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Biochemical Engineering Journal 40 (2008) 107–115
Modeling of batch fermentation kinetics for succinic acid production by Mannheimia succiniciproducens Hyohak Song a,b , Seh Hee Jang a,c , Jong Myoung Park a,b , Sang Yup Lee a,b,c,d,e,∗ a
Metabolic and Biomolecular Engineering National Research Laboratory, Korea Advanced Institute of Science and Technology (KAIST), 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea b Department of Chemical and Biomolecular Engineering (BK21 Program), Korea Advanced Institute of Science and Technology (KAIST), 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea c BioProcess Engineering Research Center, Korea Advanced Institute of Science and Technology (KAIST), 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea d Center for Systems and Synthetic Biotechnology, Institute for the BioCentry, Korea Advanced Institute of Science and Technology (KAIST), 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea e Department of Bio and Brain Engineering, Bioinformatics Research Center, Korea Advanced Institute of Science and Technology (KAIST), 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea Received 17 August 2007; received in revised form 15 November 2007; accepted 24 November 2007
Abstract Kinetic models are proposed for the batch production of succinic acid from glucose by Mannheimia succiniciproducens MBEL55E. The models include terms accounting for both substrate and product inhibitions. Experimental data collected from a series of batch fermentations with different initial glucose concentrations were used to estimate parameters and also to validate the models proposed. The optimal values of the parameters were approximated by minimizing the discrepancy between the model predictions and corresponding experimental data. The growth of M. succiniciproducens could be expressed by a modified Monod model incorporating inhibitions of glucose and organic acids accumulated in the culture broth. The Luedeking–Piret model was able to describe the formation of organic acids as the fermentation proceeded, in which succinic, acetic, and formic acids followed a mixed-growth-associated pattern. However, unexpectedly, lactic acid fermentation by M. succiniciproducens was nearly nongrowth-associated. In all cases, the model simulation matched well with the experimental observations, which made it possible to elucidate the fermentation characteristics of M. succiniciproducens during efficient succinic acid production from glucose. These models thus can be employed for the development and optimization of biobased succinic acid production processes. © 2007 Elsevier B.V. All rights reserved. Keywords: Mannheimia succiniciproducens; Succinic acid; Fermentation; Kinetic models; Inhibition
1. Introduction Succinic acid, an important four-carbon platform chemical, is mostly being produced by chemical processes using liquefied petroleum gas or petroleum oil as a starting material. However, it has been widely accepted that the current petroleum-based processes will be soon replaced by fermentation of renewable resources due to the limited nature of petroleum reserves ∗
Corresponding author at: Department of Bio and Brain Engineering, and Bioinformatics Research Center, Korea Advanced Institute of Science and Technology (KAIST), 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea. Tel.: +82 42 869 3930; fax: +82 42 869 3910. E-mail address:
[email protected] (S.Y. Lee). 1369-703X/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.bej.2007.11.021
and growing environmental concerns [1]. Indeed, researchers have screened several succinic acid-producing microorganisms, including Anaerobiospirillum succiniciproducens [2,3], Actinobacillus succinogenes [4,5], and Mannheimia succiniciproducens [6,7]. Among these, M. succiniciproducens has been studied more extensively based on its complete genome sequence, and shown to be an efficient succinic acid producer [8]. In M. succiniciproducens, phosphoenolpyruvate carboxykinase carboxylates phosphoenolpyruvate to oxaloacetate, which is further converted to succinate by the reductive tricarboxylic acid cycle and menaquinone system. On the other hand, pyruvate formed from phosphoenolpyruvate is converted to acetic, formic, and lactic acids via pyruvate dissimilation pathways. The central carbon metabolism of
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Nomenclature i j Kd KI KS ms P PCRIT rP −rs rX rAA rFA rLA rSA S X YX YAA YFA YLA YSA Z
degree of product inhibition (dimensionless) number of experimental data points (dimensionless) specific death rate (h−1 ) substrate inhibition constant (g L−1 ) substrate saturation constant (g L−1 ) specific maintenance coefficient (h−1 ) product concentration (g L−1 ) critical product concentration (g L−1 ) product formation rate (g L−1 h−1 ) substrate consumption rate (g L−1 h−1 ) biomass formation rate (g L−1 h−1 ) acetic acid formation rate (g L−1 h−1 ) formic acid formation rate (g L−1 h−1 ) lactic acid formation rate (g L−1 h−1 ) succinic acid formation rate (g L−1 h−1 ) substrate concentration (g L−1 ) biomass concentration (g L−1 ) stoichiometric yield coefficient for biomass on glucose (g g−1 ) stoichiometric yield coefficient for acetic acid on glucose (g g−1 ) stoichiometric yield coefficient for formic acid on glucose (g g−1 ) stoichiometric yield coefficient for lactic acid on glucose (g g−1 ) stoichiometric yield coefficient for succinic acid on glucose (g g−1 ) minimizing objective function of the discrepancy between experimental data and simulated results (dimensionless)
Greek letters αAA growth-associated acetic acid formation parameter (g g−1 DCW) growth-associated formic acid formation paramαFA eter (g g−1 DCW) αLA growth-associated lactic acid formation parameter (g g−1 DCW) αSA growth-associated succinic acid formation parameter (g g−1 DCW) βAA nongrowth-associated acetic acid formation parameter (g g−1 DCW h−1 ) nongrowth-associated formic acid formation βFA parameter (g g−1 DCW h−1 ) nongrowth-associated lactic acid formation βLA parameter (g g−1 DCW h−1 ) βSA nongrowth-associated succinic acid formation parameter (g g−1 DCW h−1 ) μ specific growth rate (h−1 ) μm maximum specific growth rate (h−1 )
M. succiniciproducens is described in detail by Kim et al. [8]. In order to develop a cost-competitive fermentative succinic acid production process, strain improvement maximizing the production of succinic acid but minimizing the formation of by-products through rational metabolic engineering is of vital importance. We recently developed an improved succinic acid producer, M. succiniciproducens LPK7, by genome-based metabolic engineering, resulting in much increased succinic acid production while reduced formation of by-products, such as acetic, formic, and lactic acids [9]. In addition, the possibilities of cost-effective succinic acid production by M. succiniciproducens from inexpensive and abundant feedstocks, including wood hydrolysates and whey were also investigated [10,11]. Kinetic modeling is regarded as an indispensable step in developing a fermentation process since the models can be used to determine an optimal operation condition for the production of a target metabolite. Both structured and unstructured models have been used for kinetic modeling. Although the former can explain complex microbial system at the molecular levels, relatively simpler unstructured kinetic models have frequently been used for practical applications [12,13]. Several unstructured kinetic models have been proposed to describe the fermentation of glucose to organic and amino acids such as citric, lactic, and glutamic acids [14–16]. However, little kinetic study on the fermentative production of succinic acid has been performed to date. In this paper, we present empirical kinetic models to describe the batch production of succinic acid by M. succiniciproducens MBEL55E. A series of batch fermentations with different initial glucose concentrations were conducted in a bioreactor, and the experimental data were used to estimate parameters and also to validate the models. The behaviors of M. succiniciproducens during fermentation were predicted using the models, and the predictions were compared with the experimental observations. The models developed were able to successfully explain cell growth, succinic acid production, by-products (acetic, formic, and lactic acids) formation, and glucose utilization. The fermentative characteristics of succinic acid production from glucose by M. succiniciproducens were discussed in detail. 2. Materials and methods 2.1. Organism and growth conditions Preculture of M. succiniciproducens MBEL55E (KCTC 0769BP; Korean Collection for Type Cultures, Daejeon, Korea) [6] was performed in a sealed anaerobic flask containing 250 mL medium composed of yeast extract 5 g L−1 , NaCl 1 g L−1 , CaCl2 ·2H2 O 0.2 g L−1 , MgCl2 ·6H2 O 0.2 g L−1 , and K2 HPO4 8.709 g L−1 . The medium with CO2 as the gas phase was heat sterilized at 121 ◦ C for 15 min. The pH of the medium was adjusted to 7.0 by adding 5N NaOH. Separately sterilized glucose was added into the medium to a final concentration of 50 mM as a carbon source just before inoculation. Inoculation was done by seeding 2.5 mL glycerol stock culture (15%, w/v,
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−70 ◦ C) into the medium. Anaerobic flask cultures were performed at 39 ◦ C for 9 h under a CO2 atmosphere in an anaerobic chamber (ThermoForma, Marietta, OH). Batch fermentations were carried out at 39 ◦ C in a 5-L LiFlus GX bioreactor (BioTron Inc., Puchon, Korea) at a working volume of 2.5 L. The initial glucose concentrations in the medium was varied from 1.0 to 66 g L−1 . The pH was maintained at 6.5 ± 0.1 by the addition of a 28% (v/v) ammonia solution. The agitation speed and temperature were set to 200 rpm and 39 ◦ C, respectively. The bioreactor was continuously sparged with industrial grade CO2 (Special gas, Daejeon, Korea) at a flow rate of 0.25 vvm during the whole period of fermentation. An oxygen trap (Agilent, Waldbronn, Germany) was fitted in the outlet line of the gas cylinder to completely remove residual oxygen, if any, from CO2 gas. All chemicals used in this study were of reagent grade and were obtained from either Sigma–Aldrich (St. Louis, MO) or Junsei Chemical (Tokyo, Japan), except for glucose and yeast extract which were purchased from Samyang Genex (Inchon, Korea) and Becton Dickinson Company (Le Pont de Claix, France), respectively. 2.2. Analytical procedures Quantitative analyses of glucose and organic acids, including acetic, formic, lactic, and succinic acids, were performed by a high-performance liquid chromatography (Varian ProStar 210, Palo Alto, CA) equipped with UV/VIS (Varian Prostar 320) and refractive index (Shodex RI-71, Tokyo, Japan) detectors. A MetaCarb 87H column (300 mm × 7.8 mm, Varian) was used to separate the components at 60 ◦ C using 0.01N H2 SO4 as a mobile phase at a flow rate of 0.6 mL min−1 . Cell growth was monitored by using a spectrophotometer (Ultrospec3000, Amersham Biosciences, Uppsala, Sweden) at the optical density of 600 nm (OD600 ), and dry cell weight (DCW) was determined by the method described previously [9]. 2.3. Development of kinetic models The kinetics of fermentation can be normally described by a cell growth model (rX ), a product formation model (rP ), and a substrate utilization model (rS ). In general, the Monod model is used to represent bacterial growth under substrate-limited conditions. Taking into a consideration of the excess substrate inhibition on the growth of M. succiniciproducens, this wellknown model can be modified as follows [17]: μ=
μm S (S + KS + (S 2 /KI ))
(1)
where μ is the specific growth rate (h−1 ), μm is the maximum specific growth rate (h−1 ), S is the substrate concentration (g L−1 ), KS is the substrate saturation constant (g L−1 ), and KI is the substrate inhibition constant (g L−1 ). However, Eq. (1) cannot be directly applicable to many fermentation systems since cell growth has a tendency to be inhibited gradually by the fermentation products as cultivation proceeds [18–21]. In
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addition, the presence of the high concentrations of acids in culture broth often causes to cell death [22–24]. The dependence of cell growth on products could be described by a generalized nonlinear equation suggested by Levenspiel [25]. To incorporate both substrate and product inhibitions on the growth of M. succiniciproducens, additional terms were introduced into Eq. (1) as follows: i P μm S , × 1 − μ= (S + KS + (S 2 /KI )) PCRIT when
P < PCRIT
(2)
and μ = −Kd ,
when
P ≥ PCRIT
(3)
where P is the product concentration (g L−1 ), P
CRIT is the critical product concentration at which cell growth fully stops (g L−1 ), and Kd is the specific death rate (h−1 ). Exponent i denotes the degree of product inhibition (dimensionless). M. succiniciproducens produces succinic acid as a major fermentation product and simultaneously forms acetic, formic, and lactic acids as byproducts during anaerobic glucose fermentation. Accordingly, P and PCRIT in Eq. (2) were given as the sum of the amounts of succinic and the other acids, rather than considering individual acids separately. Subsequently, the final kinetic equations for the batch growth of M. succiniciproducens could be represented by the following equations: i μm S P rX = X, × 1− (S + KS + (S 2 /KI )) PCRIT
when
P < PCRIT
(4)
and rX = −Kd X,
when
P ≥ PCRIT
(5)
where rX is the biomass formation rate (g L−1 h−1 ) and X is the biomass concentration (g L−1 ). The formation of organic acids, including succinic, acetic, formic, and lactic acids, by M. succiniciproducens could be described by the Luedeking–Piret model [26], in which the product formation rate depends on both rX and X: rSA = αSA rX + βSA X
(6)
rAA = αAA rX + βAA X
(7)
rFA = αFA rX + βFA X
(8)
rLA = αLA rX + βLA X
(9)
where rSA , rAA , rFA , and rLA represent the formation rates of succinic, acetic, formic, and lactic acids (g L−1 h−1 ), respectively. αSA , αAA , αFA , and αLA denote the growth-associated parameters for the formation of succinic, acetic, formic, and lactic acids (dimensionless), respectively. βSA , βAA , βFA , and βLA denote the non-growth-associated parameters for the formation of succinic, acetic, formic, and lactic acids (h−1 ), respectively. A carbon substrate, glucose in this study, used by M. succiniciproducens is converted to form biomass, succinic acid,
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and by-products, and also used for the maintenance. Based on the carbon mass balance, the consumption rate of glucose can be described as follows: 1 1 1 1 1 −rS = rX + rSA + rAA + rFA + rLA YX YSA YAA YFA YLA + mS X
(10) (g L−1 h−1 ).
where −rS is the consumption rate of glucose YX , YSA , YAA , YFA , and YLA are the stoichiometric yield coefficients of biomass, and succinic, acetic, formic, and lactic acids on glucose (g g−1 ). mS is the specific maintenance coefficient (h−1 ). 2.4. Model parameters estimation With the experimental data obtained from a series of batch fermentations of M. succiniciproducens, the kinetic parameters of the models were determined by minimizing an objective function of Z: ZX =
n
(Xj,exp − Xj,sim )2 ,
ZP =
j=1
or ZS =
n
(Sj,exp − Sj,sim )2 s
m (h−1 ) ms (h−1 ) i (dimensionless) YAA (g g−1 ) YFA (g g−1 ) YLA (g g−1 ) YSA (g g−1 ) YX (g g−1 ) Kd (h−1 ) KS (g L−1 ) KI (g L−1 ) PCRIT (g L−1 ) αAA (g g−1 ) αFA (g g−1 ) αLA (g g−1 ) αSA (g g−1 ) βAA (h−1 ) βFA (h−1 ) βLA (h−1 ) βSA (h−1 )
1.324 0.061 1.301 0.999 1.532 0.999 1.310 0.765 0.010 1.123 88.35 17.23 0.626 0.665 0 1.619 0.124 0.105 0.210 0.355
(Pj,exp − Pj,sim )2 ,
j=1 n
Table 1 Values of kinetic parameters
(11)
j=1
where ZX , ZP , and ZS represent the least-square discrepancies between experimental data and simulated results of biomass, product, and substrate, respectively. j denotes the number of experimental data points. Subscripts ‘exp’ and ‘sim’ denote experimental and simulated, respectively. 3. Results and discussion 3.1. Microbial growth In order to investigate the behaviors of M. succiniciproducens on the anaerobic fermentation of glucose, batch fermentations were carried out using the medium supplemented with varying concentrations (1, 3, 6, 10, 18, 28, 41, 49, and 66 g L−1 ) of glucose. First, μm and KS in Eq. (2) were estimated from the data collected from the fermentations with low initial glucose concentrations (1, 3, and 6 g L−1 ). During these fermentations, no glucose and product inhibitions were observed, implying that S2 /KI and P/PCRIT are close to zero. Consequently, Eq. (2) was reduced to the classical Monod equation μ = μm S/(S + KS ). The values of KS and μm were obtained from reciprocal plotting of the Monod equation; 1/μ versus 1/S (r2 = 0.991). Next, KI was estimated from the fermentations with high initial glucose concentrations (18, 28, 41, 49, and 66 g L−1 ) using the data collected during the exponential growth phase (until 2.5 h of inoculation) to avoid the inhibitory effect of products on the cell growth. In this case, Eq. (2) was reduced to Eq. (1) because P/PCRIT is close to zero. Reciprocal plotting of Eq. (1), 1/μ versus S provided KI (r2 = 0.981). Thus, determined values of KS , KI , and μm are listed in Table 1. Using these values, the model predictions were compared with experimental data as shown in Fig. 1. As expected, the growth of M. succiniciproducens
was negatively influenced by the high concentration of glucose in the culture broth. It was found that the maximum specific growth rate (μm ) of M. succiniciproducens can be achieved is 1.324√h−1 at a glucose concentration of 9.964 g L−1 (given by S = KS KI ) in a bioreactor. A higher specific growth rate is often desirable to grow cells faster to a desirable cell density as it can reduce the fermentation time and thus improves overall productivity. In this sense, M. succiniciproducens showing a high specific growth rate can be considered as a good succinic acid producer. The inhibitory effects of organic acids on microbial growth have been known to depend on the pH of culture broth, the dissociation constants of acids (pKa ), and their molar concentrations [27,28]. As mentioned earlier, M. succiniciproducens produces succinic acid as a major product and acetic, formic, and lactic acids as minor ones from various carbon sources under anaerobic conditions. In this study, the pH was maintained at 6.5 during the whole period of fermentation. The dissociation
Fig. 1. Effects of initial glucose concentrations on the specific growth rate of M. succiniciproducens MBEL55E. Experimental (symbol); simulated (line).
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Fig. 2. Time profiles of biomass (closed circle, solid line) and glucose (open circle, dashed line) in batch cultures of M. succiniciproducens MBEL55E with different initial glucose concentrations: (a) 10 g L−1 , (b) 18 g L−1 , (c) 28 g L−1 , (d) 41 g L−1 , (e) 49 g L−1 , and (f) 66 g L−1 . Experimental (symbols); simulated (lines).
constants of organic acids produced by M. succiniciproducens can be considered not much different for modeling purposes: acetic acid (pKa of 4.75), formic acid (pKa of 3.84), lactic acid (pKa of 3.86), and succinic acid (pKa1 of 4.21). Thus, the product term in Eqs. (2)–(4) could be assumed to be as the sum of the amounts of individual acids to simplify the models. As shown in Figs. 2–4, the growth of M. succiniciproducens gradually slowed down with increasing amounts of acids, and the growth completely ceased when it exceeded 17.23 g L−1 (PCRIT ) in all experiments. Above this critical value, the concentrations of biomass declined linearly with Kd = 0.001 h−1 as the fermentation proceeded. The parameter i was estimated to be 1.301. This is not surprising because the inhibition of microbial growth by organic acids is common and the acids accumulated to high concentrations finally cause cell lysis, even though the resistance levels against acids can be different among microorganisms [22,27,29,30]. 3.2. Succinic acid production and by-products formation The concentration of succinic acid increased proportionally to increasing the concentration of biomass in the exponential
growth phase, and its production continued even during the slow growth and stationary phases (Fig. 3). This means that the production of succinic acid by M. succiniciproducens follows the Luedeking–Piret model, indicating that the production rate of a product relies not only on the rate of cell growth (rX ) but also on the concentration of biomass (X) [26]. The nongrowth-associated coefficient for succinic acid production by M. succiniciproducens (βSA = 0.355 h−1 ) was first determined from the data collected where no cell growth was observed (P ≥ PCRIT ). Then, the growth-associated coefficient for succinic acid production by M. succiniciproducens (αSA = 1.619) was estimated when P < PCRIT . During the batch culture with the initial cell concentration of 0.04 g DCW L−1 , the maximum volumetric productivity of succinic acid was predicted to be 1.046 g L−1 h−1 at an initial glucose concentration of 18 g L−1 , which is in excellent agreement with the experimental result of 1.043 g L−1 h−1 (Fig. 3). The predictions of the model simulation were compared with the experimental data and are depicted in Fig. 3, showing a good agreement each other. In the same manner, the coefficients for the formation of acetic, formic, and lactic acids by M. succiniciproducens in
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Fig. 3. Time profiles of succinic acid concentration in batch cultures of M. succiniciproducens MBEL55E with different initial glucose concentrations: (a) 10 g L−1 , (b) 18 g L−1 , (c) 28 g L−1 , (d) 41 g L−1 , (e) 49 g L−1 , and (f) 66 g L−1 . Experimental (symbol); simulated (line).
Eqs. (7)–(9) were estimated, respectively, and the values are shown in Table 1. Like succinic acid, the formation of acetic and formic acids by M. succiniciproducens followed a mixedgrowth-associated product formation pattern (Fig. 4). However, unexpectedly, lactic acid started to be produced only when the cell growth rate was nearly zero, which means that the formation of lactic acid by M. succiniciproducens follows a nongrowth-associated product formation mode under the current experimental condition. Lactic acid fermentation is presented as a typical mixed-growth-associated product for most microorganisms including Escherichia coli, Lactobacillus, Lactococcus, and Saccharomyces [15,21,23,27,31]. Lactic acid formation and succinic acid formation share one common physiological meaning of NADH regeneration during the anaerobic fermentation; NADH produced during glycolysis needs to be regenerated to continue fermentation. However, there is a difference in that two reducing power equivalents are used for succinic acid production from phosphoenolpyruvate, while one equivalent is used for lactic acid production from pyruvate. Production of succinic acid rather than lactic acid during the cell growth phase
of M. succiniciproducens implies that the pathway involved in the production of succinic acid is much more favored in M. succiniciproducens under the given fermentation condition; anaerobic fermentation of glucose under CO2 atmosphere. M. succiniciproducens operates branched TCA cycle; a reductive branch leading to succinic acid formation from oxaloacetate and an oxidative branch producing succinyl-CoA from citrate. When the cell growth is slowed down or stopped, the oxidative branch operation may be reduced or stopped causing less generation of reducing powers. This may cause imbalance of reducing power for the production of succinic acid alone, and cells convert some pyruvate to lactic acid. 3.3. Substrate utilization The consumption rate of glucose was calculated based on the carbon mass balance using the stoichiometric yield coefficients and the estimated specific maintenance coefficient (ms = 0.061 h−1 ). The yield coefficients are shown in Table 1. In order to calculate the carbon compound incorporated into
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Fig. 4. Time profiles of the concentrations of acetic (triangle, dotted line), formic (square, solid line), and lactic (diamond, dashed line) acids in batch cultures of M. succiniciproducens MBEL55E with different initial glucose concentrations: (a) 10 g L−1 , (b) 18 g L−1 , (c) 28 g L−1 , (d) 41 g L−1 , (e) 49 g L−1 , and (f) 66 g L−1 . Experimental (symbols); simulated (lines).
the cells from glucose, the four main elements of C, H, O, and N of M. succiniciproducens cells at exponential growth phase were quantified using an Elemental Analyzer (Flash EA1112Series, CE instruments, Wigan, UK); it was found to be CH1.736 O0.367 N0.240 . M. succiniciproducens fixes one mole of CO2 to yield one mole of succinic acid via three different CO2 fixing metabolic reactions catalyzed by phosphoenolpyruvate carboxykinase, phosphoenolpyruvate carboxylase, and malic enzyme [9]. All CO2 used in the production of succinic acid was assumed to be provided externally in the calculation. Together with the experimental data, the predicted time profiles of glucose are depicted in Fig. 2. Of particular interest from Fig. 2 is that the discrepancies between the experimental data and the model simulation results were observed at the stationary growth phase, and were slightly greater when high initial concentrations (41, 49, and 66 g L−1 ) of glucose were used (Fig. 2(d)–(f)). For the simplified model development, we did not incorporate CO2 production by the cells, which occurs through the decarboxylation reactions catalyzed by oxaloacetate decarboxylase,
malic enzyme, pyruvate dehydrogenase, and formate dehydrase in M. succiniciproducens. The loss of carbon in the form of CO2 produced by cells thus seems to contribute to this discrepancy. Also, the released carbon in the form of cell debris as a result of cell lysis was not included in the calculation. Additionally, changes in cell composition [32–34] during the stationary phase might have caused this discrepancy as we used one cell composition determined at exponential growth phase. Nonetheless, the simple models developed in this study successfully describe the cell growth, glucose consumption, and products formation, which can thus be used for optimizing the process for succinic acid production. For example, the optimal substrate (glucose) concentration can be selected for the fed-batch and continuous fermentation operations. 4. Conclusion In this study, we developed the simple empirical kinetic models for the batch production of succinic acid from glucose by M.
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succiniciproducens MBEL55E. The results of the model simulations showed very good agreement with the experimental data obtained at varying initial glucose concentrations. The growth of M. succiniciproducens could be expressed by an expanded Monod model, including terms for both substrate and product inhibitions, which were suggested by Andrews [17] and Levenspiel [25], respectively. The models allow identification of the optimal concentrations of glucose and organic acids in a bioreactor for the cell growth as well as for the production of succinic acid. The production of succinic acid by M. succiniciproducens follows the Luedeking–Piret model with the growth- and nongrowth-associated parameter values of 1.619 and 0.355 h−1 , respectively. The models could elucidate the fermentative characteristics of M. succiniciproducens and also could explain its superior ability to produce succinic acid under CO2 atmosphere. The models proposed here should be useful for developing and optimizing the succinic acid production processes from renewable resources.
[11]
[12]
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[17]
Acknowledgement This work was supported by the Genome-based Integrated Bioprocess Project of the Ministry of Science and Technology through the Korea Science and Engineering Foundation (KOSEF). Further supports by the LG Chem Chair Professorship and by the Center for Ultramicrochemical Process Systems (KOSEF) are appreciated.
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