Dynamics of the Bacterial Community Associated with Phaeodactylum tricornutum Cultures

Abstract

The pennate diatom Phaeodactylum tricornutum is a model organism able to synthesize industrially-relevant molecules. Commercial-scale cultivation currently requires large monocultures, prone to bio-contamination. However, little is known about the identity of the invading organisms. To reduce the complexity of natural systems, we systematically investigated the microbiome of non-axenic P. tricornutum cultures from a culture collection in reproducible experiments. The results revealed a dynamic bacterial community that developed differently in “complete” and “minimal” media conditions. In complete media, we observed an accelerated “culture crash”, indicating a more stable culture in minimal media. The identification of only four bacterial families as major players within the microbiome suggests specific roles depending on environmental conditions. From our results we propose a network of putative interactions between P. tricornutum and these main bacterial factions. We demonstrate that, even with rather sparse data, a mathematical model can be reconstructed that qualitatively reproduces the observed population dynamics, thus indicating that our hypotheses regarding the molecular interactions are in agreement with experimental data. Whereas the model in its current state is only qualitative, we argue that it serves as a starting point to develop quantitative and predictive mathematical models, which may guide experimental efforts to synthetically construct and monitor stable communities required for robust upscaling strategies.

Full text

processes
Article
Dynamics of the Bacterial Community Associated
with Phaeodactylum tricornutum Cultures
Fiona Wanjiku Moejes 1ID , Antonella Succurro 2,3,*,† ID , Ovidiu Popa 2,4,†, Julie Maguire 1
and Oliver Ebenhöh 2,4,*ID
1Bantry Marine Research Station, Gearhies, Bantry P75 AX07, Co. Cork, Ireland;
fmoejes@bmrs.ie (F.W.M.); jmaguire@bmrs.ie (J.M.)
2Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich-Heine University, Universitätsstrasse 1,
40225 Düsseldorf, Germany; ovidiu.popa@hhu.de
3Botanical Institute, University of Cologne, Zülpicher Strasse 47b, 50674 Cologne, Germany
4Institute of Quantitative and Theoretical Biology, Heinrich-Heine University, Universitätsstrasse 1,
40225 Düsseldorf, Germany
*Correspondence: a.succurro@uni-koeln.de (A.S.); oliver.ebenhoeh@hhu.de (O.E.)
† These authors contributed equally to this work.
Received: 17 October 2017; Accepted: 28 November 2017; Published: 7 December 2017
Abstract:
The pennate diatom Phaeodactylum tricornutum is a model organism able to synthesize
industrially-relevant molecules. Commercial-scale cultivation currently requires large monocultures,
prone to bio-contamination. However, little is known about the identity of the invading organisms.
To reduce the complexity of natural systems, we systematically investigated the microbiome of
non-axenic P. tricornutum cultures from a culture collection in reproducible experiments. The results
revealed a dynamic bacterial community that developed differently in “complete” and “minimal”
media conditions. In complete media, we observed an accelerated “culture crash”, indicating
a more stable culture in minimal media. The identification of only four bacterial families as major
players within the microbiome suggests specific roles depending on environmental conditions.
From our results we propose a network of putative interactions between P. tricornutum and these
main bacterial factions. We demonstrate that, even with rather sparse data, a mathematical model can
be reconstructed that qualitatively reproduces the observed population dynamics, thus indicating
that our hypotheses regarding the molecular interactions are in agreement with experimental data.
Whereas the model in its current state is only qualitative, we argue that it serves as a starting point to
develop quantitative and predictive mathematical models, which may guide experimental efforts to
synthetically construct and monitor stable communities required for robust upscaling strategies.
Keywords:
microbial communities; host-microbe interactions; mathematical modelling; diatoms;
synthetic ecology; algal biotechnology
1. Introduction
Microalgae are photosynthesis-driven cells able to store light energy by converting carbon
dioxide into carbohydrates, lipids, proteins, and other cellular components with potential biofuel,
food, feed, and pharmaceutical and nutraceutical applications [
1
]. Novel applications also include
the use of microalgae as an alternative sustainable development tool [
2
]. One such microalgae is
the pennate diatom Phaeodactylum tricornutum that is able to synthesize a number of industrially
relevant molecules applicable in: aquaculture as feed in e.g., bivalve, echinoderm, crustacean and
fish hatcheries [
3
,
4
]; as biomass for biofuels [
5
,
6
]; pharmaceuticals and nutraceuticals [
5
,
7
–
9
];
and nanotechnology [
10
], and bioremediation industries [
11
]. To fully exploit the industrial
potential of microalgal-derived products, substantial quantities of microalgal biomass is required,
Processes 2017,5, 77; doi:10.3390/pr5040077 www.mdpi.com/journal/processes

Processes 2017,5, 77 2 of 29
preferably obtained while maintaining low production costs. This is achieved by implementation of
large-scale cultivation methods such as open raceway ponds and photobioreactors. The majority of
conventional cultivation methods rely on keeping monocultures of the desired species, particularly if
the final product is a bioactive molecule for human consumption [
12
]. Photobioreactors are closed
systems that allow for the production of monoseptic cultures, fully isolated from potential
contamination if cultivation protocols are followed correctly [
13
]. However, high operational costs
of photobioreactors might not be sustainable. Another option is open raceway ponds, which are
simple open-air cultivation systems that have been in use since the 1950s [
1
]. They are highly
susceptible to contamination, and unless the desired species is a halophile or thermophile [
14
], it is
hard to maintain monocultures. Irrespective of the cultivation method, the establishment of unwanted
organisms such as amoeba, ciliates, rotifers, bacteria, viruses, and other photosynthetic organisms in
microalgal cultures, is a serious obstacle for large-scale microalgae cultivation [
15
,
16
]. Although much
research is carried out in the field of microalgal culture upscaling, very little is known about the true
identity and characteristics of these invading organisms, responsible for microalgal culture “crashes”
which lead to loss of biomass, and therefore, loss of revenue.
Microalgae are not found in monoculture in nature and it is not surprising that imposing such an
artificial environment results in unstable large-scale cultures. By understanding rather than attempting
to push out these micro-invaders, potential alternatives such as “synthetic ecology” as novel scaling
up techniques should be explored [
17
]. This concept is based on the Competitive Exclusion Principle,
or Gause’s Law, which states that two species competing for the same natural resource scarcely ever
occupy a similar niche [
18
,
19
]. By “synthesizing” a community of organisms that fills every niche in
the ecosystem of the microalgal culture and support, rather than harm, the growth of the phototroph,
we would automatically optimize the utilization of nutrients and prevent the establishment of other
potentially harmful organisms [
17
]. In order for synthetic ecology to be a legitimate contender as
a novel scaling up technique, a greater understanding of species-specific interactions is required,
starting with the bacterial faction, which are present in all of the Earths’ biomes [
20
], and arguably
the key players in maintaining balance within a system. Theoretical ecology employs mathematical
models to study the emergent patterns in ecosystems dynamics [
21
]. Because of the many industrial
applications of microbial communities, current research has shown great interest in improving
our understanding of such systems [
22
]. In particular, mathematical models and interdisciplinary
approaches are fundamental to understanding the crucial underlying mechanisms that regulate
community dynamics [
23
,
24
]. Since the same system can be inspected at different spatio-temporal
scales and at a different degree of complexity, it is important to select the most suitable method to
describe the biological phenomena under study in mathematical terms [
25
]. The first ecosystem models
at the population-scale date back to the 1920s with the well-known Lotka-Volterra (LV) predator-prey
model [
26
,
27
]. Since then, LV models have been extensively used to represent cooperation/competition
population dynamics with a system of ordinary differential equations (ODEs). In generalized LV
models (gLV) the system includes an arbitrary number of co-existing organisms and they directly
represent positive/negative pairwise interactions as fixed parameters [
28
]. Today, a gLV model can
be developed by inferring a co-occurrence network from a time series of metagenomics data [
29
].
This however requires a reasonable number of time resolved metagenomics data and will provide
information on direct, one-on-one interactions only.
Diatoms and bacteria have co-evolved for more than 200 million years [
30
], and their co-existence
is most likely based on a “biological barter trade system”, where substances such as trace metals,
vitamins, and nutrients (nitrate, phosphate, silicate, carbon) are exchanged. In this work, we built on
previous research that investigated algae-bacterial interactions including Provasoli’s work from 1958
where he suggested that bacteria can enhance the growth of algae [
31
], and subsequent species-specific
studies that further corroborated his initial idea [
32
–
35
]. We first characterized the relative composition
of the bacterial community in non-axenic P. tricornutum cultivated in the presence and absence of
trace metals, vitamins and sodium metasilicate at different time points. Secondly, using critical

Processes 2017,5, 77 3 of 29
peer-reviewed literature we defined the most likely functional roles of the bacterial factions and
constructed a putative interaction network. Lastly, from the derived putative network of interactions,
we built an ODE model with modified Verhulst equations [
36
] for microbial growth that included the
direct effect of nutrient availability. Mortality rates were also introduced as dependent on specific
bactericidal substances. The qualitative mathematical model, with parameters fitted to the available
experimental data, served as a proof-of-concept that data as obtained here is sufficient to reconstruct a
theoretical model that (a) reproduces the experimental observations, thus demonstrating consistency of
our assumptions, and (b) allows for testing different hypotheses regarding the nature of the metabolic
interactions underlying the ecosystem dynamics. It therefore represents a starting point to gain a
deeper understanding of the principles of microbial community dynamics by an iterative experimental
and theoretical approach.
2. Materials and Methods
2.1. Strains and Culture Conditions
All P. tricornutum strains were obtained from the Culture Collection of Algae and Protozoa (CCAP)
based in Oban, Scotland [
37
]. All cultures were obtained non-axenic. Based on previous experimental
evidence [
38
], the P. tricornutum strain CCAP1052/1B displayed optimal growth in 5L cultures.
P. tricornutum was cultured in Guillard’s medium for diatoms (F/2 + Si) in filtered natural seawater
chemically sterilised using sodium hypochlorite and sodium thiosulphate pentahydrate. P. tricornutum
was grown in two media conditions; (1) complete F/2 medium containing sources of nitrogen (NaNO
3
)
and phosphorus (NaH
2
PO
4·
2H
2
O), as well as trace metals and vitamins with the addition of sodium
metasilicate, as per Guillard and Ryther 1962 [
39
] and Guillard 1975 [
40
], and (2) minimal media
which contained just sources of nitrogen (NaNO
3
) and phosphorus (NaH
2
PO
4·
2H
2
O) at the same
concentration as in the F/2 medium recipe. All cultures were grown in hanging 5L polyethylene
bags with a “V” shaped bottom prepared using a heat sealer (Supplementary Figure S1). All cultures
had a modified aeration system provided by a 10 mL pipette attached to the main pressurised air
supply via 0.2
µ
m sterile air filters. Cultures were kept at 18–20
◦
C and 24 h light at an average of
132.3 µmol m−2s−1
. All cultures, irrespective of media condition, were inoculated with 250 mL from
the same 5L stock culture of actively growing non-axenic P. tricornutum.
2.2. Growth Measurements
Growth was monitored every 24 to 48 h using a light microscope and carrying out cell counts
of each culture in quadruplicate. During the cell counts the ratios of the four different morphotypes
(oval, fusiform, triradiate and cruciform) were recorded, and descriptions of each culture noted.
Samples of each culture were subsequently taken using a sterile 10 mL syringe and placed in 50 mL
Falcon centrifuge tubes and placed in a −20 ◦C freezer.
2.3. Genomic DNA Extraction
All samples from days 1, 8, 15, and 22 were thawed in a water bath set at 25
◦
C. As per de Gouvion
Saint Cyr et al. [
41
], samples were centrifuged for 5 min at 2000 x gto gather the P. tricornutum in
the pellet while particles such as debris, other organisms, bacteria, and soluble substances remain
in the supernatant. Because the bacteria might be attached to the P. tricornutum cells in the pellet,
the pellet was washed with deionised water and then centrifuged for 5 min at 2000
×
g. This was
repeated twice to ensure that majority of the bacteria attached to the pellet were released and were
included in the community analysis. Genomic DNA extraction was carried out in the Aquaculture and
Fisheries Development Centre, University College Cork. Mo Bio’s PowerWater
R

DNA Isolation Kit
(MO BIO Laboratories, Inc., Carlsbad, CA, USA, catalogue No. 14900-100-NF) was utilized to carry
out the genomic DNA extraction. Presence of gDNA was detected by running a 1% agarose-ethidium

Processes 2017,5, 77 4 of 29
bromide gel with 72 wells. The samples were sent on dry ice to Heinrich Heine University, Düsseldorf,
for the V6 16S sequencing.
2.4. Barcoded 16S-V6-Next Generation Sequencing
Ion Torrent
TM
barcoded Next Generation Sequencing protocol (Thermo Fischer Scientific Inc.,
Waltham, MA, USA) was used to sequence the bacterial gDNA [
42
,
43
]. Amplification of the V6 hyper
variable region of 16S rRNA with forward and reverse primers (Supplementary Table S1) was carried
out. Ion Reporter
TM
software (Thermo Fischer Scientific Inc., Waltham, MA, USA) assembled all the
raw sequencing data and sorted all the reads using the unique sample-specific barcode sequences and
removed them from the reads. The outcome was raw FASTQ files which were ready for analysis using
bioinformatics tools.
2.5. Bioinformatics Analysis
A total of 87,077,374 reads were identified. The smallest sample was just over 1 million reads;
the largest sample was just under 10 million reads. The sequencing data was subjected to a pipeline
adapted and modified from Pylro et al. [
44
]. Primers were trimmed with fastq-mcf (version 1.04.807) [
45
],
the resulting sequences were quality filterted and clustered into OTUs with usearch (version 8.0.1517;
32Bit-opensource) [
46
,
47
]. Taxonomy assignment was done by QIIME (version 1.9.0) [
48
] with the
implemented uclust classifier based on 97% sequence identity to the reference 16S sequences from SILVA
111 database [
49
]. Statistical analyses were performed in R [
50
]. The complete protocol containing all
processing steps is available on GitHub (see Supplementary Materials).
2.6. Mathematical Model
Starting from our understanding of the organism-to-organism interactions, we developed
a dynamic model consisting of 13 ordinary differential equations (ODEs) and including 56 (55 free)
parameters (see Appendix B.1). The model was built from the following working hypotheses:
(1)
the growth rate
γ
of each population followed a standard Verhulst equation [
36
] parametrized
with a carrying capacity and scaled by Monod-type terms [
51
] that describe the dependency on
(micro)nutrients. These terms are in practice positive scaling factors <1.
(2)
the mortality rate of each population was inversely proportional to (1 +
γ
), to account for the fact
that cells during replication (high growth rate) were healthier;
(3)
additional contributions to population mortality was given by the presence in the environment
of noxious elements like bactericidal substances;
(4)
changes in metabolite concentrations are in general directly proportional to the growth
γ
of the
consumers and producers;
(5) in the event of micronutrient scarcity (Iron and Vitamins in our model), P. tricornutum will secrete
more organic carbons favored by those bacteria able to provide the needed micronutrients.
The initial conditions for simulations are different between minimal and complete media:
(1)
the initial quantity of Iron and Vitamins is 10 times higher in complete media;
(2)
the initial quantity of P. tricornutum biomass is matched to the first data point.
The parameters were fitted separately in minimal and complete media using a genetic
algorithm [
52
] which was run in different steps to optimise the fit of P. tricornutum growth and/or the
bacteria relative abundances to the experimental data in evolving system conditions (see Appendix B.2
and Supplementary Material 2). The model was written in Python (Python Software Foundation,
https://www.python.org/) and is available on GitHub with instructions and scripts for running (see
Supplementary Materials).

Processes 2017,5, 77 5 of 29
3. Results
3.1. Characteristics of Phaeodactylum tricornutum Growth
The media composition was shown to have a significant effect on the growth characteristics of
P. tricornutum.P. tricornutum cultivated in minimal media exhibited a statistically significantly (
p=
0.042,
unpaired Wilcoxon signed rank) higher cell density (11.2
×
10
6
cells/mL) when compared to cultivation
in complete media (9.3
×
10
6
cells/mL). The growth rates during the exponential phase in both cultures
were
µcomplete =
0.43
±
0.07 d
−1
and
µminimal =
0.51
±
0.04 d
−1
respectively. In contrast, the death rates
when the cultures “crash” are δcomplete =0.09 ±0.02 d−1and δminimal =0.08 ±0.04 d−1respectively.
3.2. Bacterial Community Profile of Phaeodactylum tricornutum Cultures
Bacterial gDNA analysis showed that most of the operational taxonomic units (OTUs) could be
assigned to the genera level (Supplementary Figure S2). Of the 9727 OTUs identified, 8109 corresponded
to known sequences in the SILVA database (v.118) [
49
]. The OTU abundance at the phylum level showed
that 99.97% of all OTUs belonged to Proteobacteria, Bacteroidetes, Actinobacteria and Firmicutes
(Figure 1a). A comparison of the number of individual reads to the number of unique OTUs showed
that the high number of reads per phyla was not the result of a single OTU (Supplementary Figure S3).
OTUs with hits to known 16S P. tricornutum sequences were discarded.
Rarefaction curves were used to evaluate the alpha diversity in the different media conditions
as well as at the different time points (Supplementary Figure S4). Species richness in both minimal
and complete media was
∼
3000. Species richness over time remained between
∼
2400 and
∼
2600,
with reduced species richness (
∼
1300) on day 8 (both minimal and complete media) possibly due
to elevated levels of 16S P. tricornutum chloroplast reads which had to be omitted. Greatest species
richness (
∼
3000) was shown on day 22. All datasets showed a diminished increase in the number
of unique species as the sample size increased, confirming adequate species richness in all culture
conditions. To compare the species composition between the different samples (days/media) we used
a non-metric multidimensional scaling (NMDS) function based on generalized UniFrac distances [
53
].
This allowed us to characterize the relationship between the particular samples on a visual level by
displaying the information contained in the distance matrix. Therefore, similar samples would be
placed together in an N-dimensional space. Here we observed a clear gradient of similarity between the
bacterial samples from the different time points. The ordination based on the sampling day indicated
that the bacterial community was dynamic with a clear divergence visible between day 1 and the
other three sampling days. Interestingly the similarity between the different time samples showed the
evolving processes of the community over time (overlaps between day 8 with day 15 and day 15 with
day 22) and the recovery to the original one (overlap day 22 and day 1) (Figure 1b).
The existence of one dominant family at each investigated time point was a particularly interesting
observation. In minimal media (Figure 2a), the lag phase of P. tricornutum growth was dominated
by Pseudoalteromonadaceae (85%). However, during the log phase, a wide diversity of bacterial
families was observed, with members of the Alteromonadaceae family (21%) beginning to dominate.
During the stationary phase, a clear dominance of Alteromonadaceae species (55%) in the community
was observed. The decline phase, however, showed the Pseudomonadaceae (39%) as a dominant family,
with Pseudoalteromonadaceae species (37%) increasing in abundance again. In complete media
(Figure 2b), the lag phase was also dominated by Pseudoalteromonadaceae (63%). During the log phase,
50% of the community was composed of members of the Flavobacteriaceae family, with the other
50% distributed among a number of different families. Flavobacteriaceae (46%) remained high in
abundance during the stationary phase, with Pseudoalteromonadaceae species (44%) beginning to
increase in abundance again. As for minimal media, Pseudoalteromonadaceae (57%) showed a clear
dominance of the community during the decline phase.

Processes 2017,5, 77 6 of 29
−0.6 −0.4 −0.2 0.0 0.2 0.4 0.6
−0.3 −0.1 0.1 0.3
NMDS 1
NMDS 2
Day1
Day15 Day22
Day8
minimal
complete
Day 1
Day 8
Day 15
Day 22
(b)
0 1 2 3 4
Frequency (log10)
*
*
*
*
(a)
Acidobacteria
Actinobacteria
Bacteroidetes
BD1−5
OD1
TM7
Chlamydiae
Chlorobi
Chloroflexi
Cyanobacteria
Deinococcus−Thermus
Elusimicrobia
Firmicutes
Fusobacteria
Gemmatimonadetes
OC31
Planctomycetes
Proteobacteria
TM6
Unknown
Verrucomicrobia
Figure 1.
(
a
) Distribution of Operational Taxonomic Unit (OTU) abundance (LOG scaled) within phyla
from complete data set. The bins marked with asterisks (*) correspond to 99.97% of all which belong to
Proteobacteria, Bacteriodetes, Actinobacteria and Firmicutes. (
b
) Ordination plot of bacterial community
in the two media conditions for all sampling points. Triangles and circles correspond to minimal
media and complete media conditions, respectively. Blue represents day 1. Red day 8. Green day 15.
Black day 22. The ellipses correspond to the 99% confidence interval to each group centroid.
An adapted version of PermanovaG was used to carry out permutational multivariate analysis of
variance using multiple distance matrices which were previously calculated based on the generalized
UniFrac distance [53]. The significance for the test was assessed by 5000 permutations. The results of
the PermanovaG tests support the NMDS ordination, confirming a statistically significant effect in the
bacterial community profile at the different sampling points and in the two media conditions whereas
no significant effect was found in the experimental replicates (Figure A1).

Processes 2017,5, 77 7 of 29
Pseudoalteromonadaceae Alteromonadaceae Pseudomonadaceae Halomonadaceae
Flammeongovirgaceae Rhodobacteraceae
Flavobacteriaceae
Bacteriovoraceae
Oceanospirillaceae Unknown
Moraxellaceae
Sphingomonadaceae Methylophilaceae
Methylobacteriaceae
Cryomorphaceae
5.8 6.2 6.6 7.0
Minimal medium
Time (days)
Cell counts (log10) / ml
12 5 89 13 14 15 16 19 21 22 23 26 27 29 30 31 33 34 36
6
Lag Phase Log Phase Stationary Phase Decline Phase
85%
1%
5%
4%
3%
2%
}
33%
55%
3%
3%
}4.5%
}1.5%
37%
18%
2%
39%
1%
}3%
20%
21%
9%
18%
2%
3%
15%
8%
3%
2%
(a)
5.8 6.2 6.6 7.0
Complete medium
Time (days)
Cell counts (log10) / ml
1258913 14 15 16 19 21 22 23 26 27 29 30 31 33 34 366
Lag Phase Log Phase Stationary Phase Decline Phase
63%
4.5%
26%
}
3%
3.5%
}
44%
6%
46%
}4%
57%
18% 8%
12%
1%
1%
2%
50%
1%
12%
7%
2%
8%
1%
11%
1%
7%
(b)
Figure 2.
Bacterial community profile of P. tricornutum (CCAP 1052/1B) over a 36 day period in culture
conditions: (
a
) minimal media; (
b
) complete media. The growth curves are partitioned into lag (green),
log (blue), stationary (red), and decline (yellow) phases. The abundance (%) of the “Top Ten” bacterial
families (corresponding colors described in the key) is depicted in pie charts on days 1, 8, 15 and 22 in
both media conditions.
3.3. Effect of Temporal Evolution and Media Composition on the Bacterial Community Profile
We compared the bacterial community profiles over time and in the different media conditions at
the family level to avoid diluting the signal of the less abundant genera. Supplementary Figures S5
and S6 show no dynamical difference within the genera that cannot be observed at the family level.
By investigating the bacterial community dynamics at the family level, we also included taxonomical
information that is unavailable at the genus level. The families over-represented in all samples were
Pseudoalteromonadaceae, Alteromonadaceae, Flavobacteriaceae and Pseudomonadaceae. Figure 2
illustrates the temporal evolution of the bacterial community in both minimal and complete media
with a unique composition at each time point. A remarkable feature is that at all investigated time
points there exist one or two dominant families.
In complete media, members of the Pseudoalteromonadaceae family were highly abundant
when P. tricornutum cell densities were low (63% and 57% on day 1 and day 22, respectively).
Flavobacteriaceae species dominated (50%) when the P. tricornutum culture was growing exponentially
(day 8). Day 15, when P. tricornutum cell densities were at their highest, showed co-dominance of both
Flavobacteriaceae (46%) and Pseudoalteromonadaceae (44%).
In minimal media, members of the Pseudoalteromonadaceae family were highly abundant when
P. tricornutum cell densities were low. On day 22 Pseudomonadaceae (39%) and Pseudoalteromonadaceae

Processes 2017,5, 77 8 of 29
(37%) were both overrepresented. When the P. tricornutum culture was in its exponential growth phase
(day 8), a cluster of Families dominated; namely Alteromonadaceae (21%), Pseudoalteromonadaceae
(20%), Pseudomonadaceae (18%), Halomonadaceae (15%) and Flavobacteriaceae (9%). When the cell
density of P. tricornutum peaked (day 15), the Alteromonadaceae species took over (55%).
The bacterial communities within the two media conditions on day 1 were more closely
related than the communities on days 8 and 15 (see Supplementary Table S2 for generalized
UniFrac distances). As the cultures begin to “crash” (day 22), the bacterial communities in the two
media conditions increased in similarity again. In general, the main families identified showed a distinct
pattern of disappearance and regeneration within the bacterial community. In the complete media,
Pseudoalteromonadaceae species started at 63% (day 1), dropped in abundance to 7% (day 8) then
recovered to 57% (day 22). Flavobacteriaceae species, in complete media, started at 4.5% (day 1),
increased in abundance to 50% (day 8), and then fell back to 8% (day 22). In the minimal media,
Alteromonadaceae species had an abundance of only 1% (day 1), peaked at 55% (day 15), and decreased
down to 18% (day 22).
3.4. Network of Putative Interactions between Phaeodactylum tricornutum and Identified Bacterial Families
The putative roles of each of the dominant families are illustrated in Figure 3. Based on an
extensive literature review, five metabolites were identified as playing a crucial role in the interactions
between P. tricornutum and the identified bacterial families. These are: bactericidal metabolites; iron;
vitamins; dissolved organic carbons; dissolved organic phosphates.
•
Bactericidal metabolites. Several species of the Pseudoalteromonadaceae family have been reported
to possess bactericidal effects [
54
]. This ability to suppress the growth of competing bacteria
could explain the dominance of Pseudoalteromonadaceae in almost all cultures irrespective of
media composition. P. tricornutum also demonstrates bactericidal properties by excreting fatty
acids (such as eicosapentaenoic acid or EPA), nucleotides, peptides, and pigment derivatives [
55
].
•
Iron. Iron acquisition is essential for biological processes such as photosynthesis, respiration and
nitrogen fixation. Bacteria produce and excrete siderophores, which scavenge iron. Diatoms are
not known to produce siderophores, but genome sequence analyses identified the presence
of a gene orthologue of a bacterial ferrichrome binding protein that suggests the possibility
of iron (III)-siderophore utilization by P. tricornutum [
56
,
57
]. Furthermore, it was shown that
P. tricornutum was able to uptake siderophores ferrioxamines B and E [58].
•
Vitamins. Prokaryotes are thought to be the main producers of B vitamins [
59
,
60
]. Although
P. tricornutum does not require cobalamin, thiamine and biotin [
61
], production of organic
compounds such as EPA can be considerably enhanced by the bioavailability of co-factors such
as cobalamin [
62
]. This provides the basis for potential mutualistic interactions. For example,
Alteromonadales, dominant in our cultures, are thought to be capable of producing B vitamins [
63
].
•
Dissolved Organic Carbon (DOC). It is estimated that up to 50% of carbon fixed via
phytoplankton-mediated photosynthesis is utilized by marine bacteria [
64
], mainly as DOC
compounds, defined as the organic material
<
0.7
µ
m in size [
65
]. DOC from diatoms originates
either from live cells or recently lysed or grazed cells, which determines the type of DOCs
available, and therefore are likely to influence the bacterial consortia associated with the
diatom [30].
•
Dissolved Organic Phosphate (DOP). Both diatoms and bacteria primarily utilize orthophosphate as
a source of phosphorus. However, to access phosphate from DOP compounds, both diatoms and
bacteria developed mechanisms to release orthophosphate (PO
3−
4
) from DOP. The mechanism is
not species-specific, which consequently means the “free” orthophosphates can be acquired by
any organism [66].

Processes 2017,5, 77 9 of 29
Figure 3.
Network of putative interactions between Phaeodactylum tricornutum and identified bacterial
families. The dotted grey line depicts the “phycosphere”; a term coined by Bell and Mitchell in 1972 as
an aquatic equivalent of the “rhizosphere”, denoting the region extending outwards from the algal cell
in which bacterial growth is stimulated by extracellular products of the alga [67].
3.5. Mathematical Model Simulations
Based on the network of putative interactions between diatoms, bacteria, and the environment,
we constructed a dynamic mathematical model, based on generalized Verhulst growth-laws [
36
]
extended with Monod-type terms [
51
] to reflect the dependencies on metabolites (see Materials
and Methods and Appendix B.1). Figure 4presents results of the model simulations after the
model parameters were fitted to the data in minimal and complete media conditions, respectively
(see Appendix B.1). Experimental data are superimposed. The top panel shows biomasses of the five
organisms (data available only for the diatom), the bottom panel shows relative bacteria abundance
versus time (individual biomass divided by total bacterial biomass). Because of the qualitative nature of
the model, units are arbitrary. The figures show that the model is able to reproduce the main features of
the bacterial community dynamics, such as the disappearance and return of Pseudoalteromonadaceae
in complete media and the peak of Alteromonadaceae at the end of the exponential growth phase of
P. tricornutum in minimal media. Supplementary Figure S7b and S7d show the dynamics of metabolite
concentrations, for which no data are available.
Due to the large number of free parameters, the fit was certainly not unique. Supplementary
Material 2 presents additional checks we performed on the parameter fitting procedures. The parameter
space could be in principle reduced to 43 free parameters, but this did not change the results. It was not
possible to find a unique parameter set valid in both minimal and complete media conditions. This is
however consistent with the fact that the model was not constructed to capture effects like metabolic
re-adjustments, something that would be observed e.g., as a different parameter value for growth
rate or metabolite consumption. With the data available, it was not possible to make any quantitative
statements about the actual interaction parameters, neither could it be assumed that simulation results
are of general validity. Despite these limitations, the model did represent a possible configuration of
diatom-bacteria-environment interactions, which was in agreement with the experimentally observed
bacterial dynamics.

Processes 2017,5, 77 10 of 29
(a)
(b)
Figure 4.
Simulation results (lines) and experimental data (squares) for communities of P. tricornutum (
D
),
Pseudoalteromonadaceae (
PA
), Flavobacteriaceae (
F
), Alteromonadaceae (
A
) and Pseudomonadaceae
(
P
) in (
a
) minimal media and (
b
) complete media conditions. The top panel shows the biomass time
course (arbitrary units) for the five organisms and the rescaled data points (squares) for the P. tricornutum.
The bottom panel shows the variations in relative abundances of the four bacteria (single bacteria
biomass/total bacteria biomass) over time and the three sets of data points from the sequencing analysis
(the first data point is used as starting condition at time 0). Also shown in the bottom plot (dotted line,
right y-axis) is the total bacterial biomass in arbitrary units.
In order to test the stability of the bacterial community and how it supports the growth of
P. tricornutum we ran simulations using the same set of parameters (either minimal or complete media
conditions) and varied the initial community composition removing one bacteria per simulation.
In complete media the simulated growth of P. tricornutum still fit the experimental data, rather
independently from the bacterial community (Figure 5). Also under axenic conditions, diatom growth
was predicted to be largely unperturbed. This situation was different in minimal media. While under
these conditions diatom growth was also unaffected upon removal of the three bacterial families
Pseudoalteromonadaceae, Flavobacteriaceae and Pseudomonadaceae, removing Alteromonadaceae
from the community resulted in the total absence of P. tricornutum growth (Figure 6). This behavior
was expected from the hypothesized central role of Alteromonadaceae in supplying the diatom with
micronutrients. Surprisingly, the removal of a single bacteria from the community in both media

Processes 2017,5, 77 11 of 29
conditions still gave, in general, a good fit of the (recomputed) relative abundances, except when
removing Alteromonadaceae in minimal media (as direct consequence of what stated previously) and
when removing Pseudoalteromonadaceae in both media conditions. This hinted to a relevant role of
Pseudoalteromonadaceae in regulating the community composition through its predatory strategy
of releasing bactericidal substances. Finally, the community composition at the last time point was
overall better captured. This suggests that the mathematical model consistently captured the general
interactions leading one bacterial family to dominate over the others on the long term.
(a) (b)
(c) (d)
Figure 5.
Reduced community simulation results (lines) and experimental data (squares)
for communities of P. tricornutum (
D
), Pseudoalteromonadaceae (
PA
), Flavobacteriaceae (
F
),
Alteromonadaceae (
A
) and Pseudomonadaceae (
P
) in complete media conditions. Simulations are run
removing from the bacterial community one member: (
a
)
PA
; (
b
)
F
; (
c
)
P
; (
d
)
A
. The top panel shows
the biomass time course (arbitrary units) for the four organisms and the rescaled data points (squares)
for the P. tricornutum. The bottom panel shows the variations in relative abundances of the three
bacteria (single bacteria biomass/total bacteria biomass) over time and the three sets of data points
from the sequencing analysis (the first data point is used as starting condition at time 0). Also shown in
the bottom plot (dotted line, right y-axis) is the total bacterial biomass in arbitrary units.

Processes 2017,5, 77 12 of 29
(a) (b)
(c) (d)
Figure 6.
Reduced community simulation results (lines) and experimental data (squares) for communities
of P. tricornutum (
D
), Pseudoalteromonadaceae (
PA
), Flavobacteriaceae (
F
), Alteromonadaceae (
A
) and
Pseudomonadaceae (
P
) in minimal media conditions. Simulations are run removing from the bacterial
community one member: (
a
)
PA
; (
b
)
F
; (
c
)
P
; (
d
)
A
. The top panel shows the biomass time course
(arbitrary units) for the four organisms and the rescaled data points (squares) for the P. tricornutum.
The bottom panel shows the variations in relative abundances of the three bacteria (single bacteria
biomass/total bacteria biomass) over time and the three sets of data points from the sequencing analysis
(the first data point is used as starting condition at time 0). Also shown in the bottom plot (dotted line,
right y-axis) is the total bacterial biomass in arbitrary units.
4. Discussion
In nature, P. tricornutum does not exist as an isolated entity. In fact, it is part of a complex ecosystem
whose complete network of interactions with both its environment and other organisms remains
poorly understood. Microbial ecosystems are of high interest for a wide range of applications in fields,
such as medicine, renewable energy, and agriculture. Within the scope of this project, the complexity
of a natural, variable system was reduced by investigating the batch growth of non-axenic laboratory
strains of P. tricornutum from a culture collection. The cultivation method we developed was designed
to compromise between highly controlled small-scale laboratory conditions and a large-scale industrial
set-up. The bacterial community was characterized and the community dynamics investigated in
two conditions: minimal and complete media. The data was then complimented with an extensive
study of existing, peer-reviewed literature to identify the putative role of the dominant bacterial
families associated with P. tricornutum. We then validated the derived network of interactions by
developing a mathematical model which could reproduce the observed dynamics. The presented
approach, integrating experiments, bioinformatics and mathematical modeling, illustrates a possible
way towards the development of a monitoring pipeline for non-axenic microalgae cultures.

Processes 2017,5, 77 13 of 29
4.1. Experimental Observation of the Dynamics of the Bacterial Community Associated to
Phaeodactylum tricornutum
The growth dynamics of P. tricornutum in the two media conditions showed an accelerated “culture
crash” in the complete media compared to the minimal media, which indicated a more stable culture
in the minimal media (Figure 2). This also suggested that non-axenic cultures of P. tricornutum might
not require expensive trace metals and vitamins for optimal growth under the conditions provide,
an observation crucial to the large-scale industrial cultivation of P. tricornutum as this would drastically
decrease the production costs. Simultaneously, the dynamics of the bacterial community revealed that
the community in the minimal media increased in complexity over time. The link between ecosystem
complexity and stability based on theoretical and experimental data has been debated by ecologists for
over half a century [
68
–
71
]. Our observations are in agreement with more recent hypotheses indicating
that diversity generally increases the stability of an ecosystem [72].
4.2. Literature-Based Assessment of the Putative Role of Each Bacterial Family
The bioinformaticsanalysis of bacterial gDNA abundance showed clear dominance of four bacterial
families: Pseudoalteromonadaceae, Alteromonadaceae, Flavobacteriaceae and Pseudomonadaceae.
These bacteria were over-represented in all samples and their relative abundances showed different
temporal dynamics among the two P. tricornutum growth conditions. In order to understand the
putative functional role of these bacteria an extensive study of peer-reviewed literature was carried out.
Pseudoalteromonadaceae.
Members of Pseudoalteromonadaceae family have been isolated
from coastal, open and deep-sea waters, sediments, marine invertebrates, as well as marine fish
and algae [
73
]. The Pseudoalteromonadaceae family has three genera, namely Pseudoalteromonas,
Algicola and Psychrosphaera [
74
]. Several species of Pseudoalteromonadaceae are reported to possess
antibiotic properties with bactericidal effects [
54
]. For example, concentrated supernatant of a marine
bacterium Pseudoalteromonas sp. strain A28 contained various enzymes including proteases, DNAses,
cellulases, and amylases, capable of causing the lysis of the diatom Skeletonema costatum [
75
].
Species of Pseudoalteromonadaceae are also capable of producing cold-adapted enzymes [
76
–
81
].
Pseudoalteromonadaceae species can produce extracellular polymeric substances allowing them
to colonise surfaces, enhancing nutrient uptake whilst limiting diffusion of particular substances
across the cell membrane [
82
]. The ability of Pseudoalteromonadaceae species to suppress the
growth of competing bacteria could explain the dominance of Pseudoalteromonadaceae in almost
all cultures irrespective of media composition, particularly when P. tricornutum abundance is limited
(Figure 2, days 1 and 22). P. tricornutum on the other hand, may protect other bacterial community
members from the bacteriolytic ability of Pseudoalteromonadaceae by producing specific antibacterial
compounds themselves. Desbois et al. showed that P. tricornutum excreted bacteriolytic fatty acids
such as eicosapentaenoic acid (EPA; 20:5n-3), nucleotides, peptides, and pigment derivatives that can
eliminate unwanted competition for nutrients such as organic phosphates from certain bacteria [55].
Alteromonadaceae.
The Alteromonadaceae family consists of 16 (yet annotated) named genera
found predominantly in marine environments [
74
]. Members of this family were isolated from
nutrient-rich environments such as coastal, open, and deep-sea waters, sediments, marine invertebrates
and vertebrates, algae, and temperate and Antarctic marine environments [
83
]. They are able to utilize
a vast array of compounds as carbon sources; from glucose to glycerol [
74
]. Members of this family
are known siderophore producers [
57
,
84
,
85
]. Greek for “iron carrier”, siderophores are a group
of iron scavengers that act by chelating iron (III) that are produced and excreted by bacteria, and
some cyanobacteria, which then reuptake the siderophores with bound iron (III) via outer-membrane
transporters that are siderophore-specific [
86
]. Most bioactive trace metals, including iron, exist at
nanomolar to picomolar concentrations in our oceans, approximately one-millionth of the intracellular
concentration in diatoms [
87
,
88
]. No trace metals, including iron (III), were provided to minimal
media cultures. However, natural seawater may contain minute traces of bioactive trace metals.
The high abundance of Alteromonadaceae in the minimal media suggests a potential supportive role in

Processes 2017,5, 77 14 of 29
sequestering traces of iron (III) that may be present in the sterile natural seawater to the P. tricornutum
(Figure 2). This is further supported by the very low level of Alteromonadaceae in the complete media
(11% in complete media compared to 55% in minimal media, both on day 15) where the culture has
been supplied with 11.7 µM of iron (III) chloride hexahydrate.
Flavobacteriaceae.
Flavobacteriaceae are members of the Bacteroidetes phylum and include over
120 genera found in soil, sediments and seawater (see [
89
] for further references). Flavobacteriaceae
belong within the Cytophaga-Flavobacterium cluster which has been shown to account for more than 10%
of the total bacterial community in coastal and offshore waters [
90
–
92
]. Members of Flavobacteriaceae
can proficiently degrade various biopolymers such as cellulose, chitin and pectin [
93
,
94
]. They were
shown to be omnipresent during phytoplankton blooms, and their preference for consuming more
complex polymers rather than monomers suggests an active role in the processing of organic matter
during these blooms [
95
,
96
]. Although the exact mechanisms behind them are not perfectly understood,
algal blooms are a consequence of exponential growth of phytoplankton [
97
]. In this respect, the phase
of exponential growth of P. tricornutum in complete media, when our results showed highest abundance
of Flavobacteriaceae, is the artificial equivalent of an algal bloom of P. tricornutum (Figure 2). In the
minimal media, the abundance of Flavobacteriaceae remains very low; at its maximum on day 8 it only
accounts for 9% of the total bacterial community. Members of the Flavobacteriaceae family could be
more demanding than other bacteria that require lower nutrient levels to thrive.
Pseudomonadaceae.
Pseudomonadaceae are an extraordinarily diverse family of bacteria found
in almost all habitats on Earth; in soils, freshwater as well as marine environments, as well as plant
and animal-associated pathogens [
98
]. Species from the Pseudomonas genus are the best studied of
the Pseudomonadaceae family, whose sheer genetic diversity explains the ability to thrive in such
a wide range of environments [
99
]. Marine isolates from the Pseudomonas genus have been shown
to produce a wide range of bioactive compounds, many of which exhibit antibacterial as well as
antiviral properties (see [
100
] for further references). Our results, indeed show an elevated level of
Pseudomonadaceae OTUs evident on day 22 of the complete media cultures, and on days 8 and 22 of
the minimal media cultures. The increased presence of Pseudomonadaceae when the P. tricornutum
culture has “crashed” could be attributed to its ability to produce antibacterial compounds allowing
members of this family to begin to thrive in the community through inhibition of its competitors.
Given its exceptional genetic diversity, and thus, its metabolic versatility, allows for members of
Pseudomonadaceae to be truly saprophytic; providing a hypothetical explanation of its abundance we
could measure when the P. tricornutum cultures crash (Figure 2, day 22 in both media conditions).
4.3. Putative Network of Interactions and Validation with a Qualitative Mathematical Model
The literature review work revealed interesting insights into the possible metabolic exchanges going
on and allowed to infer interaction links among P. tricornutum and its associated bacterial community.
We critically considered which metabolites were most relevant for survival (organic carbons for the
bacteria, iron, vitamins and phosphates for the diatom) and which ones could play a role in competition
and predation among the microbes (bactericidal metabolites). From these considerations we designed
a putative network of interactions that was then translated into a mathematical model. In particular we
chose, besides the five microbes’ biomasses, a total of eight metabolites as the variables that directly
and specifically influence the interactions among the different organisms. These were: four different
possible sources of organic carbons, each preferred by a different bacterial family [
30
]; two bactericidal
substances, one produced by Pseudoalteromonadaceae and affecting all other bacteria [
54
], the other
produced by P. tricornutum and targeting specifically Pseudoalteromonadaceae [
55
]; vitamins produced
by Alteromonadaceae and needed by P. tricornutum [
63
]; bio-available iron that is chelated by
Alteromonadaceae and efficiently absorbed by P. tricornutum [
57
]. For the scope of the model, we ignored
other free iron forms that can be uptaken by all bacteria as well as phosphates that are not species-specific
and are present in both minimal and complete media.

Processes 2017,5, 77 15 of 29
Direct metabolic exchanges are known to be central in microbial community interactions [
101
],
but usually population dynamics models like gLV [
28
] do not include this information. This work,
therefore, modified the standard formulation of the Verhulst equation [
36
] for bacterial growth to
include organism-to-organism interactions depending on the production/consumption of metabolites,
modeled as Monod-type terms [
51
]. Nutrients availability can indeed drastically change the
“metabolic state” of an organism, inducing a reprogramming of resource allocation to face nutrient
scarcity [
102
]. This was shown at the gene expression level for example for Escherichia coli [
103
] and
Shewanella oneidensis [
104
] grown in minimal and rich media condition. Micronutrients can as well
affect microbial gene expression, as is for example the case with vitamin B
12
, whose presence induces
the expression of the cofactor-dependent methionine synthase enzyme METH, while in its absence the
cofactor-independent methionine synthase enzyme METE is expressed [
105
]. An ODE model at the
population level cannot, of course, capture mechanisms such as metabolic shifts caused by changes
in the environment such as the supplementation of minimal or complete media [
25
]. Therefore, we
did not expect to find a unique set of parameters for the model in the two conditions. However,
the parameters fitted to the data of P. tricornutum with four bacterial families still provide good fits
in simulations with altered community composition. Even though the parameter values could not
directly be interpreted biologically, we could use the simulation results on metabolites dynamics
(an information absent in data) to speculate about the reason for the lower cell count of P. tricornutum
in complete media with respect to minimal media (Supplementary Material 3). In minimal media,
Alteromonadaceae maintained constant iron levels and the fitted values for parameters characterizing
P. tricornutum’s sensitivity to micronutrients levels were significantly lower. This would suggest a key
role of Alteromonadaceae in supporting P. tricornutum growth combined with the diatom’s adaptation
to scarce micronutrients availability.
Considering the limited information that can be extracted from the current experimental
data available, the model we proposed is purely qualitative and provides a proof-of-concept
that a quantitative model can, in principle, be constructed if dedicated experiments are designed
for calibration. The current qualitative model provides therefore a preliminary validation of our
putative network of interactions, and serves as motivation for further research bringing the model to
a quantitative, predictive level. Indeed, starting with systematic measurements of model parameters
in co-cultivation experiments, the simulations can gain predictive power and become a powerful tool
towards the goal of synthetic community design and control.
5. Conclusions and Outlook
This study demonstrated that the bacterial community associated with non-axenic laboratory
strains of P. tricornutum is not randomly assembled but follows dynamics that can be reproduced.
We postulate that a role within the community can be filled by a number of bacterial species capable
of carrying out a certain function (guilds) rather by one specific species of bacteria. Which bacteria
fill the role is dependent upon the environmental characteristics and the prevailing needs of the
community as a whole at any given time. Unfilled niches will be seized by bacteria with the ideal
metabolic functionality. The absence of certain micronutrients creates a new niche that can be filled by
a certain unique bacterial faction. Further work is necessary to explore the hypotheses postulated and
to further develop the qualitative mathematical model to understand the specific community roles
and the ecological niches. In the context of fundamental research, one approach would be to carry
out systematic time-resolved omics studies, which provide a holistic view of the genes (genomics)
and metabolites (metabolomics) in a specific biological sample in a non-targeted and non-biased
manner [
106
], and use them to develop an “expanded gLV” mathematical model where the species
specific interaction terms depend on the metabolite concentrations. This would allow to derive
a network of interactions independent of a priori hypotheses, and thus represent a significant step
forward in understanding community dynamics based on metabolic exchanges. In the context of
industrial scale-up, systematic co-culture experiments with culturable members of the bacterial families

Processes 2017,5, 77 16 of 29
of interest, chosen based on desired functional roles, could be used to parametrize a mathematical
model like the one we presented and develop it into a powerful predictive tool for culture monitoring.
For example, samplings assessing the community composition can be used to predict the harvesting
point and avoid “culture crash”. The development of novel co-cultivation strategies for scale-up is
extremely relevant for pharma- and nutraceutical, as well as animal feed industries. Therefore there
will be increasing interest in further research into co-cultivation approaches and in general in the field
of synthetic ecology.
Supplementary Materials:
The bioinformatics analysis steps are available online at https://github.com/QTB-
HHU/16SV6-Sequence-Analysis.git. The mathematical model in python with instructions to run simulations
is available online at https://github.com/QTB-HHU/communityODE. The following are available online at
www.mdpi.com/2227-9717/5/4/77/s1, Supplementary Material 1 contains Figures S1-S6 and Tables S1-S2,
relative to the experimental data analysis. Supplementary Material 2 contains additional checks performed for
parameter fitting of the mathematical model (Tables S7-S11). Supplementary Material 3 presents a speculative
interpretation of the simulation results (Figures S7-S8 and Table S12).
Acknowledgments:
This work was supported by the European Commission Seventh Framework Marie Curie
Initial Training Network project ‘AccliPhot’ (grant agreement number PITN-GA-2012-316427) to F.W.M. and A.S.;
and the Deutsche Forschungsgemeinschaft, Cluster of Excellence on Plant Sciences CEPLAS (EXC 1028) to O.P.,
A.S. and O.E. Genomic DNA extraction was carried out at the Aquaculture and Fisheries Development Centre,
University College Cork, Ireland (funded by Beaufort Marine Research Award in Fish Population Genetics funded
by the Irish Government under the Sea Change Programme). Barcoded 16S-V6-Next Generation Sequencing was
carried out by the Genomics and Transcriptomics Laboratory at Heinrich-Heine University, Düsseldorf, Germany.
Author Contributions:
F.W.M., O.E. and J.M. conceived and designed the experiments; F.W.M. performed the
experiments; O.P. performed the bioinformatics and statistical data analysis; A.S. developed the mathematical
model and performed simulations; all authors contributed to the interpretation of the results and wrote the paper.
Conflicts of Interest:
The authors declare no conflict of interest. The founding sponsors had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the
decision to publish the results.
Appendix A. Data Analysis
Randomizing ’replicates’ variable
R² random distribution
Frequency
0.04 0.06 0.08 0.10
0 200 400 600 800
true R² = 0.0601,
p = 0.4384
Randomizing ’time’ variable
Frequency
0.05 0.10 0.15 0.20 0.25 0.30 0.35
0 200 400 600 800 1000 1200
true R² = 0.2869,
p = 0.001
R² random distribution
Randomizing ’medium’ variable
R² random distribution
Frequency
0.00 0.05 0.10 0.15
0 500 1000 1500
true R2 = 0.1092,
p = 0.007
(a) (b)
(c)
Figure A1.
Beta diversity. A modified version of PermanovaG was used to carry out permutational
multivariate analysis of variance using multiple distance matrices. The distance matrices [24
×
24]
were previously calculated based on the generalized UniFrac distance [
53
], weighted UniFrac and
unweighted UniFrac [
107
] distance. The significance for the test was assessed by 5000 permutations.
(
a
) shows no significant effect between the replicates (
p
-value of 0.4384). (
b
) shows a significant effect
for the time variable (
p
-value of 0.001). (
c
) shows also shows a significant effect for the medium variable
(p-value of 0.007).

Processes 2017,5, 77 17 of 29
Appendix B. Mathematical Model Additional Material
The mathematical model is a system of 13 ODEs describing the variation in time of the populations
(cell counts) of
•P. tricornutum (D);
•Pseudoalteromonadaceae (PA);
•Flavobacteriaceae (F);
•Alteromonadaceae (A);
•Pseudomonadaceae (P);
and the production and consumption of the metabolites we consider as mainly contributing to drive
the community dynamics:
•the dissolved organic carbons of preference for PA and A(DOCPA and DOCA, respectively);
•the complex polymers (COP) consumed by F;
•generic vitamins (Vit) and iron (Fe) needed by Dand produced by A;
•bactericidial molecules (EPA and Bac, produced by Dand by PA respectively);
•the dissolved organic matter (DOM).
The model has 55 unknown free parameters:
•5 carrying capacities CC;
•34 maximal rates v;
•15 Monod-type coefficients K;
•the fraction of DOCA-dependent growth of A,eDOCA.
Appendix B.1. ODEs System
Five ODEs describe the variation in time of the populations of organism
O
, with
γO
and
δO
being
its growth and death rate:
dD
dt =γDD−δDD(A1)
dPA
dt =γPAPA −δPAPA (A2)
dF
dt =γFF−δFF(A3)
dA
dt =γAA−δAA(A4)
dP
dt =γPP−δPP(A5)
Eight ODEs describe the variation in time of the metabolites
J
, with
vprod/cons(O)
J
being the
maximal production/consumption rate of Jby organism O:
dVit
dt =vprod(A)
Vit γAA−vcons(D)
Vit γDD(A6)
dFe
dt =vprod(A)
Fe γAA−vcons(D)
Fe γDD(A7)
dDOCPA
dt =vprod(D)
DOCPA γDD−vcons(PA)
DOCPA γPAPA (A8)
dDOCA
dt = (vprod(D)
DOCA+φ)γDD−vcons(A)
DOCAγAA(A9)

Processes 2017,5, 77 18 of 29
dCOP
dt = (vprod(D)
COP +ψ)γDD−vcons(F)
COP γFF(A10)
dEPA
dt =vprod(D)
EPA γDD−vdeg
EPAEPA (A11)
dBac
dt =vprod(PA)
Bac γPAPA −vdeg
Bac Bac (A12)
dDOM
dt =vprod(D)
DOM δDD−vcons(A)
DOM γAA−vcons(P)
DOM γPP(A13)
φ
and
ψ
are additional terms for
DOCA
and
COP
production respectively (see Appendix B.1.1).
vdeg
J
is
the degradation rate of the bactericidal substances. Organism
O
growth and death rates depend
in general on carrying capacity
CCO
, maximal rates
vO
γ/δ
and on metabolites concentrations
J
with
Monod-type coefficient KO
Jand eventually maximal rates vO
J:
γD=vD
γ·Vit
Vit +KD
Vit
Fe
Fe +KD
Fe
(1−D
CCD)(A14)
δD=vD
δ
1
1+γD(A15)
γPA =vPA
γ
DOCPA
DOCPA +KDOCPA
(1−PA
CCPA )(A16)
δPA =vPA
δ(1+vPA
EPA ·EPA
EPA +KEPA )1
1+γPA (A17)
γF=vF
γ
COP
COP +KCOP (1−F
CCF)(A18)
δF=vF
δ(1+vF
Bac ·Bac
Bac +KF
Bac
)1
1+γF(A19)
γA=γA
DOCA+γA
DOM (A20)
γA
DOCA=vA
γ
eDOCA·DOCA
DOCA+KA
DOCA
(1−A
CCA)(A21)
γA
DOM =vA
γ
(1−eDOCA)·DOM
DOM +KA
DOM
(1−A
CCA)(A22)
δA=vA
δ(1+vA
Bac ·Bac
Bac +KA
Bac
)1
1+γA(A23)
γP=vP
γ
DOM
DOM +KP
DOM
(1−P
CCP)(A24)
δP=vP
δ(1+vP
Bac ·Bac
Bac +KP
Bac
)1
1+γP(A25)
For example in Equation (A14), describing the growth rate of the diatom, the Verhulst growth
equation
dD/dt =vD
γ(
1
−D/CCD)
describes a standard logistic growth, while adding the Monod-type
coefficients of the form
X/(X+KX)
introduce a dependency on the micronutrients Vit and Fe,
in practice scaling down the effective growth rate if micronutrients are scarce. In the case of
A
,
where growth is thought to be sustained by two different complementary nutrients, the final growth
γ
can be represented as the sum of two terms
γA
DOCA
and
γA
DOM
(Equations (A21) and (A22)), with the
parameter 0 <eDOCA<1.
Appendix B.1.1. DOCAand COP Production
When
D
is grown in minimal media conditions, the emergence of
A
is observed over
F
. From this
observation we hypothesise that
D
can produce extra organic carbons for either
A
or
F
depending

Processes 2017,5, 77 19 of 29
on the scarcicity of micronutrients to favor the growth of
A
if more
Vit
or
Fe
is needed. We model
the production of
DOCA
and
COP
(Equations (A9) and (A10)) introducing the functions
φ
and
ψ
defined as:
φ=vD
DOCACOP ·(1−ξ)(A26)
ψ=vD
DOCACOP ·ξ(A27)
ξ=Vit4
Vit4+K0D
Vit
Fe4
Fe4+K0D
Fe
(A28)
where
vD
DOCACOP
is the maximal additional production rate and 0
<ξ<
1 depends on
Vit
and
Fe
with
fourth order Hill equations terms parametrised with K0D
Vit and K0D
Fe (see Figure A2).
(a) (b)
Figure A2.
Example for
DOCA
((
a
), 1
−ξ
) and
COP
((
b
),
ξ
) additional production rates dependent on
Vit and Fe availability in the media. Here K0D
Vit =0.1, K0D
Fe =0.5.
Appendix B.2. Parameter Fitting
The model has 56 parameters, of which 55 are free parameters (see Table A1). Being a qualitative
model, we do not aim at interpreting the absolute parameter values in a biological sense.
Table A1.
Total number of parameters for each parameter set. The dependent parameter is
eDOM =1−eDOCAin the sub-set of Aparameters P(A).
Parameter Sub-Set P(D)P(PA)P(A)P(F)P(P)Degradation
Sub-set size 15 9 14 8 8 2
The available data that can be used to fit the model parameters are the diatom biomass growth
in two media conditions and four time points with bacteria relative abundances again in two
media conditions. We can therefore fit the diatom biomass
D
evolution and the four relative bacteria
i
abundances Bi/∑jBjtime-course.
We implement as general strategy a genetic algorithm, where an “individual”
i
is a full set of
56 parameters
Pi
, a “population” is an ensemble of parameter sets
{Pi}
, a population at a certain
evolution step is a “generation” and “evolution” goes as:
(1)
the first generation
{Pi}0
is populated by extracting the parameters from random uniform
distributions within user-chosen ranges;
(2)
for each Pithe ODE system is solved and a fitness score (see Appendix B.2.1) is computed;

Processes 2017,5, 77 20 of 29
(3)
the most fit 10% individuals are retained as parents for the next generation;
(4)
the remaining individuals have a probability p=0.05 to be also selected as parents;
(5)
parents are crossed to obtain enough children to reach the original population size;
(6)
crossing means randomly pick a parameter sub-set from one parent or the other;
(7)
each children has a probability p=0.3 to randomly mutate one parameter;
(8)
the process is repeated from step 2. until generation {Pi}Gm ax .
Appendix B.2.1. Fitness Score
Fitness scores are computed in a different way when fitting the diatom growth or the bacteria
relative abundances. When fitting to the diatom biomass data we compute the score as a simple
euclidean distance:
s=r∑
t
(xt−Xt)2(A29)
where the sum over time extends over 22 time points,
xt
is the
D
biomass at time
t
and
Xt
is the biomass
data at time
t
. The lower
s
, the better the fit. This score definition works well to fit the measurements of
diatom biomass, but presents a big problem when used with bacteria relative abundances. A relative
abundance is a number between 0 and 1, and we observe high variations including bacteria population
going from very close to 0 to high abundance. Having only three time points to fit (the first 16S
measurement is used as initial point), it can happen that constantly low abundant population are kept
by the algorithm. We therefore define for the fit of bacteria relative abundances the following score:
s=∑
ts∑
o1−e
rot−Rot
rot2(A30)
where the sum over time extends over 3 time points and the sum over organisms over the
4 bacterial species,
rot
is the relative abundance from the ODEs system solution for organism
o
at time
t
and
Rot
is it the corresponding experimental relative abundance. This score definition allows
to penalize the event of population extinction: when
r
is 0, the exponential term is 0 and the score is 1,
while when r=Rthe exponential term is 1 and the score is 0.
Table A2.
Datasets used to fit diatom growth in minimal and complete media (MM and CM respectively).
Time is scaled (1/3 of a day) to fit reasonably the growth phases (lag-log-exp-decay) using
parameters
O(
1
)
. For the same reason cell counts are scaled to bring the lower count close to 0,
but not feature-scaled to avoid loosing information on differences among MM and CM conditions.
Only average values, and not experimental errors, are taken into account.
T 8 16 40 48 64 72 104 112 120 128 152
MM 0.004 0.021 0.133 0.325 0.820 1.012 1.121 1.187 1.192 1.233 1.209
CM 0.050 0.044 0.162 0.605 0.733 0.919 1.037 1.099 1.134 1.108 0.859
T 168 176 184 208 216 232 240 248 264 272 288
MM 1.104 1.096 0.951 1.015 0.965 0.851 0.869 0.704 0.481 0.504 0.394
CM 0.821 0.844 0.624 0.682 0.624 0.556 0.535 0.478 0.199 0.282 0.303
Table A3.
Relative abundances of the four bacterial families at three intermediate time points (days 8, 15
and 22). The abundances were scaled from the experimental values (where more families were present)
to add to unity.
Complete Media Minimal Media
t PA F A P PA F A P
64 0.101 0.724 0.159 0.014 0.294 0.132 0.308 0.264
120 0.453 0.474 0.061 0.010 0.351 0.031 0.585 0.031
176 0.600 0.084 0.189 0.126 0.385 0.020 0.187 0.406

Processes 2017,5, 77 21 of 29
Appendix B.2.2. Results of the Genetic Algorithm
The chosen population size is 200 and the algorithm stops either after non significant increase in
fitness or at generation number 50. The algorithm can be run to fit six scenarios:
•D-MM: DBiomass in Minimal Media;
•D-CM: DBiomass in Complete Media;
•B-MM: Bacteria relative abundances in Minimal Media;
•B-CM: Bacteria relative abundances in Complete Media;
•D*B-MM: DBiomass and Bacteria relative abundances in Minimal Media;
•D*B-CM: DBiomass and Bacteria relative abundances in Complete Media;
For D-type fits, the fitness score of Equation (A29) is used. For B-type fits, the fitness score of
Equation (A30) is used. For D*B-type fits, the fitness score is the product of the two scores. We will
refer to D-fit, B-fit and D*B-fit in the following if media is not to be specified.
Considering the fact that a simple ODE model cannot capture metabolic readjustment, we do not
expect to obtain the same parameters for CM and MM conditions. The fitting is therefore performed
separately in the two conditions and in the following steps:
1. B-fit is run 20 times varying all 55 parameters in O(1)ranges
2. The parameters from the best B-fits are kept (PMM1and PC M1)
3.
After checking the effect of varying the different parameters sets, different variation ranges are
chosen to perform refits
4. D*B-CM is run 5 times varying P(D,deg)CM1±50%, P(A,F,P)CM1±20%, P(PA)C M1±10%
5. D*B-MM is run 5 times varying PMM1±50%, and the best parameters are kept (PMM2)
6. D*B-MM is run again 5 times varying P(D)MM2±5%, P(A,F,P,PA,deg)M M2±80%
The last rounds of fitting were run on different sets of parameters considered equally good.
The final parameter sets Pare presented in Table A4.
Table A4.
Final parameter sets used for simulation in CM (
PCM
) and MM (
PMM
). Also reported are
the overall average and standard deviation values from all the last rounds of fitting.
PCM µ(PC M )σ(PC M )PMM µ(PM M )σ(PM M )
KA
Bac 0.562780 0.476829 0.235751 0.23821 0.281329 0.257813
KA
DOCA0.463690 0.249183 0.152283 0.02253 0.332969 0.365116
KA
DOM 1.043490 0.526842 0.339699 0.82552 0.671145 0.475618
vA
Bac 1.884690 1.433304 0.495578 0.94702 1.085873 0.889537
CCA1.230920 1.112280 0.351905 2.74047 1.564099 0.668637
vA
δ0.036310 0.067159 0.085177 0.01697 0.058289 0.113161
vcons(A)
DOCA0.504220 0.504324 0.149340 1.30073 0.806015 0.315439
eDOCA0.204470 0.464587 0.226651 0.99257 0.670187 0.250496
vcons(A)
DOM 0.186340 0.493623 0.298676 0.74598 0.349684 0.246408
eDOM 0.795530 0.508826 0.229097 0.00743 0.323377 0.248408
vprod(A)
DOM 0.030470 0.052871 0.094576 0.06702 0.130514 0.176652
vprod(A)
Fe 0.134290 0.118939 0.028298 0.19948 0.206115 0.091565
vA
γ0.329520 0.434683 0.340005 0.34841 1.037434 0.510106
vprod(A)
Vit 0.954240 0.644251 0.317607 1.09226 1.241136 0.618984
vdeg
Bac 0.108110 0.409152 0.213390 0.07769 0.263422 0.330743
vdeg
EPA 0.350050 0.373353 0.117858 0.57995 0.784058 0.412260

Processes 2017,5, 77 22 of 29
Table A4. Cont.
PCM µ(PC M )σ(PC M )PMM µ(PM M )σ(PM M )
KD
Fe 0.488680 0.583597 0.358354 0.02979 0.124157 0.134217
K0D
Fe 1.199730 1.048777 0.343732 0.33321 0.486298 0.256807
KD
Vit 0.844900 0.645544 0.226955 0.46274 0.346839 0.088571
K0D
Vit 0.469600 0.903463 0.501364 0.09782 0.339058 0.356208
vD
DOCACOP 0.314330 0.853140 0.557108 0.36480 0.546552 0.330353
CCD1.875200 1.584920 0.515701 1.57897 1.427444 0.420945
vprod(D)
COP 1.005110 1.268507 0.463020 0.70666 0.736754 0.370734
vD
δ0.007180 0.016960 0.051891 0.00681 0.013765 0.049375
vprod(D)
DOCA1.770740 0.987437 0.490546 1.65657 1.350378 0.421454
vprod(D)
DOCPA 1.055270 0.990547 0.415673 0.83897 1.081959 0.591094
vprod(D)
DOM 0.135980 0.653181 0.351953 0.54133 0.565364 0.231813
vprod(D)
EPA 1.214350 0.899207 0.360058 1.28659 1.070999 0.478554
vcons(D)
Fe 0.665740 0.755699 0.241111 0.31684 0.363974 0.099207
vD
γ0.194310 0.200395 0.069030 0.52737 0.562459 0.149546
vcons(D)
Vit 0.367880 0.566566 0.416514 1.78450 0.909564 0.404000
KF
Bac 0.936420 0.583317 0.263447 0.16731 0.299761 0.198921
KCOP 0.477700 0.588674 0.234155 0.74525 0.451922 0.315203
vF
Bac 0.184360 0.311845 0.105780 0.23234 1.237169 1.012458
CCF1.351050 1.206888 0.384417 0.54187 1.074951 0.758107
vcons(F)
COP 0.139320 0.175972 0.045416 0.57005 0.330531 0.127086
vF
δ0.382820 0.318181 0.144775 0.18005 0.200895 0.150824
vprod(F)
DOM 0.092860 0.080066 0.074871 0.00984 0.135875 0.283818
vF
γ0.765450 0.726578 0.223690 1.50156 0.888556 0.545041
KP
Bac 0.020100 0.148399 0.132143 0.16823 0.326145 0.294570
KP
DOM 0.609800 0.560853 0.171693 1.12080 0.688621 0.413129
vP
Bac 1.009740 1.238831 0.430709 2.11081 1.419892 0.958683
CCP1.301320 1.277678 0.407513 1.17750 2.585117 0.869802
vP
δ0.020440 0.069033 0.167148 0.01591 0.036821 0.150349
vcons(P)
DOM 0.698330 0.523151 0.203572 0.17625 0.124345 0.136063
vprod(P)
DOM 0.195450 0.189091 0.103414 0.03107 0.116528 0.203261
vP
γ0.820720 0.440066 0.249502 0.57938 0.527859 0.284980
KDOCPA 0.245720 0.351941 0.221873 0.42128 0.564689 0.489764
KEPA 0.755570 0.541606 0.297743 0.05329 0.404319 0.414359
vPA
EPA 1.577050 1.484135 0.474372 2.65508 1.368551 0.892632
vprod(PA)
Bac 0.819580 0.848959 0.264185 0.28618 0.568550 0.438164
CCPA 0.995130 1.029216 0.323852 1.28138 1.477045 0.533872
vPA
δ0.221040 0.284309 0.181709 0.01861 0.052638 0.102986
vcons(PA)
DOCPA 0.236820 0.254966 0.144860 0.41130 0.249994 0.182493
vprod(PA)
DOM 0.130620 0.110548 0.125387 0.01816 0.108930 0.154334
vPA
γ0.327430 0.468045 0.287832 0.12769 0.350329 0.210662

Processes 2017,5, 77 23 of 29
Appendix B.2.3. Sanity Checks of the Parameter Fits
The parameters of the algorithm were chosen to obtain a satisfactory convergence of the fit
(Figure A3).
(a) (b)
Figure A3.
Distribution of fitness scores in populations over generations for the genetic algorithm runs
chosen to perform the last fitting iteration in minimal (a) and complete (b) media conditions.
We checked the effect of varying the parameters
δA
,
vcons(A)
DOCA
and
vprod(A)
Fe
(the only bacterial
parameters observed to influence the biomass growth curve in CM) by
±
10% and
±
50%. The diatom
growth is almost insensitive to these variations in CM (Figure A4), while it shows greater effects in
MM (Figure A5).
(a) (b) (c)
(d) (e) (f)
Figure A4.
Diatom growth in CM simulation results. The parameters
δA
,
vcons(A)
DOCA
and
vprod(A)
Fe
,
are varied by ±10% ((a–c) respectively) and by ±50% ((d–f) respectively).

Processes 2017,5, 77 24 of 29
(a) (b) (c)
(d) (e) (f)
Figure A5.
Diatom growth in MM simulation results. The parameters
δA
,
vcons(A)
DOCA
and
vprod(A)
Fe
,
are varied by ±10% ((a–c) respectively) and by ±50% ((d–f) respectively).
Parameter profiling shows that the algorithm correctly converges towards local minima and
that in general those minima are rather stable to perturbation
p±
50%. Figure A6 shows examples
of the most unstable profiles from this first set of fits. Additional information is provided in the
Supplementary Material 2.
(a) (b)
Figure A6. Cont.

Processes 2017,5, 77 25 of 29
(c) (d)
Figure A6.
Profiling of the parameters
vcons(D)
Fe
in CM (
a
) and MM (
c
) and
γD
in CM (
b
) and MM (
d
).
The red line shows the value chosen by the fit.
References
1. Chisti, Y. Biodiesel from microalgae. Biotechnol. Adv. 2007,25, 294–306.
2.
Moejes, F.W.; Moejes, K.B. Algae for Africa: Microalgae as a source of food, feed and fuel in Kenya.
Afr. J.Biotechnol. 2017,16, 288–301.
3. Ryther, J.; Goldman, J. Microbes as food in mariculture. Annu. Rev. Microbiol. 1975,29, 429–443.
4.
Tredici, M.R.; Biondi, N.; Ponis, E.; Rodolfi, L.; Zittelli, G.C.; Burnell, G.; Allan, G. Advances in microalgal
culture for aquaculture feed and other uses. In New Technologies in Aquaculture: Improving Production
Efficiency, Quality and Environmental Management; Burnell, G., Allan, G., Eds.; Woodhead Publishing Ltd.:
Cambridge, UK, 2009; pp. 610–676.
5.
Rebolloso-Fuentes, M.; Navarro-Pérez, A.; Ramos-Miras, J.; Guil-Guerrero, J. Biomass nutrient profiles of the
microalga Phaeodactylum tricornutum. J. Food Biochem. 2001,25, 57–76.
6. Kates, M.; Volcani, B.E. Lipid components of diatoms. Biochim. Biophys. Acta 1966,116, 264–278.
7.
Fajardo, A.R.; Cerdán, L.E.; Medina, A.R.; Fernández, F.G.A.; Moreno, P.A.G.; Grima, E.M. Lipid extraction
from the microalga Phaeodactylum tricornutum. Eur. J. Lipid Sci. Technol. 2007,109, 120–126.
8.
Siron, R.; Giusti, G.; Berland, B. Changes in the fatty acid composition of Phaeodactylum tricornutum and
Dunaliella tertiolecta during growth and under phosphorus deficiency. Mar. Ecol. Prog. Ser.
1989
,55, 95–100.
9.
Yashodhara, B.M.; Umakanth, S.; Pappachan, J.M.; Bhat, S.K.; Kamath, R.; Choo, B.H. Omega-3 fatty acids:
A comprehensive review of their role in health and disease. Postgrad. Med. J. 2009,85, 84–90.
10.
Francius, G.; Tesson, B.; Dague, E.; Martin-jézéquel, V.; Dufrêne, Y.F. Nanostructure and nanomechanics of
live Phaeodactylum tricornutum morphotypes. Environ. Microbiol. 2008,10, 1344–1356.
11.
Santaeufemia, S.; Torres, E.; Mera, R.; Abalde, J. Bioremediation of oxytetracycline in seawater by living and
dead biomass of the microalga Phaeodactylum tricornutum. J. Hazard. Mater. 2016,320, 315–325.
12.
Mata, T.; Martins, A.; Caetano, N. Microalgae for biodiesel production and other applications: A review.
Renew. Sustain. Energy Rev. 2010,14, 217–232.
13.
Grima, E.; Fernández, F. Photobioreactors: Light regime, mass transfer, and scaleup. J. Biotechnol.
1999
,
70, 231–247.
14.
Parmar, A.; Singh, N.; Pandey, A. Cyanobacteria and microalgae: A positive prospect for biofuels.
Bioresour. Technol. 2011,102, 10163–10172.
15.
Day, J.G.; Thomas, N.J.; Achilles-Day, U.E.M.; Leakey, R.J.G. Early detection of protozoan grazers in algal
biofuel cultures. Bioresour. Technol. 2012,114, 715–719.
16.
Wang, H.; Zhang, W.; Chen, L.; Wang, J.; Liu, T. The contamination and control of biological pollutants in
mass cultivation of microalgae. Bioresour. Technol. 2013,128, 745–750.

Processes 2017,5, 77 26 of 29
17.
Kazamia, E.; Aldridge, D.C.; Smith, A.G. Synthetic ecology—A way forward for sustainable algal biofuel
production? J. Biotechnol. 2012,162, 163–169.
18. Gause, G. The Struggle for Existence; The Williams & Wilkins company: Baltimore, MD, USA, 1934; p. 163.
19. Hardin, G. The competitive exclusion principle. Science 1960,131, 1292–1297.
20.
Dykhuizen, D. Santa Rosalia revisited: Why are there so many species of bacteria? Antonie Van Leeuwenhoek
1998,73, 25–33.
21.
Hagstrom, G.I.; Levin, S.A. Marine Ecosystems as Complex Adaptive Systems: Emergent Patterns,
Critical Transitions, and Public Goods. Ecosystems 2017,20, 458–476.
22.
Widder, S.; Allen, R.J.; Pfeiffer, T.; Curtis, T.P.; Wiuf, C.; Sloan, W.T.; Cordero, O.X.; Brown, S.P.; Momeni, B.;
Shou, W.; et al. Challenges in microbial ecology: Building predictive understanding of community function
and dynamics. ISME J. 2016,10, 2557–2568.
23.
Song, H.S.; Cannon, W.; Beliaev, A.; Konopka, A. Mathematical Modeling of Microbial Community Dynamics:
A Methodological Review. Processes 2014,2, 711–752.
24.
Zomorrodi, A.R.; Segrè, D. Synthetic Ecology of Microbes: Mathematical Models and Applications.
J. Mol. Biol.
2015,428, 837–861.
25.
Succurro, A.; Moejes, F.W.; Ebenhöh, O. A Diverse Community To Study Communities: Integration
of Experiments and Mathematical Models To Study Microbial Consortia. J. Bacteriol.
2017
,199,
doi:10.1128/JB.00865-16.
26. Lotka, A. Elements of Physical Biology; Williams & Wilkins Company: Philadelphia, PA, USA, 1925.
27.
Volterra, V. Fluctuations in the abundance of a species considered mathematically. Nature
1926
,118, 558–560.
28.
Hofbauer, J.; Hutson, V.; Jansen, W. Coexistence for systems governed by difference equations of
Lotka-Volterra type. J. Math. Biol. 1987,25, 553–570.
29.
Berry, D.; Widder, S. Deciphering microbial interactions and detecting keystone species with co-occurrence
networks. Front. Microbiol. 2014,5, 219.
30.
Amin, S.A.; Parker, M.S.; Armbrust, E.V. Interactions between diatoms and bacteria.
Microbiol. Mol. Biol. Rev.
2012,76, 667–684.
31. Provasoli, L. Nutrition and ecology of Protozoa and Algae. Annu. Rev. Microbiol. 1958,12, 279–308.
32.
Delucca, R.; McCracken, M.D. Observations on interactions between naturally-collected bacteria and several
species of algae. Hydrobiologia 1977,55, 71–75.
33.
Suminto; Hirayama, K. Application of a growth-promoting bacteria for stable mass culture of three marine
microalgae. Hydrobiologia 1997,358, 223–230.
34.
Bruckner, C.G.; Rehm, C.; Grossart, H.P.; Kroth, P.G. Growth and release of extracellular organic compounds
by benthic diatoms depend on interactions with bacteria. Environ. Microbiol. 2011,13, 1052–1063.
35.
Amin, S.A.; Hmelo, L.R.; van Tol, H.M.; Durham, B.P.; Carlson, L.T.; Heal, K.R.; Morales, R.L.;
Berthiaume, C.T.; Parker, M.S.; Djunaedi, B.; et al. Interaction and signalling between a cosmopolitan
phytoplankton and associated bacteria. Nature 2015,522, 98–101.
36.
Verhulst, P. Notice sur la loi que la population suit dans son accroissement. Corresp. Math. Phys. l’Obs. Brux.
1838,10, 113–121.
37.
Gachon, C.M.; Day, J.G.; Campbell, C.N.; Pröschold, T.; Saxon, R.J.; Küpper, F.C. The Culture Collection of
Algae and Protozoa (CCAP): A biological resource for protistan genomics. Gene 2007,406, 51–57.
38.
Moejes, F.W. Dynamics of the bacterial community associated with Phaeodactylum tricornutum cultures:
A novel approach to scaling up microalgal cultures. Ph.D. Thesis, Heinrich Heine University Dusseldorf,
Düsseldorf, Germany, 2016.
39.
Guillard, R.; Ryther, J. Studies of marine planktonic diatoms: I. Cyclotella nana Hutedt, and Detonula
confervacea (Cleve) Gran. Can. J. Microbiol. 1962,8, 229–239.
40.
Guillard, R. Culture of phytoplankton for feeding marine invertebrates. In Culture of Marine Invertebrate
Animals; Smith, W.L., Chanley, M.H., Eds.; Plenum Press: New York, NY, USA, 1975; pp. 29–60.
41.
de Gouvion Saint Cyr, D.; Wisniewski, C.; Schrive, L.; Farhi, E.; Rivasseau, C. Feasibility study
of microfiltration for algae separation in an innovative nuclear effluents decontamination process.
Sep. Purif. Technol. 2014,125, 126–135.
42.
Quail, M.; Smith, M.E.; Coupland, P.; Otto, T.D.; Harris, S.R.; Connor, T.R.; Bertoni, A.; Swerdlow, H.P.; Gu, Y.
A tale of three next generation sequencing platforms: Comparison of Ion torrent, pacific biosciences and
illumina MiSeq sequencers. BMC Genom. 2012,13, 341.

Processes 2017,5, 77 27 of 29
43.
Grada, A.; Weinbrecht, K. Next-Generation Sequencing: Methodology and Application. J. Investig. Dermatol.
2013,133, e11.
44.
Pylro, V.S.; Roesch, L.F.W.; Morais, D.K.; Clark, I.M.; Hirsch, P.R.; Tótola, M.R. Data analysis for 16S microbial
profiling from different benchtop sequencing platforms. J. Microbiol. Methods 2014,107, 30–37.
45.
Aronesty, E. ea-utils: Command-Line Tools for Processing Biological Sequencing Data, Durham, NC, USA,
2011. Available online: https://github.com/ExpressionAnalysis/ea-utils (accessed on 30 November 2017).
46.
Edgar, R.C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics
2010
,26, 2460–2461.
47.
Edgar, R.C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods
2013,10, 996–998.
48.
Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.;
Peña, A.G.
;
Goodrich, J.K.; Gordon, J.I.; et al. QIIME allows analysis of high-throughput community sequencing data.
Nat. Methods 2010,7, 335–336.
49.
Quast, C.; Pruesse, E.; Yilmaz, P.; Gerken, J.; Schweer, T.; Yarza, P.; Peplies, J.; Glockner, F.O. The SILVA
ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res.
2013,41, D590–D596.
50.
R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for
Statistical Computing, Vienna, Austria. 2015. Available online: http://www.R-project.org/ (accessed on
30 November 2017).
51. Monod, J. The Growth of Bacterial Cultures. Annu. Rev. Microbiol. 1949,3, 371–394.
52. Mitchell, M. An Introduction to Genetic Algorithms; MIT Press: Cambridge, MA, USA, 1996.
53.
Chen, J.; Bittinger, K.; Charlson, E.S.; Hoffmann, C.; Lewis, J.; Wu, G.D.; Collman, R.G.; Bushman, F.D.; Li, H.
Associating microbiome composition with environmental covariates using generalized UniFrac distances.
Bioinformatics 2012,28, 2106–2113.
54.
Bowman, J. Bioactive compound synthetic capacity and ecological significance of marine bacterial genus
Pseudoalteromonas. Mar. Drugs 2007,5, 220–241.
55.
Desbois, A.P.; Mearns-Spragg, A.; Smith, V.J. A Fatty Acid from the Diatom Phaeodactylum tricornutum
is Antibacterial Against Diverse Bacteria Including Multi-resistant Staphylococcus aureus (MRSA).
Mar. Biotechnol. 2009,11, 45–52.
56.
Soria-Dengg, S.; Reissbrodt, R.; Horstmann, U. Siderophores in marine coastal waters and their relevance
for iron uptake by phytoplankton: Experiments with the diatom Phaeodactylum tricornutum. Mar. Ecol.
Prog. Ser. 2001,220, 73–82.
57.
Amin, S.A.; Green, D.H.; Hart, M.C.; Küpper, F.C.; Sunda, W.G.; Carrano, C.J. Photolysis of iron‚ siderophore
chelates promotes bacterial‚ algal mutualism. Proc. Natl. Acad. Sci. USA 2009,106, 17071–17076.
58.
Soria-Dengg, S.; Horstmann, U. Ferrioxamines B and E as iron sources for the marine diatom Phaeodactylum
tricornutum. Mar. Ecol. Prog. Ser. 1995,127, 269–277.
59.
Provasoli, L. Organic regulation of phytoplankton fertility. In The Sea: Ideas and Observations on Progress in
the Study of the Seas; Hill, M., Ed.; Wiley-Interscience: New York, NY, USA, 1963; pp. 165–219.
60.
Provasoli, L.; Carlucci, A. Vitamins and growth regulators. In Algal Physiology and Biochemistry, Botanical
Monographs, 10; Stewart, W., Ed.; Blackwell Scientific Publications: San Diego, CA, USA, 1974; pp. 741–787.
61. Croft, M.T.; Warren, M.J.; Smith, A.G. Algae need their vitamins. Eukaryot. Cell 2006,5, 1175–1183.
62.
Yongmanitchai, W.; Ward, O.P. Growth of and omega-3 fatty acid production by Phaeodactylum tricornutum
under different culture conditions. Appl. Environ. Microbiol. 1991,57, 419–425.
63. Sañudo-Wilhelmy, S.A.; Gómez-Consarnau, L.; Suffridge, C.; Webb, E.A. The Role of B Vitamins in Marine
Biogeochemistry. Annu. Rev. Mar. Sci. 2014,6, 339–367.
64.
Azam, F.; Fenchel, T.; Field, J.; Gray, J.; Meyer-Reil, L.; Thingstad, F. The Ecological Role of Water-Column
Microbes in the Sea. Mar. Ecol. Prog. Ser. 1983,10, 257–263.
65. Stocker, R. Marine Microbes See a Sea of Gradients. Science 2012,338, 628–633.
66.
Persson, G.; Jansson, M.; Kluwer, C. Phosphate uptake and utilizaton by bacteria and algae. Hydrobiologia
1988,170, 177–189.
67.
Bell, W.; Mitchell, R. Chemotactic and growth responses of marine bacteria to algal extracellular products.
Biol. Bull. 1972,143, 265–277.
68.
MacArthur, R. Fluctuations of animal populations and a measure of community stability. Ecology
1955
,
36, 533–536.

Processes 2017,5, 77 28 of 29
69.
Gardner, M.; Ashby, W. Connectance of large dynamic (cybernetic) systems: Critical values for stability.
Nature 1970,228, 784.
70. Pimm, S. The complexity and stability of ecosystems. Nature 1984,307, 321–326.
71. Elton, C. The Ecology of Invasions by Animals and Plants; Springer: Dordrecht, The Netherlands, 1958.
72. McCann, K.S. The diversity-stability debate. Nature 2000,405, 228–233.
73.
Ivanova, E.P.; Flavier, S.; Christen, R. Phylogenetic relationships among marine Alteromonas-like proteobacteria:
Emended description of the family Alteromonadaceae and proposal of Pseudoalteromonadaceae fam. nov.,
Colwelliaceae fam. nov., Shewanellaceae fam. nov., Moritellaceae fam. nov., Ferri. Int. J. Syst. Evolut. Microbiol.
2004,54, 1773–1788.
74.
Rosenberg, E.; DeLong, E.F.; Lory, S.; Stackebrandt, E.; Thompson, F. The Prokaryotes; Springer:
Berlin/Heidelberg, Germany, 2014.
75.
Lee, S.O.; Kato, J.; Takiguchi, N.; Kuroda, A.; Ikeda, T. Involvement of an Extracellular Protease in
Algicidal Activity of the Marine Bacterium Pseudoalteromonas sp. Strain A28. Appl. Environ. Microbiol.
2000,66, 4334–4339.
76.
Venkateswaran, K.; Dohmoto, N. Pseudoalteromonas peptidolytica sp. nov., a novel marine
mussel-thread-degrading bacterium isolated from the Sea of Japan. Int. J. Syst. Evolut. Microbiol.
2000
,50,
565–574.
77.
Chen, X.; Xie, B.; Lu, J.; He, H.; Zhang, Y. A novel type of subtilase from the psychrotolerant bacterium
Pseudoalteromonas sp. SM9913: Catalytic and structural properties of deseasin MCP-01. Microbiology
2007
,
153, 2116–2125.
78.
Khudary, R.A.; Venkatachalam, R.; Katzer, M. A cold-adapted esterase of a novel marine isolate,
Pseudoalteromonas arctica: Gene cloning, enzyme purification and characterization. Extremophiles
2010
,
14, 273–285.
79.
Lu, M.; Wang, S.; Fang, Y.; Li, H.; Liu, S.; Liu, H. Cloning, expression, purification, and characterization of
cold-adapted α-amylase from Pseudoalteromonas arctica GS230. Protein J. 2010,29, 591–597.
80.
Albino, A.; Marco, S.; Maro, A.D. Characterization of a cold-adapted glutathione synthetase from the
psychrophile Pseudoalteromonas haloplanktis. Mol. Biosyst. 2012,8, 2405–2414.
81.
He, H.; Guo, J.; Chen, X.; Xie, B.; Zhang, X. Structural and functional characterization of mature forms of
metalloprotease E495 from Arctic sea-ice bacterium Pseudoalteromonas sp. SM495. PLoS ONE
2012
,7, e35442.
82.
Holmström, C.; Kjelleberg, S. Marine Pseudoalteromonas species are associated with higher organisms and
produce biologically active extracellular agents. FEMS Microbiol. Ecol. 1999,30, 285–293.
83.
Ivanova, E.P.; Mikha
˘
ılov, V.V. A new family of Alteromonadaceae fam. nov., including the marine
proteobacteria species Alteromonas, Pseudoalteromonas, Idiomarina i Colwellia. Mikrobiologiia
2001
,
70, 15–23.
84.
Reid, R.; Butler, A. Investigation of the mechanism of iron acquisition by the marine bacterium Alteromonas
luteoviolaceus: Characterization of siderophore production. Limnol. Oceanogr. 1991,36, 1783–1792.
85.
Holt, P.D.; Reid, R.R.; Lewis, B.L.; Luther, G.W.; Butler, A. Iron(III) coordination chemistry of alterobactin A:
A siderophore from the marine bacterium Alteromonas luteoviolacea. Inorg. Chem. 2005,44, 7671–7677.
86. Vraspir, J.M.; Butler, A. Chemistry of marine ligands and siderophores. Annu. Rev. Mar. Sci. 2009,1, 43–63.
87.
Bruland, K.W.; Donat, J.R.; Hutchins, D.a. Interactive influences of bioactive trace metals on biological
production in oceanic waters. Limnol. Oceanogr. 1991,36, 1555–1577.
88.
Morel, F.M.M.; Price, N.M. The biogeochemical cycles of trace metals in the oceans. Science
2003
,300, 944–947.
89.
Yoon, J.; Jo, Y.; Kim, G.J.; Choi, H. Gramella lutea sp. nov., a Novel Species of the Family Flavobacteriaceae
Isolated from Marine Sediment. Curr. Microbiol. 2015,71, 252–258.
90.
Glöckner, F.O.; Fuchs, B.M.; Amann, R. Bacterioplankton compositions of lakes and oceans: A first
comparison based on fluorescence in situ hybridization. Appl. Environ. Microbiol. 1999,65, 3721–3726.
91.
Abell, G.; Bowman, J. Ecological and biogeographic relationships of class Flavobacteria in the Southern
Ocean. FEMS Microbiol. Ecol. 2005,51, 265–277.
92.
DeLong, E.F.; Preston, C.M.; Mincer, T.; Rich, V.; Hallam, S.J.; Frigaard, N.U.; Martinez, A.; Sullivan, M.B.;
Edwards, R.; Brito, B.R.; et al. Community genomics among stratified microbial assemblages in the ocean’s
interior. Science 2006,311, 496–503.

Processes 2017,5, 77 29 of 29
93.
Manz, W.; Amann, R.; Ludwig, W.; Vancanneyt, M.; Schleifer, K.H. Application of a suite of 16S rRNA-specific
oligonucleotide probes designed to investigate bacteria of the phylum cytophaga-flavobacter-bacteroides in
the natural environment. Microbiology 1996,142, 1097–1106.
94.
Kirchman, D.L. The ecology of Cytophaga-Flavobacteria in aquatic environments. FEMS Microbiol. Ecol.
2002,39, 91–100.
95.
Cottrell, M.T.; Kirchman, D.L. Natural assemblages of marine proteobacteria and members of the
Cytophaga-Flavobacter cluster consuming low- and high-molecular-weight dissolved organic matter.
Appl. Environ. Microbiol. 2000,66, 1692–1697.
96.
Pinhassi, J.; Sala, M.M.; Havskum, H.; Peters, F.; Guadayol, Ò.; Malits, A.; Marrasé, C. Changes
in bacterioplankton composition under different phytoplankton regimens. Appl. Environ. Microbiol.
2004,70, 6753–6766.
97.
Smayda, T. Harmful algal blooms: Their ecophysiology and general relevance to phytoplankton blooms in
the sea. Limnol. Oceanogr. 1997,42, 1137–1153.
98.
Starr, M.P.; Stolp, H.; Trüper, H.G.; Balows, A.; Schlegel, H.G. The Prokaryotes; Springer: Berlin/Heidelberg,
Germany, 1981.
99.
Anzai, Y.; Kim, H.; Park, J.Y.; Wakabayashi, H.; Oyaizu, H. Phylogenetic affiliation of the pseudomonads
based on 16S rRNA sequence. Int. J. Syst. Evolut. Microbiol. 2000,50, 1563–1589.
100.
Isnansetyo, A.; Kamei, Y. Bioactive substances produced by marine isolates of Pseudomonas.J. Ind.
Microbiol. Biotechnol. 2009,36, 1239–1248.
101.
Zelezniak, A.; Andrejev, S.; Ponomarova, O.; Mende, D.R.; Bork, P.; Patil, K.R. Metabolic dependencies drive
species co-occurrence in diverse microbial communities. Proc. Natl. Acad. Sci. USA 2015,112, 6449–6454.
102.
Ponomarova, O.; Patil, K.R. Metabolic interactions in microbial communities: Untangling the Gordian knot.
Curr. Opin. Microbiol. 2015,27, 37–44.
103.
Tao, H.; Bausch, C.; Richmond, C.; Blattner, F.R.; Conway, T. Functional genomics: Expression analysis of
Escherichia coli growing on minimal and rich media. J. Bacteriol. 1999,181, 6425–6440.
104.
Beg, Q.K.; Zampieri, M.; Klitgord, N.; Collins, S.B.; Altafini, C.; Serres, M.H.; Segrè, D. Detection of
transcriptional triggers in the dynamics of microbial growth: Application to the respiratorily versatile
bacterium Shewanella oneidensis. Nucleic Acids Res. 2012,40, 7132–7149.
105. Bertrand, E.M.; Allen, A.E.; Dupont, C.L.; Norden-Krichmar, T.M.; Bai, J.; Valas, R.E.; Saito, M.A. Influence
of cobalamin scarcity on diatom molecular physiology and identification of a cobalamin acquisition protein.
Proc. Natl. Acad. Sci. USA 2012,109, E1762–E1771.
106.
Horgan, R.P.; Kenny, L.C. ‘Omic’ technologies: Genomics, transcriptomics, proteomics and metabolomics.
Obstet. Gynaecol. 2011,13, 189–195.
107.
Lozupone, C.; Knight, R. UniFrac: A New Phylogenetic Method for Comparing Microbial Communities.
Appl. Environ. Microbiol. 2005,71, 8228–8235.
c
2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).