration rate followed Monod's kinetics. 4- Intraparticle oxygen diffusivity, Deo2, was constant. 5- Cell concentration was constant from Rp to a critical radius, Rcr (1.05 × 10.3 m), being zero otherwise. The differential mass balance for intraparticle oxygen concentration in the dimentionless form yields:
[ ( R p - Rcr)~ + Rcr]2 . ( a p - Rcr) 'cl~ [ where: C = C°2 rinlet ~--o2
and
~-
r-Rcr Rp -Rcr
(Rp- Rcr)
"De°2"
=
RO2
(13)
M. L. G. C. ARAUJOet al.
2838 with the boundary conditions:
~=o,
d~=O
(14)
tL~=I, C(l) + =c b The mass balance for the dissolved oxygen in the reaction vessel liquid is: 4rtNpRp2Deo2 . d_~ ~=1
F(I-C
(Rp-Rcr)
(15)
where F is the medium flow rate (l/h) and Np is the bioparticles number. These equations were discretized througli orthogonal collocation and the resulting non-linear algebraic system was solved by Newton-Raphson method. To estimate oxygen effective diffusivity in the bioparticles containing viable cells, the Marquardt, 1963, method was applied. RESULTS AND DISCUSSION Initially, free cells experiments were carried out and a typical time course of the bioprocess is shown in Figure 1. As it can be observed, glucose is preferencially consumed while high growth rate takes place. After its depletion, sucrose starts to be metabolized in a slower rate showing the phenomenum of diauxie. During this phase higher production rate occurs while the growth is somewhat hindered. Specific experiments for determination of ttmax 1 , lamax2 and Rmax as well as the yield factors Yxs 1 and Yxs2 were also performed with samples taken in shorter periods. Linear and non-linear regression analysis allowed their calculation and it was observed that Rmax had the same value during both glucose and sucrose consumption phases. Based on these results, the kinetic model shown beforehand was elaborated. Oxygen effective diffusivity was determined through the results obtained in the experimental apparatus described earlier. Mention should be made here that this method allowed determination of the effective diffusion coefficient of oxygen inside the gel bead containing viable cells of the fungus. The calculated value compares quite well with that used by Khang e t al., 1988b, 2.11 xl0 -9 m2/s, although this value has been determined for a different bioprocess and microorganism. These values were applied in the heterogeneous batch reactor simulation, together with sugars diffusivities in calcium alginate from literature (Tanaka et al., 1984). The remaining kinetic constants were evaluated by sensitivity parametric analysis through resolution of the non-linear differencial balance equations (Equation 1 to 6) as described beforehand. Table 1 shows all estimated values of these parameters. This procedure allowed determination of all components concentration profiles inside the bioparticles at any time of the bioprocess, as well as the behavior of the process regarding liquid bulk concentrations.
36 _
-A
.A
$
\
''25°
\,
o/11ooo 8" L)
•
~' 18
+ [ r+ ,2
~o
~/~ - ~
\
\j.z
/
"--
\,
--,,oo
+ I
O
o+ J?___,,,+_-+--- ~>~-----. 24
48
__.__~_;
I
72 96 120 144 time(h) Figure 1, Experimental data of cephalosporin C fermentation by C . a c r e m o n i u m C-10 free cells Figure 2, (a) and Co), show the profiles of cell mass and oxygen respectively, at several process times. It can be seen that although most of the viable cell mass was confined in a small part of the pellet, similar to the dead core
model, cell mass concentration was much higher when compared with the free cells system, positively affecting the production rate. Figure 3 shows the general behavior of this reactor. As it can be observed, the model explains
2839
Cephalospodn C production process
quite well the complex features of this bioprocess, such as diauxic growth and higher production rate taking place mostly during the phase of sucrose consumption. Also the effectiveness of the process with immobilized ceils showed to be fairly high, even though the major part of bead contained no active cells. This fact has been compensated by the higher cell concentration in the outer ring of the beads, as predicted by the model. 200
-O--30h -A-~OOh
150
25
~
--o--30h --~--50h
~20 ~-- 15
--V--170h
~
100
10
'~
50
5
--
100h
o
120h --
o°
o
170h
o
/
o
o
,
2
4 6 r/Rpx10 (a)
8
10
[
.. ° . . - . . ~ : : z J
0 I
0
•
0
2
,
I
4 6 r/Rp×10 Cb)
~
1
8
,
I
10
Figure 2. Simulated total cell mass concentration (Cxl+Cx2) profiles, (a), and oxygen concentration profiles, (b), inside the pellet at various times for the heterogeneous batch reactor process. Table 1, Estimated parameters, with 95% confidence intervals, used for the heterogeneous batch bioreactor simulation of the cephalosporin C production process by C, acremonium C-10 immobilized in Ca-alginate beads maximum specific growth rates
tXmaxl
0.0645 + 0.0078 (h "1)
P~na~
0.0143 + 0.0028 (h "l)
Contois constant
kx I
0.33 5:0.4 (gS1/gXl)
saturation constant (substrate inhibition kinetics)
ks 2
10.0 -+ 8.0 (gS2//)
inhibition constant
Ki
0.15 (gS2//) "l
death rate constants
kd I
0.00612 (h q)
kd 2
0.0324 (hq)"
yield coefficients (sugar comsumption)
yield coefficients (product formation)
Yxsl
0.655 5:0.074 (gXl/gSl)
Yxs2
0.244 5:0.04 (gX2/gS2)
YPl x 103
6.0 (gP/gXl)
YP2 × 103
9.0 (gP/gX2)
YP3 × 101
1.0 (gP/gS2)
maintenance coefficient
m
maximum specific respiration rate
Rmax x 103
kinetic constant (respiration rate)
ko2 x 107
kinetic constants (morphological differentiation rate)
kT
3.6 (gXl/l.h)
k1
0.01 (gXl//)
-of glucose
Des I x 106
1.408 (m2/h)
-of sucrose
Des2 × 107
6.624 (m2/h)
-of oxygen in 2% Ca-alginate gel beads (42.8 g viable ceils X / I gel) volumetric gas-liquid mass transfer coefficient
Deo2 x 106
8.737 + 0.046 (m2/h)
0.036 5:0.008 (gS2/gX2.h) 1.310 + 0.011 (molO2/gX.h) 1.132 :t: 0.154 (molO2//)
effective diffussion coefficients in Ca-alginate gel:
kLa
162.0 (h -1)
The productivity with flee cells and with immobilized cells were evaluated as 0.55 and 0.44 mg CPC/g cell/h respectivelly. It has been observed that sucrose consumption, in the process utilizing bioparticles, was much lower
M.L.G.C. ArAuJo et al.
2840
than that with free cells, resulting in a higher productivity based on sucrose consumption for this process (0.33 mg CPC/g $2/h) as compared with that in free cells process (0.24 mg CPC/g $2/h). At present, in our laboratories, a tower bioreactor (1.7 t working volum) is being utilized for this process and the model presented here will be utilized for its simulation, together with correlations between mass transfer coefficient and operating variables.
75
J1,250
II
1,030
750
45
aSS
A
A
,
II •
A
250 O
t5i
05 0
jg______---y-50
,
3-..~ 103 time 0a)
A.
-.• ~ 150
~
,
o 203
Figure 3. Simulation results and experimental data of cephalosporin C batch production process by C.acremonium C-10 immobilized in Ca-alginate gel beads. References
Araujo, M.L.G.C.; Gomes, L.M.S.; Santana, M.H.A.; Oliveira, R.P., and Hokka, C.O., 1994, Estudo cin6tico do processo de produ~[to de cefalosporina C, Proceedings of the 10th Brazilian Congress of Chemical Engineering (10 COBEQ S. Paulo SP), 2 ,. 1240-1247. Behmer, C.J. and Demain, A.L., 1983, Further studies on carbon catabolite regulation of 1~- lactam antibiotic synthesis in Cephalosporium acremonium, Current Microbiology, 8, 107-114. Demain, A.L.; Newkirk, J.F., and Hendlin, D., 1963, Effect of methionine, norleucine, and lysine derivatives on cephalosporin C formation in chemically defined media, J.BacterioL, 85, 339-344. Khang, Y. -H.; Shankar, H. , and Senatore, F., 1988, Comparison of free and immobilized Cephalosporium acremonium for I3 - lactam antibiotic production, BiotechnoLLetters, 10,719-724. Khang, Y., Shankar, H. and Senatore, F., 1988b, Modeling the effect of oxygen mass transfer on fl - lactam antibiotic production by immobilized Cephalosporium acremonium, BiotechnoLLetters 10, 861-866. Kurosawa, H.; Matsumura, M., and Tanaka, H., 1989, Oxygen diffusivity in gel beads containing viable cells, BiotechnoLBioeng., 34, 926-932. Marquardt, D.W., 1963, An algorithm for least-squares estimation of nonlinear parameters, d. ,Sbc. lndust. AppL Math. 11 431-441. Petzold, L.R., 1989, Subroutine DDASSL, Computing and Mathematics Research Division, Lawrence Livermore National Laboratory, Livermore, California. Shen, Y. -Q; Wolfe, S., and Demain, A.L., 1986,Levels of isopeniciUin N synthetase and deacetoxycephalosporin C synthetase in Cephalosporium acremonium producing high and low levels of cephalosporin C, BiotechnoL, 4, 61-64 Tanaka, H., Matsumura, M. and Veliky, I.A., 1984, Diffusion characteristics of substrates in Ca-alginate beads BiotechnoLBioeng. 26, 53-58. Villadsen, J. and Michelsen, M.L., 1978 ,Solution of Differential Equation Models by Polynomial Approximation, Prentice Hall, Englewood Cliffs, New Jersey.