Biotechnology Letters

, Volume 37, Issue 10, pp 1965–1971 | Cite as

Microbial kinetics of Clostridium termitidis on cellobiose and glucose for biohydrogen production

  • Maritza Gomez-Flores
  • George Nakhla
  • Hisham Hafez
Original Research Paper



To determine Monod kinetics parameters (µmax, Ks, kd and YX/S) of the mesophilic H2 producer Clostridium termitidis grown on glucose and cellobiose by modeling in MATLAB.


Maximum specific growth rates (µmax) were 0.22 and 0.24 h−1 for glucose and cellobiose respectively; saturation constants (Ks) were 0.17 and 0.38 g l−1 respectively and the biomass yields (YX/S) were 0.26 and 0.257 g dry wt g−1 substrate. H2 yields of 1.99 and 1.11 mol H2 mol−1 hexose equivalent were also determined for glucose and cellobiose respectively.


The microbial kinetics of this model microorganism will enhance engineering biofuel production applications.


Clostridium termitidis H2 production Modeling Monod kinetics parameters 


Fermentative H2 production from carbohydrate-rich wastes is attracting the attention due to its environmental impact and high energy content. Among carbohydrates, cellulose is the most abundant (Fang 2010) and biohydrogen production from lignocellulosic wastes would be sustainable. The primary hydrolysis product of cellulose is cellobiose, which comprises two glucose molecules (Levin et al. 2009).

The most complex and best investigated cellulosome is that of the thermophilic bacterium Clostridium thermocellum (Schwarz 2001). A number of anaerobic, mesophilic, cellulolytic bacteria have been isolated and described. These include Clostridium cellulolyticum, C. cellulovorans, C. phytofermentans, and Clostridium termitidis (Levin et al. 2009). All utilize cellulose, cellobiose and glucose as carbon sources (Giallo et al. 1985; Hethener et al. 1992; Sleat et al. 1984; Warnick et al. 2002).

Hethener et al. (1992) reported the isolation of the mesophile, C. termitidis, from the gut of a wood-feeding termite, Nasutitermes lujae, and described it as an anaerobic, spore-forming, cellulolytic bacterium able to utilize cellulose, cellobiose, glucose, fructose, etc. to produce acetate, ethanol, H2 and CO2. There are only four publications that have focused on C. termitidis strain CT1112 (ATCC 51846). Ramachandran et al. (2008) studied the end-product synthesis and H2 production from cellobiose and cellulose adding lactate and formate to the previously reported end-products (Hethener et al. 1992) and obtained maximum yields of acetate, ethanol, H2 and formate from cellobiose of 5.9, 3.7, 4.6 and 4.2 mmol l−1 culture, respectively, with respective yields from cellulose of 7.2, 3.1, 7.7 and 2.9 mmol l−1 culture, respectively. Lal et al. (2013) reported the draft genome sequence of C. termitidis, while recently, Munir et al. (2014) analyzed C. termitidis for carbohydrate-active enzymes (CAZymes) and cellulosomal components, identifying significantly higher 355 CAZymes sequences than other Clostridial species.

Growth kinetics of various mesophilic cellulose-degrading microorganisms excluding C. termitidis have been studied (Alalayah et al. 2010; Lin et al. 2007; Srivastava and Volesky 1990; Yang and Tsao 1994), Thus, the aim of this study was to obtain the Monod kinetic parameters (µmax, Ks, kd and YX/S) of C. termitidis to facilitate the engineering design of bioreactors.

Materials and methods

Microbial strain and media

Clostridium termitidis ATCC 51846 was used. All chemicals for media and substrates were obtained from Sigma-Aldrich. Fresh cells were maintained by successively transferring 10 % (v/v) of inoculum to ATCC 1191 medium containing filter-sterilized glucose or cellobiose at 2 g l−1. This medium contained (per liter of distilled water): KH2PO4, 1.5 g; Na2HPO4, 3.35 g; NH4Cl, 0.5 g; MgCl2.6H2O, 0.18 g; yeast extract, 2 g; 0.25 ml 1 g resazurin l−1; mineral solution, 1 ml; vitamin solution, 0.5 ml, and l-cysteine, as reducing agent, 1 g. The mineral solution contained (g per 500 ml): trisodium nitrilotriacetate 10.1; FeCl3·6H2O, 1.05; CoCl2·6H2O, 1; MnCl2·4H2O, 0.5; ZnCl2, 0.5; NiCl2·6H2O, 0.5; CaCl2·2H2O, 0.25; CuSO4·5H2O, 0.32; and Na2MoO4·2H2O, 0.25. The vitamin solution contained (mg per 500 ml): pyridoxine–Hcl, 50; riboflavin, 25; thiamine, 25; nicotinic acid, 25; p-aminobenzoic acid, 25; lipoic acid (thioctic acid), 25; biotin, 10; folic acid, 10; and cyanocobalamin, 5. The initial pH was 7.2 but was not controlled during growth.

Experimental conditions

Batch anaerobic fermentations were performed in serum bottles (Wheaton) with a working volume of 400 ml and 310 ml of headspace. Bottles containing 344 ml of ATCC 1191 medium were tightly capped with rubber stoppers, degassed by applying vacuum and sparged with high purity N2 gas, and autoclaved. Duplicate bottles were inoculated with 10 % (v/v) of fresh cultures. Bottles were incubated at 37 °C and 90 rpm for 48 h when grown with glucose and 58 h when grown with cellobiose.

Analytical methods

Cell growth was monitored by measuring the OD600 value, cellular protein content, and dry wt. Duplicates using the 1191 Media with the same concentration of glucose or cellobiose without the culture subjected to the same procedure as the fermentation bottles, served as controls. To measure proteins, samples were placed in microcentrifuge tubes and centrifuged at 10,000×g for 15 min. Supernatants were discarded and pellets re-suspended with 0.9 % (w/v) NaCl and centrifuged at the same aforementioned conditions. Supernatants were discarded; 1 ml 0.2 M NaOH was added to microcentrifuge tubes and vortexed to re-suspend the pellet. Microcentrifuge tubes were held at 100 °C for 10 min. When cool, samples were analyzed following the Bradford method. Soluble samples (filtered through 0.45 µm) were used to analyze for glucose and cellobiose. Glucose was measured using a UV-test kit and cellobiose was measured by the phenol sulfuric acid method. Chemical oxygen demand (COD) was measured using a standard kit (Hach Co.).

Gas measurements

Gas volume was measured by releasing the gas pressure in the bottles using appropriately sized glass syringes in the range of 5–100 ml to equilibrate with the ambient pressure (Owen et al. 1979). H2 analysis was conducted by employing a GC equipped with a thermal conductivity detector and a molecular sieve column (5Å, mesh 80/100, 1.83 m × 0.32 cm). The temperatures of the column and the TCD detector were 90 and 105 °C, respectively. Argon was used as the carrier gas at a flow rate of 30 ml/min.

H2 gas production was calculated from headspace measurements of gas composition and the total volume of biogas produced, at each time interval, using the mass balance equation.

$$ V_{{H_{2} , i}} = V_{{H_{2} ,i - 1}} + C_{{H_{2} ,i}} \times V_{G,i} + V_{h,i} (C_{{H_{2} ,i}} - C_{{H_{2} ,i - 1}} ), $$
where \( V_{{H_{2} , i}} \) and \( V_{{H_{2} ,i - 1}} \) are cumulative H2 gas volumes at the current (i) and previous (i−1) time intervals.\( V_{G,i} \) is the total biogas volume accumulated between the previous and current time intervals. \( C_{{H_{2} ,i}} \) and \( C_{{H_{2} ,i - 1}} \) are the fractions of H2 gas in the headspace of the reactor in the current and previous intervals, and \( V_{h,i} \) is the total volume of the headspace of the reactor in the current interval (López et al. 2007).


Monod kinetics parameters (µmax, Ks, kd and YX/S) were determined by using MATLAB R2014a. The objective function employed was lsqcurvefit, a non-linear least square fit. The solver function used to estimate numerical integration of the ordinary differential equations (Eqs. 2 and 3) was Ode45, which implements fourth/fifth order Runge–Kutta methods. A first approximation for the Monod kinetics parameters was obtained by linearization with Lineweaver–Burk plots in Excel; these results were used as the initial conditions in MATLAB.

$$ \frac{{{\text{d}}X}}{{{\text{d}}t}} = \frac{{\mu_{max} SX}}{{K_{s} + S}} - k_{d} X, $$
$$ \frac{{{\text{d}}S}}{{{\text{d}}t}} = \frac{{ - \mu_{max} SX}}{{Y_{{{\raise0.7ex\hbox{$X$} \!\mathord{\left/ {\vphantom {X S}}\right.\kern-0pt} \!\lower0.7ex\hbox{$S$}}}} (K_{s} + S)}}, $$
where µmax (h−1) is the maximum specific growth rate, Ks (g l−1) is the saturation constant or half-velocity constant and is equal to the concentration of the rate-limiting substrate when the specific growth rate is equal to one half of the maximum, kd (h−1) is the decay coefficient, and YX/S (g dry wt g−1 substrate consumed) is the biomass yield (Shuler and Kargı 2002).

Average percentage errors (APE), root mean square errors (RMSE) and ANOVA analysis of the modeled data with experimental data were calculated.

Results and discussion

Monod growth kinetics and substrate utilization

Biomass growth kinetics were determined using cellular protein content. As shown in the Supplementary Fig. 1, the correlation between dry wt and cellular protein content was calculated, resulting in a 19 % protein content per g dry wt. Atkinson and Mavituna (1991) reported that the protein content of a bacterium typically varied from 40 to 50 % dry wt, while Giallo et al. (1985) observed that the protein content of Clostridium cellulolyticum was 62 % dry wt. The relatively low protein content measured here reflects the low protein extraction efficiency estimated at 33–8 %. It must be asserted however that this extraction efficiency does not impact the estimation of biokinetic constants.

Monod kinetic parameters were first calculated by linearization using Lineweaver–Burk plots and these values were used as initial conditions for modeling in MATLAB (Table 1).
Table 1

Monod kinetic parameters of Clostridium termitidis grown in glucose and cellobiose (2 g l−1) by linearization


µmax (h−1)

\( K_{s} ( {\text{gl}}^{ - 1} ) \)

\( k_{d} ( {\text{h}}^{ - 1} ) \)

\( Y_{{{\raise0.7ex\hbox{$x$} \!\mathord{\left/ {\vphantom {x s}}\right.\kern-0pt} \!\lower0.7ex\hbox{$s$}}}} \)a

R2(1/µ vs. 1/S)













ag dry wt g−1 substrate

Figure 1 shows the changes of substrate and biomass concentrations with time in both glucose and cellobiose experiments. Glucose and cellobiose were completely depleted after 20 and 35 h, respectively. To determine Monod kinetics, the data after the lag phase until the decay phase was taken into account. An initial lag phase of 10 h was observed with cellobiose. For glucose, the experimental data used for modeling was from 0 to 48 h whereas for cellobiose the experimental data from 10 to 58 h was considered.
Fig. 1

Experimental and modeled growth kinetics of C. termitidis. a On glucose (2 g l−1). Experimental (crosses) and modeled (dashed line) glucose concentration, experimental (diamonds) and modeled (solid line) dry wt. b On cellobiose (2 g l−1). Experimental (crosses) and modeled (dashed line) cellobiose concentration, experimental (diamonds) and modeled (solid line) dry wt. Experimental data points represent mean values of duplicate experiments. Modeled data was determined in MATLAB R2014a

Linearization is not the best option to determine kinetic parameters because of the low R2 value and the clear cluster of points. Experimental data can be visually compared with the modeled data from MATLAB in Fig. 1. In order to evaluate the modeling, average percentage errors (APE) and root mean square errors (RMSE) values were calculated with the results shown in Table 2. Furthermore a correlation between the experimental data and the modeled data is illustrated in Fig. 2 together with the correlation coefficient. All R2 values were greater than 0.98, and RMSE values were low, between 0.021 and 0.16 g l−1. The highest APE was for cellobiose (8.6 %) and the lowest APE is for dry wt in the same experiment (4.17 %). As shown in Fig. 2, the modelled dry wt and glucose concentrations deviated from the experimental values by 0.47 and 1.5 %, respectively. Similarly the modelled dry wt and cellobiose concentrations differed from the experimental observations by 1.5 and 12 %, respectively.
Table 2

Monod kinetic parameters of C. termitidis grown in glucose and cellobiose (2 g l−1) obtained in MATLAB, APE, RMSE and H2 yields




Monod kinetic parameters

µmax (h−1)



\( K_{s} ( {\text{gl}}^{ - 1} ) \)



\( k_{d} ( {\text{h}}^{ - 1} ) \)



\( Y_{{{\raise0.7ex\hbox{$x$} \!\mathord{\left/ {\vphantom {x s}}\right.\kern-0pt} \!\lower0.7ex\hbox{$s$}}}} \)a



\( K_{m} \)b



APE (%)c

Dry wt






RMSE (g l−1)d

Dry wt






H2 yield (mol H2 mol−1 hexose equivalent)



ag dry wt g−1 substrate

bg substrate g−1 dry wt h−1

cAverage percentage error

dRoot mean square error

Fig. 2

Linear regression of experimental data against modeled data. a Glucose experiment. Glucose concentration (crosses) and glucose linear regression (dotted line). Dry wt (diamonds) and dry wt linear regression (solid line) b Cellobiose experiment Cellobiose concentration (crosses) and cellobiose linear regression (dotted line). Dry wt (diamonds) and dry wt linear regression (solid line)

To determine the goodness of fit for the modeling, linear regression (Fig. 2) and ANOVA analysis (data not shown) were performed for both experiments. All p values obtained from the F-Distribution were lower than 0.01, concluding there is evidence to suggest a good fit for the linear relationship with α = 0.05. Similarly, all p values obtained from the t-Distribution were greater than α = 0.05, inferring slope = 1 and intercept = 0 in all cases. In conclusion, Monod kinetics modelled in MATLAB for the C. termitidis were statistically proven to be a good fit.

Table 2 shows the Monod kinetic parameters obtained for C. termitidis i.e. µmax and Yx/s values for both experiments were similar whereas Ks for cellobiose was more than twice that for glucose. Upon comparing the growth biokinetic coefficients for cellobiose based on linearization (Table 1) and nonlinear modeling (Table 2), it is evident the biomass yield and Ks were relatively close within 14 and 2 % of the larger values while µmax differed by 29 %. It must be asserted, however, that the accuracy of determining Ks from a single batch test is not high, as generally several batches at a wide range of initial substrate concentrations are employed to ensure accurate delineation of Ks. The discrepancy between the biokinetic constants for glucose using linear and nonlinear methods are even larger (19–80 %) than for cellobiose except for the biomass yield, thus emphasizing the limitations of linearization techniques. As C. termitidis Monod kinetic parameters have not been reported before, other Clostridium species kinetic parameters were reviewed (Alalayah et al. 2010; Lin et al. 2007; Linville et al. 2013; Srivastava and Volesky 1990; Yang and Tsao 1994). Notwithstanding the accuracy of Ks determination from the single batch tests used here, the Ks value for glucose observed in this study of 0.17 g l−1 is similar to the 0.18 g l−1 reported for the mesophile C. acetobutylicum (Lin et al. 2007) while that obtained for cellobiose, 0.38 g l−1, is lower than for the thermophile C. thermocellum, 0.92 g l−1. The µmax value for C. termitidis with glucose is slightly lower than those reported in the literature (0.39–0.58 h−1) and for cellobiose it was also lower than the reported range (0.57–1.22 h−1). It is important to note that the maximum cellobiose utilization rate (Km) for the mesophilic C. termitidis is 10 % greater than the maximum glucose utilization rate. Although kd was significantly lower than µmax, kd for glucose was atypically 45 % higher than for cellobiose clearly highlighting the low determination accuracy.

H2 production

Figure 3 shows the cumulative H2 production after 20 and 30 h from glucose and cellobiose, respectively, along with the changes in pH. C. termitidis stopped H2 production the same time glucose was depleted and biomass concentration reached its maximum, with pH decreasing to its minimum value of 5.8, while on cellobiose, C. termitidis reached the complete substrate utilization at 30 h and pH decreased to 6.1. The H2 yields presented in Table 2 indicate that the yield was higher for glucose (1.99 mol H2 mol−1 hexose equivalent) than cellobiose (1.11 mol H2 mol−1 hexose equivalent). Nevertheless, H2 yield by C. termitidis in cellobiose was more than two times higher than the 0.39 mol H2 mol−1 hexose equivalent previously reported by Ramachandran et al. (2008). Both experiments were run in batches but the main difference was the reactor size, 10 ml working volume compared to 400 ml used in this study. The same authors (Ramachandran et al. 2008) obtained a higher H2 yield of 0.62 mol H2 mol−1 hexose equivalent when C. termitidis was fed with cellulose, as compared to the cellobiose yield of 0.39 mol H2 mol−1 hexose equivalent, which is not plausible since cellobiose is the product of hydrolysis of cellulose.
Fig. 3

Cumulative H2 production and pH. aC. termitidis on glucose (2 g l−1). Cumulative H2 profile (squares) and pH changes (triangles) bC. termitidis on cellobiose (2 g l−1). Cumulative H2 profile (squares) and pH changes (triangles). Data points represent the mean values of duplicate experiments

The closures of COD balances at 99 ± 1 % verifies the reliability of the data. Based on the final COD measurements in the glucose experiment, the biomass yield was 0.15 g dry wt g−1 glucose. This agrees with the biomass yield of 0.18 g dry wt g−1 glucose considering the Monod theoretical yield and decay coefficient. In the case of cellobiose, the observed biomass yield at the end of the fermentation was 0.24 g dry wt g−1 cellobiose, as compared with 0.2 g dry wt g−1 cellobiose based on the Monod model kinetics.


Clostridium termitidis CT1112, isolated from the gut of a wood-feeding termite, Nasutitermes lujae, has become of great industrial interest because of its ability to degrade cellulose at mesophilic temperatures and to produce H2.

Growth kinetics on glucose and cellobiose were modeled in MATLAB by fitting the data to experimental results and Monod kinetic parameters (µmax, Ks, kd and YX/S) were determined. In glucose µmax was 0.22 h−1 and 0.24 h−1 for cellobiose, Ks was 0.17 and 0.38 g l−1 respectively and finally biomass yield was 0.26 and 0.257 g dry wt g−1 substrate. H2 yields of 1.99 and 1.11 mol H2 mol−1 hexose equivalent were also determined for glucose and cellobiose respectively. C. termitidis exhibited a higher biomass yield and a lower H2 yield when grown in cellobiose than in glucose.

This study has provided valuable insights into the fermentation of mono and disaccharides by C. termitidis. The microbial kinetics of this model microorganism will enhance engineering biofuel production applications. Furthermore, studies of substrate consumption and microbial growth will provide an understanding of microbial metabolism under specific fermentation conditions.



This work was supported by the Eastern platform of the Biofuel Network. The authors acknowledge the support by Consejo Nacional de Ciencia y Tecnologia de Mexico (CONACYT) and Alianza para la Formacion e Investigacion en Infraestructura para el Desarrollo de Mexico, awarded to Maritza Gomez-Flores.

Supporting information

Supplementary Figure 1—Correlation between dry wt and cellular protein content in Clostridium termitidis.

Supplementary material

10529_2015_1891_MOESM1_ESM.docx (28 kb)
Supplementary material 1 (DOCX 29 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Maritza Gomez-Flores
    • 1
  • George Nakhla
    • 1
    • 2
  • Hisham Hafez
    • 2
  1. 1.Department of Chemical and Biochemical Engineering, Faculty of EngineeringUniversity of Western OntarioLondonCanada
  2. 2.Department of Civil and Environmental Engineering, Faculty of EngineeringUniversity of Western OntarioLondonCanada

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