aspergillus tamarii growth

7/30/2019 Aspergillus Tamarii Growth

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Chp 4 16 January 2012 v4

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CHAPTER 4

Energetics of Growth ofAspergil lus tamari iin a Biological Real Time Reaction Calorimeter

4.1 Introduction

Filamentous fungi are widely used in the production of homologous and heterologous

proteins due to their desirable growth characteristics, limited space required for their cultivation

and ease of downstream processing (Srinubabu et al. 2007). Fungi which exhibit literal

glycosylation/post transitional modification of proteins are classified as GRAS (generally

regarded as safe) by the FDA (Food and Drug Administration) and can be grown on a wide

variety of inexpensive substrates (Li et al. 2000). Growth can be defined as the increases in cell

size and number that take place during the life history of an organism (Encyclopedia-Britannica,

2010). Growth and product formation from the cultured microorganisms are governed by a wide

range of parameters such as culture medium, pH, temperature, shear stress and fungal

morphology. Fungal fermentation is a complicated multi-phase, multi-component process (Wang

et al. 2003). For an effective fungal fermentation process, knowledge of the influence of

operation parameters and morphology of fungi is needed. General methodologies adopted in

studying the fungal growth are colony diameter, hypal elongation rate, biomass, ergosterol

content and heat production rate (Li et al. 2011). Each parameter has a significant role in

quantifying the different aspects of fungal growth process.

Recently, biocalorimetry has been found to have potential for real-time bioprocess

monitoring due to its non-invasive and instantaneous mode of operation (Surianarayanan et al.

2011). Calorimetry also plays an important role in quantitative engineering and in bioreactor

control. Indeed, heat signals during a bioprocess provide a global insight into metabolic activity

of living cells. In some of the reported studies (von Stockar and Marison 1989; Voisard et al.

2002; Surianarayanan and Senthilkumar 2009), the metabolic heat production has been well

correlated to microbial growth, oxygen uptake and product formation. Improvements in

sensitivity of bench scale calorimeters have made it possible to undertake real-time monitoring

of several bioprocesses (Marison et al. 1998). Heat yield values and stoichiometric yields were

also established in literature for different microbes (Sandler and Orbey 1991). As the metabolism

is a coupled effect of both anabolic and catabolic reactions, a thorough and detailed study is

required to distinguish the individual activities of microbes. Both anabolic and catabolic


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activities of a cell culture contribute to the total enthalpy change of a biological reaction. In some

cases, catabolic process appears to be dominantly exothermic as compared to anabolic process

(Surianarayanan and Senthilkumar 2008). Heat production rates depend on the nature of

catabolism. Calorimetrically analyzing the catabolic reactions can give insight into the energy

changes involved in a growth process.

Isothermal calorimetry has the advantage of being a non-destructive technique and offers

a continuous recording of the thermal power as a function of time. The features of isothermal

calorimetry method in measuring the activity of fungi are

It is a general and unspecific technique that can be used for any types of substrateand organisms.

During a calorimetric measurement, the thermal power is continuously measured.One can thus monitor processes in detail while they take place.

It is a non-destructive technique. As heat flows through all materials, one can monitor processes taking places

inside opaque materials and packages.

It is often a sensitive technique.Biomass and thermal power are two quite different measures of growth. In an external

aspect (biomass) and the other one relates to its internal activity (thermal power). However,

during the active growth period of an organism, the biomass accumulation is closely related to its

anabolic and catabolic activities since the biomass is the result of organisms anabolismthe

conversion of carbon substrate into biomass coupled to the transformation of ADP to ATP.

Anabolism is driven by catabolism-the combustion of substrate carbon with oxygen to give

carbon dioxide, which generates the energy carrier ATP from ADP. From this perspective

biomass and thermal power are related; the thermal power comes from the catabolism that drives

the anabolism that results in new biomass. The thermal power from an organism can be

measured and used as an indicator of the organisms activity level. The measurement of thermal

power has therefore been used in studying the growth and production of filamentous fungi. Thus,

thermo kinetics is one of the vital information required for consistent yield, successful scale-up

and design of bioprocesses.

Assessing the enthalpy balances for the fermentation process is important for process

calculations. Enthalpy balance for a fermentation processes can be understood from the enthalpy


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content of major substrates, products and biomass. The enthalpy of dried biomass depends upon

origin and classification, hence data reported in the literature for the same generic species may

vary 1.5 times (Ishikawa and Shoda 1983) and it may be determined experimentally by burning

samples in a bomb calorimeter. In our study four models (Corider et al. 1987) that relate the heat

of combustion of organic matter to its elemental composition are compared with experimentally

determined value forA. tamarii.

The change of mycelia morphology of filamentous microorganisms in BioRTCal will

have remarkable effect on the primary or secondary metabolites production and also on enzyme

production. In submerged fermentation, filamentous fungal morphology is classified as either

dispersed or pelleted, and the dispersed form can be further divided as freely dispersed and

clumps. Many studies have discussed the advantages and disadvantages of growth morphologies

in terms of targeting different type of products (Liao et al. 2007). It has been concluded that the

fungal growth in pellet form was a favorable alternative to benefit the most of fungal

fermentations because it not only made fungal biomass reuse possible but also significantly

improved the culture rheology, resulting in better mass and oxygen transfer into the biomass, and

lower energy consumption for aeration and agitation (Papagianni and Moo-Young, 2002;

Dobson et al. 2008). Agitation rate, initial glucose concentration and DO will have significant

effect on morphology (Wang et al. 2003). In cold mode experiments, the pellet size are

controlled by growth more than by mechanical force and the pellets are stable for several hours

(Cui et al. 1997).

Aspergillus tamarii MTCC5152, a typical filamentous fungus was selected as a model

system to study the feasibility of monitoring growth in BioRTCal based on metabolic heat flux.

Coupling of hydrodynamics and bioreaction highlighted the complex relationship between

energy dissipation, substrate uptake rate and fungal physiology. An attempt was made to relate

the morphological changes during the fungal growth process to the heat flux. The enthalpy of the

dried biomass depends on the cellular composition and can be characterized by elemental

analysis are discussed in detail.

4.2 Materials and methods

The details about the fungal strain A. tamarii, growth media composition, analysis,

chemicals and reagents employed here were the same as in Chapter 3.


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Growth ofA. tamari i

The growth ofA. tamarii was monitored by gravimetric method. Samples taken from the

calorimeter were centrifuged at 10000 g, for 15 min, at 4C (Sigma 3-18k, Germany). The

supernatant was decanted and cells were separated. The harvested cells were freed from soluble

salts, nutrients and waste products by washing thrice with sterile water and dried at 80C to a

constant weight.

Metabolic heat evaluation in a Biological Real Time Reaction Calorimeter (BioRTCal)

The BioRTCal is a heat-flux bench-scale calorimeter of Mettler-Toledo (Switzerland)

shown in Figure 4.1(a). The system was equipped with pH probe, turbidity probe, and DO probe

(Mettler-Toledo, Switzerland). The inbuilt DO probe was used to monitor the variations in

dissolved oxygen (DO) value. Heat generated from the BioRTCal was monitored and controlled

by iControl RC1e 4.0 software. The calorimeter shown in Figure 4.1 was a 2 liter jacketed glass

reactor that can operate in isothermal, adiabatic, or isoperibolic mode. For biological reactions,

the calorimeter was operated in isothermal mode to avoid excessive accumulation of heat inside

the reactor that might affect the growth of cell culture. In isothermal mode, the temperature of

the reactor (r

T ) was held constant by proper control of the jacket temperature ( jT ) by

circulating low-viscosity silicone oil at a high rate through the reactor jacket. Blending oils from

hot and cold oil circuits, through an electronically controlled metering valve, controlled the

jacket temperature. Agitation was achieved through a 4-blade pitched turbine impeller for

effective mixing. The inlet air for aeration was controlled by a Rotameter, sterilized through a

membrane filter (0.2m) and sparged through the bottom of the reactor and exited through a

second membrane filter (0.2m). Thus, a process dissipating or taking up heat resulted in a

decrease or increase in jT , leading to a temperature gradient across the reactor wall directly

proportional to the thermal flux liberated or absorbed by the process ( rq ) as per

)( jrr TTUAq (4.1)

where, UA was the overall heat transfer coefficient,rT the reactor temperature (C) and jT the

jacket temperature (C). The heat flow, RTCq , was estimated online by the vertical and horizontal

inbuilt sensors bands shown in Figure 4.1(b). Heat flux sensors attached to the outer wall of the


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reaction vessel measured the specific heat flow through the horizontal sensor band. The fill level

was determined by the vertical sensor band. This allowed the heat flow through the reactor wall

to be calculated using the equation (4.2)

SORTC qAq . (4.2)

where RTCq was heat flow through the part of the reaction vessel wall that is wetted by its

contents (W), A is the effective heat exchange area, determined by the sensors of the vertical

band (m2), and SOq is the specific heat flow through the horizontal sensor band (W/m

2). The

principle behind, heat determinations from the calorimeter were well documented (Turker, 2004

and Senthilkumar et al. 2008).

The metabolic heat (heat flux) measured from microbial growth ( rq ) given by Equation

(4.1) was corrected for the constant baseline heat production. Baseline heat was the sum of

external heat loss (loss due to aeration, environmental heat loss) and heat input in to the system

(stirring power, heat accumulation, and dosing heat). This baseline heat value ( bq ) was carefully

eliminated from the heat evolved due to biological processes. The heat flow balance in

BioRTCal is depicted in Figure 4.1(c). The value of metabolic heat flux during the bioprocess (

q ) was determined by deducting the measured rq (from Equation 4.1) from that of the base-line

signal ( bq ):

br qqq (4.3)

Power-time curve was obtained by plotting heat flow rate ( q ) against the corresponding

time. Heat-time curves were obtained by plotting cumulative heat values (calculated from power-

time curve by integrating for specified time intervals) against the corresponding time. Scatter and

errors in the heat evolution rate measurements were effectively smoothed by integration of the

signal. All experiments were performed with a working volume of 1.2 L and the reaction

temperature was maintained constant at 28C. Agitation rate and aeration was maintained at 350

rpm, 1vvm (volume of air per volume of medium per minute) throughout the experiment.

Cultures ofA. tamarii were grown in batch mode until depletion of the carbon source. Dissolved

oxygen (DO) sensor in BioRTCal was initially calibrated by purging with pure oxygen and

nitrogen. A minimum DO value of 4 ppm was always maintained to ensure aerobic conditions


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throughout the reaction. Oxygen uptake rate (OUR) was calculated by the dynamic method

(Garcia-Ochoa et al. 2010).

The total heat liberated depended on the nature of the carbon and energy source,

particularly to the degree of reduction, the biomass yield coefficient and physical conditions such

as temperature, pH and the aerobicity of the culture, as well as the specific chemical composition

of the medium. For these reasons, the use of calorimetry for the finger-printing of strains had

to be undertaken in strictly controlled conditions.

Heat-yield coefficients

Microbial heat evolution data would be used indirectly to determine biomass

concentration (von Stockar & Marison 1989) and OUR (Garcia-Ochoa et al. 2010). The heat

yields might vary during the growth process, and also, during the production of secondary

metabolites in non-growth associated processes. The heat production rate is an important process

variable, since the heat yields are dependent on the other process yields such as biomass,

substrate and oxygen.

Experimental determination of Oxygen Uptake Rate (OUR) & volumetric mass transfer

coefficient ( akL

)

Dissolved oxygen (DO) sensor in BioRTCal was initially calibrated by purging with pure

oxygen and nitrogen. A minimum DO value of 4 ppm was always maintained to ensure aerobic

conditions throughout the reaction. The macroscopic mass balance for the dissolved oxygen in

the well mixed liquid phase could be written as

XOLLLL CqCCak

dt

dC.).(

2

* (4.4)

where dtdCL is the accumulation of oxygen in the liquid phase, the first term on the right hand

side in Equation (4.4) is the oxygen transfer rate (OTR) and the second term is the oxygen uptake

rate (OUR). Rearranging of Equation (4.4) yielded


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aK

dtdCCqCC

L

LXO

LL

)(.2*

(4.5)

OUR was calculated by the dynamic method. It is based on the respiratory activity of

microorganisms [Garcia-Ochoa et al. 2010] actively growing in the BioRTCal. The air flow to

the broth is interrupted for a few minutes as the DO concentration decreases, values are recorded

by the iControl 4.0 software interfaced with the DO probe. The slope of the linear decrease in

LC value with time, yields OUR. When the aeration is turned on again, the DO concentration

increases and reaches a steady-state concentration. The slope of the response curve at a given

point is measured to get dtdCL , and from Eq. (4.5) on plotting LC against [ )(.2 dtdCCq LXO ],

slope becomes equal to the reciprocal of akL . The procedure was repeated several times during

the production process [Potumarthi et al. 2007]. Microbial heat evolution data would be used

indirectly to determine biomass concentration and OUR.

Energy dissipation per circulation function (EDCF) and Aeration Number in BioRTCal

Power was measured directly from electrical consumption of the motor, and a correction

was applied to compensate for power drawn due to friction in the gearbox and fittings. Power,

agitator speed and airflow rate were reported as energy dissipation/circulation function (EDCF)and aeration number ( AeN ) as described here. EDCF (W/m

3.s), was found to correlate better with

morphological and fragmentation data than power per unit liquid volume ( Lg VP / ) [Amanullah et

al., 1999; Justen et al., 1998 and Li et al. 2000]. In high-viscosity system the gas-filled cavities

behind the impeller blades were quite stable, and their size was almost independent of gassing

rate [Nienow, 1990]. From the reported information, it was evident that the turbulent regime

ungassed power numbers (PN ) were similar for shear thinning fluids (e.g., filamentous fungal

broth) and for water [Nienow et al., 1983]. Thus, to determine the power number in BioRTCal, a

previously worked out strategy was adopted [Senthilkumar et al. 2008],PN for the pitched blade

turbine was approximately 1.25 [Houcine et al, 2000]. To characterize mixing for the agitation

and aeration conditions, Reynolds number ( ReN ) was employed. The ratio of gassed to ungassed

power (og PP / ) was determined as per Doran [1995]. Thus, to arrive at EDCF for fungal


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fermentation in the BioRTCal, total gassed power was taken as proportional to 5iD . In addition to

power distribution, geometric constant, k(Eq. 4), and ungassed flow number, FlN (Eq. 4.11),

were determined as previously reported (Smith et al., 1990). Gassed flow numbergFlN , (Eq.

4.10) and gassed circulation time, ct (Eq. 4.9) were also determined as previously reported

(Justen et al., 1998). Agitation and aeration rates were reported in terms of aeration number ( AeN

), determined according to Equation 4.12, below.

ci

g

tkD

PEDCF

3

(4.6)

2.0

3/2

425.0

1.0

Li

L

g

o

g

gWV

ND

NV

F

P

P

(4.7)

iD

Wk

4

(4.8)

3

, igFl

Lc

NDN

Vt

(4.9)

P

gPFlgFl

NNNN ,,

(4.10)

5.0

91.0

i

PFlD

WNN

(4.11)

3

i

g

AeND

FN

(4.12)

Stoichiometry of fungal growth process

If heat flux calorimetry is to play a more important role in quantitative engineering

related studies and in bioprocess monitoring, the black box model for cell growth process need

to be revealed. The quantitative relationship of the heat evolution rate with other relevant process

variables, such as biomass concentration, growth rate and so on must be elucidated. Heat flow


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has often been considered as non specific information, which may account for some prejudices in

the field of biocalorimetry. However, it has been shown that specific, quantitative information

on the above mentioned parameters may readily be deduced from heat evolution measurements

on analysing them in terms of combined enthalpic and elemental balances of microbial growth

(von Stockar and Marison, 1989). The stoichiometry of a general growth process under pure

aerobic conditions (no side product) may be described in terms of a chemical equation as

follows:

OHYCOYNOCHYNHYOYNOCH SWSCXXXSXSNSOSSS 2'

/2

'

/321

'

/3

'

/2

'

/321 (4.13)

This equation has been formulated in terms of C-moles, which means that each chemical formula

has been reduced to the basis of one carbon atom. In this notation, the letters appearing in the

subscript stand for substrate (S), biomass (X), O2 (O), NH3 (N), CO2 (C) or H2O (W), whereas

the numbers designate the elements H (1), O (2), and N (3). In order to use the combined

elemental and enthalpy balances the chemical formula for all compounds listed in Equation

(4.13) as well as data on their heats of combustion is required.

Elemental composition and heat of combustion ofA. tamari i

Estimation of the enthalpic content of dried microbial biomass from the literature is

difficult due to difference in isolation nature of microorganism, growth pattern and medium

composition.

Preparation ofA. tamari isample for elemental analysis

Colonies ofA. tamarii were harvested in the exponential phase, in a vessel cooled by ice

water to prevent lysis. Washing was performed thrice with ice-cold water to remove cell debris

and the cells were resuspended in water, poured into petri dishes as thin layer, frozen overnight

at -20C and lyophilized. The lyophilized and dried cells obtained in powder form were sealed in

a sample bottle used for combustion and elemental analysis.

Combustion calorimetry

The enthalpy content of dried biomass can be estimated experimentally by combustion

calorimetry, but the measurements are tedious and time consuming. An isoperibol Bomb

Calorimeter (Parr, USA, Model: 6200) was used for the determination of heat of combustion.

The calorimeter controlled at constant temperature using a circulating water bath, enables the

control section of the calorimeter to determine the heat leak corrections and apply them in real


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time. This combination makes it possible to operate the calorimeter in dynamic mode for rapid

testing without a detectable difference in the precision of the test.

The bomb features an automatic inlet check valve and an adjustable needle valve for

controlled release of residual gasses following combustion. They are intended for samples

ranging from 0.6 to 1.2 g with a maximum energy release of 8000 calories. The removable

bucket has been designed to hold the bomb, stirrer and thermistor with a minimum volume of

water and to provide an effective circulating system which will bring the calorimeter to rapid

thermal equilibrium both before and after firing. The bomb is made of a high-strength, high

nickel stainless steel designed to resist the corrosive acids produced. The calorimeter can be

operated on an open system where tap water is used to cool the jacket and to fill the bucket. The

heat capacity of the calorimeter is determined by burning a specified mass of benzoic acid in

oxygen. A comparable amount of the analysis sample is burned under the same condition in the

calorimeter as per the ASTM D 5865-02 protocol. The calorific value of the analysis sample is

computed by multiplying the corrected temperature rise, adjusted for extraneous heat effects, by

the heat capacity and dividing by the mass of sample.

Elemental analysis

The general stoichiometric equation for the combustion of ill-defined biomass is

represented as

2222222

1

4

1 )( NOHCOxOxxxNOHC NHNOHC

xx

COHCxxxx (4.14)

where ONHC xxxx ,,, are the stoichiometric indices of C, H, N and O respectively. The energy

content of fungal biomass ultimately depends on the cellular composition which can be

characterized by elemental analysis. It is an indirect determination of the heat of combustion and

is less time consuming. 5 mg of the lyophilized fungal sample was used for elemental analysis

(C, H and N) (Perkin-Elmer, Model PE2400, Perkin Elmer Co., Norwalk, CT, USA). The

stoichiometric indices can be computed from elemental analysis as follows

12

CTC

fx (4.15)

HTH fx (4.16)

14

NTN

fx

(4.17)


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16

OTO

fx

(4.18)

where OTNTHTCT ffff ,,, represents mass fraction of the content of C, H, N and O corrected for

ash and residual water. From the analysis results the H was corrected for the contribution ofresidual water as shown in Equation (4.19)

OHappHH fff 218

2, (4.19)

where OHappHH fff 2,, , is the true content of H, the content of H is indicated by the elemental

analyser, the content of residual water respectively. The fraction of O was computed as follows

)(12OHashNHCO

ffffff (4.20)

where OHashNHC fffff 2,,,, are mass fraction of the contents of C, H, N, ash and residual water.

The number of moles of oxygen consumed during combustion of organic compound is found

from the stoichiometric Equation (4.14).

Four models were taken from the literature that relates the heat of combustion of organic

matter to its elemental composition.

Thornton Model

The molar heat of combustion of many organic compounds is directly proportional to thenumber of oxygen atoms during combustion, represented by Equation (4.21)

CO xQH (4.21)

C

OHC

x

xxx )(421

41

(4.22)

whereQ (assigned as 110.88 kJ/mol) is the heat evolved per number of available electron

equivalents transferred to one gram atom of oxygen during combustion; is degree of

reductance, defined as the number of electrons transferred to oxygen per atom of carbon defined

by Equation (4.22). The enthalpy content of many organic compounds can be predicted

accurately using Equation (4.21) and thus could also be used to predict the heat of combustion of

various types of ill-defined organic mixtures (fungal biomass) at high degree of accuracy.


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Giese Model

Giese proposed a procedure for estimating the energy content of microbial biomass based

on reduction level (RL). The RL is one fourth of the reductance degree () as defined by

Equation (4.23)

CO xQH RL4 (4.23)

C

OHC

x

xxx

2

5.02RL

(4.24)

where Q assigned as 115.06 kJ/mol and the RL indicate the number of molecules of oxygen

utilized per carbon atom during the combustion.

Ho and Payne Model

The calculation procedure proposed by Ho and Payne is also based on the oxygen

consumption during combustion as obtained from the combustion stoichiometric Equation

(4.14).

CTO fH 852.44 (4.25)

Dulong Model

The energy content of fuels was estimated based on the correlation

805.14476.33 OTHTCTO

fffH

(4.26)

The coefficients in Equation (4.26) are derived from the enthalpy contents of carbon and

hydrogen in their elemental states.

Pellet size, apparent density and pellet porosity

The particle characteristics of the A. tamarii fungal pellets were studied by randomly

choosing 20 pellets from the sample. The sizes of the pellets were measured under a microscope

on a hemocytometer. The average diameter (D) was calculated as

idn

D1

(4.27)


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where n is the number of pellets and di is the diameter of each pellet. The volume related specific

surface ( vS ) was determined by assuming that all the pellets have the same spherical shape

defined as

i

vd

S 6 (4.28)

The same samples were used to measure the apparent density of the pellets. The terminal

settling velocity ( u ) of the fungal pellets were determined by dropping the pellets into a long

narrow tube of water at 25C and allowed to settle freely. Using a stopwatch, the time taken for a

pellet to pass 20cm length at a uniform velocity was recorded. The apparent density of the wet-

pellets ( ap ) was calculated using Stokes Law (Bird et al. 1960).

18

2 wapgDu , for 1Re10 4 (4.29)

or

6.0

2

Re27.0w

wapgDu

for 310Re1 (4.30)

where ap was the apparent wet pellet density, w is the water density (997 kg/m3

at 25C) and

g is acceleration due to gravity. From published sources wet hypae density would be assumed as

1100 kg/m3 (Cui et al. 1997). Thus, from the apparent density of wet pellets calculated above, the

porosity of the pellets )( was estimated as

ppap )1( (4.31)

where p was the wet hyphae skeleton density. The calculated pellet porosity represented the

comprehensive features of a pellets structure (dense or loose) and surface conditions (smooth or

fluffy, hairs being long or short). Loose and fluffy pellets would have higher porosity values. The

data then could be related to the fermentation conditions. The culture morphology was analyzed

by 2D photographs using a microscope (Nikon Eclipse 80i, USA ).

Batch kinetics (unstructured growth model)

The metabolism of growth of fungiA. tamarii was analyzed in single substrate (glucose)

growth medium using metabolic heat obtained from BioRTCal. For designing a fermentor, it is


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necessary to consider the bioreactor performance and the microbial kinetics. Description of the

bioreactor performance involves modeling of mass transfer effects and flow pattern in gas and

liquid phases. Microbial kinetics deals with the individual cell level and the level of the entire

cell population. Single substrate driven growth (Bailey and Ollis, 1987) of the biomass is

governed by the following equations

Xdt

dX , oXXt ;0 (4.32)

SXY

X

dt

dS

/

, oSSt ;0 (4.33)

where X is the biomass concentration, Sis the substrate concentration, oX is the initial biomass

concentration, oS is the initial substrate concentration, is the specific growth rate and S/XY is

the yield coefficient. Essential parameters for developing correlation were obtained from

BioRTCal experimental results.

Results and discussion

Measurement ofA. tamari igrowth in BioRTCal

The heat flux due to cultivation ofA. tamarii in a BioRTCal for different glucose

concentrations is presented in Figure 4.2. At 2.5 g/L glucose concentration the heat release rates

are less when compared with that of 5 g/L and 7.5 g/L. The power time curve exhibits three

distinct regions of growth irrespective of the glucose concentration. The initial lag phase extends

up to 1100 min for 5 g/L and 7.5 g/L glucose concentration, while for 2.5 g/L glucose

concentration it is 1400 min. The culture A. tamarii obviously needs more time for adaptation

and relaxation before it enters the exponential growth phase. In the exponential phase, the heat

release rate peaks as a result of both cell multiplication and glucose consumption. In the case of

7.5 g/L glucose concentration experiment, the exponential phase extends for a long time before

the culture returns to decay or endogenous state. In 5 g/L glucose concentration experiments the

exponential phase ends sharply at 1700 min, the shape and trend of the power time curve are

comparable to the previously reported studies with different types of microorganisms. The

details of pellet characteristics and other growth data are summarized in Table 4.1. A close


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examination of the data indicates that 5 g/L glucose concentration is the limiting concentration

for maximum growth.

In Figures 4.3-4.5, a comparative plot of power time curve, biomass growth-cell dry

weight (CDW), glucose consumption and OUR is presented for 2.5, 5 and 7.5 g/L initial glucose

concentrations to correlate growth to heat release profiles. In Figure 4.4 glucose consumption

begins right from the addition of inoculum ofA. tamarii, to the reactor. Maximum consumption

(80%) occurs within the first lag phase (0-1100 min). However it may be noticed that during this

phase, the biomass growth is not appreciable. The sudden increase of heat flow rate shows that

the fungal cells enter the log phase and this is validated by the increase in CDW in Figure 4.4.

Interestingly growth or cell multiplication (marked by biomass increase) rapidly occurs during

the later part of the exponential phase. After reaching maximum growth rate, both biomass and

OUR remained almost flat. While the heat profile is not gradually decreasing, it exhibited a

short dip at the end of exponential phase and was fluctuating around 0.25 W/L, suggesting

stationary behavior. Thus the heat signal finger printed well all phases of growth.

The power time curve is quite characteristic of A. tamarii; biomass concentration

increases exponentially until the carbon source glucose becomes limiting. The heat generation

rate, q, follows the growth curve until the end of the exponential growth phase 1560 min. Heat

signal falls rapidly during the endogenous metabolism of the cells at 1740 min. This power-time

curve has a distinct shape that can be used for the identification of specific strains.

Biomass heat yield

In order to determine the energy efficient process in a fungal metabolism (anabolic or

catabolic), to observe the substrate shifts and for validating stoichiometry, it was necessary to

estimate the yield coefficients. Results published (Senthilkumar et al. 2008) reveals that the

amount of heat evolved by a microbial culture is directly related to growth and can be

represented by the heat yield coefficient with respect to biomass, XQY / (kJ heat evolved per g of

CDW). Heat yield coefficient can be determined from a plot between the total heat evolved by

the culture (kJ/L) and the biomass concentration (g/L) or from the rate of heat evolution by the

culture (W/L) and the biomass production rate (g/L.hr). Figure 4.6 shows the values obtained for

an aerobic batch culture of the fungiA. tamarii grown on a glucose-limited mineral salt medium.

At low biomass concentrations, i.e. the period immediately after inoculation, the rate of heat


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evolution is very small and close to the limits of calorimetric detection, whereas at the end of the

exponential growth phase, q varies as a function of the limiting substrate concentration.

The biomass yield coefficient GXY / (deduced from slope of CDW and residual glucose

concentration) had a pronounced effect on XQY / . The effects of GXY / on XQY / for three different

glucose concentrations (2.5, 5 and 7.5 g/L) are shown in Figure 4.7-4.9. The first growth phase

was characterized by high substrate consumption per unit biomass formed, and thus a low value

of GXY / (0.182 g of CDW/g of glucose). The second phase was characterized by lower substrate

consumption and thus a higher value of GXY / (1.611 g of CDW/g of glucose). Similar behavior

was observed for 2.5 g/L and 7.5 g/L glucose levels (0.255 g/g to 1.257 g/g & 0.177 g/g to 0.594

g/g) from Figure 4.8 & 4.9 respectively. This showed that the culture utilizes maximum amount

of glucose in medium at phase I and reproduces at very slow rate. The energy available in the

reduced substrate (Glucose) was effectively used by the culture for adaptation on medium during

the growth in Phase I.

The total heat evolved was dependent on the biomass formation (Figure 4.6), this plot

also, exhibited three distinct phases. The first phase corresponded to a lower level of heat

dissipation per unit biomass and was clearly influenced by the low value of GXY / . The second

phase corresponded to a higher level of heat dissipation ( XQY / ) resulting from a higher biomass

yield ( GXY / ). The third phase in Figure 4.6 was due to the non-growth related oxidation of by-

product. Consequently, GXY / was zero and XQY / tends to infinity.

The fungal metabolism appeared to behave quite differently from that of any other

microorganism (Rao et al. 2006), which needs substrate during exponential growth. The

organism A. tamarii appeared to build-up a strong cell structure in contrast to other

microorganisms such as Pseudomonas aeruginosa, Staphylococcus lentus, Bacillus badius

reported earlier. The OUR profile followed the heat profile very closely in all the three phases of

the growth as evidenced with other types of microorganisms (Karthikeyan et al. 2011).

Influence of agitation intensity and aeration on protease production in BioRTCal


17/36


Page 17 of 36

Figure 4.10 depicts the influence of agitation intensities on total metabolic heat, akL and

CDW. Higher agitation intensity resulted in lower growth and lower metabolic heat. Although

akL continued to increase with increase in agitation intensity, both fungal growth and metabolic

heat decreased beyond 350 rpm. This was because the fungal hypae got severely damaged with

increasing impeller shear resulting in poor biomass development.

The heat flux profile during growth ofA. tamarii for different agitation intensities (250,

350 & 450rpm) are presented in Figures 4.11, 4.4 & 4.12. In Figure 4.11, at 250 rpm agitation

the metabolic heat release rate was higher than that of 350 & 450 rpm respectively. At 250 rpm

the fungal hypae remained stable and the metabolism lasted longer, i.e., at lower agitation

intensities growth related processes were dominating. At 350 rpm, A. tamarii growth, substrate

heat yield and biomass heat yield were maximum. When the agitation intensity was raisedbeyond 350 rpm, a decrease in fungal growth and heat yield was observed. It was concluded that

450 rpm agitation intensity was not favorable for promoting growth. Thus, 350 rpm agitation rate

was found to be the optimal rate forA. tamarii growth in BioRTCal. A growing fungal pellet was

to be carefully nurtured in a bioreactor so that the natural morphology did not get disturbed and

at the same time oxygen mass transfer promoted by agitation had to be in its the optimum range;

for example 350 rpm in the present study.

The growth ofA. tamarii in BioRTCal as a function of aeration rate is presented in Table4.2 and time course of parameters is shown in Fig 13, Fig 4 & Fig 14. The aeration rates varied

from 0.5 to 2 vvm. During fermentation, the DO levels decreased rapidly during the first 24 hr in

BioRTCal due to high viscosity (0.091 kg/m.s) of the broth as shown in Fig 15. On initiation of

pellet formation, the viscosity of the broth reduced. Later, on complete pellet formation, the DO

level went up slowly to 26 ppm. The change in morphology from free filamentous form to pellets

helped in reducing the viscosity observed initially. Again, a comparison of the data presented in

Table 4.2 showed that 1 vvm aeration was the optimum for maximum fungal growth. Decrease

and increase of aeration supply rates affected the fungal growth levels marginally. The data in

Table 4.2 clearly indicated that variation in aeration rates was not as influential as the agitation

rates for maximum growth. The power requirement increased with increase in impeller speed.

At optimal conditions, 350 rpm and 1 vvm, the gassed power required was 0.0899 W. An

examination of Table 4.2 also revealed that for each set of variation in air flow and agitation rate,


18/36


Page 18 of 36

growth ofA. tamarii was different, indicating that these parameters could act as metabolic

regulating parameters.

DO tension affects productivity, cell autolysis, the rigidness of the cell wall, and many

other features in fungal fermentation [Cui et al 1997]. Wang et al. [2003] reported that oxygen

enrichment in the gas supply resulted in a higher percent of active lengths in the hyphal

elements and glucoamylase activity. In this study, A. tamarii growth was found affected by the

DO level, as higher DO concentration resulted in higher growth. Therefore, optimum balance

between aeration (oxygen tension) and agitation intensity (shear), for maximum fungal growth,

was achieved in BioRTCal.

Morphology

In order to explore relationship between morphology and heat release pattern, the surface

structures ofA. tamarii at various time intervals (Figure 4.16) was superimposed on the

metabolic heat curve. It is essential to understand physiological phenomena such as the breakage

of hypae and its relationship to mechanical stress. The spore started to germinate at about 480

min after inoculation resulting in the formation of clumps. Small pellets were found formed after

1440 min of inoculation. The dense, hairy pellets kept growing larger until the stationary phase.

Since the pellet was subjected to impeller shear stress for a longer period, after 2160 min, the

hypae gets weakened and the hairs continued to be shaved off and the pellet surface became

smoother than they were at 1800 min. Bristles were observed in the broth; their mass fraction

was small compared to total biomass. Sample pellets were collected at the end of exponential

phase for the measurement of pellet size, apparent density and porosity. Fungal pellets with

diameter of 1 to 4 mm were formed, no hollow and broken pellets were observed in our

experimental conditions. The porosity of the pellets varied from 79.2 % to 92.6 %. At low

agitation rate (250 rpm) the medium became more viscous and poor mixing was observed.

Agitation intensity and DO tension also affected the pellet porosity significantly. On increasing

the substrate concentration the spores were found to aggregate together and fluffier and loose

pellets were formed.

A close examination of Figure 4.16 suggests that under lag and exponential phase, the

growth process involves, germination, small pellet formation followed by fully matured dense


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Page 19 of 36

hairy pellets. After the peak exponential growth at 1700 min, depletion of carbon source and

longer shear stress give rise to weakness of the hypae and it starts to wear off to form smooth

pellets. No quantitative comments at this stage can be made, but it is certain that the heat release

pattern does reflect the changes in morphology during the formation ofA. tamarii. This will

allow better understanding of production process and help to establish process control schemes

on a true physiological basis.

In submerged fermentation, morphology of filamentous microorganisms varies between

pellet and filamentous forms and the exact morphology was found to depend on the culture

conditions and genotype of the strain (Thomas, 1992). Fungal morphology was influenced by

inoculum level, pH and agitation. Filamentous growth is common in industrial fermentation.

However, reduced extracellular protease secretion was found in pelleted growth and it is

beneficial for heterologous protein production (Xu et al. 2000). Pelleted growth could result in

reduced cell mass due to substrate and oxygen limitation in the dense core of the pellet when the

pellets exceeded critical radius (Cui et al. 1997). In the center of pellets, oxygen depletion

would cause autolysis of the cells and eventually leads to the formation of a hollow center. Pellet

morphology was a preferred one because of decrease in the viscosity of the culture fluid,

resulting in improved mixing and mass transfer properties (Teng et al. 2009).

Fungal growth in pellet form can be monitored calorimetrically and reliable information

on the growth dynamics of the organism achieved. Growth in terms of fungal biomass profile

closely follows the metabolic heat profile. Respirogram too follows the powertime curve in all

the phases of growth and a linear correlation between respirometric and calorimetric data is

visible. The heat yields estimated due to fungi biomass growth, oxygen uptake, and biomass

yield, help to understand the energetics of the organism under study. The oxycalorific coefficient

agrees well with results published suggesting that the process is aerobic. Heat of combustion

determination from the four models seems to lie quite close together, but differs significantly

from experimental values. Hence, for fungal cultures exact determination of heat of combustion

can be found experimentally in a bomb calorimeter. A quantitative relationship between

morphology and heat release pattern is discussed. Further work in necessary to establish process

control strategies based on morphology. This study reveals that both growth and non-growth

related reactions involved in this cell culture metabolism can be monitored efficiently by

calorimeter and the heat yield values used for better design and scale up of fermentor.


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Elemental and Enthalpy Balance for Analyzing Microbial Growth Processes

The composition ofA. tamarii obtained from elemental analysis and the stoichiometric

index is shown in Table 4.3 & 4.4. Residual water and oxygen contents were calculated

according to the reported procedure (Dermoun, Z 1980). Ash content was determined by burning

1 g of the dried cells in a muffle furnace at 500C to a constant weight. Based on the elemental

analysis, the molecular formula for the fungal strainA. tamarii was deduced as 129.0878.0693.2 NOCH

. The molecular weight of this culture was found to be 30.547 g/gmol. Stoichiometric equation

for pure aerobic growth ofA. tamarii was obtained as follows:

20042.00288.00881.00327.0326126 69.5493.90399.0558.5 CONOHCNaNOOOHC

NaOH 0399.0582.5 2 (4.34)Several models are available to predict the heat of combustion based on its elemental

composition. Ho, Dulong, Thornton and Giese models (Corider et al. 1987) are found to correlate

well with experimentally predicted heat of combustion values. The values are shown in Table 4.5

and the best fit between predicted and observed was obtained using Gieses model followed by

Thornton; Ho and Payne, the worst fit was obtained for Dulong model. On comparison of the

model equations it differs only with respect to the numerical values assigned to the various

coefficients. For precise measurements the combustion of fungal cells is reliable because thepredicted values are significantly different from the experimentally observed ones. The number

of available electrons to be transferred to oxygen upon complete oxidation of whole cells was

4.94 electrons/(C mol) according to the following combustion reaction:

2222129.0878.0693.2 0021.00441.00327.004.0 NOHCOONOCH (4.35)

Table 4.6 shows the comparison of theoretical and experimental heat yields (BioRTCal)

for growth ofA. tamarii in a glucose-limited mineral salt medium. A good agreement was

observed between these values. This proves the efficiency of heat-flux calorimetry (BioRTCal)

in monitoring metabolic activity ofA. tamarii.

Coupling biokinetics and bioenergetics may help to understand microbial growth

processes deeply. The discussion of growth stoichiometry is limited to a simple equation (with

one or two by-products apart from new biomass). Since metabolic pathway of organisms

comprises a large number of parallel reactions, it is impossible to provide a well defined


21/36


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stoichiometry for all reaction steps. Heat evolved from living systems is considered as overall

output of all metabolic actions i.e. all parallel reactions; it is justifiable to approximate growth

stoichiometry to a single overall reaction since most other reactions are either endothermic or

non contributors to overall heat.

Determination of rate of biomass generation and substrate consumption based on

metabolic heat

The relationship between the metabolic heat generated due to biomass growth and

substrate consumption at time (t) is obtained as follows: let,*

SH ,*

XH and*

PH be the

heat liberated on burning a unit mass of substrate, fungal biomass, and products respectively. By

Hesss law,

t

0

meto*Po

*Xo

*S dt)t(q)PP(H)XX(H)SS(H (4.36)

where metq is the rate at which metabolic heat is liberated per unit volume of the reactor, and

P,X,S is concentrations of substrate, biomass, and product, respectively. In this studyA. tamarii

was cultivated in pure aerobic respiration mode and hence there is no byproduct formation.

Hence Equation (4.36) becomes

t

0

meto*Xo*S dt)t(q)XX(H)SS(H (4.37)

On differentiating Equation (4.37) with respect to time:

me t

*

X

*

S qdt

dXH

dt

dSH (4.38)

Substituting fordt

dSand

dt

dXfrom Equation (4.32) and (4.33) in Equation (4.38)

me t

*

X

S/X

*

S qXHY

XH

(4.39)

Solving Equation (4.39) for me tq


22/36


Page 22 of 36

S/X

*

XS/X

*

Sme t

Y

)H(YHXq (4.40)

Biomass growth rate can be represented in terms of heat rate by substituting for X from

Equation (4.40) in Equation (4.32) as follows:

S/X

*

XS/X

*

S

me t

X

Y)H(YH

q

dt

dXr

(4.41)

where gkJHS /9.15* and gkJHX /95.18

* (Corider et al. 1987). The value of biomass

yield ( S/XY ) was obtained experimentally as 1.611 g/g, details shown in Figure 4.8. From

Equation (4.41), all the parameters on the right side except the metabolic heat ( me tq ) can be

estimated. The rate of biomass generation was determined by substituting the experimental

values of me tq in Equation (4.41). In Figure 4.17 a linear correlation between the rate of biomass

generation and metabolic heat was observed, which further substantiates the importance of the

relationship developed to estimate growth rate instantaneously from the powertime profile.

Similarly a relationship for substrate (glucose) consumption was developed for the growth ofA.

tamarii. From Equation (4.33), the rate of substrate consumption ( Sr ) can be represented by

dt

dX

Y

1

dt

dSr

S/X

S (4.42)

On substituting Equation (4.41) in (4.42) and solving

S/X

*

XS/X

*

S

me t

S/X

S

Y)H(YH

q

Y

1r

(4.43)

A linear plot is obtained between estimated values of substrate consumption rate by Equation

(4.43) and experimental metabolic heat values (Figure 4.18). Since the substrate is consumed

during the course of the biological reaction by the metabolic activity of the cell culture, a straight

line with a negative slope is observed. Figure 4.6 shows the linear plot between experimental

values of cumulative metabolic heat (Q) and biomass concentration (cell dry weight) for growth

ofA. tamarii in glucose-limited (5 g/L) growth media under optimized conditions in BioRTCal.


23/36


Page 23 of 36

Let the denominator in Equation (4.41) can be represented as D. The values of all the parameters

in the denominator D are known. By integrating both sides of the Equation (4.41):

t

0

me to dt)t(qD

1X)t(X (4.44)

Let the total metabolic heat liberated until a given time t be )t(Qmet as shown below:

t

0

metme t dt)t(q)t(Q (4.45)

On substituting Equation (4.45) in (4.44)

D

)t(QX)t(X meto (4.46)

On rearranging Equation (4.46)

D

)t(QX)t(X meto (4.47)

On substituting Equation (4.47) in (4.40)

SX

XSXSmet

ometY

HYH

D

tQXq

/

*

/

* )()( (4.48)

Equation (4.48) can be simplified to

))(( tQDXq metomet (4.49)

Figure 4.12 shows a linear relationship between me tq and ))(( tQDX meto , with the slope being

the specific growth rate ( ). Specific growth rate for theA. tamarii cultivated in glucose-limited

(5 g/L) growth media under optimized conditions was observed to be 0.0029 1/min. The linearity

of the profile (R2

= 0.983) shown in Figure 4.19 further proves the feasibility of applying

calorimetric results for estimation of biokinetic parameters. Dielectric spectroscopy can be used

to estimate instantaneous biomass concentration values for determination of growth rates.

However, its applicability in practical situations has some limitations, such as generation of

electronic noise degrading the quality of output signal, modifications in dielectric properties of

cell culture due to enzyme blockage (Maskow et al. 2008). Soley et al. (2005) used impedance

spectroscopy as an in situprobe during yeast fermentation studies and found erroneous signals at

higher aeration and agitation rates. Mid-IR and fluorescence spectroscopic probes were used to

monitor online metabolites and byproducts formation in a bioprocess. However, they suffer from


24/36


Page 24 of 36

problems of drifts in the measured signal and also limited application to different bioprocess

systems. Research groups are presently involved in developing suitable calibration models to

resolve the measured signal from drifts and various interferences (Schenk et al. 2007 and Schenk

et al. 2008). Taking into account these problems with using different online probes,

biocalorimetry is a promising tool for bioprocess monitoring due to its non-invasive mode of

operation. As heat generation is a product of anabolic and catabolic reactions, the measured heat

signal, could be interpreted to determine the instantaneous growth rate, substrate consumption

and product formation.

Conclusion

Fungal growth in pellet form can be monitored calorimetrically and reliable informationon the growth dynamics of the organism achieved.

Growth in terms of fungal biomass profile closely follows the metabolic heat profile. Respirogram too follows the powertime curve in all the phases of growth and a linear

correlation between respirometric and calorimetric data is visible.

The heat yields estimated due to oxygen uptake and biomass growth, help to understandthe energetics of the organism under study.

The oxycalorific coefficient agrees well with results published suggesting that the processis aerobic.

A quantitative relationship between morphology and heat release pattern is discussed. Ho & Payne and Dulong model will directly determine the enthalpy content. Whereas

Thornton and Giese model require knowledge of the dry biomass chemical formulae

for the evaluation of heat of combustion.

Heat of combustion determination from the four models seems to lie quite close together,but differs significantly from experimental values. Hence, for microbial cultures exact

determination of heat of combustion can be found experimentally in a bomb calorimeter.

Using Hesss law the relationship between the online metabolic heat variable and theoffline bioprocess variables, i.e. biomass generation, substrate consumption were

explored and validated.


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Page 25 of 36

A linear relationship was observed between metabolic heat and other offline variables.This finding proved the feasibility of deducing the values of biokinetic variables

indirectly from calorimetric results.

This study reveals that both growth and non-growth related reactions involved in this cellculture metabolism can be monitored efficiently by calorimeter and the heat yield values

used for better design and scale up of fermentor.

Table 4.1 Comparison of heat yield coefficients and heat flux ofA. tamarii grown at varyingglucose concentrations

Initial GlucoseConcentration, g/L

Pelletdiameter, mm

Apparent wet pelletdensity, kg/m

3

Pelletporosity, %

vS ,

1/mm

2.5 1 1018.4 79.2 6

5 2 1012.5 84.9 3

7.5 4 1004.6 92.6 1.5

Table 4.2 Effect of aeration and agitation intensity on growth ofA. tamarii in BioRTCal

Aeration,

vvm0.5 1 2

Agitation,

rpm250 350 450 250 350 450 250 350 450

CDW,

g/L1.761 1.618 1.324 1.955 1.74 1.427 2.181 2.006 1.792

akL , 1/hr 27.774 35.062 45.825 38.031 47.130 58.207 51.954 63.109 72.009

EDCF,

W/m3

.s

309.1 401.0 580.6 380.6 476.9 620.9 466.6 563.1 632.0

oP , W 0.062 0.082 0.113 0.080 0.103 0.132 0.103 0.129 0.150

gP , W 0.063 0.085 0.119 0.069 0.090 0.117 0.074 0.095 0.112

ct , s 3.880 4.002 3.863 3.417 3.565 3.565 3.013 3.184 3.345

AeN 0.012 0.009 0.007 0.025 0.018 0.014 0.049 0.035 0.027

Table 4.3 Elemental analysis (in wt %) ofA. tamarii

Substrate Residual water AshElemental analysis

Cf Hf Nf Of

Glucose 1.6 0.05 0.25 0.01 38.55 0.15 8.65 0.09 5.8 0.06 45.14 0.21

Table 4.4 Stoichiometric index calculated from elemental analysis ofA. tamarii

SubstrateStoichiometric index (x 100)

'

xM Cx Hx Nx Ox

Glucose 3.27 0.01 8.81 0.08 0.42 0.01 2.88 0.06 4.94 0.05 30.55 0.12


26/36


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Table 4.5 Experimentally determined and model predicted heat of combustion ofA. tamarii

SubstrateHeat of combustion of cells, OH , kJ/g

Ho Dulong Giese Thornton Experimental

Glucose 17.62 0.1 17.67 0.12 18.59 0.14 17.9 0.09 18.95 0.06

Table 4.6 Comparison of predicted and experimental yield coefficients of aerobic strain

A. tamarii cultivated in glucose limited growth media

Initial GlucoseConcentration,

g/L

GXY / ,

g of DCW/

g of glucose

'

/ OQY ,

kJ/mol

XQY

/,

kJ/g

Theoretical Experimental Theoretical Experimental

2.5 1.257

460

479

26

24.97

5 1.611 486 29.84

10 0.594 494 26.95

4.1 (a)


27/36


Page 27 of 36

4.1 (b) 4.1 (c)

Figure 4.1 (a) Principle setup of BioRTCal (b) Measurement principle (c) Heat flowbalance

0 500 1000 1500 2000 2500 3000

0.0

0.1

0.2

0.3

0.4

0.5

III

III

III

II

II

I

7.5 g/L

2.5 g/L

q,

W/L

Time, min

5 g/L

I

Figure 4.2 Comparative BioRTCal heat flux profiles for the growth ofA. tamari iat various

glucose concentrations


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Page 28 of 36

0 500 1000 1500 2000

0.0

0.1

0.2

CDW,g/L

Time, min

Q,

kJ/L

q,

W/L

0

2

4

6

8

0 10 20 30 40

0.00

0.25

0.50

0.75

1.00

1.25

0.0

0.5

1.0

1.5

2.0

2.5

Glucoseconcentration,g/L

0.0000

0.0001

0.0002

0.0003

0.0004

OUR,mg/L.m

in

Figure 4.3 Growth ofA. tamari iin a glucose-limited medium (2.5 g/L): () cell dry weight

CDW, () oxygen uptake rate OUR, () residual glucose concentration,(--) totalheat evolved Q, () heat evolution rate q.

0 500 1000 1500 2000 2500

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

CDW,g/L

Time, min

Q,kJ/L

q,W/L

-5

0

5

10

15

20

25

0 10 20 30 40

0.0

0.5

1.0

1.5

2.0

0

1

2

3

4

5


0.000

0.005

0.010

0.015

0.020

0.025

OUR,mg/L.min

Figure 4.4 Growth ofA. tamari iin a glucose-limited medium (5 g/L): () cell dry weight



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Page 29 of 36

0 500 1000 1500 2000 2500

0.0

0.1

0.2

0.3

0.4

0.5

CDW,g/L

Time, min

Q,

kJ/L

q,

W/L

0

5

10

15

20

25

30

35

0 10 20 30 40

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

2.25

0

1

2

3

4

5

6

7

8


0.0000

0.0005

0.0010

0.0015

OUR,mg/L.m

in

Figure 4.5 Growth ofA. tamari iin a glucose-limited medium (7.5 g/L): () cell dry weight


Figure 4.6 Biomass heat yield calculation of A. tamari iin BioRTCal for 5 g/L of initial

glucose concentration.

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20

0

2

4

6

8

10

12

14

YQ/X

=10.42kJ/g

Q,

kJ/L

X-XO, g/L

YQ/X

=29.84kJ/g

I

II

III


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Page 30 of 36

0.0 0.5 1.0 1.5 2.0 2.5

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

YX/G

= 1.257 g/g

CDW,g/L

Glucose, g/L

YX/G

= 0.255 g/gI

II

Figure 4.7 Biomass yield due to glucose uptake for the growth ofA. tamari iin BioRTCal for

2.5 g/L of initial glucose concentration.


5 g/L of initial glucose concentration.


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Page 31 of 36


7.5 g/L of initial glucose concentration.

250 300 350 400 450

35

40

45

50

55

60

kLa,1

/min

Stirrer speed, rpm

CD

W,g/L

1.25

1.50

1.75

2.00

8

10

12

14

Metabolic

heat,kJ/L

Figure 4.10 Influence of Impeller speed on total metabolic heat (), akL () &CDW ()


32/36


Page 32 of 36

0 500 1000 1500 2000 2500

0.0

0.2

0.4

0.6

0.8

1.0

CDW,g/L

q,

W/L

Time, min

0 10 20 30 40

0.0

0.5

1.0

1.5

2.0

0 10 20 30 40

0

2

4

6

Glucoseconcentration

,g/L

0 10 20 30 40

0.000

0.002

0.004

0.006

0.008

0.010

OUR,mg/L.min

Figure 4.11 BioRTCal batch responses for proteolytic activity of A. tamari iat 250 rpm

agitation rate: () CDW, () residual glucose concentration, () OUR, () heat evolution

rate q.

0 500 1000 1500 2000 2500

0.0

0.1

0.2

0.3

OUR,mg/L.min

q,W/L

Time, min


0 10 20 30 40

0.0

0.5

1.0

1.50 10 20 30 40

0

1

2

3

4

5

6

CDW,g/L

0 10 20 30 40

0.00000

0.00025

0.00050

0.00075

0.00100

Figure 4.12 BioRTCal batch responses for proteolytic activity of A. tamari iat 450 rpm

agitation rate: () CDW, () residual glucose concentration, () OUR, () heat evolution

rate q.


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Page 33 of 36

0 500 1000 1500 2000 2500

0.00

0.25

0.50

0.75

1.00

OUR,mg/L-min

q,

W/L

Time, min

Glucoseconcentratio

n,g/L

0 10 20 30 40

0.0

0.5

1.0

1.5

0 10 20 30 40

0

1

2

3

4

5

6

CDW,g/L

0 10 20 30 40

-0.001

0.000

0.001

0.002

0.003

0.004

0.005

Figure 4.13 BioRTCal batch responses for proteolytic activity of A. tamari iat 0.5 vvm

aeration rate: () CDW, () residual glucose concentration, () OUR, () heat evolution

rate q.

0 500 1000 1500 2000 2500

0.0

0.5

1.0

1.5

2.0

q,W/L

Time, min

Glucoseconcentratio

n,g/L

0 10 20 30 40

0.0

0.5

1.0

1.5

2.0

2.50 10 20 30 40

0

1

2

3

4

5

6

CDW,g/L

0 10 20 30 40

0.0000

0.0025

0.0050

0.0075

0.0100

OUR,mg/L-min

Figure 4.14 BioRTCal batch responses for proteolytic activity of A. tamari iat 2 vvm

aeration rate: () CDW, () residual glucose concentration, () OUR, () heat evolution

rate q.


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Page 34 of 36

0 10 20 30 40

430

440

450

460

470

480

Viscosity,

kg/m.s

Pg

&P

o,

Watt

EDCF,

W/m3.s

Time, hr

0.080

0.085

0.090

0.095

0.100

0.105

0.082

0.084

0.086

0.088

0.090

0.092

0.094

0.096

Figure 4.15 EDCF as a function of time for the growth of A. tamari iin BioRTCal at

impeller speed of 350rpm and 1vvm aeration () viscosity, () EDCF, () Gassed Power,

() Ungassed Power

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0 500 1000 1500 2000 2500 3000

q,

W/L

Time, min

a

b c

d


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Page 35 of 36

Figure 4.16 Growth phases ofA. tamari iin BioRTCal. a. small pellet with short hypae b.

pellet with long hypae c. pellets with hypae becoming shorter d. smoother pellet with short

hairs

0.000000 0.000005 0.000010 0.000015 0.000020 0.000025 0.000030

0.00

0.05

0.10

0.15

0.20

0.25

0.30

q,W/L

rX, g/L.s

Figure 4.17 Linear correlation between metabolic heat and rate of biomass growth ofA.

tamariiin BioRTCal

-0.000016 -0.000012 -0.000008 -0.000004 0.000000

0.00

0.05

0.10

0.15

0.20

0.25

q,W/L

rs, g/L.s

Figure 4.18 Linear correlation between metabolic heat and rate of substrate consumption

ofA. tamari iin BioRTCal


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0 1000 2000 3000 4000 5000 6000

0.00

0.05

0.10

0.15

0.20

0.25

0.30

q,W/L

DXo+Q

met

= 0.0029 1/min

Figure 4.19 Determination of specific growth rate from instantaneous heat release ofA.

tamariiin BioRTCal.

aspergillus tamarii growth

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