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Process Biochemistry 46 (2011) 701–708

Contents lists available at ScienceDirect

Process Biochemistry

journa l homepage: www.e lsev ier .com/ locate /procbio

ynamics of rate limiting enzymes involved in the sequential substrate uptakey Pseudomonas putida CSV86: Modeling and experimental validation

ebasish Dasa,1, Aditya Basub,1, Anshul Nigamb,1, Prashant S. Phaleb, Pramod P. Wangikara,∗

Department of Chemical Engineering, Indian Institute of Technology, Bombay, Powai, Mumbai 400076, IndiaDepartment of Biosciences and Bioengineering, Indian Institute of Technology, Bombay, Powai, Mumbai 400076, India

r t i c l e i n f o

rticle history:eceived 19 January 2010eceived in revised form7 November 2010ccepted 25 November 2010

eywords:

a b s t r a c t

Cybernetic model assumes that a microorganism is an optimal strategist, sets goals, sub-goals and sub-sub-goals, monitors its performance against these goals and then takes remedial action. The soil bacteriumPseudomonas putida CSV86 is known for its ability to preferentially utilize aromatic compounds overglucose. We had set up a structured kinetic model where sub-goals are based on the environmentalconditions. Upon exhaustion of the preferred hydrocarbon substrate, the organism sets a sub-goal ofutilization of the less preferred glucose substrate and in-turn induces the relevant rate limiting enzymes.

ey inducible enzymexygen uptakeinetic model

The model parameters were estimated from batch data and validated in the dynamic phase of a chemostat,which was subjected to pulse input of the preferred substrate. We find that the organism not only growsfaster on the preferred hydrocarbon substrate but also adapts faster. Specifically, the rate of synthesis ofthe rate limiting enzyme for benzyl alcohol utilization is approximately seven times greater than thatfor glucose. The recovery time from the pulse of benzyl alcohol reduced dramatically with the increasingdilution rate. This is partially attributed to a higher enzyme synthesis rate at higher dilution rates inaddition to the obvious effect of quicker benzyl alcohol washout.

. Introduction

Microbial growth under steady state condition is a rare phe-omenon. Rather, the growth of a microorganism in a naturalystem is confronted with continuous change in environmentalonditions, such as change in nutrient composition or the variationn physiological parameters. Therefore, adequate metabolic flexi-ility is a primary condition for a microorganism in order to survivend grow within a mixed population. Several studies in molec-lar biology have revealed that substrate utilization patterns arenalogous to the dynamics of the rate limiting enzymes, which areesponsible for catalyzing the uptake and catabolism of substrates1–4]. However, from the concept of metabolic control theory it hasecome clear that control in a metabolic pathway is generally dis-ributed and it is difficult to identify only one step as bottleneck.hus, in the present study the enzyme(s) with the functionality of

ontrolling the utilization of a specific substrate is termed as keynducible enzyme(s).

There have been several initiatives in modeling of microbialrowth kinetics and substrate utilization pattern via cybernetic

∗ Corresponding author. Tel.: +91 22 2576 7232; fax: +91 22 2572 6895/3480.E-mail address: wangikar@iitb.ac.in (P.P. Wangikar).

1 Equal author contribution.

359-5113/$ – see front matter © 2010 Elsevier Ltd. All rights reserved.oi:10.1016/j.procbio.2010.11.017

© 2010 Elsevier Ltd. All rights reserved.

principles [4–18]. However, all these studies were restricted tomixed substrate growth kinetics under fixed medium composi-tion in a batch cultivation process. Further, substrate consumptionkinetics under transient state conditions has been reported forvarious microbial systems. Transient states were achieved by puls-ing a secondary substrate into a steady state culture of primarysubstrate in chemostat [19–21] or by feed switching [22] andfeed shift up experiments [23]. However, one of the potentialbottlenecks is that majority of the current studies lack the exper-imental data in terms of synthesis and activity of key inducibleenzymes. Therefore, the current literature has treated these rate-limiting enzymes as latent parameters [24]. We argue that whilethe molecular mechanisms underlying the feedback control loopto maintain these enzyme levels at their respective set points maybe unknown presently, it is feasible to experimentally measure thelevels of rate limiting enzymes. In a changing environment, theseset points may evolve dynamically. Further, metabolic flexibilitynecessitates that a microorganism not only grows faster on pre-ferred substrate but also adapts faster when subject to suddenchange in medium composition. Therefore, substrate utilization

pattern under transient condition is governed by mainly two fac-tors: time needed to express a specific enzyme to a certain level(rate of induction) and the rate of degradation of enzyme. Quan-tification of these kinetic parameters is sparse in the literatureand needs to be evaluated via standard engineering methodolo-

7 chemi

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02 D. Das et al. / Process Bio

ies for modeling and simulation of biological systems. We haveelected Pseudomonas putida CSV86 as a model system in this study.he strain preferentially utilizes hydrocarbon compounds suchs naphthalene, methylnaphthalene, benzylalcohol, and salicylatever glucose [25,26]. Some of the enzymes involved in hydro-arbon uptake such as 1,2-dihydroxynaphthalene dioxygenase,enzyl alcohol dehydrogenase, catechol-1,2-dioxygenase (C12O),nd catechol-2,3-dioxygenase (C23O) were found to be inducible25]. Hydrocarbon compounds repressed some of the enzymesnvolved in glucose uptake such as the glucose transport pro-ein present in the periplasmic space and glucose-6-phosphateehydrogenase [25,27]. On the contrary, glucose was not found toepress the enzymes involved in hydrocarbon uptake.

Here we present a kinetic model to predict the sequence ofubstrate uptake by hydrocarbon degrading P. putida CSV86 in aultisubstrate environment. The model is based on a cascade of

egulatory networks and the concept of inducible enzymes; bothf which are all pervasive in biological systems. The basic premisef our model is that the uptake of substitutable carbon substratess regulated by certain rate limiting enzymes. The model is set ups a structured kinetic model without regard to the actual mecha-ism by which the enzyme levels are controlled. The experimentalerification of the model is based on the measurements of dynamicvolution of the rate limiting enzymes in a chemostat, which haseen subjected to pulse input of a competing substrate. The find-

ngs would be useful in bioremediation in predicting the behavior ofnvironmental systems affected by changes in substrate spectrumnd availability.

. Materials and methods

.1. Organism, medium and culture conditions

P. putida CSV86 was grown in minimal salt medium (MSM) [28]. Carbon lim-ted chemostat cultivations were carried out a in a bioreactor equipped with pH,emperature and dissolved oxygen controllers (BIOSTAT® B PLUS; B. Braun Biotechnternational, Schwarzenberger, Germany) at a working volume of 1.0 L. Glucose atconcentration of 2.7 g L−1 was the sole carbon substrate in the feed to the biore-

ctor. A pulse input of 1 g of benzyl alcohol was given to a steady state culture andhe dynamic response of the culture was monitored via online measurements ofH, dissolved oxygen and stirrer speed. The offline measurements were obtained byollecting samples from the exit stream of the chemostat, which included biomass,esidual glucose, benzyl alcohol, specific enzyme activity and whole cell oxygenptake. The pulse tracer experiments were carried out with four different dilutionates of 0.03 h−1, 0.06 h−1, 0.09 h−1 and 0.12 h−1. The temperature was maintainedt 28 ◦C ± 0.2. The pH was maintained at a set point of 7.2 ± 0.1 by addition of alkali.eration was at the rate of 1 vvm. The dissolved oxygen level was maintained at 40%y cascade control with the agitator.

.2. Analytical techniques

The growth was monitored at 540 nm using spectrophotometer (Model v750,asco, Japan). Liquid phase concentrations of glucose and benzyl alcohol measuredia High Performance Liquid Chromatography as described by Basu et al. [29]. Wholeell oxygen uptake rate was measured for cells harvested at different points of timerom the pulse input chemostat experiments. The cells were centrifuged, washednd resuspended in phosphate buffer for oxygen uptake measurements by usingxygraph (Hansatech Instruments, Norfolk, UK) fitted with Clark type oxygen elec-

rode at 30 ◦C. The assay mixture of 2 ml in the oxygraph reaction chamber containedmg cells (wet mass), substrate (10 mM) and appropriate amount of potassiumhosphate buffer (50 mM, pH 7.5). The rates were corrected for endogenous oxygenonsumption. The net oxygen uptake in the presence of added carbon substratesas expressed in nmole of O2 min−1 mg of cells−1 (wet mass).

.2.1. Enzyme activity assayIn order to measure the specific enzyme activity, cells were washed with ice

old Tris–HCl buffer (50 mM; pH = 7.5), and re-suspended in Tris–HCl buffer in 1:4w/v) ratio of wet cell mass. The cells were maintained at 4 ◦C and were sonicated

or four cycles of 15 pulses each (1 s pulse, 1 s interval, cycle duration 30 s, out-ut 15 W), using an Ultrasonic processor (model GE130, USA), and then centrifugedt 20,000 × g for 20 min. The supernatant was used for measuring enzyme activ-ties. The protein content was measured by Bradford method [30]. Benzylalcoholehydrogenase (BADH) activity was estimated by measuring the rate of forma-ion of NADH at 340 nm [27,31]. One ml reaction mixture contained benzyl alcohol

stry 46 (2011) 701–708

(100 �M), NAD+ (200 �M), suitable amount of enzyme and glycine–KOH buffer(100 mM, pH 9.0). Activity of catechol-1,2-dioxygenase (C12O) was measured bymeasuring the rate of increase in absorbance at 260 nm due to the formation of theproduct cis,cis-muconic acid [25,27,32]. One ml reaction mixture contained catechol(100 �M) and suitable amount of enzyme in Tris–HCl buffer (50 mM, pH 7.5). Glu-cose 6-phosphate dehydrogenase (ZWF) activity was measured by measuring therate of increase in absorbance at 340 nm due to formation of NADPH [27,33]. Oneml reaction mixture contained glucose 6-phosphate (4 mM), NADP (200 �M) andsuitable amount of enzyme in Tris-HCl buffer (100 mM, pH 8.75).

2.3. Model development

The various carbon substrates such as glucose (Sglc) and aromatic hydrocar-bon compounds (SHC) are assumed to be truly substitutable. Thus, we considerone growth reaction per carbon substrate (Eq. (1)). The specific rate of each of thegrowth reactions is modeled in the form of Monod kinetics with the possibility oftwo limiting substrates and substrate inhibition (Eqs. (2)–(5)). Moreover, the ratesare assumed to be dependent on a rate-limiting enzyme XEi. The overall growth rateis thus the sum total of the two growth rates (Eq. (6)). The model is set up as astructured kinetic model with the main goal of optimizing the growth rate whilesthe sub-goals being the maintenance of the rate limiting enzymes. The organismis assumed to be an optimal strategist and is assumed to decide sub-goals with aview to optimize the overall goal of optimizing the growth rate. The sub-goals areassumed to be achieved via enzyme synthesis (Eqs. (7) and (8)). The equations forenzyme synthesis have been proposed based on the observed pattern of inductionand repression. Specifically, each enzyme is induced by the presence of the respec-tive substrate but the enzyme for glucose uptake is repressed by the presence ofhydrocarbon. The mass balance equations for a flow process for cell mass, glucoseand hydrocarbon are given by Eqs. (10)–(12), respectively. The model equation wassolved by solving the differential equations for growth, substrates and enzymesusing ODE solver “ode15s” (gear type stiff solver) available in the software packageMATLAB (Mathworks, Natick, MA).

Model equations

X + YC/X CO2 + YW/X H2O − YN/XiSamm − YSi/X Si − YO/X O2 = 0 (1)

Here, Si represents SHC or Sglc

�HC = �maxHC

(XEHC

XRefEHC

)r∗

HC (2)

�glc = �maxglc

(XEglc

XRefEglc

)r∗

glc (3)

r∗HC = SHC

KsHC + SHC + (S2HC/KI,HC)

Samm

Ksamm + Samm + (S2amm/KI,amm)

(4)

r∗glc = Sglc

Ksglc + Sglc + (S2glc

/KI,glc)

Samm

Ksamm + Samm + (S2amm/KI,amm)

(5)

� = �HC + �glc (6)

d(XEglc /XRefEglc

)

dt= Kglc

Er∗

glcQHC − (� + ˇglc)

(XEglc

XRefEglc

)(7)

d(XEHC /XRefEHC

)

dt= KHC

E r∗HC − (� + ˇHC)

(XEHC

XRefEHC

)(8)

QHC = (KHC)n

(KHC)n + (SHC)n (9)

Mass balance for a flow reactor:

dX

dt= D(XF − X) + �X (10)

dSglc

dt= D(SglcF

− Sglc) −[

�glc

YX/glc

]X (11)

dSHC

dt= D(SHCF

− SHC) −[

�HC

YX/HC

]X (12)

2.4. Parameter estimation

In a previous study a large number of batch cultivations of P. putida CSV86have been performed for various substrate combinations of aromatic compounds,organic acids and glucose [29]. This experimental data has been used for estimationof the various model parameters: stoichiometric coefficients and kinetic parame-ters. The estimated kinetic parameters were further validated in the dynamic phase

D. Das et al. / Process Biochemistry 46 (2011) 701–708 703

Table 1Model parameter values for growth of Pseudomonas putida CSV86 on benzylalcohol and glucose.

Substrate Model parametersa

�max (h−1) KS (g L−1) KI (g L−1) KE (h−1) ˇ (h−1) YX/S (g g−1)

Benzyl alcohol (HC) 0.5 0.045 2.3 13.84 0.03 1.281

ss, subd

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This discrepancy may be due to a higher amount of glucose beingutilized for maintenance at the low D value. Cell washout wasobserved at D values of greater than 0.16 h−1, which was consistentwith the specific growth rates observed in batch cultures.

1.02

1.03

1.04

1.05

1.06

1.07

0.150.120.090.060.030

-1

Bio

mass (

g/L

)

0

0.04

0.08

0.12

0.16

Glu

co

se (

g/L

)

Glucose (glc) 0.22 0.07

a The model parameters were obtained by fitting simulated profiles of biomaetermined from batch cultivations. Batch data taken from Basu et al. [29].

f a chemostat. The kinetic parameter values were estimated by fitting the sim-lated profiles of biomass, substrates and profile of rate limiting enzymes (Eqs.7)–(12)) with the corresponding experimentally measured values in batch cul-ivations. Further, the parameter values were fine tuned in order to obtain bestt between experimental data and model prediction. The goodness of fit is mea-ured by the R2 value [5]. The parameter values corresponding to best fit have beenchieved by searching the parameter space by using fmincon, a built-in routinerom MATLAB optimization toolbox (Mathworks, Natick, MA). The routine is uti-ized to find a constrained minimum of objective function of several variables suchs biomass, glucose, and hydrocarbon. The objective function is defined as the dif-erence between the experimentally measured time profiles of the variables and theorresponding values obtained from simulations. In the present study medium scaleptimization technique was adopted which uses a sequential quadratic program-ing (SQP) method to solve quadratic programming sub problem at each iteration.

he values that gave the minimum deviation between experimental and simulatedalues were taken as the best estimates for the parameters. Objective functionormulated for parameter estimation is mathematically considered as non-linearptimization problem which suffers from various limitations: nonlinear dynamicptimization is computationally very expensive and high nonlinearity and naturef dynamic constraints result in local optimum instead of global optimum [34,35].herefore, the parameters estimated in the present study can be considered as onef the feasible solutions in a large solution space. There are few reports which sug-est the use of deterministic [36,37] and stochastic methods [38,39] in order to findlobal optimum. However, in case of deterministic method, while convergence tolobal optimum is guaranteed, it is feasible only when a lesser number of parame-er is involved. On the other hand, finding global optimum by stochastic method isomputationally very expensive.

. Results

P. putida CSV86 is known to prefer aromatics over glucose [27].t is therefore reasonable to expect that the hydrocarbon sub-trate exerts a repressive effect on one or more of the rate limitingnzymes for the utilization of glucose; either directly or throughne of the intermediates of hydrocarbon metabolism. Based on thisypothesis, a structured model is proposed, whose form is similaro the genetically structured model proposed for the lac operonystem where the presence of glucose represses the lactose uti-izing enzymes such as �-galactosidase, and permease while theresence of lactose induces these enzymes [40]. For experimentalalidation of the model, we have obtained several direct (specificctivity of enzyme) and indirect measurements (whole cell oxy-en uptake rate) of rate limiting enzymes. In general, the enzymeatalyzing the reaction step with lowest velocity and hence, con-rolling the overall reaction rate is termed as late limiting enzyme.owever, theory and experiments suggest that the control of ratef a metabolic pathway is spread across multiple reactions ratherhan a single reaction. Therefore, in the present study the enzyme(s)hich control the utilization of a specific substrate is(are) termed

s key inducible enzyme(s). Although, the rate limiting steps inegradation pathway of aromatic compounds and glucose have noteen unequivocally elucidated, several enzymes have been rec-gnized as inducible enzymes such as BADH and C12O in benzyllcohol metabolism and ZWF and glucose binding protein in theeriplasmic space in glucose metabolism [25,27]. In the present

tudy, we have measured the whole cell oxygen uptake rate inhe presence of various substrates and metabolic intermediates, asn indicator of the substrate associated respiratory activity of theulture. In a recent study, it has been demonstrated that Oxygenptake Rate (OUR) can be utilized as an indicator of cellular activ-

.35 1.8 0.02 0.39

strates and intermediates metabolites with corresponding experimental values

ity which enables indirect measurement of substrate utilization[41,42]. Whole cell oxygen uptake rate therefore, is an indirect mea-sure of the rate limiting enzyme(s) in the uptake and metabolismof a substrate.

3.1. Estimation of model parameters

There are two types of model parameters; (i) the stoichiometriccoefficients of Eq. (1) and (ii) the kinetic constants of Eqs. (2)–(12).The stoichiometric coefficients were first determined from the var-ious batch growth experiments (Table 1). Some of the batch datahas been shown in Basu et al. [29]. The biomass yield coefficient onbenzyl alcohol was found to be significantly greater than that onglucose (Table 1). Further, the kinetic parameter values were esti-mated by fitting the simulated profiles of biomass and substrateswith the corresponding experimentally measured values obtainedfrom various batch runs (data not shown). While substrate inhi-bition constant for benzyl alcohol (2.3 g L−1) was estimated to bemuch higher, the KI for glucose (1.35 g L−1) was found to be half ofthe concentration of the respective substrate used in the exper-iments. Therefore, a significant substrate inhibition is expectedfor glucose utilization. The rate of induction (KE) for rate limitingenzyme of benzyl alcohol is estimated to be much faster than thatof glucose. However, the degradation rate constant (ˇ) were foundto be comparable for both the substrates.

3.2. Chemostat culture with glucose as carbon source

Before conducting the pulse input experiments, we first estab-lished the steady state chemostat culture of P. putida CSV86 atvarious dilution rates (D) with glucose as the sole carbon source(Fig. 1). The exit glucose concentration was lowest for D = 0.03 h−1

while the exit biomass concentration was highest for D = 0.12 h−1.

D (h )

Fig. 1. Effect of dilution rate on steady state exit concentrations of biomass (�) andglucose (�) in a chemostat culture of Pseudomonas putida CSV86 with glucose as solecarbon substrate. The feed contained 2.7 g L−1 of glucose in minimal salt medium(MSM) [28].

704 D. Das et al. / Process Biochemistry 46 (2011) 701–708

10090804035302520151050-5-10

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C

BA

LC

(g

/L)

Time (h)

B

Glu

co

se

(g

/L)

10090804035302520151050-5-10

0.0

0.5

1.0

1.5

2.0

A

Bio

ma

ss

(g

/L)

Fig. 2. Exit concentrations of biomass, glucose and benzyl alcohol after pulse inputof benzyl alcohol to steady state chemostat culture of Pseudomonas putida CSV86growing on glucose as sole carbon substrate at a dilution rate of 0.06 h−1. One gramof benzyl alcohol was added to the fermentor working volume of 1 L at time t = 0.The feed contained 2.7 g L−1 of glucose in MSM. (A) Biomass; (B) Glucose; (C) Benzylalcohol. Model predicted values are shown with continuous lines and the experi-mma

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adtuwt

10090804035302520151050-5-10

0.0

0.2

0.4

0.6

0.8

1.0C

A

BA

LC

(g

/L)

Time (h)

10090804035302520151050-5-10

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0.1

0.2

0.3

0.4B

Glu

co

se (

g/L

)

10090804035302520151050-5-10

1.0

1.5

2.0

Bio

mass (

g/L

)

Fig. 3. Exit concentrations of biomass, glucose and benzyl alcohol after pulse input ofbenzyl alcohol to steady state chemostat culture of Pseudomonas putida CSV86 grow-ing on glucose as sole carbon substrate at a dilution rate of 0.12 h−1. (A) Biomass;(B) Glucose; (C) Benzyl alcohol. Model predicted values are shown with continuous

profile of specific activity of BADH, C12O and ZWF has shown that

ental values are shown with symbols. Experimental data shown in the figure is theean of multiple data sets. Error bars shown in the figure is the standard deviation

cross the multiple data sets.

.3. Pulse input of benzyl alcohol in chemostat culture grown onlucose

The objective of this experiment was to study the kineticsf induction, repression and turnover of rate limiting enzymesnvolved in glucose and benzyl alcohol utilization by perturbing theteady state growth of P. putida CSV86 on glucose by pulse additionf a more preferred substrate, benzyl alcohol. It is expected thathe glucose utilization would be transiently affected but restoredventually upon exhaustion/washout of benzyl alcohol. In pulsenput experiments, the microorganism was grown with glucose ashe sole carbon source in the feed stream of a chemostat. After cul-ure reaches steady state, a pulse input of 1 g of benzyl alcohol wasntroduced. These experiments were conducted at dilution ratesf 0.03, 0.06, 0.09 and 0.12 h−1. The dynamic profiles of biomass,enzyl alcohol and glucose are shown in Figs. 2 and 3 for two rep-esentative dilution rates. With the pulse input of benzyl alcohol,he model predicts a shift from glucose to benzyl alcohol utiliza-ion. Repression of glucose utilization results in transient increasen residual glucose concentration (Figs. 2B and 3B). Subsequently,lucose uptake was restored upon complete exhaustion of benzyllcohol. The model also predicts a significant but transient increasen biomass concentration during the benzyl alcohol utilizationhase. This is due to the fact that the specific growth rate on ben-yl alcohol is much greater than that on glucose (Figs. 2A and 3A).ubsequently, the biomass concentration decreases and reacheshe original steady state value. The duration and extent of glucoseepression decreased with increasing D (Figs. 2 and 3). The modelccurately predicts the profiles for exit concentrations of benzyllcohol and biomass.

The transient behavior of the chemostat upon pulse addition haslso been captured via the online measured variables such as pH,issolved oxygen concentration (DO) and stirrer speed. For all the

hree dilution rates, the pH and DO decrease sharply within 1–2 hpon addition of the pulse and reach the original steady state valueithin the next 5–20 h (Fig. 4). In general, the perturbations in all

hree of the online measured variables last longer at lower dilution

lines and the experimental values are shown with symbols. Refer to legend to Fig. 2for details. Experimental data shown in the figure is the mean of multiple data sets.Error bars shown in the figure is the standard deviation across the multiple datasets.

rates (Fig. 4). This may be due to the fact that benzyl alcohol gets uti-lized and/or washed out faster at higher dilution rates. The stirrerspeed, which is controlled in cascade mode to maintain DO above40%, increases sharply soon after the pulse input. Note that the stir-rer speed is a function of the volumetric oxygen consumption rateas the concentration difference of driving force for mass transfer ofoxygen is kept unchanged [5,43,44]. Thus, the extent and durationof the stirrer speed increase are indicative of the increased respi-ration activity, possibly due to the uptake of benzyl alcohol, whichsupports faster growth rate compared to glucose. The increasedrespiratory activity lasts longer at low dilution rates compared tothat at higher dilution rate (Fig. 4C).

3.4. Effect of induction time on adaptability to transient changein environment

As glucose is a significant contributor to the pool of utilizablecarbon substrates, all the microorganisms that prefer glucose toother substrates are expected to respond rather quickly to theavailability of glucose. However, P. putida CSV86 exhibits a uniqueproperty of preferring benzyl alcohol to glucose. Thus, it was ofinterest to examine the time required for the induction of rate limit-ing enzymes for glucose and benzyl alcohol. The rate of induction ofrate limiting enzyme for glucose utilization is approximately seventimes lower than that for benzyl alcohol utilizing enzyme (Table 1).The levels of the rate limiting enzymes for benzyl alcohol andglucose uptake were predicted for glucose-benzyl alcohol-glucosetransition phase (Figs. 5 and 6). Soon after the pulse input of benzylalcohol (at time t = 0), the rate limiting enzymes for benzyl alcoholand glucose utilization were induced and repressed, respectively.The predicted enzyme profiles were found to be in agreement withthe indirect measurement of whole cell oxygen uptake rates onthe corresponding substrate (Figs. 5 and 6). Further, experimen-tal validation of model predicted rate limiting enzyme by dynamic

the indirect measurement of whole cell oxygen uptake data arewell correlated and directly proportional to the direct measure-ment of specific activity of key inducible enzymes (Fig. 5). Inductionof benzyl alcohol utilizing enzyme takes place within 1 h of addi-

D. Das et al. / Process Biochemi

Fig. 4. Profiles of online measured variables after pulse input of benzyl alcohol tosteady state chemostat culture of Pseudomonas putida CSV86 growing on glucoseas sole carbon substrate. The profiles at different dilution rates (D) are shown as:D = 0.03 h−1: thick dark line; D = 0.06 h−1: thin dark line; D = 0.09 h−1: thick grey line.(A) pH; (B) Dissolved Oxygen (%) and (C) Stirrer speed.

stry 46 (2011) 701–708 705

tion of benzyl alcohol in the medium and reaches to a maximumlevel at approximately 2 h (Figs. 5B and 6B). On the other hand,the induction of glucose utilizing enzyme takes between 5 and 20 hdepending on the dilution rate (Figs. 5A and 6A). The induction ofthe enzyme was faster at higher dilution rate.

3.5. Turnover rate of rate limiting enzymes

Swift adaptation to the rapidly changing environment is basedon the twin mechanism of induction and repression. Proteinturnover essentially appears to be the mechanism involved in sucha situation. Assuming that the rate of synthesis of benzylalcoholdehydrogenase is insignificant after benzylalcohol was completelyconsumed by the microorganism, the turnover rate of the enzymecan be estimated from the degradation rates given by the slopeof the curve. The turnover rate was calculated to be in the rangeof 1.5–4 h−1. This degradation rate is several orders of magnitudeslower than the rate of nitrilotraiacetate (NTA) degrading enzymeNTA-MO in C. heintzii [1]. However, this degradation rate is com-parable with the rate of degradation in nongrowing and growingcells of B. cereus [45].

4. Discussion

There have been several initiatives to model microbial growthkinetics and substrate utilization pattern in a mixed substrate envi-ronment. These models are based on cybernetic principle, a goalseeking principle with optimization based on a short-term perspec-tive of response to the change in environmental condition [12–15].This goal has been defined as the optimization of overall growthrate. One can argue that this overall goal of growth maximiza-tion can be broken down into several sub-goals. For instance, asub-goal may involve the uptake of a specific substrate, which sup-ports the highest growth rate, and therefore, a related sub-goalmay involve the synthesis of the rate limiting enzyme(s) for uti-lization of that substrate. However, the rate limiting enzymes havebeen considered as latent or unmeasured parameters and have notbeen verified with experimental data. Here we present a struc-tured kinetic model to predict the mixed substrate growth kineticsand substrate utilization pattern by hydrocarbon degrading organ-ism P. putida CSV86. The model is based on a regulatory networkstructure where the biomass optimization is linked to the utiliza-tion of a growth favoring substrate and in turn, maintaining thelevel of substrate specific rate limiting enzyme(s). The key contri-bution of the current manuscript is the experimental validation ofthe model by measuring the rate limiting enzymes such as BADH,C12O and ZWF and the whole cell oxygen uptake rate on the spe-cific substrates. However, while the key enzymes for hydrocarbonand glucose metabolism have been quantified and elucidated inthe literature, the rate limiting steps for the same is not very wellestablished. Therefore, the experimental validation of the presentmodel is mostly based on indirect measurements such as substratespecific whole cell oxygen uptake study. Further, a key aspect ofthe present investigation has been on the quantification of kineticparameters including rates of induction degradation of enzymeswhich in turn are a measure of microorganism’s ability to adjustwith the sudden change in environmental condition.

The experimentally derived yield coefficients on benzyl alco-hol and glucose are in agreement with those reported for variousaromatic hydrocarbons and for different pseudomonas species. For

instance, the biomass yield coefficient on toluene has been foundto be 1.28 ± 0.13 g g−1 for P. putida F1 [46] and 1.2 g g−1 for P. putidaR1 [47,48]. However, biomass yield coefficient on toluene has beenfound to be lower (0.58 g g−1) for P. putida F1 ATCC 700007 [49].Further, the yield coefficients of biomass on benzene have been

706 D. Das et al. / Process Biochemistry 46 (2011) 701–708

39342924191494-1

O2 u

pta

ke

on

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LC

(nm

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/mg

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0

1

2

3

4

5

6

7

8

9

(E/E

ref )

BA

LC

0

1

2

3

4

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1200

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on

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Fig. 5. Model predicted profiles of rate limiting enzymes involved in substrate uptake compared with whole cell oxygen uptake rates and specific activity of key inducibleenzymes after pulse input of benzyl alcohol to steady state chemostat culture of Pseudomonas putida CSV86 growing on glucose as sole carbon substrate at a dilution rate of0.06 h−1. The cells were centrifuged, washed and re-suspended in phosphate buffer for oxygen uptake measurements via a Clark type oxygen sensor at 30 ◦C in the presenceof the respective substrate. The detail of enzyme activity assay is given in Section 2. (A) Whole cell oxygen uptake rate in the presence of glucose and the specific activityof ZWF are compared with rate limiting enzyme for glucose uptake; (B) Whole cell oxygen uptake rate in the presence of benzyl alcohol and the specific activity of BADHa l oxygb 2 for dt e enzfi rd dev

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re compared with rate limiting enzyme for benzyl alcohol uptake; (C) Whole celenzyl alcohol are compared with specific activity of C12O. Refer to legend to Fig.he experimental dissolved oxygen profiles are shown with closed circle (�) and thgure is the mean of multiple data sets. Error bars shown in the figure is the standa

eported to be in the range of 1.0–1.25 g g−1 for P. putida F1 and P.ragi B1 [46] and 0.75 g g−1 for P. putida F1 ATCC 700007 [49]. In a

ixed culture system the yield on naphthalene has been reportedo be 0.48 g g−1 [47]. The yield coefficients on glucose were found

o be comparable with those for E. coli [50] with the exception oftrain E. coli ML30 [51], which shows a very high yield in compari-on to P. putida CSV86. The estimated �max for P. putida CSV86 onenzyl alcohol is found to be similar to those for P. putida ATCC

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ig. 6. Model predicted profiles of rate limiting enzymes involved in substrate uptake coteady state chemostat culture of Pseudomonas putida CSV86 growing on glucose as sole che presence of glucose compared with rate limiting enzyme for glucose uptake; (B) Whimiting enzyme for benzyl alcohol uptake; (C) Whole cell oxygen uptake rate in the pregend to Figs. 2 and 5 for details. Model predicted key enzyme profiles are shown withymbols. Experimental data shown in the figure is the mean of multiple data sets. Error b

en uptake rate in the presence of catechol, an intermediate in the degradation ofetails. Model predicted key enzyme profiles are shown with continuous lines and

yme activity data are shown with open circle (©). Experimental data shown in theiation across the multiple data sets.

17484 [52] and P. aeruginosa [53] when grown on phenol as lim-iting carbon source and P. putida BS359 (NPL-1) when grown onsalicylate [54]. However, at low substrate concentration, mixed cul-ture of P. aeruginosa and P. fluorescene showed a twofold increase

in maximum specific growth [53]. The inhibitory effect of aromaticcompounds on substrate growth kinetics of P. putida CSV86 is foundto be lower than that for phenol on P. putida ATCC 17484 [52] andP. aeruginosa [53].

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mpared with whole cell oxygen uptake rates after pulse input of benzyl alcohol toarbon substrate at a dilution rate of 0.12 h−1. (A) Whole cell oxygen uptake rate inole cell oxygen uptake rate in the presence of benzyl alcohol compared with rateesence of catechol, an intermediate in the degradation of benzyl alcohol. Refer tocontinuous lines and the experimental dissolved oxygen profiles are shown withars shown in the figure is the standard deviation across the multiple data sets.

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The model reasonably predicts the transient dynamics ofiomass, glucose and benzyl alcohol caused by pulse input of ben-yl alcohol into a steady state culture of microorganism growingn glucose as the only carbon source. However, quantitative dis-repancies between experimental data and model predictions haveeen observed for biomass and benzyl alcohol for a certain period ofime. Further, while the simulated profile of key inducible enzymesor glucose utilization is shown to be repressed to a lesser extentn comparison to the whole cell oxygen uptake profile and thepecific activity of ZWF on the respective substrate, the enzymes shown to be re-induced at a much faster rate after completexhaustion of benzyl alcohol (Figs. 5 and 6). As stated earlier, theodel was developed irrespective of actual molecular mechanism

f substrate utilization pattern due to lack of sufficient experimen-al evidence. The observed discrepancies between model predictionnd the experimental data therefore can be attributed to the facthat while the model is predominantly based on classical theoryf catabolite repression and inhibition, the control in a metabolicathway has been shown to be distributed to various nodes such asatabolite repression, catabolite inhibition and substrate exclusion.his phenomenon is well demonstrated for P. putida CSV86 grownn various aromatic compounds and glucose as the sole source ofarbon. In a recent study of [14C] glucose uptake by whole cellseveals that the glucose is taken up by active transport [55] and thelucose transport is suppressed when the cells are grown on organiccids or aromatics compounds. The results suggest that the glucosetilization by P. putida CSV86 is controlled both at metabolic and atransport level. In a study similar to what presented here, substrateonsumption and growth kinetics for Escherichia coli was investi-ated for a mixture of glucose and galactose, under transient stateondition [50]. Dynamics of substrate consumption and enzymeynthesis in Chelatobacter heintzii was also investigated for mixedubstrate in chemostat [1]. Another key point emanating from thisork is that the organism not only grows faster on benzyl alcohol,

ut also adapts faster to this substrate than to glucose, the less pre-erred substrate. The rate of the synthesis of rate limiting enzymeor benzyl alcohol is approximately 7 times greater than that forlucose. Thus, the organism adapts to benzyl alcohol within 1–2 hhile taking 5–20 h to resume utilization of glucose upon complete

xhaustion of benzyl alcohol. These results have been verified byoth the oxygen uptake rates and the specific rate limiting enzymesuch as BADH, C12O and ZWF. To the best of our knowledge, thiss the first report where levels of the rate limiting enzymes of ainetic model have been experimentally measured. Further, theate of degradation of the rate limiting enzyme for benzyl alcoholas much lower. We conjecture that these features may be com-on to the design of micro-organisms that have evolved to survive

n a certain niche.

ppendix A.

HC Specific growth rate on hydrocarbon (h−1)glc Specific growth rate on glucose (h−1)maxHC Maximum specific growth rate on hydrocarbon (h−1)maxglc Maximum specific growth rate on glucose (h−1)

i Concentration of substrate i in the broth (g L−1)EHC Concentration of enzyme responsible for utilization of

hydrocarbon (g L−1)Eglc

Concentration of enzyme responsible for utilization of

glucose (g L−1)glcE Induction rate constant of enzyme for glucose utilization

(h−1)HCE Induction rate constant of enzyme for hydrocarbon uti-

lization (h−1)

[

[

stry 46 (2011) 701–708 707

ˇglc Degradation rate constant of enzyme for glucose utiliza-tion (h−1)

ˇHC Degradation rate constant of enzyme for hydrocarbon uti-lization (h−1)

KIi Substrate inhibition constant for substrate i (g L−1)KSi

Substrate half saturation constant for substrate i (g L−1)BALC Benzyl alcoholBADH Benzyl alcohol dehydrogenaseZWF Glucose 6-phosphate degydrogenaseC12O Catechol-1,2-dioxygenaseC23O Catechol-2,3-dioxygenase

References

[1] Bally M, Egli T. Dynamics of substrate consumption and enzyme synthesisin Chelatobacter heintzii during growth in carbon-limited continuous culturewith different mixtures of glucose and nitrilotriacetate. Appl Environ Microbiol1996;62(1):133–40.

[2] Jacob F, Monod J. Genetic regulatory mechanisms in the synthesis of proteins.J Mol Biol 1961;3:318–56.

[3] Tian L, Ma P, Zhong JJ. Kinetics and key enzyme activities of phenanthrenedegradation by Pseudomonas mendocina. Process Biochem 2002;37:1431–7.

[4] Littlejohns JV, Daugulis AJ. Kinetics and interactions of BTEX compounds duringdegradation by a bacterial consortium. Process Biochem 2008;43:1068–76.

[5] Bapat PM, Sohoni SV, Moses TA, Wangikar PP. A cybernetic model to predictthe effect of freely available nitrogen substrate on rifamycin B production incomplex media. Appl Microbiol Biotechnol 2006;72(4):662–70.

[6] Bapat PM, Bhartiya S, Venkatesh KV, Wangikar PP. A structured kinetic modelto represent the utilization of multiple substrates in complex media duringrifamycin B fermentation. Biotechnol Bioeng 2006;93:779–90.

[7] Bapat PM, Das D, Sujata S, Wangikar PP. Hierarchical amino acid utilization andits influence on fermentation dynamics: rifamycin B fermentation using Amy-colatopsis mediterranei S699, a case study. Microbial Cell Factories 2006;5:32.

[8] Dhurjati P, Ramkrishna D, Flickinger MC, Tsao GT. Cybernetic modelling ofmicrobial growth in multiple substrate systems. Proc Sec World Cong ChemEng 1981;16(5):252–5.

[9] Dhurjati P, Ramkrishna D, Flickinger MC, Tsao GT. A cybernetic view ofmicrobial growth: modeling of cells as optimal strategists. Biotechnol Bioeng1985;27:1–9.

10] Kompala DS, Ramkrishna D, Tsao GT. Cybernetic modeling of microbial growthon multiple substrates. Biotechnol Bioeng 1984;26:1272–81.

11] Ramkrishna D. A cybernetic perspective of microbial growth. In: Foundations ofBiochemical Engineering: Kinetics and Thermodynamics in Biological Systems.American Chemical Society; 1982.

12] Ramakrishna R, Ramkrishna D, Konopka AE. Cybernetic modeling of growth inmixed, substitutable substrate environments: preferential and simultaneousutilization. Biotechnol Bioeng 1996;52:141–51.

13] Ramakrishna R, Ramkrishna D, Konopka AE. Microbial growth on substitutablesubstrates: characterizing the consumer–resource relationship. BiotechnolBioeng 1997;54:77–90.

14] Venkatesh KV, Doshi P, Rengaswamy R. An optimal strategy to modelmicrobial growth in a multiple substrate environment. Biotechnol Bioeng1997;56:635–44.

15] Venkatesh KV. An optimal model to simulate growth of micro-organism on adissacharide. Process Biochem 1998;33:483–90.

16] Doshi P, Venkatesh KV. An optimal model for microbial growth in a multi-ple substrate environment: simultaneous and sequential utilization. ProcessBiochem 1998;33(6):663–70.

17] Doshi P, Rengaswamy R, Venkatesh KV. Modelling of microbial growth forsequential utilization in a multisubstrate environment. Process Biochem1997;32(8):643–50.

18] Altıntas MM, Kırdar B, Önsan ZI, Ülgen KO. Cybernetic modelling of growth andethanol production in a recombinant Saccharomyces cerevisiae strain secretinga bifunctional fusion protein. Process Biochem 2002;37:1439–45.

19] Dijken JR, Weusthuis RA, Pronk JT. Kinetics of growth and sugar consumptionin yeasts. Antonie Van Leeuwenhoek 1993;63:343–52.

20] Al-Awadhi N, Egli T, Hamer G, Manson CA. The process utility of thermotoler-ant methylotrophic bacteria: II. An evolution of transient response. BiotechnolBioeng 1990;36:821–5.

21] Sipkema EM, Koning WD, Ganzeveld KJ, Janssen DB, Beenackers AACM. Exper-imental pulse technique for the study of microbial kinetics in continuousculture. J Biotechnol 1998;64:159–76.

22] Bally M, Egli T. Dynamics of substrate consumption and enzyme synthesisin Chelatobacter heintzii during growth in carbon-limited continuous culturewith different mixtures of glucose and nitriloacetate. Appl Environ Microbiol

1996;62(1):133–40.

23] Standing CN, Fredrickson AG, Tsuchiya HM. Batch- and continuous-culturetransients for two substrate systems. Appl Microbiol 1972;23(2):354–9.

24] Kompala D, Ramkrishna D, Jansen NB, Tsao GT. Investigation of bacterial growthon multiple substrates: experimental evolution of cybernetic models. Biotech-nol Bioeng 1986;28:1044–55.

7 chemi

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[[

[

[

[

[

[

[

[

[The efficiency of salicylate utilization by Pseudomonas putida strains catab-

08 D. Das et al. / Process Bio

25] Basu A, Dixit SS, Phale PS. Metabolism of benzyl alcohol via catechol ortho-pathway in methylnaphthalene-degrading Pseudomonas putida CSV86. ApplMicrobiol Biotechnol 2003;62:579–85.

26] Mahajan MC, Phale PS, Vaidyanathan CS. Evidence for the involve-ment of multiple pathways in the biodegradation of 1-methylnaphthaleneand 2-methylnaphthalene by Pseudomonas Putida CSV86. Arch Microbiol1994;161:425–33.

27] Basu A, Apte SK, Phale PS. Preferential utilization of aromatic com-pounds over glucose by Pseudomonas putida CSV86. Appl Environ Microbiol2006;72:2226–30.

28] Phale PS, Mahajan MC, Vaidyanathan CS. A pathway for biodegradationof 1-naphthoic acid by Pseudomonas maltophilia CSV89. Arch Microbiol1995;163:42–7.

29] Basu A, Das D, Bapat PM, Wangikar PP, Phale PS. Sequential utilization of sub-strates by Pseudomonas putida CSV86: signatures of intermediate metabolitesand on-line measurements. Microbiol Res 2009;164(4):429–37.

30] Bradford MM. A rapid and sensitive method for the quantitation of micro-gram quantities of protein utilizing the principle of protein-dye binding. AnalBiochem 1976;72:248–54.

31] Shaw JP, Harayama S. Purification and characterisation of TOL plasmid-encodedbenzyl alcohol dehydrogenase and benzaldehyde dehydrogenase of Pseu-domonas putida. Eur J Biochem 1990;191:705–14.

32] Kojima Y, Fujisawa H, Nakazawa A, Nakazawa T, Kanetsuna F, Taniuchi H,Nozaki M, Hayaishi O. Studies on pyrocatechase. I. Purification and spectralproperties. J Biol Chem 1967;242:3270–8.

33] Lessmann D, Schimz KL, Kurz G. d-Glucose-6-phosphate dehydrogenase(Entner-Doudoroff enzyme) from Pseudomonas fluorescens. Purification, prop-erties and regulation. Eur J Biochem 1975;59:545–59.

34] Rodriguez-Fernandez M, Egea JA, Banga JR. Novel metaheuristic for parame-ter estimation in nonlinear dynamic biological systems. BMC Bioinformatics2006;7:483.

35] Rodriguez-Fernandez M, Mendes P, Banga J. A hybrid approach for effi-cient and robust parameter estimation in biochemical pathways. Biosystems2006;83:248–65.

36] Polisetty P, Voit E, Gatzke E. Identification of metabolic system parametersusing global optimization methods. Theor Biol Med Model 2006;3:4.

37] Zwolak J, Tyson J, Watson L. Globally optimised parameters for a model ofmitotic control in frog egg extracts. IEE Proc Syst Biol 2005;152(2):81–92.

38] Moles C, Mendes P, Banga J. Parameter estimation in biochemical pathways: a

comparison of global optimization methods. Genome Res 2003;13:2467–74.

39] Egea JA, Rodriguez-Fernandez M, Banga J, Marti R. Scatter search for chemicaland bio-process optimization. J Global Optim 2007;37(3):481–503.

40] Wong P, Gladney S, Keasling JD. Mathematical model of the lac operon: inducerexclusion, catabolite repression, and diauxic growth on glucose and lactose.Biotechnol Prog 1997;13:132–214.

[

stry 46 (2011) 701–708

41] Dumsday GJ, Ocal G, Bridger JS, Zachariou M. The use of oxygen uptake rate tomonitor discovery of microbial and enzymatic biocatalysts. Biotechnol Bioeng2008;102(3):673–83.

42] Schäfer S, Schrader J, Sell D. Oxygen uptake rate measurements to mon-itor the activity of terpene transforming fungi. Process Biochem 2004;39:2221–8.

43] Doan XT, Srinivasan R, Bapat PM, Wangikar PP. Detection of phase shifts inbatch fermentation via statistical analysis of the online measurements: a casestudy with rifamycin B fermentation. J Biotechnol 2007;132:156–66.

44] Maiti SK, Srivastava RK, Bhushan M, Wangikar PP. Real time phase detectionbased online monitoring of batch fermentation processes. Process Biochem2009;44:799–811.

45] Urba RC. Protein breakdown in Bacillus cereus. Biochem J 1959;71:513–8.46] Reardon KF, Mosteller DC, Rogers J. Biodegradation kinetics of benzene, toluene,

and phenol as single and mixed substrates for Pseudomonas putida F1. Biotech-nol Bioeng 2000;69(4):385–400.

47] Guha S, Peters C, Jaffe PR. Multisubstrate biodegradation kinetics ofnaphthalene, phenanthrene, and pyrene mixtures. Biotechnol Bioeng1999;65(5):491–9.

48] Pedersen AR, Møller S, Molin S, Arvin E. Activity of toluene degrading Pseu-domonas putida in the early growth phase of a biofilm for waste gas treatment.Biotechnol Bioeng 1997;54:131–41.

49] Abuhamed T, Bayraktar E, Mehmetoglu T, Mehmetoglu U. Kinetics modelfor growth of Pseudomonas putida F1 during benzene, toluene and phenolbiodegradation. Process Biochem 2004;39:983–8.

50] Egli T, Lendenmann U, Snozzi M. Kinetics of microbial-growth with mixturesof carbon-sources. Antonie Van Leeuwenhoek Int J General Mol Microbiol1993;63:289–98.

51] Kovar K, Kach A, Zehnder AJB, Egli T. Cultivation of Escherichia coli with mix-tures of 3-phenylpropionic acid and glucose: steady-state growth kinetics. ApplEnviron Microbiol 1997;63(7):2619–24.

52] Hill GA, Robinson CW. Substrate inhibition kinetics: phenol degradation byPseudomonas putida. Biotechnol Bioeng 1975;XVII1:599–1615.

53] Oboirien BO, Amigun B, Ojumu TV, Ogunkunle OA, Adetunji OA, BetikuE, Solomon BO. Substrate inhibition kinetics of phenol degradationby Pseudomonas aeruginosa and Pseudomonas fluorescence. Biotechnology2005;4(1):56–61.

54] Filonov AE, Karpov AV, Kosheleva IA, Puntus IF, Balashova NV, Boronin AM.

olizing naphthalene via different biochemical pathways. Process Biochem2000;35:983–7.

55] Basu A, Phale PS. Inducible uptake and metabolism of glucose by the phos-phorylative pathway in Pseudomonas putida CSV86. FEMS Microbiol Lett2006;259:311–6.