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Fed-batch cultivation and bioprocess modeling of Cyclotella sp. for enhanced fattyacid production by controlled silicon limitation

Clayton Jeffryes a,, Jennifer Rosenberger b,1, Gregory L. Rorrer b,1

a Fonds de la RechercheScientifique- FNRS,Earth & Life Institute Bioengineering Laboratory, Universit Catholique de Louvain,PlaceCroix du Sud2, bte.L07.05.19, B-1348 Louvain-la-Neuve, Belgiumb School of Chemical, Biological & Environmental Engineering, Oregon State University, 102 Gleeson Hall, Corvallis, OR, 97331, USA

a b s t r a c ta r t i c l e i n f o

Article history:

Received 12 May 2012Accepted 5 November 2012

Available online 5 December 2012

Keywords:

Algae

Diatom

Lipid

Biofuel

Photobioreactor

Biorefinery

There is significant interest in utilizing microalgae as a source for biofuel and bioactive products such as poly-

unsaturated fatty acids. Diatoms are a class of single-celled microalgae which make silica cell walls and re-

quire silicic acid as a substrate for cell division. Therefore, manipulation of soluble silicon delivery to the

culture offers a route to control the cell cycle and secondary or stress-induced metabolite production. A

multi-stage, semi-continuous photobioreactor cultivation process was developed to produce lipid-rich algal

biomass from the centric diatom Cyclotella sp. In the initial phase, algal cells were grown until the silicic

acid in the medium was depleted. Next, fresh medium containing silicic acid was perfused for durations of

48, 72 or 96 h. The silicic acid was rapidly consumed by the cells, which resulted in cell growth while silicon

depleted conditions were maintained. Perfusions of 48 and 72 h yielded high lipid concentrations (>45% of

dry cell weight) while maintaining biomass generation, and showed higher lipid productivity than when sil-

icon was added as a single pulse. Seven fatty acids were identified by GCMS, including the three main fatty

acids: palmitoleic acid, palmitic acid and eicosapentanoic acid. The lipid fatty acid distribution remained con-

stant once silicon starvation was achieved, regardless of perfusion strategy. This study illustrates that con-

trolled delivery of silicic acid to a cell culture of Cyclotella sp. can enhance lipid production. Additionally, a

mechanistic model to predict biomass and lipid production of under silicon-stress conditions has been devel-

oped, which will aid in the development of future silicon-stress based bioprocesses for the production of fatty

acids or high-valued metabolites. 2012 Elsevier B.V. All rights reserved.

1. Introduction

As concern for global warming and dependence on fossil fuels

grows, interest in clean and renewable energy sources has increased

dramatically. Alternative energy sources such as wind, solar, geother-

mal and biofuels have risen to the forefront as possible solutions to

this energy crisis. Biofuels are attractive because they utilize existing

technologies and are compatible with current infrastructures.

Because biofuels are derived from plants, they can be considered

carbon neutral energy sources. Additionally, microalgae have simple

cellular structures, short cultivation cycles (110 days), the ability

to accumulate large quantities of lipids (4080%) per dry cell weight

[13], can grow in harsh conditions and on land that is inadequate

for food crops [4], require less nutrient and fertilizer [5,6], and can

grow year round in controlled conditions by using specially designed

bioreactors [7]. While there are many advantages to algal biodiesel

there are some challenges as well, such as the high cost of producing

algal biomass [8,9]. Another important area of improvement is

increasing algal lipid productivity. Nutrient deprivation can be used

to increase lipid concentrations within cells by altering the lipid

biosynthetic pathways. Typically nitrogen is limited but other limit-

ing nutrients can have a similar effect [10]. However, nutritionally

stressed algae have much lower growth rates then when grown in a

healthy environment, which yields high lipid concentrations within

the cells but low lipid productivity overall.

Diatoms are by far the most prolific class of microalgae which

make lipids that are readily converted to biodiesel [11]. Diatoms are

single-celled photosynthetic algae of the class Bacillariophyceae that

possess silica shells called frustules, which they make by a process

called biosilicification.

Biosilicification is the process by which an organism uptakes solu-

ble silicon from the environment, precipitates the silicon into amor-

phous silicon oxides and creates a frustule. Each diatom frustule is

composed of two overlapping valves (Fig. 1).

As a diatom prepares to divide, silicon in the form of Si(OH)4 is

transported across the cell wall and into the cytoplasm (Fig. 1) by

silicon transport proteins [12]. Soluble silicon in the cytoplasm is

transported to the silicon deposition vesicle (SDV), [13], where solu-

ble silicon is polymerized to form new diatom frustules made of solid,

Algal Research 2 (2013) 1627

Corresponding author. Tel.: +32 1047 3149; fax: +32 1047 3062.

E-mail addresses: [email protected] (C. Jeffryes),

[email protected] (G.L. Rorrer).1 Tel.: +1 541 737 3370.

2211-9264/$ see front matter 2012 Elsevier B.V. All rights reserved.

http://dx.doi.org/10.1016/j.algal.2012.11.002

Contents lists available at SciVerse ScienceDirect

Algal Research

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amorphous silica. When the new frustule is completed, the diatom di-

vides. A complete review of silicon metabolism in diatoms [14] and

diatom frustule formation [15,16] can be found elsewhere.

Silicon metabolism also controls metabolic pathways not related

to the development of silica structures. Of particular interest to this

study are the effects of silicon on lipid metabolism which produces

lipids of various classes, including triglycerides, the major raw lipid

precursor for biodiesel production. Indeed, manipulating the silicon

metabolism to increase fatty acid production has shown advantages

over the application of other stresses such as nitrogen limitation

[17]. Triglyceride synthesis can be divided into three steps [7]: step

one is the formation of acetyl-CoA, which occurs with photosynthesis.

Step two is fatty acid chain elongation. Step three is the formation oftriacylglycerides.

Diatoms synthesize fatty acids for esterification into membrane

lipids by a de novo biosynthesis pathway, of which the generalized

pathway is presented in Fig. 2. Fatty acids perform a structural role

in the cell and are necessary for cell division. De novo synthesis of

fatty acids in algae occurs mainly in the chloroplast [18]. In this syn-

thesis scheme the committed step is Reaction 1, in which acetyl CoA

is converted to malonyl CoA. This reaction is catalyzed by the enzyme

acetyl CoA carboxylase (ACCase) [7].

ACCase catalyzed triacylglyceride synthesis from the glycerol-

3-phosphate pathway is depicted in Fig. 3. In silicon-deficient cells a

substantial increase in the activity of acetyl-CoA carboxylase has

been observed. It was determined that silicon deficiency promotes

transcription of nuclear genes responsible for the formation of ACCase[19], thus increasing fatty acid content within the diatom. This in-

creased fatty acid production drives the formation of triacylglycerides

(TAGs) by the glycerol-3-phosphate pathway [20]. Unlike membrane

lipids, TAGs do not perform a structural role but instead are trans-

ferred to the cytoplasm as lipid vesicles and serve primarily to store

carbon and energy.

Investigations into the extent of enhanced lipid production as a

consequence of silicon-starvation in batch culture systems demon-

strated lipid content doubling during exponential growth and in-

creasing by as much as 20% when maintained at silicon starvation

[19,21,22], but with biomass limited by the amount of initial silicon

provided to the system. Further studies considered two-stage sys-

tems in which a second charge addition of silicon was added to the

reactor once silicon-starvation had been achieved. These experiments

saw additional biomass generation but lipid concentrations decreased

as cells began to uptake silicon and divide [2325]. One experiment

utilized a turbidostat to deliver a steady flow of silicon at low concen-

trations [26] and saw increased lipid concentrations but low total

lipid production due to a decrease in cell growth rate. These studies

made no attempt to optimize silicon delivery, but simply studied

systems of silicon depletion or added instant charges of additional sil-

icon for cell growth.

This study designs and tests a unique bioprocess strategy whichhopes to take advantage of increased lipid formation during times

of silicon-starvation but also enable cell growth and allow for a

truly enhanced lipid production. A multi-stage photobioreactor culti-

vation scheme will mimic a fed-batch system with controlled silicon

delivery. The silicon will be added at low concentrations over extend-

ed time periods, such that silicon-starved cells with increased lipid

concentrations will receive silicon by perfusion at rates low enough

to maintain a silicon-depleted state and yet receive sufficient silicon

for continued biomass production while maintaining high lipid levels.

The first objective of this study was to measure biomass and lipid

production during cultivation in a fed-batch system. The second

objective was to develop a mechanistic model to describe and predict

biomass productivity and silicon-stress related metabolite produc-

tion, in this case lipids. The third objective was to determine if the

lipid profile is affected by the growth state of the cell culture. Based

upon these results, a method for enhanced lipid production by using

a multi-stage photobioreactor with perfusion was developed and

bioprocess strategies to create a continuous process for the produc-

tion of biomass of high lipid content were proposed.

2. Materials and methods

2.1. Cell culture

Cyclotella sp. was obtained from UTEX, the Culture Collection of

Algae and was maintained at 22 C under cool white fluorescent

lights at an intensity of 100 E m2 s1 in unagitated 500 mL Erlen-

meyer flasks with foam stoppers as previously described [27]. The

culture medium wasa modified Harrison's artificial seawater medium[28] as described in a previous work [29]. Subculturing was

performed every 14 days.

2.2. Photobioreactor design

A bubble-column photobioreactor was used to cultivate the

Cyclotella sp. cell suspension under controlled conditions. The biore-

actor vessel was constructed of a cylindrical glass column of

10.5 cm inner diameter (R=5.25 cm), 4.8 mm wall thickness, and

70.5 cm height with a total volume of 6.1 l. The glass column was

mounted onto two stainless steel support plates, which formed a

headplate and a baseplate. The photobioreactor was supplied with

compressed and humidified air and was illuminated by six 20 W,

cool white fluorescent lights mounted vertically along the axis ofthe bubble column. The complete headplate, illumination, sampling,

control and monitoring assembly is described elsewhere [30]. The

perfusion addition of fresh, sterile medium was supplied by a Cole

Parmer Masterflex C/L dual-channel variable-speed tubing pump.

2.3. Photobioreactor cultivation experiments

Medium composition for the cultivation of Cyclotella sp. in the

photobioreactor was identical to the flask culture with the exception

of the dissolved silicon concentration. Flask cultured diatom cells

harvested 14 days after subculture were used for bioreactor inocula-

tion. The incident light intensity to the surface of the cell culture

was adjusted to 150 mE m2 s1 [30] with a 14:10 light:dark

photoperiod.

SiO2

Cytoplasm

SDV

Si(OH)4

Culture

Medium

Cytoplasm

SDV

diatom cross

section

Silica frustule

SiO2

Fig. 1. Diatom cell structure and silicic acid uptake and transport.

17C. Jeffryes et al. / Algal Research 2 (2013) 1627


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In Stage I of the photobioreactor cultivation process, culture growth

wasdesignedfor silicon depletion.Sterile transfer of a defined inoculum

volume targeted an initial cell density of 21053105 cells mL1 in

4.5 L of cell culture. The initial dissolved silicon concentration (as solu-

ble Na2SiO3) was targeted to provide 1.61062.4106 cells mL1, or

three to four cell doublings. After the soluble silicon was consumed

and achieved an unchanging near-zero concentration, the culture was

kept in a silicon-starved state at constant stationary phase cell density

for at least 24 h. At the point of silicon starvation, and after at least24 h at constant cell density, Stage I was complete. During Stage I, the

cell suspension was sampled at 24 hour intervals for the determination

of cell number density, pH, and dissolved silicon concentration, and at

72 hour intervals for total lipid content. As a result of sampling, the

reactor volume was drawn down to approximately 2 L by the end of

Stage I.

Stage II began with a perfusion of 2 L medium with a composition

identical to Stage I with the exception of the dissolved silicon concen-

tration. The dissolved silicon concentration was calculated to provide

sufficient silicon for one cell division based on the cell density at the

end of Stage I. The medium was perfused for 48, 72, or 96 h (depending

on the experimentaldesign) by using a peristaltic pump. DuringStage II,

the culture was sampled twice during each illuminated portion of the

photoperiod throughout the perfusion period for cell number density,pH, dissolved silicon concentration and total lipid content. A control

experiment was performed in which the fresh culture medium was

delivered as a single charge instead of a controlled perfusion. Stage II

was complete at the end of silicon addition.

Stage IIIof the cultivationbegan after the completion of silicon addi-

tion and ended after a second stationary phase was achieved. During

Stage III the cell culture was sampled at 24 hour intervals for cell num-

ber density, pH, dissolved silicon concentration and total lipid content.

Each photobioreactor experiment: Si pulse and Si perfusions of 48,

72 and 96 h, were performed in duplicate.

2.4. Cell density

To determine cell number density a 0.1 mL sample of the cell

suspension was diluted in 10.0 mL of Isoton II electrolyte solution

(Beckman-Coulter). Cells in the cell suspension were counted on a

Beckman Z2 Coulter Counter at a minimum threshold of 6 m. Tripli-

cate cell counts were conducted on each of the two samples. Immedi-

ately following the cell count, the cell suspension was centrifuged at

1000 rpm for 15 min to separate the cell mass from the supernatant.

To correlate cell mass density to cell numberdensity, cells were washed

with distilled water three times by centrifugation. Aliquots (n =74) of

varying, but known, cell numbers of these washed cells were dried for

24 h at 80 C. The cells were removed from the drying oven and their

mass was measured directly. The cell number to cell mass conversionfactor was found to be (5.00.1)109 cells (g DW)1, where DW is

dry cell weight.

Fig. 3. ACCase catalyzed triacylglyceride synthesis from the glycerol-3-phosphate pathway. G-3-P (glycerol-3-phosphate), lyso-PA (lysophosphatidic acid), PA (phosphatidic acid),

DAG (diacylglycerol), and TAG (triacylglycerol).

Fig. 2. Fatty acid de novo synthesis pathway. Reaction 1 is the committed step.

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2.5. Silicon and nitrate analysis

The dissolved silicon concentration in the supernatant was deter-

mined by spectrophotometric assay at 360 nm following derivatiza-

tion of 1.0 mL of cell culture supernatant (diluted to 5.0 mL with

silicon-free distilled H2O) with 0.2 mL 13.3%w/v ammonium molyb-

date reagent in water and 0.1 mL 13.7%v/v HCl in distilled water

[31] and by inductively coupled plasma (ICP) analysis using a Varian

Liberty 150 ICP atomic emission spectrometer as previously described[29]. The nitrate concentration in the medium was assayed by using a

LaMotte nitrate test kit (model NCR 3110). The nitrate concentration

was measured spectrophotometrically at 530 nm.

2.6. Lipid analysis

2.6.1. Lipid extraction

To isolate lipids, cells collected from the centrifugation step were

first washed three times with 100 mM sodium chloride and then

lyophilized for 12 h. Once lyophilized, 25.0 mg dry cell mass was

homogenized and rehydrated for 12 h in 5.0 mL 2:1 (v:v) CHCl3:

MeOH and the lipid extract was separated from the cell mass. Twice

more 5.0 mL 2:1 (v:v) CHCl3:MeOH was added to the cell mass and

vortexed for 60 s before allowing the cell mass solids to settle. Once

the solids had settled the solvent was separated and all of the extrac-

tions were pooled. The pooled lipid extract was combined with

5.0 mL 0.88% (w/v) KCl in H2O, vortexed for 60 s and then separated

again by centrifugation at 1000 rpm for 10 min. The lipid extract was

then combined with 5.0 mL 1:1 MeOH:H2O, vortexed for 60 s and

then separated by centrifugation at 1000 rpm for 10 min. Finally,

the lipid extract was dried by using anhydrous Na2SO4, the final

volume of extract was recorded and then stored at 20 C. Thisprocess is described as a modified Folch method [32].

2.6.2. Lipid quantification

The total lipid content as a weight percent of the dry cell mass was

determined after a reaction with K2Cr2O7. A 0.2 mL aliquot of lipid

isolate was evaporated under nitrogen and combined with 1.0 mL

(2.5 g L1) K2Cr2O7 in H2SO4. The mixture was heated at 100 C for45 min then cooled to room temperature. After cooling, 0.5 mL of

the reaction mixture was diluted to 5.0 mL with distilled H2O. The

diluted reaction solution was allowed to cool to room temperature

and then assayed spectrophotometrically for total lipids at 350 nm

by using previously published methods [33,34]. The lipid weight

percent in the biomass was calculated from the mass of biomass

from which lipid was extracted, the volume of lipid extracted from

the biomass and the lipid content of the lipid extract.

2.6.3. Transesterification of fatty acids

The fatty acid components of lipids must be converted to low

molecular weight, non-polar derivatives for analysis by gas chroma-

tography (GC). In this case, the fatty acids were transesterified to

fatty acid methyl esters by the following procedure: first, a 1.0 mLaliquot of lipid extract was evaporated to dryness with nitrogen and

followed by the addition 0.5 mL 1% (v/v) H2SO4/MeOH to the sample.

The mixture was heated at 100 C for 60 min then cooled to room

temperature. Once cooled, 0.5 mL H2O was added and followed by

0.5 mL hexane. The sample was vortexed for 60 s then separated by

centrifugation at 2000 rpm for 3 min. The upper solvent phase

which contained the fatty acid methyl esters was collected by pipette

and dried with anhydrous Na2SO4.

2.6.4. Fatty acid quantification by GC

The major fatty acid constituents present in the lipid extract were

quantified by GC. GC analyses were performed by using an HP5890

GC equipped with an HP-5 capillary column (30 m0.32 mm i.d.;

film thickness 0.25 m). The carrier gas was helium at a flowrate of

1 mL min1 and the inlet temperature was 280 C. The column

temperature was initially 40 C (held for 3 min) and then increased

to 200 C at 20 C min1, held for 10 min, and then increased to

210 C at 10 C min1. Three representative transesterified time

point samples from 48 hour and 72 hour perfusion experiments

were chosen for GCMS analysis: one sample each from the Stage I

mid-exponential phase, Stage I stationary phase and Stage III. A

1.0 mg mL1 methyl nonadecanoate solution (Sigma) in hexane

was used as an internal standard. The area counts were convertedto concentrations by using external calibration curves.

2.6.5. Fatty acid identification by GCMS

The major fatty acid constituents present in the lipid extract were

identified by GCMS. GCMS analyses were performed by using a

Hewlitt Packard HP-6890 GCMS with the same column and separa-

tion conditions as for fatty acid quantification by GC. The mass

spectrometer was operated in positive EI mode with an ionization

energy of 70 eV. Spectra were monitored from 45 to 400 m/z and

the fatty acid components were identified by comparison of their

mass spectra with reference mass spectra from the American Oil

Chemists Society database. Three representative transesterified time

point samples were chosen for GCMS analysis: one sample each

from Stage I mid-exponential phase, Stage I stationary phase, and

Stage III. A 1.0 mg mL1 methyl nonadecanoate solution (Sigma) in

hexane was used as an internal standard.

2.6.6. Nile Red fluorescence determination of lipids

In addition to direct measurement by isolation of the lipids, the

lipid content, location, and morphology within the cells themselves

can be qualitatively analyzed by staining. A 0.4 mL aliquot of cell

suspension of known cell density, usually between 3 106 and

4106 cells mL1, was combined with 0.03 mL of 50 g Nile

Red mL1 in acetone. 2.57 mL 25% (v/v) DMSO/H2O was added and

the mixture was held at 40 C for 10 min. After the algal cell suspen-

sion was stained with Nile Red, fluorescence was observed by using a

Leica DMIL inverted microscope at 1800 (100, 18) with a 535

and 610 nm excitation and emission wavelengths, respectively. Im-

ages of both the unstained and stained cells were recorded during atwo-stage cultivation experiment with a 48 hour perfusion of fresh

media for a qualitative assessment of changing lipid content within

the cells. Images were taken during the early exponential growth

phase, late exponential growth phase, stationary phase, mid-

perfusion and post-perfusion. The lipids were extracted from samples

taken at the same time points and the total lipid content was deter-

mined as previously described.

3. Model development

3.1. Model introduction

In this section, cell culture growth, substrate consumption and

lipid production models under both silicon replete and starvationconditions during both constant culture removal and discontinuous

fresh medium delivery are developed for a cell suspension culture

of the marine diatom Cyclotella sp. Modeling of neutral lipid produc-

tion by microalgae under nitrogen limitation has recently been devel-

oped elsewhere [35]. The model development nomenclature is found

in Table 1. In addition to micro- and macronutrients, cell cultures of

diatoms require light, inorganic carbon from CO2 and soluble silicon

in the form of silicic acid. In this study all micro- and macro-

nutrients were supplied in excess (except silicic acid) and the CO2transfer rate was in excess of the carbon demand even under the

highest observed productivities. The cultivation comprised three

stages, which are depicted in Fig. 4. Stage I operation was adepletion batch with a constant outflow term due to sampling,

Stage II operation also had a continual outflow term due to sampling,



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but also had an inflow of fresh medium, and Stage III was again a de-

pletion batch with only an outflow term. It is important to note that

in Stage II the volumetric inflow of fresh medium was greater than

the volumetric outflow of culture, so the culture volume increased

during this stage. Below, the combined effects of silicic acid concen-

tration and uptake, light intensity, and culture inflow and outflow

on the biomass and lipid production rates are incorporated into the

model.

3.2. Mass balances

In Stage I, the reactor was initially charged with Si(OH)4 (silicic

acid) giving an initial concentration of CSi,0,I in the culture and was in-

oculated with cell culture to an initial cell density of CDW,0,I. There was

no additional input of Si or biomass during the course of Stage I. The

outflow term is a result of regular, non-trivial volumes of reactor sam-

pling, which was assumed to occur at a constant volumetric rate, QI. A

Stage I molar balance on Si is determined by Eq. (1):

d VCSi

dt CDWV

YX=SiQICSi: 1

The Stage I mass balance on biomass is given by Eq. (2):

d VCDW

dt

CDWVQICDW: 2

The culture volume at time, t, is:

V V0;IQ1t: 3

The balances can be transformed in terms of the total moles of Si

and grams of cells in the cell culture by substituting MSi= CSiV,

MDW= CDWV into Eqs. (1) and (2) and rearranging to give:

dMSidt

MDWYX=Si

QIMSiV 4

dMDWdt

MDWQIMDW

V5

where V=V0,IQIt and with the initial condition of MSi=V0,ICSi,0,Iand MDW=V0,ICDW,0,I at tI=0. Silicic acid is a substrate for biomass

production, so Eqs. (4) and (5) are coupled and non-linear because

they are linked to the growth rate, . As the concentration of Si in

the culture medium decreases, so does the biomass production rate.

As described later, the lipid production rate is also coupled to silicic

acid concentration and biomass productivity.

Stage II began when fresh medium with a silicic acid feed concen-

tration of CSi,fwas pumped into the cell culture at a rate of Qf. The du-

ration of the medium delivery was 48, 72 or 96 h, depending onexperimental design, but all experiments delivered the same volume

of medium fresh medium and a cumulative quantity of silicic acid suf-

ficient for one cell doubling. The reactor sampling rate in Stage II was

QII.

Thebalanceson Si and biomass in Stage II aredescribed as follows:

d VCSi

dt CDWV

YDW=Si QfCSi;fQIICSi 6

d VCDW

dt CDWVQIICDW 7

which leads to the following differential equations in terms of total

moles of Si and mass of cells within the reactor:

dMSidt

MDWYX=Si

QfCSi;fQIIMSi

V8

dMDWdt

MDWQIIMDW

V9

where the reactor volume, V, during Stage II is

V V0;II tII QfQII 10

with initial conditions MSi=CSi,II,0 V0,II and MDW=CDW,0,IIV0,II at

tII=0.

Stage III began upon the completion of the medium addition in

Stage II. The balance equations for Stage III are identical to Stage I,with an outflow rate of QIII and initial conditions MSi=V0,IIICSi,0,IIIand MDW=V0,IIICDW,0,III at tIII=0. Also, it can be seen that when Qfis set to zero, the balance equations for Stage II reduce to those of

Stages I and III. Additionally, the concentrations of biomass and silicic

acid in the cell culture at time, t, can be found by dividing the total

quantities of biomass and silicic acid at time, t, by the culture volume

at time, t.

3.3. Growth rate

The growth rate, , can be a function of many factors, such as tem-

perature, pH, aeration, and nutrient levels. In this case, the CO2 trans-

fer rate was sufficient to maintain cell growth even under the highest

observed productivities, the culture medium was replete with respect

Table 1

Notation.

0 Growth related lipid production rate (g P g DW1)

Non-growth related lipid production rate (g P g DW1 h1)

max Maximum non-growth related lipid production rate

(g P g DW1 h1)

Specific growth rate (h1)

max Maximum specific growth rate (h1)

CDW Cell density in liquid suspension culture (g DW L1)

CDW,0,I Initial cell density in Stage I (g DW L1)

CSi Bioavailable silicic acid concentration (mmol Si L1)

CSi,0,I Initial silicon concentration in Stage I (mmol Si L1)

CSi,f Silicon concentration in the feed (mmol Si L1)

I0 150 Light intensity incident to the cell culture (mol photons m

2 s1)

Ik 77 Light intensity half saturation (mol photons m2 s1)

Im Mean light intensity within the cell culture (mol

photons m2 s1)

kc 0.576 Light attenuation constant for cell suspension culture

(L g DW1 cm1)

KP,Si Silicon dependent lipid productivity constant (mmol Si L1)

KSi 7.6010

3

Silicic acid half saturation constant (mmol Si L1)

KSi,n Silicon lipid production sensitivity parameter ()MP Total mass of lipids in cell culture (g P)

MP,0,I Initial mass of lipids in cell culture (g P)

MSi Total moles of Si in liquid phase (mmol Si)

MSi,0,I Total moles of Si initially charged into culture (mmol Si)MDW Total mass of cells in culture (g DW)

MDW,0,

I

Total mass of cells initially charged into culture (g DW)

Qi Culture removal rate in Stage I (I, II, or III) (L h1)

Qf Flowrate of fresh medium into reactor (L h1)

R 5 .2 5 Photobior ea ct or ra dius ( cm)

t Time (h)

V Reactor volume (L)

V0,i Initial reactor volume for Stage I (I, II, or III) (L)

yP Mass fraction of lipids in the cell mass (g P g DW1)

yProd Measured specific lipid productivity (g P g DW1 h1)

yP,K 0.02 Lipid productivity transinhibition factor ()yP,max 0.60 Maximum mass fraction of lipids in the cell mass (g P g DW

1)

YDW/Si Biomass yield on silicon (g DW mmol Si1)



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to all micro and macronutrients except for silicic acid and max was

considered specific to the pH and temperature of this cultivation.

Therefore, the specific growth rate exhibits saturation kinetics with

respect to only the silicon concentration and the mean light intensity

within the cell culture as seen in Eq. (11):

Im

Ik Im

CSiKSi CSi

max 11

where Ik and KSi are the half saturation constants for mean light

intensity and silicic acid concentration, respectively, and CSi is the

concentration of silicic acid available for consumption by the cell

culture. Ik was determined in previous works (unpublished data)

by methods previously described [36,37] and found to be 77

10 E m2 s1. The Ksi value of for Cyclotella meneghiniana

(7.6106 mol L1) was taken from the literature [14].

As a result of self-shading, the mean light intensity within the

cell culture is a function of the illumination intensity incident tothe cell culture (I0), cell culture density (CDW), photobioreactor

radius (R), and the cell culture light attenuation constant (k c),

analogous to the BeerLambert absorption coefficient. The param-

eter kc was determined by methods previously described [38] and

found to be 0.580.02 L gDW1 cm1. The geometry of the

photobioreactor is cylindrical and the external illumination was

assumed to be uniform about the exterior and with respect to the

axial and angular dimensions. For simplicity, effects which are

not taken into account by the BeerLambert Law, e.g. fluorescence

and backscattering,are acknowledged to exist but are not considered

in this analysis. The mean light intensity within the photobioreactor

was determined by using a photon shell balance and the Beer

Lambert law to determine the local light intensity within the

photobioreactor, integration over the circular photobioreactor cross

section, and dividing by the photobioreactor cross sectional area as

follows,

Im 1

R2

2

0

R

0

2RI0eRkcCx

r

cosh RkcCDW rdrd 12

where Im is the mean light intensity within the photobioreactor, I0is the photon flux incident to the cell culture just inside the reactor

wall, R is the radius of the photobioreactor, and kc is the specific

light attenuation constant for the cell culture. Internal light scattering

and light attenuation by the transparent, cell-free medium was negli-

gible and therefore not considered in this analysis. Integration of

Eq. (12) gives:

Im 2I0 1 exp

2RkcCDW

RkcCDW: 13

The specific growth rate is therefore a combination of Eqs. (11)

and (13). This system of nonlinear differential equations does not

have a closed-form, analytical solution.

3.4. Lipid production

Product formation can be either primary, concomitant with pri-

mary metabolism and growth, or secondary, which is not coupled to

cellular growth and normally occurs in response to a stress or exter-

nal stimuli. It was assumed that fatty acid production is partially

growth-associated. That is, they can be produced by both primary

production, where (g Pg DW1) is the amount of product (fatty

acids) produced with each unit of biomass and by secondary produc-

tion, where (g Pg DW1

h1

) is the rate of production induced by

air outlet

lamp

air

sampling

lamp

air

Si

air outlet

lamp

air

sampling

air outlet

sampling

a b c

Stage I Stage II Stage III

Fig. 4. A representation of the cell culture volume and silicon concentration at the start of each cultivation stage. A darker grayscale indicates a higher Si concentration. (a) Stage I:

high volume, high Si, (b) Stage II: low volume, Si starvation, and (c) high volume, low Si.

http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B4%80


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silicon stress. A Stage I mass balance on lipids, assuming a partially

growth-associated lipid formation rate is found in Eq. (14),

d VCDWyP

dt V

dCDWdt

VCDWQICP 14

where yP is the mass fraction of lipids in the biomass. After substitut-

ing MP=VCDWyP; MDW=VCDW and dMDW/dt from Eq. (5), the

following expression was obtained for the total lipids in the cell cul-ture in Stage I:

dMPdt

MDW QIMP

V15

with initial condition MP,I=V0,ICDW,0,IyP,0 at tI=0, and the culture

volume described by Eq. (3). The expressions for total lipids in

Stage II and Stage III are identical to Stage I except for the outflow

terms, which become QII and QIII, respectively. There is no inflow of

lipids. The initial conditions reflected the initial volumes and concen-

trations at the start of Stages II and III.

The non-growth associated production of lipids was a function of

the concentration of silicic acid in the medium and the fatty acid com-

position of the cells. Nitrogen stress did not play a role in lipidproduction since nitrate was present in excess in the culture medium

(>3 mM, data not shown) at all times. Specifically, as the concentra-

tion of the silicic acid decreased the metabolism of the organism

shifted from growth to lipid accumulation. A maximum lipid content

attainable in the cell mass, yp,max (g Pg DW1), was also assumed. As

this limit is approached the production rate of lipids decreases. This

transinhibition model was used previously to predict diatom growth

[39] by using Monod-form equations. In this model, exponential

form equations have replaced the Monod equations to model second-

ary lipid production as a function of nutrient stress and lipid

transinhibition.

max exp CSiKP;Si !KSi;n" #

exp yP yP;KyP;maxyP !

16

where max is the maximum rate of lipid production, yP,max is the

maximum mass fraction of lipids achievable in the cell mass, KP,Si,

KSi,n, and yP,K are parameters that describe the sensitivity of lipid pro-

duction to CSi and yP, respectively. In this model we observe that

when there is no lipid transinhibition (yp=) that as CSi0,max and as CSi, 0. In the absence of silicon transinhibition

(CSi=0), we also observe that as yp, max and as ypyp,max,0.

4. Results

4.1. Final model and parameter estimation

The final model for the semi-continuous production of lipids from

a cell culture ofCyclotella sp. predicts the total mass of cells and lipids

(MDW and MP) and the total mmol of Si (MSi) in the cell culture with

respect to time. These state variables and the bioprocess variables (V0,I,

MDW,0,I, MSi,0,I, Qi, Qf, CSi,f, I0) along with the other parameters tabulated

in Table 1 can be used to calculate the mean light intensity (Im),

specific growth rate (), mass fraction of lipids in the biomass (yP),

biomass production rate (g DW h1), lipid production rate (g Ph1),

total consumption of Si(OH)4 (mmol) and total biomass (g DW) and

lipids (g P) produced during the cultivation.

The values for the adjustable parameters were determined from

least-squares optimizations and are found in Table 2. The modelpredictions were calculated by using the 4th-order RungeKutta

method with a timestep of 1 h. Specifically, least-squares optimiza-

tions between the experimental data and model predictions for

total biomass in the cell culture and total soluble silicon in the cell

culture were performed to determine yield coefficient (YDW/Si) and

maximum specific growth rate (max). The same method was used

between the model prediction and total lipids produced to determine

the theoretical maximum lipid content of the cells (yP,max), the

maximum specific production rate of lipids due to silicon stress

(max) and the lipids produced through primary metabolism ().

The constraints on the optimization were 0.0 gP gDW1, andyp0.60 gP gDW1 because this was the approximate maximumlipid content observed in this study.

The optimizations revealed that =0.0 glipids gDW1, which

indicated lipid production was predominantly a result of Si-stress

and not primary metabolism. The parameter was subsequently

dropped from the model. A response analysis of the silicon sensitivity

coefficients (KP,Si and KSi,n) revealed that the sum of the least squares

had low sensitivity to changes in these parameters and that values of

unity were acceptable although the presence of KP,Si is still assumed

in order to maintain a unitless exponential term. As a result, these pa-

rameters were also dropped from the model, which left the following

simplified model for :

max exp CSi expyPyP;K

yP;maxy

!: 17

However, Eq. (16) may be required to adequately describe the

production of other metabolites or bioprocesses by using other

organisms.

From Table 2 a comparison of adjustable parameters canbe made for

each bioprocess examined in this study. Parameter values expected to

be intrinsic to the organism (max, YDW/Si) independent of the

bioprocess were found to be the same within the margin of error for

both parameters in all experiments except for YDW/Si for the 72 hour

perfusion experiment. While the magnitude of cells produced during

this cultivation was lower, the general shape of the growth curve was

the same, withall experiments reaching a stationary phase at t200 h.The values of yP,max determined in the least-squares optimization

were the same for the Si-pulse, 48 and 96 hour perfusion experi-ments. The yP,max for the 72 hour perfusion experiment was

0.60 g P g DW1, the upper constraint set for the parameter during

the optimization. In this cultivation the predictive model slightly

underpredicted the total biomass which led to an overestimation in

Table 2

Growth and lipid production parameters for each cultivation strategy.

Perfusion duration Y DW/Si max max yP,max yprod

g DW (mmol Si)1 h1 mg lipids g DW1 h1 g lipids g DW1 g lipids g DW1

0 1.16 0.02 0.021 0.001 4.70.9 0.54 0.09 1.6 1.0

48 1.17 0.02 0.021 0.004 5.41.8 0.52 0.06 6.2 2.0

72 0.66 0.08 0.021 0.007 1.50.1 0.60 0.00 5.5 0.3

96 1.13 0.03 0.023 0.002 0.90.1 0.52 0.12 3.10.9



8/12

the content of the biomass in order to minimize the least squares for

the total lipids. This is because the total lipids are calculated from the

product of total biomass and biomass lipid fraction. The highest

biomass lipid content measured in the 72 hour perfusion experi-

ments was 535%, which was consistent with the yP,max determined

in the other cultivations.

All parameters within the cultivation were held constant, whilemax, YX/Si, yP,max and max were determined from the least squares

minimization. The values for max, YX/Si, yP,max were consistent acrossthe experiments, so the only true adjustable parameter to describe

lipid productivity in this optimization was max. As seen in Table 2,

values for max were not consistent across the experiments. This indi-

cates that the bioprocess strategy has a large influence on the specific

lipid production rate. The value for max was highest for the pulse

addition and the 48 hour perfusion with decreasing max as the

perfusion time increases. However, when max was calculated based

on data from only Stages II and III for the 72 hour perfusion experi-

ment, the value increased to 3.91.4 mg P g DW1 h1, which

approached the value of the pulse and 48 hour perfusion experi-

ments. The 96 hour perfusion cultivation produced minimal lipids.

4.2. Biomass and lipid productivity

Model predictions and experimental data for the total mass of

cells produced, lipids produced and the total consumption of silicic

acid with respect to time of representative runs for the Si(OH)4pulse, 48 hour perfusion and 72 hour perfusion are presented in

Fig. 5. The model predictions follow the trend of the data except for

the 96 hour perfusion experiments (Fig. 5d), which showed no lipid

productivity and minimal cell growth during Stages II and III despite

the addition of silicon. As a result, this bioprocess was discounted as

a viable lipid production strategy and the model predictions have

been omitted.

The biomass and lipids produced by time, t, is a combination of the

biomass and lipids in the cell culture at time, t, and the sum of the

biomass and lipids removed from the cell culture before and includ-

ing time, t. The total Si consumed by time, t, is the total of Si up

until and including time, t, which has been consumed and converted

to biomass. The biomass produced and the silicic acid consumed are

proportional throughout the prediction because the overall biomassyield coefficient is assumed to be constant throughout the cultivation,

even though the cell cycle and synchronicity effects do affect the in-

stantaneous yield coefficient [39]. This is because non-trivial amounts

of intracellular Si can be stored within the cytoplasm which are later

incorporated into the diatom frustule during cell growth and division

or which remain as a Si storage pool. Intracellular Si was not

measured in this study and was therefore not accounted for in this

model. The quantity of lipids produced is a function of the biomass

produced and the mass fraction of lipid in the biomass. The lipids

produced are not linearly related to the biomass produced because

the lipid mass fraction within the cells is changing with time. Namely,

lipid production is promoted when the silicic acid in the medium is

depleted and lipid production is suppressed when the medium is

replete with silicic acid.

Model predictions and experimental data for the total amounts of

biomass, lipids and silicic acid in the cell culture with respect to time

are presented in Fig. 6. The model predictions follow the trend of the

experimental data. The gradual decrease in total biomass and total

liquid phase silicic acid in the stationary phase in Stages I and III are

a result of the continual outflow of material due to sampling. More

specifically, while the concentrations remain nearly constant, the

reactor volume is decreasing, thus the total amount of each material

in the reactor is decreasing.

StartStop

0

1

2

0 100 200 300 400 500

Time (hr)

gCellsorLipids

Produced

0

1

2

Siconsumed(mmol)

Total Cells Produced

Total Cells Produced - SimTotal Lipids Produced

Total Lipids Produced - Sim

Total Si Consumedtotal Si consumed - SimPulse

0

1

2

3

100 200 300 400 500

Time (hr)

gCellsorLipids

Prod

uced

0

1

2

3

Siconsumed(mmol)

Total Cells ProducedTotal Cells Produced - SimTotal Lipids ProducedTotal Lipids Produced - Sim

Total Si Consumedtotal Si consumed - SimStartStop

Time (hr)

Total Cells ProducedTotal Cells Produced - Sim

Total Lipids ProducedTotal Lipids Produced - SimTotal Si Consumedtotal Si consumed - Sim

a b

c d

0

1

2

3

4

200 400 600 800 1000

Time (hr)

gCellsorLipids

Produced

0

1

2

3

4

Siconsumed(mmol)

Total Cells Produced

Total Lipids Produced

Total Si Consumed

Start

Stop

0

1

2

3

Siconsumed(mmol)

0

1

2

3

gCellsorLipids

Produc

ed

0 200 400 600 800

0

0

Fig. 5. The total cells produced, total lipids produced and total Si consumed with respect to time for the cultivation data and the model predictions for the (a) Si-pulse, (b) 48 hour

perfusion, (c) 72 hour perfusion, and (d) 96 hour perfusion experiments.



9/12

4.3. Effect of silicic acid concentration on lipid production

Thelipid biomass composition (wt.%=100 yP) and the silicic acid

concentration in the medium with respect to time for the Si(OH)4pulse, 48 hour perfusion and 72 hour perfusion is presented in

Fig. 7. Initially, the lipid content of the cells remained low until the

silicic acid was depleted. Silicic acid depletion promoted the

non-growth associated production of lipids, and the cellular lipid

content continued to rise towards the maximum cellular value until

the addition of silicic acid at the start of Stage II. During Stage II, the

intracellular lipid content began to fall in the presence of the newly

added Si, but remained well above the lipid concentrations at the

onset of the cultivation. At the end of Stage II the inflow of fresh silicic

acid was stopped, the silicic acid was depleted and the lipid content of

the cells increased in Stage III for the 48 hour and 72 hour perfusion

cultivations. The measured lipid content of the cells decreased in

the Si-pulse cultivation for the next five days, at which time the culti-

vation was stopped. This indicates that the perfusion addition strate-gy was more effective at maintaining a high lipid content in the cells

after the addition of Si. However, there was only minimal biomass

productivity during Stages II and III for the 96 hour perfusion experi-

ment (Fig. 5) and the lipid productivity was negative because the

wt.% of lipids in the biomass decreased. This indicated that Si-stress

which is prolonged beyond a certain duration can be detrimental to

productivity.

4.4. Effect of silicic acid addition strategy on lipid productivity

The measured lipid production rate, yprod (mg P g DW1 h1) for

the five day period after the startof the silicon addition (start of Stage

II) is presented in Table 2. This value was calculated by dividing the

change in total lipids produced during this five day period by the

0

1

2

0 100 200 300 400 500

Time (hr)

gCellor

mmolSiinCulture

0.0

0.5

1.0

gLi

pidsinCulture

Total Cell MassTotal Cell Mass - SimTotal SiTotal Si - SimTotal LipidsTotal Lipids - SimPulse

0

1

2

3

gCellorm

molSiinCulture

Total Cell MassTotal Cell Mass - SimTotal SiTotal Si - SimTotal LipidsTotal Lipids - SimStartStop

0 200 400 600 800

Time (hr)

Total Cell MassTotal Cell Mass - SimTotal SiTotal Si - SimTotal LipidsTotal Lipids - SimStartStop

0 100 200 300 400 500

Time (hr)

0.0

0.5

1.0

gLip

idsinCulture

0.0

0.5

1.0

gLipidsinCulture

0

1

2

3

gCellorm

molSiinCulture

a

b

c

Fig. 6. The total cell and lipid mass and mmol of silicic acid in the cell culture with re-

spect to time for the cultivation data and the model predictions for the (a) Si-pulse, (b)

48 hour perfusion, and (c) 72 hour perfusion experiments. The dashed vertical lines

represent the time of the Si-pulse or the start and stop of the Si-perfusion.

0

20

40

60

0 100 200 300 400 500

Time (hr)

wt%l

ipidsinbiomass

0.0

0.1

0.2

0.3

0.4

0.5

SiCon

c.

(mmolSiL-1)

wt% lipidswt% lipids - simSi concSi conc. - simPulse

0

20

40

60

wt%l

ipidsinbiomass

wt% lipidswt% lipids - simSi concSi conc. - sim

0

20

40

60

0 200 400 600 800

Time (hr)

wt%lip

idsinbiomass

0.0

0.2

0.4

0.6wt% lipidswt% lipids - simSi concSi conc. - simStartStop

a

b

c

0.0

0.1

0.2

0.3

0.4

0.5

SiConc

.(mmolSiL-1)

0 100 200 300 400 500

Time (hr)

SiConc.

(mmolSiL-1)

Fig. 7. The weight percent of lipids in the biomass and the silicic acid concentration in

the culture medium with respect to time for the cultivation data and the model predic-

tions for the (a) Si-pulse, (b) 48 hour perfusion, and (c) 72 hour perfusion experi-

ments. The dashed vertical lines indicate the start and stop of fresh medium perfusion.



10/12

amount of biomass in the cell culture at the startof Stage II. Biomass

concentration increased significantly after the start of Stage II in

some cultivations which allowed yprod to be greater than max. The

measured production rate was highest for the 48 and 72 hour perfu-

sion additions with 6.2 2.0 and 5.5 0.3, mg P g DW1 h1

respectively. The pulse addition strategy produced only 1.6

1.0 mg P g DW1 h1, even though the max for the cultivation

was the same, within error, as the 48 hour perfusion experiment, at

4.70.9 and 5.41.8 mg P g DW

1

h

1

, respectively. These ratesare comparable to Pruvost et al. [40], where approximately

3.8 mg P g DW1 h1 was produced from the same cell culture

density (0.4 g DW L1) of Neochloris oleoabundans during the first

48 h of nitrate starvation. This indicated that the 48 and 72 hour

perfusion experiments applied silicon stress to the cell culture more

efficiently than the pulse addition. However, the 96 hour perfusion

experiment lost lipids during Stages II and III. In the 96 hour cultiva-

tion there was no cell growth and the lipid fraction decreased. It is

believed that the extended time spent in the G2 phase of the cell

cycle may have induced a dormant state in the cell culture. However,

this cannot be verified without future work.

4.5. Fatty acid composition and profile

Cyclotella sp. cells harvested at three distinct cultivation points for

both 48 and 72 hour perfusion experiments were analyzed for fatty

acid composition as seen in Fig. 8a,b. These experiments were chosen

for analysis because they were the most promising bioprocess

platforms. Fatty acid composition was determined by GC as seen in

the representative chromatogram in Fig. 8c. Those time points are

as follows: Stage I mid-exponential growth, Stage I stationary phase,

and late Stage III (shutdown). Seven fatty acid constituents were

identified by GC/MS (data not shown) including myristic acid

(14:0), hexadecatrienoic acid (16:3), palmitoleic acid (16:1), palmitic

acid (16:0), stearic acid (18:0), octadecanoic acid (18:1), and

eicosapentanoic acid (20:5). Several minor, unlabeled peaks in

Fig. 8c were determined to be impurities in the sample, because

their mass spectra did not match with any fatty acids. The predomi-

nant fatty acids present in both experiments were palmitoleic acid,

palmitic acid and eicosapentanoic acid. The relative amounts of each

component remained unchanged over the course of the experiment

and thus remained unchanged regardless of the growth state or

silicon concentration. Therefore, changing the silicon delivery strategy

was not a viable method to steer fatty acid production towards one

product or another. The effect of other parameters, such as light inten-

sity and temperature, on the fatty acid distribution was beyond the

scope of the study.

4.6. Nile Red fluorescence determination of lipids

An additional 48 hour perfusion experiment was conducted to

qualitatively analyze the location and amount of intracellular lipidsby Nile Red staining, as seen in Fig. 9aj, which enabled the visualiza-

tion of intracellular lipids by fluorescent microscopy. The samples

included three time points in Stage I (early exponential growth

phase, mid-exponential growth phase, and stationary phase), one

time point from Stage II (mid-perfusion), and one time point in

Stage III (shutdown). It was possible to qualitatively assess the lipid

content within the cell by comparing the presence and relative size

of lipid vesicles within the cells. Fig. 9b clearly showed a lack of

distinct lipid vesicles within the cell. Instead, the cell walls and a

few internal membranes, which comprised lipids, were stained.

Fig. 9d,f, h and j all showedsignificant lipid increases when compared

with Fig. 8b and clearly exhibit lipid vesicles within the cells.

5. Discussion

This study demonstrated that lipid production by Cyclotella sp. can

be enhanced by using a bioprocess strategy of controlled silicon addi-

tion. This was achieved by utilizing a multi-stage photobioreactor

cultivation scheme in which silicon-starved cells with increased

lipid concentrations received silicon by perfusion at rates low enough

to maintain a silicon-depleted state and yet provided sufficient silicon

for continued biomass production while maintaining high lipid levels.

While it is known that silicon-starvation induces high lipid concen-

trations within diatoms by driving fatty acid synthesis with increased

formation of ACCase (Fig. 3) [19], this was the first study to actively

design and implement a system by which lipid production was en-

hanced along with lipid concentrations (Figs. 5, 7). Prior to this

study, investigations into the extent of enhanced lipid production as

Fig. 8. The fatty acid composition within the biomass in the Stage I mid-exponential,

Stage I stationary and Stage III phases for the (a) 48 h perfusion and (b) 72 h perfusion

cultivations. (c) A representative gas chromatograph for the fatty acids present in the

biomass.



11/12

a consequence of silicon-starvation in batch systems demonstrated

lipid content doubling during exponential growth and increasing

even further when maintained at silicon starvation by as much as

20% [19,21,22], but with biomass limited by the amount of initial

silicon provided to the system. Further studies considered two-stage

systems in which a second charge addition of silicon was added to

the reactor once silicon-starvation had been achieved. These experi-

ments saw additional biomass generation but lipid concentrations

decreased as cells began to uptake silicon and divide [2325]. One

experiment utilized a turbidostat to deliver a steady flow of silicon

at low concentrations [26] and saw increased lipid concentrations

but low lipid production overall due to a decrease in cell growth rate.

These experiments were similar to the charge-addition control

experiment performed in this study, in which a surge uptake mecha-

nism was utilized for silicon replenishment. Lipid concentration

decreased as the cellular uptake of silicon resumed and cells doubled,

allowing for low lipid productivity and high biomass productivity. By

considering an alternative delivery system for silicon, more control

over the cellular lipid metabolic pathways can be exerted. By usingthe perfusion addition of a large-volume, low-silicon-concentration

medium maintained starvation concentrations of silicon, which

improved lipid productivity when compared to silicon added as a

single pulse (Table 2), while having no effect on the fatty acid distri-

bution (Fig. 8). Since the intracellular concentrations of silicon would

have been quite low this new silicon supply was taken up quickly by

the starved cells. Had the cellular uptake not been rapid, the silicon in

the system would have accumulated to significant levels, causing

intracellular lipids to decrease. The lipid concentrations of both the

48 and 72 hour experiments decreased somewhat during the

silicon-uptake and cell division but the overall lipid productivity

continued to increase (Figs. 5, 7) because the biomass continued to

increase (Fig. 6).

However, the 96 hour perfusion showed decreased lipid produc-

tivity in Stages II and III (Fig. 5). This demonstrated a maximum

limit to the length of perfusion, or likewise, the existence of a mini-

mum silicon addition rate. This slow rate of addition would have

affected the metabolism of the cells. These cells did not effectively

take up the newly added dissolved Si, unlike the 48 and 72 hour

experiments, but instead slowly removed the silicon from the medi-

um until the experiment was shut down. As a consequence, silicon

starvation was not maintained and lipid concentrations decreased

from their Stage I stationary phase levels. The increased presence of

silicon would have slowed the formation of ACCase and thus the

fatty acid synthesis. Additionally, fatty acids would be used in cellular

components when generating new cells.

Towards the understanding and development of this bioprocess a

mechanistic model to describe cell growth and lipid production in

response to Si-stress was developed (Figs. 57). Based on thismodel, predictions could be made about the productivity of Cyclotella

sp. cell cultures by using a Stage II perfusion addition of up to 72 h.

Model predictions indicate that the formation of fatty acids was

primarily related to silicon stress, and not primary metabolism and

cellular growth. This is supported by Fig. 9, which demonstrated

that lipid accumulation was primarily in lipid storage vesicles and

not a result of cell wall or cell membrane formation. Likewise, the

increased lipid productivity in the perfusion experiments can be at-

tributed to a more effective application of silicon stress (Table 2),

because the ratio of the measured lipid productivity to the theoretical

maximum lipid productivity was high in these experiments when

compared to silicon pulse experiments. Based on the measured

results and model predictions, an improved bioprocess could be to

cycle the perfusion addition and culture removal by using cycletimes of between 48 and 72 h. Additionally, this model could be

applied to the production of other partially growth-associated or

secondary metabolites.

6. Conclusion

By utilizing a silicon perfusion-addition strategy and taking

advantage of the externally controlled silicon uptake mechanism, a

silicon-depleted state was conserved within the cell culture which

enhanced lipid productivity while producing biomass. To date,

enhanced productivity of lipids in combination with biomass produc-

tion has not been achieved. As a consequence of this study a new

method has been proposed to cultivate microalgae for enhanced

lipid production along with biomass production. In a comprehensive

Fig. 9. Light and Nile Red fluorescence microscopy during a 48 hour perfusion experi-

ment for samples taken at (a,b) early exponential growth phase, 15.12.0 wt.% lipid,(c,d) mid-exponential growth phase, 32.6 8.8 wt.% lipid, (e,f) Stage I stationary

phase, 32.68.8 wt.% lipid, (g,h) Stage II mid-perfusion, 37.36.4 wt.% lipid, and

(i,j) shutdown, 42.52.1 wt.% lipid.

http://localhost/var/www/apps/conversion/tmp/scratch_1/image%20of%20Fig.%E0%B9%80


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review of biofuels from microalgae [41], it was concluded that contin-

ued development of technologies to optimize microalgae lipid

production are needed in order to provide a sustainable biofuel

source that can meet large-scale demands cost-effectively. This

study develops a unique method which progresses towards this goal.

Acknowledgment

This work was supported by The Oregon Built Environmental andSustainable Technologies Center (BEST), the National Science Founda-

tion (NSF) Emerging Frontiers for Research and Innovation program

under award number 1240488, the European Coordinative Project

FP7-KBEE-2010-4-BAMMBO (265896) and the Belgian Fonds de la

Recherche Scientifique FNRS (F.N.S.-FNRS) project FOTOFUNDS. We

would also like to acknowledge Professor Spiros Agathos of the

Universit Catholique de Louvain for his support during the writing of

this document.

The authors have no conflict of interest or financial involvement

with any organization of entity with a financial interest, conflict, or

motive which could influence the content of this manuscript.

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