Congressional Ethics, Third Party Candidates, and Federalism

Assignment 2: It May Not Work in Politics
Due Week 10 and worth 225 points
Write a three to four (3-4) page paper in which the student
addresses the following three (3) items using headers to
separate each response:
Congressional Ethics. Identify one (1) member of Congress
who has been charged with ethics violations. Briefly discuss the
reason for the charges and provide two (2) reasons why you
agree or disagree with the verdict and any penalties. Provide
examples to support your answer. Note: Consider how the
verdict and penalties impacts your trust of the members of
Congress.
Third Party Candidates. Discuss two (2) political reasons
why a third party candidate has never been successful in
winning a presidential election. Provide examples to support the
answer. Note: Consider the political impact of the Republican
and Democratic Party if a third party was successful.
Federal and State Authority. Identify one (1) current issue
facing the United States today. Analyze the respective roles of
Federal and state authorities in addressing the issue. Determine
whether the U. S. Constitution constrains the Federal and state
responses to the issue. Explain.
In your research, you cannot use Wikipedia, online dictionaries,
Sparknotes, Cliffnotes, or any other Website do that do not
qualify as an academic resource.
Your assignment must follow these formatting requirements:
Be typed, double spaced, using Times New Roman font (size
12), with one-inch margins on all sides; references must follow

APA or school-specific format. Check with your professor for
any additional instructions.
Include a cover page containing the title of the assignment,
the student’s name, the professor’s name, the course title, and
the date. The cover page and the reference page are not included
in the required page length.
The specific course learning outcomes associated with this
assignment are to:
Identify informed opinions on issues and questions involving
the U.S. government, national political processes, policy
making, and the notion of democracy.
Employ terminology used to study political science and
American government.
Develop reasoned written and spoken presentations on issues
and questions involving the U.S. government and national
political processes using information in the course.
Describe the basic values of American political culture.
Explain how the federal system of government works.
Explore different perspectives on issues and questions about
the U.S. government and national political processes.
Describe the importance of an informed, effective citizenship
for the national government and political processes.
Use concepts from our study of U.S. national government and
politics (such as models of democracy) to discuss government
and politics in state, local, and international contexts.
Examine the evolution of presidential power in military
affairs.
Use technology and information resources to research issues
in the field of U.S. government and politics.
Write clearly and concisely about U.S. government and
politics using proper writing
Points: 225
Assignment 2: It May Not Work in Politics
Criteria

Unacceptable
Below 60% F
Meets Minimum Expectations
60-69% D
Fair
70-79% C
Proficient
80-89% B
Exemplary
90-100% A
1. Identify one (1) member of Congress who has been charged
with ethics violations.. Briefly discuss the reason for the
charges and provide two (2) reasons why you agree or disagree
with the verdict and any penalties
Weight: 25%
Did not submit or incompletely identified Identify one (1)
member of Congress who has been charged with ethics
violations.. Briefly discuss the reason for the charges and
provide two (2) reasons why you agree or disagree with the
verdict and any penalties
Insufficiently Identify one (1) member of Congress who has
been charged with ethics violations.. Briefly discuss the reason
for the charges and provide two (2) reasons why you agree or
disagree with the verdict and any penalties
Partially identified Identify one (1) member of Congress who
has been charged with ethics violations.. Briefly discuss the
agree or disagree with the verdict and any penalties
Satisfactorily identified Identify one (1) member of Congress
who has been charged with ethics violations.. Briefly discuss
the reason for the charges and provide two (2) reasons why you

Thoroughly identified Identify one (1) member of Congress who
has been charged with ethics violations.. Briefly discuss the
2. Discuss two (2) reasons why a Third Party candidate has
never been successful in the presidential election.
Weight: 25%
Did not submit or incompletely discussed two (2) reasons why a
Third Party candidate has never been successful in the
presidential election.
Insufficiently discussed two (2) reasons why a Third Party
candidate has never been successful in the presidential election.
Partially discussed two (2) reasons why a Third Party candidate
has never been successful in the presidential election.
Satisfactorily discussed two (2) reasons why a Third Party
Thoroughly discussed two (2) reasons why a Third Party
3. Identify one (1) current issue facing the United States today.
Analyze the respective roles of Federal and state authorities in
addressing the issue. Determine whether the U. S. Constitution
constrains the Federal and state responses to the issue. Weight:
25%
Did not submit or incompletely identified one (1) current issue
facing the United States today. Did not submit or incompletely
analyzed the respective roles of Federal and state authorities in
addressing the issue. Determine whether the U. S. Constitution
constrains the Federal and state responses to the issue.
Insufficiently identified one (1) current issue facing the United
States today. Insufficiently analyzed the respective roles of
responses to the issue.
Partially identified one (1) current issue facing the United
States today. Partially analyzed the respective roles of Federal
and state authorities in addressing the issue. Determine whether

the U. S. Constitution constrains the Federal and state responses
to the issue.
Satisfactorily identified one (1) current issue facing the United
States today. Satisfactorily analyzed the respective roles of
Thoroughly identified one (1) current issue facing the United
States today. Thoroughly analyzed the respective roles of
4. Writing / Support for ideas (8%)
Never uses reasons and evidence that logically support ideas
Rarely uses reasons and evidence that logically support ideas
Partially uses reasons and evidence that logically support ideas
Mostly uses reasons and evidence that logically support ideas
Consistently uses reasons and evidence that logically support
ideas.
5. Writing / Grammar and mechanics
(5%)
Serious and persistent errors in grammar, spelling, and
punctuation
Numerous errors in grammar, spelling, and punctuation
Partially free of errors in grammar, spelling, and punctuation
Mostly free of errors in grammar, spelling, and punctuation
Free of errors in grammar, spelling, and punctuation
6. Writing and Information Literacy / Integration and Crediting
of Sources
(7%)
Serious errors in the integration of sources, such as intentional
or accidental plagiarism, or failure to use in-text citations.
Sources are rarely integrated using effective techniques of
quoting, paraphrasing, and summarizing, using in-text citations
Sources are partially integrated using effective techniques of

Sources are mostly integrated using effective techniques of
Sources are consistently integrated using effective techniques of
7. Information Literacy / Research (5%)
Quantity and/or quality of sources are unacceptable
Too few references and/or references are of poor quality
Number of sources is less than expected and/or the quality of
sources is questionable.
Number of sources is sufficient and the quality of sources is
mostly good.
Number of sources is sufficient and the quality of sources is
good.
Homework 7 Due: 3/21/2016 (Monday)
Question: Each student should write a page of review summary
from one of the following
research articles. You can combine the knowledge that you learn
from the biofuels classes.
Please choose your interested article and upload your homework
on the Blackboard.
The summary should contain following scientific component: 1)
Background, 2) experimental
design; 3) interpretation of the data; 4) statistical analysis; 5)
impact; and 6) conclusion.
Research articles: (The full version of the articles are attached
below)
1. Sivakumar et al. 2014. Bioprocessing of Stichococcus

bacillaris strain siva2011.
Biotechnology for Biofuels, 7:62.
2. Sivakumar et al. 2014. Biomass and RRR-a-tocopherol
production in Stichococcus
bacillaris strain siva2011 in a balloon bioreactor. Microbial
Cell Factories, 13: 79
RESEARCH Open Access
Bioprocessing of Stichococcus bacillaris strain
siva2011
Ganapathy Sivakumar1*, Kwangkook Jeong2 and Jackson O Lay
Jr3
Abstract
Background: Globally, the development of a cost-effective long-
term renewable energy infrastructure is one of the
most challenging problems faced by society today. Microalgae
are rich in potential biofuel substrates such as lipids,
including triacylglycerols (TAGs). Some of these algae also
biosynthesize small molecule hydrocarbons. These
hydrocarbons can often be used as liquid fuels, often with more
versatility and by a more direct approach than
some TAGs. However, the appropriate TAGs, accumulated from
microalgae biomass, can be used as substrates for
different kinds of renewable liquid fuels such as biodiesel and
jet fuel.
Results: This article describes the isolation and identification of

a lipid-rich, hydrocarbon-producing alga, Stichococcus
bacillaris strain siva2011, together with its bioprocessing,
hydrocarbon and fatty acid methyl ester (FAME) profiles. The
S. bacillaris strain siva2011 was scaled-up in an 8 L bioreactor
with 0.2% CO2. The C16:0, C16:3, C18:1, C18:2 and C18:3
were 112.2, 9.4, 51.3, 74.1 and 69.2 mg/g dry weight (DW),
respectively. This new strain produced a significant amount
of biomass of 3.79 g/L DW on day 6 in the 8 L bioreactor and
also produced three hydrocarbons.
Conclusions: A new oil-rich microalga S. bacillaris strain
siva2011 was discovered and its biomass has been scaled-up
in a newly designed balloon-type bioreactor. The TAGs and
hydrocarbons produced by this organism could be used as
substrates for jet fuel or biodiesel.
Keywords: Algae, Bioreactor, Hydrocarbon, Jet fuel,
Triacylglycerol
Background
Worldwide consumption of crude oil is predicted to grow
continuously. It is clear that in spite of improvements in
the recovery of traditional fossil fuels, alternative renew-
able energy resources will at some point be needed.
Moreover, such renewable fuels offer the prospect of
minimizing increases in atmospheric CO2 by recycling
carbon from the atmosphere back into a viable liquid
fuel (or perhaps eventually sequestering it entirely).
Over a large number of cycles, the net effect could be a
significant reduction in the addition of CO2 into the at-
mosphere compared to continued reliance only on fossil
fuels. A wide variety of existing biofuel technologies have
been tested, but none have proven to provide a suitable
source of replacement liquid fuels. Although current alter-
natives such as ethanol and biodiesel can provide carbon
neutrality, fuels derived largely from normally edible plant

sources affect the food supply negatively [1-3]. For these
reasons algae feedstocks are being explored as an alterna-
tive [4]. The development of a suitable algal-based jet fuel
from algal biomass may also impact air transportation.
The jet fuel approach is to chemically process tria-
cylglycerols (TAGs) to alkanes. This could be done by
catalytic hydrotreating, breaking the TAG molecule and
removing the oxygen to form alkanes. While this product
meets diesel specifications, it can be further upgraded into
jet fuel or naphtha by hydrocracking, isomerization and
catalytic reforming [5]. The by-product propane can be
used for residential central heating. However, not all
microalgae are capable of producing sufficient TAGs and
hydrocarbons for effective fuel production. While others
might produce abundant TAGs, they might not necessar-
ily be the optimum TAGs for production of high-value
products such as aviation fuel. For production of such spe-
cialized fuels, the selection of the algal species is the key
to success. Carbon profiles for selecting algal strains [6]
and catalytic hydrothermal decarboxylation of fatty acids
for aviation fuel [7] have been studied.
* Correspondence: [email protected]
1Arkansas Biosciences Institute and College of Agriculture and
Technology,
Arkansas State University, PO Box 639, Jonesboro, AR 72401,
USA
Full list of author information is available at the end of the
article
© 2014 Sivakumar et al.; licensee BioMed Central Ltd. This is
an Open Access article distributed under the terms of the
Creative Commons Attribution License
(http://creativecommons.org/licenses/by/2.0), which permits

unrestricted use,
distribution, and reproduction in any medium, provided the
original work is properly credited.
Sivakumar et al. Biotechnology for Biofuels 2014, 7:62
http://www.biotechnologyforbiofuels.com/content/7/1/62
mailto:[email protected]
http://creativecommons.org/licenses/by/2.0
Recently, Stichococcus bacillaris Naegeli was proposed
as a potential and dedicated candidate for use in fuel pro-
duction [8]. S. bacillaris is a green soil microalga which in-
cludes over 14 species [9]. Cells are approximately 2 to
3 μm in diameter. The state of filamentous or unicellular
structures depends on salinity [10]. This species can grow
both in freshwater and seawater with different growth kin-
etics [11], while tolerating high salinities [12]. In addition,
this alga has adapted to low temperatures and is found in
ice-free areas of Antarctica [13]. Moreover, it also contains
high NADPH-GDH activity [14], low CO2 resistance [15]
and has unique microtubule organization in prophase
[16]. The NADPH-GDH plays an important role in photo-
synthetic microalgae, which is associated with photore-
gulation and the incorporation of ammonia into amino
acids. The changes in NADPH-GDH were shown in
different culture conditions such as photoautotrophic,
heterotrophic and mixotrophic [17]. Compared to am-
monium, nitrate-grown S. bacillaris had higher activity
of NADPH-GDH [18]. S. bacillaris is fairly abundant glo-
bally, can remove heavy metals from hazardous environ-
ments [19] and is also capable of biotransforming phenols
[20]. These characteristics suggest that S. bacillaris could
minimize water contamination or improve water quality.
In addition, this organism has a short life cycle and is

tolerant to different ranges of pH. Most importantly,
over 30% of its dry mass can be produced as oil that can
be readily converted to biodiesel [21]. Moreover, this
alga produced a high percentage of C16 to C18 carbon
fatty acids. Therefore, the goal was to isolate Stichococ-
cus species for the study of aviation fuel. Other proposed
algal strains either produced triterpene hydrocarbons
that are difficult to convert cost-effectively to usable
fuels or grew too slowly to be useful [22,23]. Some other
TAGs are produced from algae but they typically yield a
low biomass [24]. Thus, the aim of this research has
been: 1) to isolate new Stichococcus algal species produ-
cing significant quantities of lipids and hydrocarbons, es-
pecially those suitable for production of aviation fuel;
and 2) to evaluate the scale-up potential of this alga in a
new design balloon-type bioreactor.
Results and discussions
Stichococcus bacillaris strain siva2011 identification
A new axenic microalga S. bacillaris strain siva2011 was
isolated from an in vitro plant. Microscopic examination
demonstrated green rod-shaped cells 5 to 10 μm in
length and 2 to 3 μm in diameter. The cells are often
presented in chains. The 18S sequence data confirmed
that this new alga is a strain of genus Stichococcus with
the greatest similarity to S. bacillaris. However, there is a
large difference between this and existing strains at nu-
cleotides 610 to 980 of the 18S rDNA (see Additional
file 1). The 23S sequence did not match with existing S.
bacillaris sequences, providing further confirmation that
this is a new species. Due to taxonomic problems of the
Stichococcus species and intraspecific biodiversity [25],
this new alga was named ‘Stichococcus bacillaris strain
siva2011’. The two partial sequences were deposited into

the National Center for Biotechnology Information (NCBI)
[GenBank:JN168788 and JN168789]. A neighbor-joining
tree was created using a Clustal X2.0.12 set to exclude posi-
tions with gaps and correct for multiple substitutions.
Based on the 18S rDNA sequences, 1,000 bootstrap trials
were used to show the relationship of the S. bacillaris and
the strain siva2011 (Figure 1). Previously, a similar phylo-
genetic tree was reported for S. bacillaris strains NJ-10 and
NJ-17 [13].
Bioreactor culture of S. bacillaris strain siva2011
To further explore the potential for enhanced fuel applica-
tion, production of the S. bacillaris strain siva2011 was cul-
tured using a low-cost indoor balloon-type bioreactor
(Figure 2). This alga required both light and sugar sub-
strates for optimum growth. The higher biomass accu-
mulation was noticed in 1% fructose supplemented
medium when compared to other sugars. It is also im-
portant to know that in a 6-day culture, a comparison of
Trebouxiophyte sp. UR55/3
Trebouxiophyte sp. UR47/41
Stichococcus deasonii UTEX 1706
Stichococcus bacillaris
K4-4
Stichococcus mirabilis
CCAP 379/3
Stichococcus sp.
MBIC10465

D10-1
Stichococcus bacillaris strain siva2011
Stichococcus jenerensis
D4
716
582
591
695
409
311
0.001
Figure 1 Phylogenetic tree showing the relationship of S.
bacillaris strain siva2011 based on 18S rDNA sequences. A
distance of 0.001 is indicated by the scale.
Sivakumar et al. Biotechnology for Biofuels 2014, 7:62 Page 2
of 9
the sugar concentrations and the total inorganic carbon in
the media showed no significant difference (data not
shown). Light limitation is one of the critical factors in
algal biomass scale-up in the photobioreactor. This strain
grows well under a low light intensity (15 to 30 μE m-2 s-1

for 10 hours) and requires room temperature. The newly
designed reactor has a larger headspace, which efficiently
captures light and also has good media circulation to en-
hance photosynthesis. In other words, this unique bioreac-
tor configuration could minimize the factors limiting the
overall rate of photosynthesis in a high density culture.
After autoclaving the media, the pH dropped from 6.0
to 4.2 because of fructose degradation. The sugar deg-
radation could be minimized by sterilizing filtration;
however, this procedure increases the percentage of cul-
ture contamination. Figure 3A,B illustrates the kinetics
of S. bacillaris strain siva2011 grown for 6 days in 4 L
and 8 L airlift balloon bioreactors with 0.05, 0.1, 0.2 or
0.5% CO2, respectively. Of the four concentrations of
CO2 tested, 0.2% yielded the highest biomass. The expo-
nential growth phase was noticed on day 4 and station-
ary phase on day 6. Although the culture medium
nutrients did not deplete in 6 days, the pH dropped to
highly acidic levels (Figure 4A,B). The dropping pH
could be caused by the degradation of fructose or CO2
effects or HNO3 accumulation in the culture medium.
For instance, in the aqueous phase, nitrogen oxide from
the media could react with oxygen to form nitrogen di-
oxide, which could then react with the hydroxyl radical
to form nitric acid. In order to bring the pH back to an
optimum level of 4.5 to 5.0 in fructose supplemented
medium, every 6 days the culture was subcultured and
old media was removed by centrifugation. Alternatively,
adding sodium hydroxide to the culture could perhaps
have maintained the pH; further studies are needed for
verification. This alga did not grow well in algal Bold’s
Basal Medium (BBM) (data not shown).
On day 6 with 0.2% CO2, a maximum biomass of

3.79 g/L dry weight (DW) was achieved in 8 L and 3.45 g/
L DW in 4 L, respectively. Since the new strain requires a
very low 0.2% of CO2, the input cost on large-scale could
be minimized. When compared to 0.5% CO2, algal cells
grown in a 4 L bioreactor were higher in biomass, with
0.55, 0.986 and 1.45 g/L DW in the 0.05, 0.1 and 0.2%
CO2, respectively. Similarly, in the 8 L bioreactor, biomass
accumulation was 0.793, 1.107 and 1.79 g/L DW after
Figure 2 Balloon-type bioreactor 20 L culture of S. bacillaris
strain siva2011 (working volume 8 L).
0
0.5
1
1.5
2
2.5
3
3.5
4
0 1 2 3 4 5 6
D
ry

W
ei
gh
t
(g
/L
)
Days
0.05% CO2
0.10% CO2
0.20% CO2
0.50% CO2
A
0
0.5
1
1.5
2
2.5
3
3.5
4

4.5
0 1 2 3 4 5 6
D
ry
W
ei
gh
t
(g
/L
)
Days
0.05% CO2
0.10% CO2
0.20% CO2
0.50% CO2
B
Figure 3 Growth kinetics of S. bacillaris strain siva2011. (A)
Growth for 6 days in a 4 L airlift balloon bioreactor with 0.05,
0.1, 0.2 or 0.5% CO2.
(B) Growth for 6 days in an 8 L airlift balloon bioreactor with
0.05, 0.1, 0.2 or 0.5% CO2.
of 9

6 days of culture. Both growth and pH kinetic trends were
similar in 4 L and 8 L bioreactors. The data supports the
notion that this strain does not require light intensity over
15 to 30 μE m-2 s-1; therefore, achieving high density bio-
mass may not be a problem. However, the optimization of
air flow is needed for better media circulation and growth.
The media circulation is critical for efficient photosyn-
thesis. The oil-rich alga Ettlia oleoabundans accumulates
2.28 g/L dry biomass in BBM in approximately 22 to
27 days [24], while S. bacillaris strain siva2011 accumu-
lates approximately 1.5 g/L higher biomass within 6 days
in modified Murashige and Skoog (MS) medium.
For scale-up studies, modeling is necessary because it
provides detailed estimates regarding prediction and
validation. For instance, DW of S. bacillaris strain
siva2011 has been regarded as an indicator for produc-
tivity in the bioprocess. Prediction of DW is needed
and helpful for scale-up of S. bacillaris strain siva2011
biomass in large-scale reactors. Quinn et al. [26] mod-
eled microalgae growth and lipid accumulation in an
outdoor photobioreactor by using MATLAB for biofuel
applications. This model qualitatively captures the growth
trends with variations in time. Figure 5 shows the pre-
diction of normalized DW for 0.2% CO2 in 4 L and 8 L
bioreactors compared with experimental/measured data
obtained from each test. An overall trend of predicted
DW for 8 L was similar to the 4 L data. The R2 value has
been estimated in the 8 L as 85.8%, while the 4 L exhibits
0

0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 1 2 3 4 5 6
p
H
Days
0.05% CO2
0.10% CO2
0.20% CO2
0.50% CO2
A
0

0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 1 2 3 4 5 6
p
H
Days
0.05% CO2
0.10% CO2
0.20% CO2
0.50% CO2
B

Figure 4 pH kinetics of S. bacillaris strain siva2011. (A) Grown
medium pH for 6 days in a 4 L airlift balloon bioreactor with
0.05, 0.1, 0.2 or
0.5% CO2. (B) Grown medium pH for 6 days in an 8 L airlift
balloon bioreactor with 0.05, 0.1, 0.2 or 0.5% CO2.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
N
or
m
al
iz
ed

D
ry
W
ei
gh
t,
D
W
*
Normalized Time, t*
4L Measured with 0.20%
8L Measured with 0.20%
4L Predicted with 0.20%
8L Predicted with 0.20%
CO2
CO2
CO2
CO2
Figure 5 Prediction of normalized S. bacillaris strain siva2011
dry weight (DW) for 0.2% CO2 in 4 L and 8 L balloon-type
bioreactors.

of 9
87.4%. Both tests give an error under 15%. This suggests
that the selected variables are not adequate for accurate
validation and mapping within an acceptable error. Appli-
cation of polynomial equations with more variables in the
modeling or another mathematical approach might be
warranted for future optimization if better accuracy is
needed.
Hydrocarbons and FAMEs
Figure 6 shows the gas chromatography–mass spectrom-
etry (GC-MS) total ion chromatogram of the hydro-
carbon fraction harvested from S. bacillaris strain
siva2011 biomass. This chromatogram shows that this
strain biosynthesizes three free hydrocarbons, namely
n-nonadecane (C19H40), nonacosane (C29H60) and hep-
tadecane (C17H36). These alkanes are also found in trad-
itional and non-traditional liquid fuels. The S. bacillaris
strain siva2011 contained 1.36% of total hydrocarbons: the
C19H40, C29H60 and C17H36 were 6.3, 4.1 and 3.2 mg/g
DW, respectively, with 0.2% CO2. In addition, it was un-
changed in both the 4 L and 8 L bioreactor studies. Some
of these hydrocarbons were previously reported in other
algal species [27,28]. In cyanobacteria, a two-step alkane
biosynthetic pathway was reported: 1) acyl-acyl carrier
protein (ACP) reductase converted ACP into aldehyde
by reduction; and 2) an aldehyde-deformylating oxygenase
converted aldehyde into alkane or alkene by oxidation
[29,30]. The overexpressed ACP reductase and aldehyde-
deformylating oxygenase cyanobacteria LX56 strain bio-
mass accumulated 1.1% DW of alkane in a column

photobioreactor [30]. In general, alkane accumulation
was toxic to algal cells, therefore, production was lowered.
However, the longer chain (over C12) of alkane accumula-
tion was insignificant with respect to toxicity in Saccharo-
myces cerevisiae cells, while C8 to C11 alkanes were
cytotoxic [31]. Although the S. bacillaris strain siva2011
biosynthesize longer chain hydrocarbons, the alkane level
is lower than in the TAGs. Since the S. bacillaris strain
siva2011 is a new species, a reference genome is necessary
for transcriptome analysis, and subsequent metabolic en-
gineering is unavailable.
The S. bacillaris strain siva2011 biomass contains two
free fatty acids (FFAs) (palmitic acid (C16:0), linolenic
acid (C18:3)) and phytol (Figure 6). Figure 7 illustrates
the fatty acid methyl ester (FAME) profile and total lipid
(35.82%) content of S. bacillaris strain siva2011 biomass
from the 8 L bioreactor culture with 0.2% CO2. This
data shows that this strain contains 31.62% of total
FAMEs, 2.3% of FFAs and 1.9% of unidentified fatty
acids, which is higher compared to the 4 L bioreactor. In
addition, 1.2% of phytol was also found. The S. bacillaris
strain 158/11 biomass contained 32% of total lipid and
1% of phytol [21]. The results show that this strain con-
tains a high degree of unsaturated fatty acids. The main
unsaturated FAMEs detected are methyl hexadecatrieno-
ate (C16:3), methyl oleate (C18:1), methyl linoleate
(C18:2) and methyl linolenate (C18:3). The predominant
saturated FAME is methyl palmitate (C16:0). This profile
is consistent with the other S. bacillaris strains, NJ-10
and NJ-17 [13]. When S. bacillaris strain siva2011 was
scaled-up in the 8 L bioreactor with 0.2% CO2, the
C16:0, C16:3, C18:1, C18:2 and C18:3 were 112.2, 9.4,
51.3, 74.1 and 69.2 mg/g DW, respectively; in the 4 L bio-
reactor the FAMEs were 102, 8.1, 49.4, 71.7 and 65.3 mg/g,

respectively, which is higher compared to the 0.5% CO2. It
suggests not only that S. bacillaris strain siva2011 biomass
was scaled-up, but also that the TAG production metabol-
ism appeared to be scaled-up as well. However, the FAME
profiles were unchanged. Previously this reactor was used
for plant root culture and was successful at commercial-
scale (10,000 L) cultivation of small molecules [32,33]. It
was also demonstrated that the resveratrol metabolic
600
500
400
300
200
100
0
7.5 10.0 12.5 15.0 17.5 20.0
Minutes
k
C
ou
n
ts
n
-N

on
ad
ec
an
e
C
16
:0
P
h
yt
ol
C
18
:3
N
on
ac
os
an
e
H
ep

ta
d
ec
an
e
Figure 6 GC-MS profile of the hydrocarbons and free fatty acids
from S. bacillaris strain siva2011 biomass. GC-MS, gas
chromatography–mass spectrometry.
of 9
pathway gene, chalcone synthase, was highly expressed in
this type of reactor. Moreover, this reactor design has not
only non-agitation hydrodynamics but also enhanced
geometry, flow dynamics and kinematics [33,34]. Thus, this
configuration could enhance light capturing by mixing the
algal cells evenly, which facilitates photosynthesis and bio-
mass accumulation as well as possibly up-regulating fatty
acid pathway genes. Olivieri et al. [21] showed that the S.
bacillaris 158/11 contains C16:0 6.5%, C18:3 5.2%, C18:2
4.6% and C18:1 13.8%. This suggests that the Stichococcus
species has unique fatty acid profiles which could be used
for high-quality liquid fuel production. However, the actual
large-scale feasibility test for algal biomass scale-up is
needed for aviation fuel production.
Conclusions
Long-term energy demands will eventually greatly out-

weigh the world supply of fossil fuels, and their use in-
creases greenhouse gases. Therefore, alternative sources
and methods of producing fuels must be found. Although
algae can capture greenhouse gas emissions while produ-
cing oxygen, the need for high biomass and oil accumula-
tion are challenging for algal-based bioenergy production.
S. bacillaris strain siva2011 is rich in lipids, presumably
TAGs, with a suitable carbon range for aviation or other
liquid fuels. Indeed, scaling studies showed that at 0.2%
CO2 supplementation S. bacillaris strain siva2011 had bet-
ter growth and increased FAMEs in the 8 L bioreactor
than 4 L. It is likely that the 20 L bioreactor could have
substantially lower hydrodynamic stress. However, fur-
ther studies on mass transfer at larger scales seem to be
warranted. The culture conditions vary from alga spe-
cies to species. S. bacillaris strain siva2011 can grow in
conditions mentioned in this study. Irrespective of the
need to further characterize the biochemical pathways
for this organism, it is nevertheless important to point
out that there is already sufficient empirical evidence
that it will likely be a possible candidate for renewable
production of light liquid fuels based on the copious
production of lipids and hydrocarbons, and especially
the relatively high degree of unsaturation found therein.
Materials and methods
Isolation and identification of Stichococcus bacillaris strain
siva2011
Axenic S. bacillaris strain siva2011 cells were isolated
from in vitro Lagerstroemia seedlings. The morphology of
algal cells was identified by light microscopy. The genus
and species were identified by the 18S rDNA region of the
nuclear chromosome and the 23S region of the chloro-
plast rDNA. PCR was performed using primers to amplify
the 23S and the 18S region of the rDNA. The products

were then sequenced. The genus Stichococcus was iden-
tified based on the 18S rDNA sequence in the NCBI
database. The identification was confirmed and authen-
ticated by The Culture Collection of Algae at the Uni-
versity of Texas at Austin (UTEX), Austin, TX, USA.
The phylogenetic tree was created based on the 18S
rDNA sequence using a Clustal X2.0.12 set to exclude
positions with gaps, correct for multiple substitutions
and run 1,000 bootstrap trials.
Bioreactor culture
The new alga S. bacillaris strain siva2011 was cultured in
5 L and 20 L liquid-phase airlift balloon-type bioreactors
[32-34] with modified MS [35] liquid medium for 6 days.
This alga was also tested in BBM for comparison [36]. To
evaluate the scale-up potential of the balloon-type bioreac-
tor for larger-scale use, the 5 L bioreactor was used for the
4 L working volume and a 20 L bioreactor was used for the
0
2
4
6
8
10
12
%
o

f
L
ip
id
FAMEs
Figure 7 Total lipid content from S. bacillaris strain siva2011,
biomass from 20 L bioreactor and working volume 8 L with
0.2% CO2 on
day 6. FA, fatty acid; FAME, fatty acid methyl ester; FFA, free
fatty acid.
of 9
8 L working volume in order to gain a linear biomass pat-
tern for prediction or modeling. Working volumes of bio-
reactors for scale-up studies were previously published [37]
for root culture and were not repeated here. Balloon biore-
actors have a larger headspace. The 5 L bioreactor has an 8
inch diameter and the 20 L has a 12 inch diameter, which
may facilitate efficient light absorption and medium circu-
lation for algal culture. The modified MS medium contains
reduced NH4NO3 0.6 g, KNO3 1.5 g [32,33] with 1% fruc-
tose and pH 6.0. The cool white fluorescent room lights at
15 to 30 μE m-2 s-1 for 10 hours followed by 14 hours of
dark and 23 to 25°C culture conditions were used. After
autoclaving the medium and the bioreactor, the axenic
algal cells were cultured into the bioreactor. The inoculum
was active cells that were 3 days old and 0.05 g fresh
weight (FW)/L. The bioreactor cultures were supple-

mented with different concentrations of sterile filtered CO2
such as 0.05, 0.1, 0.2 or 0.5%. The input air mixture CO2
gas flow was set at 0.1 vvm (volume (of air) per volume (of
liquid) per minute). To screen growth kinetics of S. bacil-
laris strain siva2011, algal biomass was harvested, and
medium pH was measured after 1, 2, 3, 4, 5 and 6 days.
Algal cells were harvested by centrifugation at 10,000 rpm
for 5 minutes. After harvesting, algal biomass were frozen
in liquid nitrogen and freeze-dried. DW was recorded after
the samples were freeze-dried to a constant weight.
Linear regression
A linear regression model has been developed based on
0.2% CO2 experimental data to predict DW of S. bacillaris
strain siva2011 production ranging from 4 L to 8 L. Re-
lated variables including DW, yCO2, pH, time (t) and vol-
ume (V) are listed from constituents in S. bacillaris strain
siva2011 production tests, as follows:
DW ¼ f yCO2; pH; t; Vð Þ ð1Þ
To test its feasibility, it has been simplified into a lin-
ear equation with two variables, t and V, among the
whole variables as shown in Equation (2):
DW ¼ f t; Vð Þ
¼ at þ bð Þ⋅ V c ð2Þ
Experimental data and variables have been normalized
using maximum DW and t to establish a mathematical
model as shown in Equations (3) and (4).
DW% ¼ DW=DW f ð3Þ
t% ¼ t=tf ð4Þ

Where DWf is maximum DW (g/L) produced from 8 L
test under 0.2% CO2 fraction and tf is maximum t to reach
the maximum DW. The DWf and tf were 3.79 g/L on day
6. V has been standardized by a baseline 8 L in this model.
Constants a, b and c in Equation (2) have been determined
using test data and a linear least squares method. The 4 L
and 8 L data were used to determine the constants: a, b
and c. The final non-dimensional equation is suggested by
Equation (5):
DW% ¼ 1:0676⋅ t% þ 0:042ð Þ⋅
V 2
V 1
! "0:19177
ð5Þ
V1 was base volume at 4 L and V2 was extended at 8 L
volume.
Analysis of hydrocarbons and FAMEs
One gram of 6-day-old freeze-dried algal cells were used
for analysis of hydrocarbons and FAMEs. The total lipids
were evaluated according to Jones et al. [38]. FAMEs
were processed according to the AOAC method 996.06
and AOCS method Ce 1 h-05 [39,40]. Each FAME GC-
MS spectra were acquired using a Clarus 500 gas chroma-
tography (PerkinElmer, Waltham, MA, USA) coupled to a
Clarus A mass spectrometer (PerkinElmer). A FAME-
WAX column (Restek, Bellefonte, PA, USA) was used for
separation of FAMEs (30 m length, 0.25 mm ID, 0.25 μm
film thickness). The column conditions were determined
prior to analysis using a FAME and hydrocarbon reference
mixture. Initially, the gas chromatography temperature
was 30°C and ramped 10°C/min to a final temp of 220°C

and held for 15 minutes at 220°C. Helium was used as the
carrier gas. The flow rate was set at 1 mL/min and the
spilt ratio was 1:20. The sample injection volume was
1 μL. The mass spectrometer was set to record ranges of
spectra from 50 to 500 m/z. The inlet line temperature
was set at 300°C and the source temperature was 180°C.
Hydrocarbons were processed and analyzed in GC-MS
according to Wang et al. [30]. Quantitative analysis of
hydrocarbons and FAMEs in the algal biomass was calcu-
lated from the calibration curve of the respective standard.
Data acquisition and processing were performed with Tur-
boMass software (PerkinElmer).
Statistical analysis
All experiments were repeated at least three times, each
with three replications except sequencing. The experimen-
tal variations were expressed as a mean standard error.
Additional file
Additional file 1: NCBI basic local alignment search tool
comparison of the S. bacillaris strain siva2011 18S rDNA
sequences
with different Stichococcus species. A large difference was
found in
the Clustal X2.0.12 multiple sequence alignment between the S.
bacillaris
strain siva2011 and the existing strains at nucleotides 610 to
980. SIVA, S.
bacillaris strain siva2011.
Abbreviations
ACP: Acyl-acyl carrier protein; BBM: Bold’s Basal Medium;
CO2: Carbon
dioxide; DW: Dry weight; FA: Fatty acid; FAME: Fatty acid
methyl ester;

of 9
http://www.biomedcentral.com/content/supplementary/1754-
6834-7-62-S1.pdf
FFA: Free fatty acid; FW: Fresh weight; GC-MS: Gas
chromatography–mass
spectrometry; GDH: Glutamate dehydrogenase; HNO3: Nitric
acid;
KNO3: Potassium nitrate; MS: Murashige and Skoog; NADPH:
Nicotinamide
adenine dinucleotide phosphate; NCBI: National Center for
Biotechnology
Information; NH4NO3: Ammonium nitrate; PCR: Polymerase
chain reaction;
TAG: Triacylglycerol; UTEX: The Culture Collection of Algae
at the University
of Texas at Austin.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
GS, JL and KJ made substantial contributions to experimental
design or
analysis or interpretation of data. Specifically, GS led and
designed the
experiments and performed the organism isolation,
bioprocessing and
GC-MS quantitative studies. JL performed the gas
chromatography separation
and mass spectrometry experiments for compound

identification. KJ performed
the mathematical model to validate the variables. GS wrote the
manuscript,
which was reviewed and approved by all authors. All authors
read and
approved the final manuscript.
Acknowledgments
This research was funded by the Arkansas Biosciences Institute
(grants
262178 and 200109). Part of the mass spectrometry work was
supported by
the National Institutes of Health (NIH) (P30 GM103450)
through the National
Institute of General Medicine, and organism identification was
supported by
the Department of Energy (DOE) (DE-FG36-08BO88036)
through the
Midsouth/Southeast Bioenergy Consortium. The authors thank
Dr David
Nobles at UTEX for identification and authentication of S.
bacillaris strain
siva2011. The technical assistance of Dr Jianfeng Xu,
Christopher Easley,
Kelsea Brewer, Veronica Hawes and Saritha Kontham (Arkansas
State
University, Jonesboro, AR, USA), and Dr Jennifer Gidden
(University of
Arkansas, Fayetteville, AR, USA) was appreciated.
Author details
Technology,
USA. 2College of
Engineering, Arkansas State University, Jonesboro, AR 72401,

USA. 3Arkansas
Statewide Mass Spectrometry Facility, University of Arkansas,
Fayetteville, AR
72701, USA.
Received: 26 July 2013 Accepted: 17 March 2014
Published: 15 April 2014
References
1. Sivakumar G, Vail DR, Xu J, Burner DM, Lay JO, Ge X,
Weathers PJ:
Bioethanol and biodiesel: alternative liquid fuels for future
generations.
Eng Life Sci 2010, 10:8–18.
2. Ge X, Burner DM, Xu J, Phillips GC, Sivakumar G:
Bioethanol production
from dedicated energy crops in Arkansas, USA. Biotechnol J
2011, 6:66–73.
3. Ge X, Green S, Zhang N, Sivakumar G, Xu J: Eastern
gamagrass as an
alternative cellulosic feedstock for bioethanol production.
Process
Biochem 2012, 47:335–339.
4. Sivakumar G, Xu J, Thompson RW, Yang Y, Randol-Smith P,
Weathers PJ:
Integrated green algal technology for bioremediation and
biofuel.
Bioresour Technol 2012, 107:1–9.
5. Tran NH, Bartlett JR, Kannangara GSK, Milev AS, Volk H,
Wilson MA: Catalytic
upgrading of biorefinery oil from micro-algae. Fuel 2010,

89:265–274.
6. Fadock MN: Carbon profile matching, algae fatty acids and
Jet A fuel
properties. Guelph Eng J 2010, 3:1–8.
7. Yang C, Nie R, Fu J, Hou Z, Lu X: Production of aviation
fuel via catalytic
hydrothermal decarboxylation of fatty acids in microalgae oil.
Bioresour
Technol 2013, 146:569–573.
8. Olivieri G, Gargano I, Andreozzi R, Marotta R, Marzocchella
A, Pinto G, Pollio A:
Effects of photobioreactors design and operating conditions on
Stichococcus bacillaris biomass and biodiesel production.
Biochem Eng J
2013, 74:8–14.
9. Neustupa J, Elias M, Sejnohova L: A taxonomic study of two
Stichococcus
species (Trebouxiophyceae, Chlorophyta) with a starch-
enveloped
pyrenoid. Nova Hedwigia 2007, 84:51–63.
10. Brown LM, Hellebust JA: Some new taxonomic
characteristics applied to
Stichococcus bacillaris (Chlorophyceae). Can J Bot 1980,
58:1405–1411.
11. George EA: A note on Stichococcus bacillaris Naeg. and
some species of
Chlorella as marine algae. J Mar Biol Assoc UK 1957, 36:111–
114.
12. Hayward J: Studies on the growth of Stichococcus bacillaris

Naeg in
culture. J Mar Biol Assoc 1974, 54:261–268.
13. Chen Z, He C, Hu H: Temperature responses of growth,
photosynthesis,
fatty acid and nitrate reductase in Antarctic and temperate
Stichococcus.
Extremophiles 2012, 16:127–133.
14. Ahmad I, Hellebust JA: Transport and assimilation of
nitrogen by
Stichococcus bacillaris grown in the presence of methionine
sulfoximine.
Plant Physiol 1985, 79:1125–1126.
15. Iwasaki I, Hu Q, Kurano N, Miyachi S: Effect of extremely
high-CO2 stress on
energy distribution between photosystem I and photosystem II
in a
'high-CO2' tolerant green alga, Chlorococcum littorale and the
intolerant
green alga Stichococcus bacillaris. Photochem Photobiol B
1998, 44:184–190.
16. Katsaros CI, Varvarigos V, Gachon GMM, Brand J,
Motomura T, Nagasato C,
Küpper FC: Comparative immunofluorescence and
ultrastructural analysis
of microtubule organization in Uronema sp., Klebsormidium
flaccidum, K.
subtilissimum, Stichococcus bacillaris and S. chloranthus
(Chlorophyta).
Protist 2011, 162:315–331.
17. Ahmad I, Hellebust JA: Control of nitrogen assimilation in
Stichococcus

bacillaris by growth conditions. Can J Bot 1987, 65:432–437.
18. Hellebust JA, Ahmad I: Nitrogen metabolism and amino
acid nutrition in
the soil alga Stichococcus bacillaris (Chlorophyceae). J Phycol
1989,
25:48–54.
19. Zhang W, Majidi V: Monitoring the cellular response of
to exposure of several different metals using in vivo 31P NMR
and other
spectroscopic techniques. Environ Sci Technol 1994, 28:1577–
1581.
20. DellaGreca M, Pinto G, Pollio A, Previtera L, Temussi F:
Biotransformation of
sinapic acid by the green algae Stichococcus bacillaris
155LTAP and
Ankistrodesmus braunii C202.7a. Tetrahedron Lett 2003,
44:2779–2780.
21. Olivieri G, Marzocchella A, Andreozzi R, Pinto G, Pollio A:
Biodiesel
production from Stichococcus strains at laboratory scale. J
Chem Technol
Biotechnol 2011, 86:776–783.
22. Lohr M, Schwender J, Polle JEW: Isoprenoid biosynthesis in
eukaryotic
phototrophs: a spotlight on algae. Plant Sci 2012, 185–186:9–
22.
23. Niehaus TD, Okada S, Devarenne TP, Watt DS, Sviripa V,
Chappell J:
Identification of unique mechanisms for triterpene biosynthesis

in
Botryococcus braunii. Proc Natl Acad Sci U S A 2011,
108:12260–12265.
24. Yang Y, Xu J, Vail D, Weathers PJ: Ettlia oleoabundans
growth and oil
production on agricultural anaerobic waste effluents. Bioresour
Technol
2011, 102:5076–5082.
25. Handa S, Nakahara M, Tsubota H, Deguchi H, Nakano T: A
new aerial alga,
Stichococcus ampulliformis sp. nov. (Trebouxiophyceae,
Chlorophyta)
from Japan. Psychol Res 2003, 51:203–210.
26. Quinn J, Winter L, Bradly T: Microalgae bulk growth model
with application
to industrial scale systems. Bioresour Technol 2011, 102:5083–
5092.
27. Thangalazhy-Gopakumar S, Adhikari S, Chattanathan SA,
Gupta RB: Catalytic
pyrolysis of green algae for hydrocarbon production using
H+ZSM-5
catalyst. Bioresour Technol 2012, 118:150–157.
28. Dembitsky VM, Shkrob I, Dor I: Separation and
identification of
hydrocarbons and other volatile compounds from cultured blue-
green
alga Nostoc sp. by gas chromatography-mass spectrometry using
serially
coupled capillary columns with consecutive nonpolar and
semipolar
stationary phases. J Chromatogr A 1999, 862:221–229.

29. Li N, Chang WC, Warui DM, Booker SJ, Krebs C, Bollinger
JM: Evidence for
only oxygenative cleavage of aldehydes to alk(a/e)nes and
formate by
cyanobacterial aldehyde decarbonylases. Biochemistry 2012,
51:7908–7916.
30. Wang W, Liu J, Lu J: Engineering cyanobacteria to improve
photosynthetic production of alka(e)nes. Biotechnol Biofuels
2013, 6:69.
31. Chen B, Ling H, Chang MW: Transporter engineering for
improved
tolerance against alkane biofuels in Saccharomyces cerevisiae.
Biotechnol
Biofuels 2013, 6:21.
32. Sivakumar G, Yu KW, Paek KY: Production of biomass and
ginsenoides
from adventitious roots of Panax ginseng in bioreactor cultures.
Eng Life
Sci 2005, 5:333–342.
33. Sivakumar G, Medina-Bolivar F, Lay JO, Dolan MC,
Condori J, Grubbs SK, Wright
SM, Baque MA, Lee EJ, Paek KY: Bioprocess and bioreactor:
next generation
technology for production of potential plant-based antidiabetic
and
antioxidant molecules. Curr Med Chem 2011, 18:79–90.
of 9

34. Sivakumar G: Bioreactor technology: a novel industrial tool
for high-tech
production of bioactive molecules and biopharmaceuticals from
plant
roots. Biotechnol J 2006, 1:1419–1427.
35. Murashige T, Skoog F: A revised medium for rapid growth
and bioassays
with tobacco tissue cultures. Physiol Plant 1962, 15:23–28.
36. Nichols HW, Bold HC: Trichosarcina polymorpha gen. et
sp. nov. J Phycol
1965, 1:34–38.
37. Sivakumar G, Liu C, Towler MJ, Weathers PJ: Biomass
production of hairy
roots of Artemisia annua and Arachis hypogea in a scaled-up
mist
bioreactor. Biotechnol Bioeng 2010, 107:802–813.
38. Jones J, Manning S, Montoya M, Keller K, Poenie M:
Extraction of algal
lipids and their analysis by HPLC and mass spectrometry. J Am
Oil Chem
Soc 2012, 89:1371–1381.
39. International AOAC: AOAC Official Method 996.06: Fat
(Total, Saturated, and
Unsaturated) in Foods. Hydrolytic Extraction Gas
Chromatography Method.
Gaithersburg, MD: AOAC International; 2001.
40. International AOAC: AOCS Official Method Ce 1 h-05:
Determination of cis-, trans-,

Saturated, Monounsaturated and Polyunsaturated Fatty Acids in
Vegetable or
Nonruminant Animal Oils and Fats by Capillary GLC.
Gaithersburg, MD: AOAC
International; 2005.
doi:10.1186/1754-6834-7-62
Cite this article as: Sivakumar et al.: Bioprocessing of
Stichococcus
bacillaris strain siva2011. Biotechnology for Biofuels 2014
7:62.
Submit your next manuscript to BioMed Central
and take full advantage of:
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at
www.biomedcentral.com/submit
of 9

RESEARCH Open Access
Biomass and RRR-α-tocopherol production in
Stichococcus bacillaris strain siva2011 in a balloon
bioreactor
Ganapathy Sivakumar1*, Kwangkook Jeong2 and Jackson O Lay
Jr3
Abstract
Background: Green microalgae represent a renewable natural
source of vitamin E. Its most bioactive form is the
naturally occurring RRR-α-tocopherol which is biosynthesized
in photosynthetic organisms as a single stereoisomer.
It is noteworthy that the natural and synthetic α-tocopherols are
different biomolecular entities. This article focuses
on RRR-α-tocopherol production in Stichococcus bacillaris
strain siva2011 biomass in a bioreactor culture with methyl
jasmonate (MeJa) elicitor. Additionally, a nonlinear
mathematical model was used to quantitatively scale-up and
predict the biomass production in a 20 L balloon bioreactor with
dual variables such as time and volume.
Results: Approximately 0.6 mg/g dry weight (DW) of RRR-α-
tocopherol was enhanced in S. bacillaris strain siva2011
biomass with the MeJa 50 μL/L for 24 hrs elicitations when
compared to the control. The R2 value from the
nonlinear model was enhanced up to 95% when compared to the
linear model which significantly improved the
accuracy for estimating S. bacillaris strain siva2011 biomass
production in a balloon bioreactor.
Conclusions: S. bacillaris strain siva2011 is a new green
microalga which biosynthesizes significant amounts of
RRR-α-tocopherol. Systematically validated dual variable
empirical data should provide key insights to multivariable

or fourth order modeling for algal biomass scale-up. This
bioprocess engineering should provide valuable
information for industrial production of RRR-α-tocopherol from
green cells.
Keywords: Antioxidant, Biomass, Bioreactor, Microalgae,
Vitamin E
Background
RRR-α-tocopherol is a lipid soluble small molecule and
the biologically active form of natural vitamin E. RRR-α-
tocopherol is exclusively biosynthesized by photosyn-
thetic organisms or green cells including algae, plants,
and cyanobacteria [1-3]. Plant-based products are a pri-
mary source of RRR-α-tocopherol in the human diet.
For example, hazelnut is one of the richest sources of
vitamin E [4]. It is known that vitamin E plays an im-
portant role in human nutrition as a natural antioxidant.
It was recently proposed that vitamin E is active against
oxidative stress-related diseases [5]. Reportedly, it sup-
presses telomerase activity in ovarian cancer cells [6].
Vitamin E enhances IL-2 production, gene expression,
and is an effective therapeutic adjuvant [7]. Vitamin E
deficiency affects both T and B immune cell functions
[8], the α-tocopherol transfer protein (α-TTP) gene, and
neurologic dysfunctions. In animal models, vitamin E
mixtures inhibit colon, lung, mammary, and prostate
carcinogenesis [9], as well as prevent diabetes [10]. Cos-
metic industries also extensively use vitamin E in skin
care products. In addition, RRR-α-tocopherol prolongs the
shelf life of meat [11]. Thus, the use of RRR-α-tocopherol
continues to increase in nutraceuticals [12].
Bioreactor technology is the key component for the
industrial scale production of bioactive small molecules

for pharmaceutical applications. For instance, bioreactor
technology was successfully developed and scaled-up to
10,000 L for commercial-scale production of ginseng roots
used for human health-related applications [13,14]. This
reactor configuration has also been tested for RRR-α-
tocopherol production in lab-scale photosynthetic hazel-
nut root culture [15]. However, knowledge regarding the
bioprocessing of green algal cells for production of RRR-
* Correspondence: [email protected]
Technology,
USA
Full list of author information is available at the end of the
article
© 2014 Sivakumar et al.; licensee BioMed Central Ltd. This is
an Open Access article distributed under the terms of the
Creative Commons Attribution License
(http://creativecommons.org/licenses/by/4.0), which permits
unrestricted use,
distribution, and reproduction in any medium, provided the
original work is properly credited. The Creative Commons
Public
Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to
the data made available in this
article, unless otherwise stated.
Sivakumar et al. Microbial Cell Factories 2014, 13:79
http://www.microbialcellfactories.com/content/13/1/79
mailto:[email protected]
http://creativecommons.org/licenses/by/4.0
http://creativecommons.org/publicdomain/zero/1.0/

α-tocopherol in balloon bioreactors is limited. Previously,
Stichococcus bacillaris strain siva2011 biomass was scaled-
up in a lab-scale balloon bioreactor (4 L - 8 L), and a lin-
ear fitting model for predicting scale-up was proposed
[16]. The S. bacillaris strain siva2011 has unique lipid
(Figure 1) [16] and vitamin E metabolisms to lead to bio-
active RRR-α-tocopherol production. A nonlinear model
enables more accurate estimates and should provide
insight for quantitatively predicating algal biomass accu-
mulation in a large volume bioreactor. The objectives of
this study were to: 1) evaluate RRR-α-tocopherol content
from S. bacillaris strain siva2011 biomass with MeJa elicit-
ation; 2) quantitatively predict S. bacillaris strain siva2011
DW in a 20 L bioreactor with simplified stepwise dual var-
iables (time and volume) from 4 L and 8 L data using non-
linear regression. This systematic study could provide
insight regarding stepwise nonlinear scale-up of S. bacil-
laris strain siva2011 biomass for RRR-α-tocopherol pro-
duction in a balloon bioreactor.
Results and discussions
Natural vitamin E occurs in two general forms, namely
the tocopherols and tocotrienols, which are collectively
called tocochromanols. Each has four distinct isoforms
having α, β, γ, or δ substitution on the chromanol. The
natural α-tocopherol contains three methyl groups on
the chromanol moiety at positions 5, 7, and 8. The phy-
tyl or saturated side chain is attached to the C-2 position
of the chromanol ring which has three chiral centers
with a single RRR stereoisomer (Figure 2). The chroma-
nol groups have two fused rings, a phenol and a tetrahy-
dropyran, sharing a 2 carbon bridge [17]. These rings
are moderately polar, giving them an affinity for the cel-
lular membrane surface while the phytyl tail is hydro-

phobic and normally associated with membrane lipids
[18]. These structural features of RRR-α-tocopherol are
efficiently acted on by the human hepatic α-TTP which
is responsible for maintaining plasma α-tocopherol con-
centrations [19].
C
16
:0
C
16
:3
C
18
:1
C
18
:2
C
18
:3
Figure 1 Gas chromatography mass spectrometry profile of fatty
acid methyl esters from S. bacillaris strain siva2011, biomass
from
20 L bioreactor, working volume 8 L with 0.2% CO2 on day 6.
Methyl palmitate (C16:0), methyl hexadecatrienoate (C16:3),

methyl oleate
(C18:1), methyl linoleate (C18:2), and methyl linolenate
(C18:3).
Sivakumar et al. Microbial Cell Factories 2014, 13:79 Page 2 of
8
The synthetic α-tocopherol called all-racemic-α-tocopherol
is not identical to RRR-α-tocopherol. It is an equimolar
mixture of eight stereoisomers which possess three
chiral centers at positions 2’, 4’, and 8’, giving rise to four
diastereoisomeric pairs of enantiomers such as RRR,
RSR, RRS, RSS, SRR, SSR, SRS, and SSS [20]. Moreover,
α-TTP has a high affinity to RRR-α-tocopherol and has
a 3-fold greater binding half-life when compared to syn-
thetic α-tocopherol [21]. Thus, the bioactivity and the
relative safety are different. Human proteins such as en-
zymes and receptors usually exhibit high stereospecificity
[20]. Therefore, the natural RRR-α-tocopherol is more bio-
active than the synthetic form.
RRR-α-tocopherol plays a major role as an antioxidant
which prevents lipid peroxidation. In the photosynthetic
cells, it may protect photosystem II during photoinhibi-
tion and repair chloroplast mechanisms [18]. This is due
to the hydroxyl group on the C-6 position which is the
active site that donates a hydrogen atom. The phenolic
hydrogen atom is capable of scavenging lipid peroxy radi-
cals and quenching singlet oxygen [22]. RRR-α-tocopherol
is recycled in the photosynthetic cell by cytosolic ascor-
bate which oxidizes one-electron from the tocopheroxyl
radical thus regenerating vitamin E [23]. This mechanism
might protect the cell membranes.

RRR-α-tocopherol is biosynthesized in photosynthetic
cells via two different pathways [24]. The phytyl domain
precursor comes from an isoprenoid pathway, and the
chromanol domain precursor comes from an alternative
shikimate pathway homogentisic acid via complex en-
zymatic reactions [25]. RRR-α-tocopherol is found in the
chloroplast envelope, thylakoids, and plastoglobuli of the
plastid. The vitamin E biosynthetic pathway has been
elucidated in Arabidopsis [26]. The genes associated
with vitamin E biosynthesis in photosynthetic organisms
have been well described in literature [3,27]. A signifi-
cant metabolic engineering effort has been made to im-
prove vitamin E content both in plants [28] and in
cyanobacteria [29,30]. Moreover, tocopherol production
in plant green callus [31], cell [32], and root cultures
[15] have also been reported.
RRR-α-tocopherol production in S. bacillaris strain
siva2011
Photosynthetic algae are a potential alternative for pro-
duction because they biosynthesize an abundance of
RRR-α-tocopherol. For instance, freshwater Euglena gra-
cilis [33], marine Dunaliella tertiolecta and Tetraselmis
suecica [34], model system Chlamydomonas reinhardtii
[35], and commercial algae Spirulina platensis [36] have
been used to biosynthesize RRR-α-tocopherol. The new
green alga, S. bacillaris strain siva2011 [16], produces
significant amounts of RRR-α-tocopherol. This microalga
has efficient photosynthetic mechanisms which facilitate
the quick biosynthesis of vitamin E.
Tocopherol production can be enhanced by molecular
elicitation which is non-transgenic. MeJa is a plant stress
volatile signaling molecule which up-regulates several

defense-related genes [37]. In plants, jasmonates are bio-
synthesized via the octadecanoid pathway; exogenous
MeJa treatment is up-regulating secondary metabolic
pathway genes especially those encoding for stress pro-
tection [38]. Therefore, it is used as one of the potential
molecular elicitors in plant root culture to enhance sev-
eral pharmaceutical molecule productions [15]. For in-
stance, MeJa elicitation increases the activity of tyrosine
aminotransferase in plant green cell culture which is one
of the initial step enzymes involved in tocopherol bio-
synthesis [31,39]. To increase RRR-α-tocopherol content
in S. bacillaris strain siva2011, MeJa elicitation was also
used. The S. bacillaris strain siva2011 characteristics, cul-
ture conditions, and bioreactor experimental designs were
previously reported for lipid production [16].
Figure 3 illustrates the RRR-α-tocopherol content of S.
bacillaris strain siva2011 biomass under elicitation with
various concentrations of MeJa. The unelicited culture
O
OH
C H
3
C H
3
CH
3
C H
3

C H
3CH
3
CH
3
C H
3
(R) (R) (R)
8’4’
2
1
3
45
6
7
8
Chromanol
nucleus
Phytyl chain
Figure 2 Chemical structure of RRR-α-tocopherol.
8

accumulated 0.7 mg/g DW of RRR-α-tocopherol which
was detected starting during the early exponential growth
phase. The lower concentration of MeJa, 50 μL/L, after
24 hrs elicitation, enhanced the production of RRR-α-
tocopherol to the highest concentration, 1.3 mg/g DW.
The higher MeJa concentrations or longer elicitation pe-
riods were inhibiting both to the biomass growth and the
resultant RRR-α-tocopherol production. MeJa can diffuse
to cells either by intercellular migration or while in the
vapor phase [40]. In plants, MeJa can transport from
leaves to roots [41]. The vapor signaling can be trans-
ported to distal plants via air, and the intercellular sig-
naling can be transported via vascular process [42]. For
instance, in an in vitro root culture the MeJa elicitation
could trigger the defensive molecules accumulation via
intercellular transport [13-15], whereas in in vivo plants
during herbivore attack it can act as a volatile signal
[42]. In the algal culture, MeJa elicitation could trigger
the RRR-α-tocopherol production by either intercellular
signaling or both. The optimum concentration can up-
regulate the tocopherol biosynthetic pathway enzymes in
S. bacillaris strain siva2011 which could increase the anti-
oxidant. MeJa elicitation rapidly activates the defensive
genes which also down regulates the photosynthetic sys-
tem genes [38]. In addition, MeJa induces reactive oxygen
species (ROS) which alter the mitochondrial and chloro-
plast dynamics [43]. Thus, the higher MeJa concentrations
or longer elicitation periods can produce uncontrolled
ROS which can precede chloroplast or photosynthetic
dysfunctions which could be inhibiting the tocopherol
biosynthesis and biomass accumulation in S. bacillaris
strain siva2011. In green cells, chloroplasts are an essential
organelle for energy capture and transduction; a decline in
photosynthetic activity is closely related to the decrease in

the biomass. The typical MeJa elicited cells’ symptoms
were loss of chlorophyll, which causes the decline in
the net photosynthetic rate, and degradation of
ribulose bisphosphate carboxylase, etc. [44]. For in-
stance, 100 μL MeJa at 9 hrs elicitation altered chloroplast
morphology and function which is associated with cell
death [43]. Even though RRR-α-tocopherol indirectly reg-
ulates the amounts of jasmonic acid [18], the decline in
chloroplast efficiency could down regulate the RRR-α-toc-
opherol metabolism. This suggests that higher MeJa con-
centrations or longer elicitation could be cytotoxic beyond
what was studied.
Nonlinear regression
Compared to green adventitious roots, photosynthetic algae
have a higher ability to biosynthesize RRR-α-tocopherol in
bioreactor cultures. The balloon bioreactor is a liquid-phase
reactor with enhanced geometry and efficient fluid flow
dynamics which could help provide higher mass transfer
efficiency [14]. When compared to other photobioreac-
tors, the balloon bioreactor had a larger headspace which
efficiently captured light and enhanced photosynthesis
[16]. Thus, S. bacillaris strain siva2011 was scaled-up in a
balloon bioreactor (Figure 4) to investigate enhanced bio-
mass accumulation. Of the four concentrations of CO2
tested, 0.2% yielded the highest biomass of 3.45 g/L in 4 L
and 3.79 g/L (DW) in 8 L on day 6 [16]. The RRR-α-toc-
opherol production was unchanged by the 0.2% CO2 (data
not shown).
To quantitatively predict larger-scale algal biomass
production based on lab-scale test data, the following
stepwise structured approach was proposed [45,46]: 1)
to set up the simplest model to linearize with dual vari-
ables; 2) to model a nonlinear regression with dual vari-

ables; and 3) to demonstrate multiple variables based
on a nonlinear regression. Although scale-up predication
Figure 3 RRR-α-tocopherol content in S. bacillaris strain
siva2011, unelicited and methyl jasmonate elicited culture on
day 4, 5, and 6.
8
requires multiple variables, the 6 days algal culture does
not significantly utilize all the media components. There-
fore, selection of important dual variables can give insight
on the efficiency of parameter selection for lab-scale valid-
ation and initial scale-up prediction. In addition, the mul-
tiple linear regression models might not incorporate the
underlying nonlinear relationships [47]. So, in this study
the second systematic approach was conducted to evalu-
ate nonlinear modeling for scale-up prediction in a 20 L
balloon reactor using dual variables. Nonlinear regres-
sion models are more important tools than linear
models because they provide better parsimony, inter-
pretability, and prediction [48]. Figure 5 illustrates non-
linear modeling of predicted S. bacillaris strain siva2011
biomass accumulation in a 20 L balloon bioreactor with
0.2% CO2. This model was generated using 4 L to 8 L
data of S. bacillaris strain siva2011 and shows only small
discrepancies between measured and predicted data. The
Figure 4 Biomass production of S. bacillaris strain siva2011 in
5 L balloon type bioreactors (working volume 4 L).

Figure 5 Nonlinear modeling for nondimensionalized dry weight
from 4 L to 20 L.
8
nondimensional DW* can be converted into dimensional
DW in g/L by multiplying by the maximum DW experi-
mentally obtained from the baseline test which in this case
is 4 L. The nonlinear modeling agrees with measured data
both qualitatively and quantitatively, where the modeling
has enhanced the R2 value up to 95% compared to the lin-
ear model value 87.4% [16]. This suggests that the nonlin-
ear regression approach enhances accuracy of modeling
which provides key scale-up informations of S. bacillaris
strain siva2011 biomass for RRR-α-tocopherol production.
In this study, the significance of this empirical approach
provides insights into the application of a nonlinear re-
gression model that increases the R2 value and enhances
the quantitative predication of S. bacillaris strain siva2011
scaled-up in a 20 L bioreactor. This allows for the sys-
tematic understanding and design of a multi-variable
nonlinear regression experiment for significant biomass
production.
Conclusions
Photosynthetic microalgae are rich in RRR-α-tocopherol
and a potential source for this natural antioxidant which
is an essential human micronutrient. A significant ad-
vantage of this natural source is the maintenance of the
specific bioactive form needed for nutrition and the
elimination of possible issues with potential unknown
or unexpected toxicities from the synthetic conforma-

tions. S. bacillaris strain siva2011 has a unique vitamin
E biosynthetic mechanism capable of sustaining high
levels of production, including inducible enhanced pro-
duction which could provide a possible production plat-
form of RRR-α-tocopherol for pharmaceutical industries.
A nonlinear mathematical model was developed to model
scale-up to production in 20 L reactors using an approach
with a higher accuracy based on the dual variables tested
in 4 L and 8 L reactors. The R2 value from this study dem-
onstrates that this nonlinear approach significantly im-
proves estimation of S. bacillaris strain siva2011 biomass
production in the bioreactor than does the linear model.
Additional studies with progressively larger reactors (and
models) will be needed to bridge the gap between labora-
tory and industrial scale. Nevertheless, this data provides
enhanced bioprocess engineering information in the pro-
gression towards large-scale pharmaceutical RRR-α-tocoph-
erol production from S. bacillaris strain siva2011 biomass.
Methods
Bioreactor culture, elicitation and analytics
S. bacillaris strain siva2011 cells were cultured in a balloon
type bioreactor (4 L and 8 L). The bioreactor experimen-
tal design and the biomass harvesting were performed
as described by Sivakumar et al. [16]. For elicitation,
three concentrations of filter sterilized MeJa 50, 100,
and 200 μL/L were added to the S. bacillaris strain
siva2011 culture on the 3rd day. MeJa dissolved in etha-
nol and MeJa not dissolved in ethanol were tested, and
both had a similar effect. Elicitations were carried out
for 24, 48, or 72 hrs. Algal cells were harvested and
freeze-dried according to the Sivakumar et al. [16]
method. One gram of MeJa elicited and unelicited
freeze-dried algal cells were used for analysis of RRR-α-
tocopherol. RRR-α-tocopherol was processed according

to the Sivakumar et al. [4] method. The reversed phase-
high performance liquid chromatography chromato-
gram was acquired using a Dionex 3000 (LPG3400A)
system (Thermo Scientific, Sunnyvale, CA) equipped
with a thermostatted UltiMate 3000 autosampler and a
Dionex RF 2000 fluorescence detector. The system was
monitored by the software’s chromeleon (6.80) for in-
strument control and data acquisition, data reproces-
sing, and solute quantification, respectively. An Agilent
Zorbax Eclipse XDB-C8 (250 mm × 4.6 mm, mean par-
ticle size 5 μm) column or C18 (250 mm × 4.6 mm,
mean particle size 5 μm) was used to separate RRR-α-
tocopherol. The mobile phase consists of a linear gradi-
ent of 90% methanol in water. The flow rate was 1 ml/
min. The total acquisition time was 35 min. The wave-
length was set at 290 nm for excitation and 330 nm for
emission. The authenticated RRR-α-tocopherol fluores-
cence spectra and retention time was used for HPLC
confirmation of RRR-α-tocopherol in the samples. The
RRR-α-tocopherol liquid chromatography mass spec-
trometry spectrum confirmation was acquired using a
Shimadzu 8050 mass spectrometer.
Nonlinear regression
For a stepwise approach, the first phase of the nonlinear
regression method begins with the simplification from
five variables to two variables, time (t) and reactor vol-
ume (V), as shown in equation 1. The regression will be
extended to multivariables when the two variables
method is validated.
DW ¼ f yCO2; EC; OrP; pH; t; Vð Þ≈f t; Vð Þ ð1Þ
The associated variables DW, t, and V in equation 1
are nondimensionalized into DW*, t*, and V* as shown
in equation (2) to (4).

DW$ ¼
DW
DW max
ð2Þ
t$ ¼
t
tmax
ð3Þ
V $ ¼
V L½ &
4L
ð4Þ
Where DWmax is the maximum DW [g/L] produced
from 4 L test under 0.02% CO2 fraction, and tmax is the
8
maximum time to reach the maximum DW. In this
study, DWmax and tmax were 3.45 g/L and 6 days, respect-
ively. The volume was standardized by the baseline: 4 L in
this modeling.
A nonlinear model was assumed by combining 4th
order polynomials and the power form of the equation as
shown in equation (5) in consideration of measured data

of 4 L and 8 L.
DW$ ¼ at$4 þ bt$3 þ ct$2 þ dt$2 þ et þ f
! "
V $g ð5Þ
All associated constants a, b, c, and d in equation (5)
were determined from a nonlinear regression method as
shown in equation (6).
DW$ ¼
#
2:686t$4−6:942t$3 þ 5:109t$2
þ 0:127t þ 0:0184
$
V $0:151 ð6Þ
Equation (6) allows the predicting of DW in reactor
volume 20 L at 0.02% CO2 fraction from the correlation
obtained from 4 L and 8 L measured data.
Statistical analysis
Bioreactor culture, elicitations, and analytical experiments
were repeated at least three times, each with three replica-
tions. Results were expressed as the mean with standard
errors. Stepwise dual nonlinear regressions were used to
investigate the relationship between 4 L and 8 L in order
to predict 20 L bioreactor scale-up.
Abbreviations
C: Carbon; CO2: Carbon dioxide; DW: Dry weight; G: Gram;
hrs: Hours;
α: Trimethyl; β, γ: Dimethyl; δ: Monomethyl; α-TTP: α-
Tocopherol transfer

protein; MeJa: Methyl jasmonate; Mg: Milligram; μL:
Microliter; L: Liter;
%: Percentage; ROS: Reactive oxygen species.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
GS, JL, and KJ made substantial contributions to the
experimental design,
analysis, and/or interpretation of data. Specifically, GS led and
designed the
experiments and performed the bioprocessing and HPLC
studies. JL
performed the mass spectrometry experiments for compound
confirmation.
KJ performed the mathematical modeling to validate the
variables. GS wrote
the manuscript which was reviewed and approved by all authors.
All authors
read and approved the final manuscript.
Acknowledgments
This research was funded by the Arkansas Biosciences Institute
(grants
262178 and 200109). The technical assistance of Kelsea Brewer
and Dez
Jones (Arkansas State University, Jonesboro, AR, USA), and
Dr. Jennifer
Gidden (University of Arkansas, Fayetteville, AR, USA) was
appreciated.
Author details
Technology,

USA. 2College of
Engineering, Arkansas State University, Jonesboro, AR 72401,
USA. 3Arkansas
Statewide Mass Spectrometry Facility, University of Arkansas,
Fayetteville, AR
72701, USA.
Received: 11 April 2014 Accepted: 30 May 2014
Published: 3 June 2014
References
1. Dasilva EJ, Jensen A: Content of α-tocopherol in some blue-
green algae.
Biochim Biophys Acta 1971, 239:345–347.
2. Sivakumar G, Bacchetta L: Determination of natural vitamin
E from Italian
hazelnut leaves. Chem Nat Compd 2005, 6:654–656.
3. Maeda H, DellaPenna D: Tocopherol functions in
photosynthetic
organisms. Curr Opin Plant Biol 2007, 10:260–265.
4. Sivakumar G, Bacchetta L, Gatti R, Zappa G: HPLC
screening of natural
vitamin E from Mediterranean plant biofactories – a basic tool
for
pilot-scale bioreactors production of α-tocopherol. J Plant
Physiol 2005,
162:1280–1283.
5. Niki E: Role of vitamin E as lipid-soluble peroxyl radical
scavenger:
in vitro and in vivo evidence. Free Radic Biol Med 2013, 66:3–
12.

6. Bermudez Y, Ahmadi S, Lowell NE, Kruk PA: Vitamin E
suppresses
telomerase activity in ovarian cancer cells. Cancer Detect Prev
2007,
31:119–128.
7. Molano A, Meydani SK: Vitamin E, signalosomes and gene
expression in T
cells. Mol Aspects Med 2012, 33:55–62.
8. Pekmezci D: Vitamin E and immunity. Vitam Horm 2011,
86:179–215.
9. Yang CS, Li G, Yang Z, Guan F, Chen A, Ju J: Cancer
prevention by
tocopherols and tea polyphenols. Cancer Lett 2013, 334:79–85.
10. Kamimura W, Doi W, Takemoto K, Ishihara K, Wang DH,
Sugiyama H, Oda S,
Masuoka N: Effect of vitamin E on alloxan-induced mouse
diabetes.
Clin Biochem 2013, 46:795–798.
11. Röhrle FT, Moloney AP, Black A, Osorio MT, Sweeney T,
Schmidt O,
Monahan FJ: α-Tocopherol stereoisomers in beef as an indicator
of
vitamin E supplementation in cattle diets. Food Chem 2011,
124:935–940.
12. Ajjawi I, Shintani D: Engineered plants with elevated
vitamin E: a nutraceutical
success story. Trends Biotechnol 2004, 22:104–107.
13. Sivakumar G, Yu KW, Paek KY: Production of biomass and

ginsenosides
from adventitious roots of Panax ginseng in bioreactor cultures.
Eng Life
Sci 2005, 5:333–342.
14. Sivakumar G, Medina-Bolivar F, Lay JO, Dolan MC,
Condori J, Grubbs SK,
Wright SM, Baque MA, Lee EJ, Paek KY: Bioprocess and
bioreactor: next
generation technology for production of potential plant-based
antidiabetic
and antioxidant molecules. Curr Med Chem 2011, 18:79–90.
15. Sivakumar G: Bioreactor technology: a novel industrial tool
for high-tech
production of bioactive molecules and biopharmaceuticals from
plant
roots. Biotechnol J 2006, 1:1419–1427.
16. Sivakumar G, Jeong K, Lay JO: Bioprocessing of
strain siva2011. Biotechnol Biofuels 2014, 7:62.
17. Zingg ZM: Vitamin E: an overview of major research
directions. Mol Aspects
Med 2007, 28:400–422.
18. Munne-Bosch S: α-Tocopherol: a multifaceted molecule in
plants. Vitam Horm
2007, 76:375–392.
19. Liu K, Luo HL, Yue DB, Ge SY, Yuan F, Yan LY, Jia HN:
Molecular cloning
and characterization of the sheep α-TTP gene and its expression
in
response to different vitamin E status. Gene 2011, 494:225–230.

20. Jensen SK, Lauridsen C: Alpha-tocopherol stereoisomers.
Vitam Horm 2007,
76:281–308.
21. Traber MG, Ramakrishnan R, Kayden HJ: Human plasma
vitamin E kinetics
demonstrate rapid recycling of plasma RRR-α-tocopherol. Proc
Natl Acad
Sci U S A 1994, 91:10005–10008.
22. Traber MG, Atkinson J: Vitamin E, antioxidant and nothing
more. Free
Radic Biol Med 2007, 43:4–15.
23. Nowicka B, Kruk J: Plastoquinol is more active than α-
tocopherol in singlet
oxygen scavenging during high light stress of Chlamydomonas
reinhardtii.
Biochim Biophys Acta 1817, 2012:389–394.
24. Sarah C, Hunter SC, Cahoon EB: Enhancing vitamin E in
oilseeds: unraveling
tocopherol and tocotrienol biosynthesis. Lipids 2007, 42:97–
108.
25. Porfirova S, Bergmuller E, Tropf S, Lemke R, Dormann P:
Isolation of an
Arabidopsis mutant lacking vitamin E and identification of a
cyclase
essential for all tocopherol biosynthesis. Proc Natl Acad Sci U
S A 2002,
99:12495–12500.
26. Tsegaye Y, Shintani DK, DellaPenna D: Overexpression of
the enzyme

p-hydroxyphenylpyruvate dioxygenase in Arabidopsis and its
relation
to tocopherol biosynthesis. Plant Physiol Biochem 2002,
40:913–920.
8
27. DellaPenna D: Progress in the dissection and manipulation
of vitamin E
synthesis. Trends Plant Sci 2005, 10:574–579.
28. Cahoon EB, Hall SE, Ripp KG, Ganzke TS, Hitz WD,
Coughlan SJ: Metabolic
redesign of vitamin E biosynthesis in plants for tocotrienol
production
and increased antioxidant content. Nat Biotechnol 2003,
21:1082–1087.
29. Sattler SE, Cahoon EB, Coughlan SJ, DellaPenna D:
Characterization of
tocopherol cyclases from higher plants and cyanobacteria.
Evolutionary
implications for tocopherol synthesis and function. Plant
Physiol 2003,
132:2184–2195.
30. Backasch N, Schulz-Friedrich R, Appel J: Influences on
tocopherol biosynthesis
in the cyanobacterium Synechocystis sp. PCC 6803. J Plant
Physiol 2005,
162:758–766.

31. Antognoni F, Faudale M, Poli F, Biondi S: Methyl
jasmonate differentially
affects tocopherol content and tyrosine amino transferase
activity in
cultured cells of Amaranthus caudatus and Chenopodium
quinoa.
Plant Biol (Stuttg) 2009, 11:161–169.
32. Caretto S, Nisi R, Paradiso A, De Gara L: Tocopherol
production in plant
cell cultures. Mol Nutr Food Res 2010, 54:726–730.
33. Kusmic C, Barsacchi R, Barsanti L, Gualtieri P, Passarelli
V: Euglena gracilis
as source of the antioxidant vitamin E. Effects of culture
conditions in
the wild strain and in the natural mutant WZSL. J Appl Psychol
1999,
10:555–559.
34. Carballo-Cardenas EC, Tuan PM, Janssen M, Wijff RH:
Vitamin E (α-tocopherol)
production by the marine microalgae Dunaliella tertiolecta and
Tetraselmis
suecica in batch cultivation. Biomol Eng 2003, 20:139–147.
35. Kruk J, Szymanska R, Krupinska K: Tocopherol quinone
content of green
algae and higher plants revised by a new high-sensitive
fluorescence
detection method using HPLC–Effects of high light stress and
senescence. J Plant Physiol 2008, 165:1238–1247.
36. Mendiola JA, Garcıa-Martınez D, Ruperez FJ, Martın-
Alvarez PJ, Reglero G,
Cifuentes A, Barbas C, Ibanez E, Senorans FJ: Enrichment of

vitamin E from
Spirulina platensis microalga by SFE. J Supercrit Fluids 2008,
43:484–489.
37. Turner JG, Ellis C, Devoto A: The jasmonate signal
pathway. Plant Cell 2002,
14:153–164.
38. Cheong JJ, Choi YD: Methyl jasmonate as a vital substance
in plants.
Trends Genet 2003, 19:409–413.
39. Sandorf I, Holländer-Czytko H: Jasmonate is involved in the
induction of
tyrosine aminotransferase and tocopherol biosynthesis in
Arabidopsis
thaliana. Planta 2002, 216:173–179.
40. Seo HS, Song JT, Cheong JJ, Lee YH, Lee YW, Hwang I,
Lee JS, Choi YD:
Jasmonic acid carboxyl methyltransferase: a key enzyme for
jasmonate-regulated
plant responses. Proc Natl Acad Sci U S A 2001, 98:4788–4793.
41. Zhang ZP, Baldwin IT: Transport of [2-14C]jasmonic acid
from leaves to
roots mimics wound-induced changes in endogenous jasmonic
acid
pools in Nicotiana sylvestris. Planta 1997, 203:436–441.
42. Tamogami S, Noge K, Abe M, Agrawal GK, Rakwal R:
Methyl jasmonate is
transported to distal leaves via vascular process metabolizing
itself into
JA-Ile and triggering VOCs emission as defensive metabolites.
Plant Signal Behav 2012, 7:1–4.

43. Zhang L, Xing D: Methyl jasmonate induces production of
reactive
oxygen species and alterations in mitochondrial dynamics that
precede
photosynthetic dysfunction and subsequent cell death. Plant
Cell Physiol
2008, 49:1092–1111.
44. Wierstra I, Kloppstech K: Differential effects of methyl
jasmonate on the
expression of the early light-inducible proteins and other light-
regulated
genes in barley. Plant Physiol 2000, 124:833–844.
45. Bates D, Watts D: Nonlinear regression analysis and its
applications. NY, USA:
Wiley-Interscience; 2007.
46. Liu S: Bioprocess engineering: kinetics, biosystems,
sustainability, and reactor
design. Oxford, UK: Elsevier; 2012.
47. Hassan SS, Farhan M, Mangayil R, Huttunen H, Aho T:
Bioprocess data mining
using regularized regression and random forests. BMC Syst Biol
2013, 7:S5.
48. Archontoulis SV, Miguez FE: Non-linear regression models
and
applications in agricultural research. Agronomy J 2013, 105:1–
13.
doi:10.1186/1475-2859-13-79
Cite this article as: Sivakumar et al.: Biomass and RRR-α-
tocopherol

production in Stichococcus bacillaris strain siva2011 in a
balloon
bioreactor. Microbial Cell Factories 2014 13:79.
Submit your next manuscript to BioMed Central
and take full advantage of:
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at
www.biomedcentral.com/submit
8
Homework 7 Due 3.21.16

Congressional Ethics, Third Party Candidates, and Federalism

Recommended

Recommended

More Related Content

More from rock73

More from rock73 (20)

Recently uploaded

Recently uploaded (20)

Congressional Ethics, Third Party Candidates, and Federalism