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O R I G I N A L P A P E R
Metabolic modelling of syntrophic-like growthof a 1,3-propanediol producer, Clostridium butyricum,
and a methanogenic archeon, Methanosarcina mazei,under anaerobic conditions
Marcin Bizukojc
David Dietz
Jibin Sun
An-Ping Zeng
Received: 8 April 2009 / Accepted: 21 July 2009/ Published online: 13 August 2009
Springer-Verlag 2009
Abstract Clostridium butyricum can convert glycerol
into 1,3-propanediol, thereby generating unfortunately ahigh amount of acetate, formate and butyrate as inhibiting
by-products. We have proposed a novel mixed culture
comprising C. butyricum and a methane bacterium, Met-
hanosarcina mazei, to relieve the inhibition and to utilise
the by-products for energy production. In order to examine
the efficiency of such a mixed culture, metabolic modelling
of the culture system was performed in this work. The
metabolic networks for the organisms were reconstructed
from genomic and physiological data. Several scenarios
were analysed to examine the preference of M. mazei
in scavenging acetate and formate under conditions of
different substrate availability, including methanol as a
co-substrate, since it may exist in glycerol solution from
biodiesel production. The calculations revealed that if
methanol is present, the methane production can increase
by 130%. M. mazei can scavenge over 70% of the acetate
secreted by C. butyricum.
Keywords Metabolic network 1,3-Propanediol
Clostridium butyricum Methanosarcina mazei
Mixed culture
Introduction
In nature, hardly any microorganisms grow separately as
a monoculture. They almost always develop in consortia,
in which many different microbial species live together.
Amongst the individual species, various ecological and
metabolic interactions exist, which may be either positive
or negative. Organisms may cooperate together and utilise
the common food (commensalism), e.g. one species is
provided food by another. These species may be able to
survive separately and the interaction between them is not
obligatory [1, 2]. The co-existence of two species some-
times profits both sides, resulting in symbiosis. Symbiosis
can be facultative and then the organisms can also sur-
vive separately. If two species can live only together,
such an interaction is called mutualism. A syntrophy is a
specific form of microbial mutualism occurring amongst
microorganisms, which utilise organic or inorganic
substances. In syntrophy the metabolites, which are
essential for their growth, are transferred between the
species [3, 4].
In the world of anaerobic bacteria, such a syntrophic
relationship is observed between acetogenic or sulphate-
reducing bacteria and methanogenic bacteria [3]. Aceto-
gens or many heterotrophic sulphate reducers excrete
hydrogen and carbon dioxide or acetic and formic acid,
which are immediately utilised by methanogens [58].
Both species profit, as, for example, hydrogen is an
inhibitor for acetogenic and sulphate-reducing bacteria.
At the same time, methanogens cannot assimilate carbon
Electronic supplementary material The online version of thisarticle (doi:10.1007/s00449-009-0359-0 ) contains supplementary
material, which is available to authorized users.
M. Bizukojc D. Dietz J. Sun A.-P. Zeng (&)
Institute of Bioprocess and Biosystems Engineering,
Hamburg University of Technology, Denickestr. 15,
21071 Hamburg, Germany
e-mail: [email protected]
M. Bizukojc (&)
Department of Bioprocess Engineering,
Technical University of Lodz, ul. Wolczanska 213,
90-924 Lodz, Poland
e-mail: [email protected]
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dioxide without molecular hydrogen, which is the irre-
placeable carrier of the reductive potential for them [3,5].
There is also a group of anaerobic bacteria, which are
capable of utilising glycerol as the sole carbon source.
They transform glycerol via 3-hydroxypropionaldehyde
into 1,3-propanediol (PDO). The product of this biotrans-
formation is an attractive monomer for the manufacturing
of new polymers. Amongst these bacteria, Clostridiumbutyricum is one of the most efficient glycerol degraders.
Apart from PDO, it also forms by-products, mainly organic
acids like formic, acetic, butyric and lactic acids [9]. These
organic acids, especially acetic acid, inhibit C. butyricum
growth and deteriorate its ability to biotransform glycerol
into PDO. Due to the fact that the dissociated forms of
these acids are less toxic, the pH of the culture needs to be
kept close to the neutral level within fermentation [10].
Therefore, whilst using C. butyricum as a PDO pro-
ducer, the removal of organic acids from the system,
especially the most inhibitive acetic acid, would benefit
this biotransformation process. It is well known that Met-hanosarcinasp. is very efficient in the utilisation of acetic
and formic acids. Thus, the production of PDO in a two-
species syntrophic-like system composed ofC. butyricum
and a Methanosarcina sp. appears to be attractive.
Defined mixed cultures comprisingClostridium sp. and
Methanosarcinasp., mostly under thermophilic conditions,
have been described previously [1113]. They mainly
aimed at methane production from such substrates as glu-
cose, cellulose or lactate, which are unavailable for indi-
vidual methanogens. An effect of the syntrophic growth
was assumed in these works, but not investigated in detail.
Furthermore,Clostridiumsp. andMethanosarcinasp. have
been proved to be involved in the degradation of glycerol
containing synthetic wastes by 16S rRNA analysis [14].
Regarding the biodiesel process, the aimed co-culture
provides another advantage. When raw glycerol from
biodiesel plants is to be utilised for PDO production,
methanol usually present in it at the level of about 1% can
be utilised by Methanosarcina sp. too. Then, any potential
inhibition that methanol could exert onC. butyricumcan be
avoided. Last, but not least, methane, which is a desired
and valuable energy carrier, can be obtained from such a
two-species system.
Mathematical description, especially metabolic model-
ling at a network level, for a simultaneous growth of two
microbial species is hardly found in literature. Only Stolyar
et al. [7] have recently analysed the natural syntrophic
association between Desulphovibrio vulgaris and Methan-
ococcus maripuladis with the use of metabolic modelling.
They reconstructed the metabolic networks for both species
upon genome data and tested whether in the absence of
sulphate, which is a typical electron acceptor for D. vul-
garis, methanogenic bacteria can play the role of a
scavenger of inhibitive hydrogen, enabling in this way an
undisturbed growth of the sulphate reducer by fermenting
an organic carbon source such as lactate. They also tested a
genetically perturbed system to gain insight into the prev-
alence of formate, another metabolite excreted by D. vul-
garis, in the system.
In this study, a metabolic model for the syntrophic-like
growth of C. butyricum and Methanosarcina mazei ispresented. The two-species system studied by Stolyar et al.
[7] and ours differ in many aspects. The metabolic path-
ways of C. butyricum are very different from those of
D. vulgaris, although both are anaerobic organisms. Firstly,
a different carbon source, i.e. glycerol, is utilised by
C. butyricum in our study. Secondly, only a few excreted
metabolite products, i.e. formate and hydrogen, are com-
mon for D. vulgaris and C. butyricum. Furthermore, the
metabolism ofM. mazei is more complicated than that of
Methanococcus sp. Methanosarcina sp. is capable of uti-
lising at least four carbon sources: carbon dioxide, formate,
methanol and acetate. Therefore, the presented model goesbeyond the one presented by Stolyar et al. [7], as more
reactions have to be included in it, and the balance of
reduced hydrogen carriers as well as the form of catabolic
pathways have to describe the situation, in which both the
utilisation of a single and a mixture of the aforementioned
carbon substrates takes place. Thus, the model should be
able to predict shifts in Methanosarcina sp. metabolism
caused by competing substrates: hydrogen and carbon
dioxide, formate, methanol and acetate.
The aim of this conceptual study, having formulated the
metabolic model, is, first of all, to test how the application
of M. mazei can influence the excretion fluxes of two
C. butyricum by-products, formate and acetate. Further-
more, it is interesting to know to what extent the compe-
tition between the available substrates may influence
the ultimate utilisation of acetic acid, which is the most
troublesome. The measures that might be undertaken to
maximise the removal of the inhibitive organic acids are
also tested. Furthermore, the situation when raw glycerol,
which contains methanol, is used for PDO production is
examined in such a two-species culture system.
Materials and methods
The metabolic networks for both species were formulated
based on various data sources. For C. butyricum, the basic
reactions of reductive and oxidative branches of central
carbon metabolism were taken from Zeng [15], Jung [16]
and Zhang et al. [17]. These reactions were verified and
supplemented by genome annotation data from the Web
page of NIAID Bioinformatics Resource Center Pathema
(Pathema, http://pathema.jcvi.org/cgi-bin/Clostridium/
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PathemaHomePage.cgi). The data from Pathema were also
used to establish the anabolic pathways of amino acids
biosynthesis. The reactions of macromolecules biosynthe-
sis were formulated based on bacterial biomass composi-
tion given by Neidhardt et al. [18].
The formulation of M. mazei network was performed
mainly based on Kyoto Encyclopaedia of Genes and
Genomes (KEGG) and Metacyc (SRI International) dat-abases. Additionally, the detailed description of the meta-
bolic pathways responsible for the utilisation of methanol,
acetate, formate and carbon dioxide by methanogens
presented by Deppenheimer [19] was used. Some missing
reactions were taken from Feist et al. [20], who presented a
detailed metabolic model for Methanosarcina barkeri.
The network for C. butyricum consists of 77 reactions,
thereof 13 reversible, and 69 metabolites. The network for
M. mazei consists of 85 reactions, thereof 31 reversible,
and 74 metabolites. Four exchange reactions and four
additional external metabolites for the substances excreted
by C. butyricum and consumed by M. mazei connect bothnetworks. Altogether, 166 reactions, thereof 44 reversible,
are included in the two-species network. The network
comprises 147 metabolites. The calculations of metabolic
fluxes were first performed for each network separately, so
as to check the correctness of their formulations, and then
for the two-species system.
CellNetAnalyzer v. 8.0 (http://www.mpi-magdeburg.
mpg.de/projects/cna/cna.html), which is a MATLAB
add-on developed by Steffen Klammt from Max-Planck-
Institute for Dynamics of Complex Technical Systems
(Magdeburg, Germany), was used to perform linear opti-
misation and metabolic flux calculations. Owing to the
literature data [16], the individual C. butyricum network
could be treated as an overdetermined system; therefore,
metabolic flux calculations were performed to test the
network and additionally some fluxes, which were not
involved in the flux calculations, were used to check the
correctness of the calculations.The two-species network, as well as the separate
M. mazeinetwork, are underdetermined and therefore two
linear programming solvers were simultaneously applied.
Most of the values of fluxes were sought in the range from
0 to 100 mmol g X-1 h-1 for the irreversible reactions and
from -100 to 100 mmol g X-1 h-1 for the reversible
ones.
Nevertheless, several range constraints for selected
fluxes were set narrower according to available literature
data, as well as linear objective functions in the optimisa-
tion procedure were used. The constraints and these func-
tions are mentioned, where required, in the Results anddiscussion section.
Networks and modelling concept
Metabolic network forC. butyricum
The reconstructed network for C. butyricumconsists of two
main parts (Fig.1). First, carbon metabolism (catabolism)
is taken into account starting with the reductive pathway of
Fig. 1 Reconstructed metabolic
network for C. butyricum
presented in the form of a
background map used for the
calculation of fluxes in CNA
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glycerol biotransformation into PDO, oxidative pathway
(glycolysis and partially active TCA cycle) leading from
glycerol into acetyl-CoA and by-products, lactic, acetic,
butyric and formic acids and ethanol, up to the pentose
phosphate pathway, which supplies the precursors for
anabolic pathways and biomass formation. Second, the
anabolic pathways for the formation of all 20 amino acids
are also added to the reconstructed network. Biosynthesisof the cellular components (macromolecules) is presented
in the form of equations, in which amino acids and other
biomass precursors are substrates. In the following, the
metabolism ofC. butyricum is described in detail.
In the reductive pathway, glycerol is initially dehydrated
to 3-hydroxypropionaldehyde by glycerol dehydratase (EC
4.2.1.30) and then reduced by PDO dehydrogenase (EC
1.1.1.202). The presence of this pathway is characteristic
for the organisms, which are capable of utilising glycerol
as a sole carbon source under anaerobic conditions. The
reduction of 3-hydroxypropionaldehyde into PDO is the
important way to sustain the equilibrium of the hydrogencarriers in the cells [15, 16, 21]. It is also sometimes
observed that C. butyricum, apart from PDO, also releases
3-hydroxypropionaldehyde out of the cells [16].
In the oxidative pathway, which is responsible for the
energy and supply of biomass precursors, glycerol is
initially oxidised into dihydroxyacetone by glycerol
dehydrogenase (EC 1.1.1.6). Being activated by ATP,
dihydroxyacetone in its phosphorylated form is further
incorporated into typical glycolysis reactions via 3-phos-
phoglyceraldehyde, phosphoenolpyruvate and pyruvate up
to acetyl-CoA. In order to decarboxylate pyruvate and
form acetyl-CoA,C. butyricumutilises pyruvate:ferredoxin
2-oxidoreductase (CoA-acetylating), instead of pyruvate
dehydrogenase with NAD as a cofactor [16].
Only few TCA cycle reactions are active in C. butyri-
cum and their main task is to supply the precursors for the
formation of amino acids and other biomass building
blocks. The formation of by-products is another way to
decrease the intracellular concentration of the reduced
hydrogen carriers in the cells. Therefore, pyruvate is partly
reduced to lactate by lactate dehydrogenase (EC 1.1.1.27)
and formate by pyruvate:formate lyase (EC 2.3.1.54).
Formate is either excreted from the cell or further oxidised
into carbon dioxide with the accompanying release of
hydrogen. Formate dehydrogenase (EC 1.2.1.2) is respon-
sible for this reaction. The other three main by-products
originate from acetyl-CoA. Acetyl-CoA is either reduced to
ethanol by acetaldehyde dehydrogenase (EC 1.2.1.10) or
transformed into acetic or butyric acid. Because the amount
of ethanol produced by C. butyricum is not very high, the
two above-mentioned acids are the dominating by-prod-
ucts. It is for their pH-decreasing effect and degree of
dissociation-dependent toxic action that the inhibition of
biomass growth is observed both in batch and fed-batch
cultures ofC. butyricum [22]. Acetate is formed in a one-
step reaction from AcCoA catalysed by acetyl-CoA
hydrolase (EC 3.1.2.1), generating one ATP molecule.
Butyric acid formation takes place in a pathway, in which
five enzymes are involved (Supplementary Table 1 in
Electronic Supplementary Material), and two molecules of
acetyl-CoA and NADH are utilised to form one moleculeof butyrate [16].
Referring to the genome annotation data, it is noticed
that the pentose phosphate pathway in Clostridia does not
have the oxidative branch. The enzyme, which is respon-
sible for decarboxylation of glucose-6-phosphate to ribu-
lose-5-phosphate, is not found in the genome of C.
butyricum (Pathema). It is in disagreement with the earlier
approach to metabolically model the growth ofC. butyri-
cum and PDO formation by Jung [16]. She assumed that
this reaction is present in C. butyricum. Therefore, as the
oxidative branch of the pentose phosphate pathway is
omitted, only four reactions of the pentose phosphatepathway are involved in the reconstructed network (Fig. 1).
They are all catalysed by transaldolases and transketolases
(Supplementary Table 1).
The pathways leading to the formation of amino acids,
then to proteins and other macromolecules, are universal
for the variety of microorganisms. Generally, amino acids
and other biomass precursors originate from the following
primary metabolism intermediates: oxalacetate, a-ketoglu-
tarate, pyruvate, erythrose-4-phosphate, acetyl-CoA,
ribose-5-phosphate and glucose-6-phosphate. The multi-
step pathways of amino acids formation were reconstructed
from the data supplied in the Pathema Web page.
Due to the lack of specific data for C. butyricum, the
percentage composition of biomass for the averaged bac-
terial cell, consisting of protein, peptidoglycan, lipopoly-
saccharides, glycogen, lipids, DNA and RNA, was taken
from Neidhardt et al. [18], and so were the biosynthesis
reactions of the above-mentioned biomass components
formulated on these data.
Metabolic network forM. mazei
In Fig.2a, the reconstructed network for M. mazei is
depicted. Of the various genera of methane bacteria, Met-
hanosarcina sp. is the most versatile in the utilisation of
various carbon sources. Therefore, the reconstruction of
M. mazei pathways, that are responsible for methanogen-
esis and biomass growth with the use of various carbon
compounds, is of particular interest. These important
reactions are extracted from the network and shown sepa-
rately in Fig. 2b for a better understanding.
One major type of carbon sources for methanogens are
C1 compounds, such as methanol, methylamines and
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methylthiols or formate. However, some bacteria, such as
Methanosarcina sp., are also capable of utilising C2 com-
pounds, i.e. acetic acid. Hydrogenotrophic growth on car-
bon dioxide and hydrogen is observed in Methanosarcina,
as in all other methanogenic Archea. Nevertheless,
hydrogen is then an obligatory molecule, playing the role
of a donor of the reductive potential [19]. These optional
types of growth and methanogenesis are bound to the three
basic pathways: CO2-reducing pathway, methylotrophic
pathway and aceticlastic pathway.
In the CO2-reducing pathway, carbon dioxide is initially
reduced, with the use of reduced ferredoxin as a cofactor,
to formylmethanofuran (formyl-MFR). Then, after the
transfer of the formyl group onto tetrahydromethanopterin
(H4MPT), formyl-H4MPT is formed. For the sake of sim-
plicity of the reconstructed network, formyl-MFR is
Fig. 2 Reconstructed metabolic
network for M. mazei presented
in the form of a background
map used for the calculation of
fluxes in CNA (a) and the most
important pathways of carbon
metabolism in Methanosarcina
sp. (b); methylotrophic growth
is shown in solid arrows (red),
aceticlastic growth in short
dashed arrows (blue), carbon
dioxide reduction in long
dashed arrows (green) and
formate assimilation in long
dashed arrows (magenta)
(formate pathway is actually the
same as the carbon dioxide
pathway)
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omitted in M. mazei network, as this pathway has no
branches. Formyl-H4MPT is then reduced stepwise via
methenyl-H4MPT and methylene-H4MPT to methyl-
H4MPT. The reduced coenzyme F420plays the role of the
hydrogen donor in these reactions. It is regenerated
(reduced) later with the use of the molecular hydrogen in
the reactions catalysed by coenzyme F420hydrogenase (EC
1.12.99.6). The methyl group is transferred from methyl-H4MPT onto coenzyme M (CoM) to obtain methyl-CoM,
from which methane is finally formed [19].
Carbon dioxide is also partly reduced by a CO
dehydrogenase/acetyl-CoA synthase complex (carbon-
monoxide dehydrogenase EC 1.2.7.4 and CO-methylating
acetyl-CoA synthase EC 2.3.1.169), with the use of
methyl-H4MPT and reduced ferredoxin, to acetyl-CoA,
which is the precursor for the molecules required for
biomass formation.
In the methylotrophic pathway, some methanol mole-
cules are transformed directly into methyl-CoM and then
transformed to methane. Some methyl groups from meth-anol are transferred from methyl-CoM to methyl-H4MPT
and further oxidised in the reversed CO2-reducing pathway
via methylene-, methenyl- and formyl-H4MPT up to car-
bon dioxide. Reduced coenzyme F420 is retrieved in this
pathway and thus there is no need to supply molecular
hydrogen [19]. Acetyl-CoA formation is the same as in the
CO2-reducing pathway.
The aceticlastic pathway is the most complicated one.
Being assimilated, acetate is transformed into acetyl-CoA.
Part of the acetyl-CoA is oxidised to carbon dioxide by a
CO dehydrogenase/acetyl-CoA synthase complex (EC
1.2.7.4, EC 2.3.1.169). Ferredoxin is reduced within this
reaction. The methyl group from acetyl-CoA is also
transferred, with the accompanying decarboxylation, onto
tetrahydrosarcinopterin (H4SPT) to form its methyl deriv-
ative similar to methyl-H4MPT. Finally, methane is formed
via methyl-CoM. The CO2-reducing pathway is reversed,
as it is in the methylotrophic growth [19]. All these path-
ways described above are visualised in Fig. 2b.
The actual reaction of methanogenesis, already men-
tioned above as the transformation of methyl-CoM into
methane, is independent of the carbon source utilised.
Methyl-CoM reacts with coenzyme B. Methane is released
and the complex of two coenzymes B and M connected by
a disulphide bond, CoMSSCoB, is formed. Both
coenzymes are regenerated by the reductive cleavage, for
which the reduced methanophenazine is the hydrogen
donor. The reduced methanophenazine is then regenerated,
i.e. reduced back by the molecular hydrogen or coenzyme
F420, dependently on the carbon substrate utilised [Meta-
cyc,7, 19].
Methanogenic species are capable of autotrophic bind-
ing of the molecular nitrogen under the conditions of
ammonium nitrogen deficiency [19]. So, this reaction is
included in the reconstructed network as well.
The central carbon metabolism of M. mazei leading to
the formation of biomass precursors starts from acetyl-
CoA. Then, the reversed glycolysis reactions lead from
acetyl-CoA via pyruvate, phosphoenolpyruvate and 3-
phosphoglyceraldehyde to fructose- and glucose-6-phos-
phate. Fructose-6-phosphate is further incorporated into thepentose phosphate pathway (Fig. 2a). As in C. butyricum,
the oxidative branch of the pentose phosphate pathway is
not present in M. mazei either [7, KEGG].
The pathways of amino acid formation are also included
in the network according to information from KEGG and
Metacyc databases (Fig.2a). The biosynthesis of macro-
molecules and then of biomass differ inM. mazeicompared
to C. butyricum. According to Stolyar et al. [7], for
methanogenic Archea only glycogen, phospholipids, pro-
teins, DNA and RNA should be included as biomass-
forming polymers. The protein composition is established
based on data from Feist et al. [20].
The exchange of metabolites and their balance
In the two-species system, the following metabolites
excreted by C. butyricum: carbon dioxide, acetic acid,
formic acid and hydrogen are set as potential substrates
for M. mazei and included in the exchange reaction.
Each of these substances excreted by C. butyricum out of
the cells becomes the extracellular pool in the medium.
Organic acids are dissolved completely and gases up to
their maximum solubility under the given conditions. In
this way, they become the substrate for methane bacteria.
M. mazei assimilates them, and so the excretion flux
from C. butyricum for each of these metabolites splits
into two fluxes: the uptake flux by M. mazei and the
excretion flux reflecting the amount of the unused
exchanged metabolite remaining in the medium (acids)
or leaving the system (gases). In the case of carbon
dioxide, there must be an additional excretion flux taken
into account, as the metabolism of methanol or acetic
acid by M. mazei is also the source of carbon dioxide.
This concept is presented in Fig. 3 and Table 1, in which
the stoichiometric equations representing all these fluxes
are collected.
Stoichiometric balance equations for the two-species
network
On the basis of information on the biochemical pathways
of both microorganisms tested, stoichiometric equations
are formulated and listed in Supplementary Tables 1 and 2.
The graphs showing all the reactions included are also
shown in Figs. 1 and 2a.
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Results and discussion
Examination of metabolic networks for the individualspecies
To assure the reliability of simulations for the two-species
network, the networks for both species, C. butyricum and
M. mazei, were first tested as individual species networks
with the use of available experimental data from literature.
Metabolic network for C. butyricum
The metabolic network for C. butyricum was validated by
two types of tests. In the first approach, it was tested with
the use of metabolic flux analysis. Seven external fluxes
have to be measured to determine the system. Eight fluxes
of substrate and extracellular metabolites were measured
by Jung [16], i.e. glycerol uptake flux and the excretion
fluxes of PDO, lactate, acetate, butyrate, hydrogen, ethanol
and carbon dioxide, which could be used in the calcula-
tions. Thus, the system was over-determined. The glycerol
flux was thus selected to test the correctness of the
reconstructed network and its calculated value was com-
pared with the experimental one. The value of carbondioxide flux was corrected with the use of the method by
Zeng [23] to take carbon dioxide solubility in the medium
into account.
Calculations were performed with data from C. butyri-
cum chemostat cultures run under glycerol limitation and
glycerol excess (overflow) conditions at dilution rate
D = 0.1 h-1 and under glycerol limitation conditions at
D = 0.3 h-1 [16]. The calculated fluxes agree well with
the experimental results, proving that the network is cor-
rectly reconstructed (Fig.4). For example, the values of
glycerol flux from Jung [16] are very close to the calculated
ones, respectively (in mmol g X-1 h-1), 21.2 versus 20.7under glycerol-limited condition at D = 0.1 h-1, 29.8
versus 29.7 under glycerol-overflow conditions at
D = 0.1 h-1, and 40.3 versus 40.2 under glycerol-limited
condition at D = 0.3 h-1. It is worth mentioning that the
estimation of the rate of cell growth from flux data is
normally associated with large variations. In our case, the
rate of biomass growth, which is equal to the dilution rate
but was set to be unknown in the calculations, is very
accurately predicted, underlying again the rigorousness of
the network reconstructed.
The second approach used to test C. butyricum network
was different. This time, an optimisation procedure for an
underdetermined system was performed. The growth rates
Fig. 3 A scheme of the exchange of metabolites between C.
butyricum and M. mazei; the reactions from 1 to 13 are listed in
Table1
Table 1 Metabolite exchange
reactions in the metabolic model
of syntrophic-like growth of
C. butyricum and M. mazei
Reaction
no.
Process Stoichiometry Symbol, as defined
in the networks
(1) Carbon dioxide excretion byC.
butyricum
P_CO2 ) CO2(exch) P57_qCO2
(2) Hydrogen excretion byC. butyricum P_H2 ) H2(exch) P53_qH2
(3) Acetate excretion by C. butyricum P_HAc ) HAc(exch) P54_qHAc
(4) Formate excretion by C. butyricum P_FORM ) FORM(exch) P83_qFORM
(5) Carbon dioxide uptake byM. mazei CO2(exch) ) M_CO2 M_CO2_demand(6) Hydrogen uptake by M. mazei H2(exch) ) M_H2 M_H2_demand
(7) Acetate uptake by M. mazei HAc(exch) ) M_HAc M_HAc_demand
(8) Formate uptake by M. mazei FORM(exch) ) M_FORM M_FORM_demand
(9) Carbon dioxide excretion CO2(exch) ) M_CO2_excretion
(10) Hydrogen excretion H2(exch) ) M_H2_excretion
(11) Acetate excretion HAc(exch) ) M_HAc_excretion
(12) Formate excretion FORM(exch) ) M_FORM_excretion
(13) Carbon dioxide excretion connected with
M. mazeimetabolism only
M_CO2 ) M_CO2_excretion_MM
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of the biomass were set constant at the values 0.1, 0.2, 0.3,
0.4, 0.45 and 0.5 h-1, and range constraints for several
fluxes (in mmol g X-1 h-1) were also introduced. These
were P18_rmaintenance = 050, P55_qEtOH = 01.5 and
P52_qLAC = 05. The range constraints for glycerol
uptake (P56_glycuptake) rate were set in such a way that
they depended on the rate of cell growth. Also, a linear
objective function was used to maximise glycerol uptake.
The simulated fluxes are compared to the experimental
results (Fig. 5) obtained in a chemostat culture reported by
Solomon et al. [24]. The experimental data for carbon
dioxide flux were also corrected with regard to carbon
dioxide absorption in the medium according to the proce-
dure described by Zeng [23]. The results of these simula-
tions are also quite satisfactory, reinforcing the usefulness
of the network and the method used.
Metabolic network for M. mazei
The network for M. mazei is tested for the following four
cases. First, it is assumed that only methanol is the sole
carbon source for methanogenesis and growth. Second, the
aceticlastic growth is tested. Third, formate was the sole
carbon source. Finally, simulation of the hydrogenotrophic
growth on carbon dioxide as a carbon source with the
obligatory presence of hydrogen is performed. Range
constraints for biomass growth and maintenance fluxes
were set (Table 2). The latter was also minimised in the
form of a linear objective function. When the growth of
M. mazeion single substrates was consecutively tested, for
each case all substrate uptake fluxes were zeroed, exclud-
ing the flux of the substrate for which the simulation was
performed. All these constraints are listed in Table 2. The
resulting fluxes are depicted in Fig. 6. Selected fluxes arealso transferred to Table 2 to compare with available lit-
erature data. In Table2, also other important fluxes as well
as the yields that resulted from the calculated fluxes are
added.
The direction of fluxes presented in Fig. 6 shows that
the network for the methanogenic bacteria is properly
reconstructed and its behaviour is in agreement with the
contemporary knowledge of the biochemistry of metha-
nogenicArchea, as shown in Fig. 2[19].M. mazeinetwork
successfully predicts the reversibility of the CO2-reduction
pathway when methylotrophic and aceticlastic pathways
are used. It also shows the capability of the microorganismto grow without extracellular molecular hydrogen within
the methylotrophic, aceticlastic and formate metabolism. If
no hydrogen is supplied and carbon dioxide is used as the
sole carbon source, all fluxes are calculated to be zero,
which is in agreement with the knowledge of the
biochemistry of methanogens [19].
Rajoka et al. [25] presented a variety of yield and kinetic
data concerning the growth ofM. mazei on acetic acid and
methanol. The simulated values of fluxes and yields cal-
culated for them are in the range of the measured ones
(Table2). In the case of methylotrophic growth, only
methane over biomass yield is slightly lower than that
reported by Rajoka et al. [25]. Also, less carbon dioxide is
excreted. For the aceticlastic growth, the agreement is
better. The only difference is that the network predicts a
higher carbon dioxide flux and higher methane over bio-
mass yield coefficient than that found in experiments.
Some experimental data were also supplied for the
hydrogenotrophic growth by Weimer and Zeikus [26].
Although these data were obtained for M. barkeri, their
application for the purpose of comparison is justified as
these bacteria belong to the same genus and have similar
metabolic networks. Doubts may occur for growth on
formate because no data can be found for Methanosarcina
sp. Nevertheless, the range of values for the calculated
fluxes is acceptable (Table2), although the experimental
data cited were found for Methanobacterium formicicum.
From the results above, it can be concluded that the
reconstructed networks properly simulate the metabolic
activities of both C. butyricum and M. mazei under dif-
ferent conditions. Therefore, it is assumed that the net-
works can be applied to properly simulate and analyse the
behaviour of the two-species system.
Fig. 4 Selected fluxes for C. butyricum calculated with the use of
metabolic flux analysis; glycerol-limiting conditions are denoted by
top and bottom (red boxes) and glycerol-overflow conditions by
middle (green boxes). The dilution rates were: D = 0.1 h-1 for both
glycerol-limiting and -overflow conditions and D = 0.3 h-1
for
glycerol-limiting conditions only. The values of measured fluxes by
Jung [16] are depicted (in yellow) in the boxeswithencircled corners
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Simulation for the two-species system
First, simulations for the two-species system were per-
formed to examine the influence of methanol on the for-
mation of the two desired products, PDO and methane, and
the utilisation of the two by-products, acetate and formate.To do this,C. butyricumbiomass flux was set constant at the
levels 0.1, 0.2, 0.3, 0.4, 0.45 and 0.5 h-1. For each biomass
flux, the optimisation of the network for methanol-con-
taining and methanol-free media was performed. In these
calculations, only range constraints were applied to limit the
number of potential solutions. The excretion fluxes of
C. butyricummetabolites were constrained in the same way
as done in the test of the individual C. butyricum network.
The methanol uptake flux by M. mazei was additionally
constrained between 12 and 19 mmol g X-1 h-1, unless it
was set zero in the simulations for the methanol-free media.
As expected, the presence of methanol in the tested
system does not influence PDO formation (Fig.7) because
methanol is not utilised by C. butyricum. However, meth-
anol is an additional substrate for methanogenesis by
M. mazei, apart from formate, acetate and carbon dioxide.
Its presence facilitates methane formation to a high extent,
as its flux increased from about 18 mmol g X-1 h-1 to
over 25 mmol g X-1 h-1 in the optimum (for PDO flux)
biomass flux range (0.40.5 h-1). For lower C. butyricum
biomass fluxes (0.10.2 h-1), which are more adequate for
the syntrophic-like growth withM. mazei, methane flux is
even tripled. Thus, methanol, an ingredient of raw glycerol,
proves to be a useful, not troublesome as one may expect,
substrate in the tested system and assures efficient methane
production. Even if it exerts any inhibitive effect on
C. butyricum, this inhibition is avoided due to the metab-
olism ofM. mazei.
The influence of methanol on the utilisation of
by-products is quantified with the use of flux ratios, which
are defined as the net excretion flux of a given metabolite
into the medium divided by its formation flux from
C. butyricumalone. The higher this ratio, the worse is the
utilisation of a given by-product.
The presence of methanol in the system exerts an
equivocal effect on the utilisation of by-products (Fig. 8).
At lowerC. butyricumbiomass fluxes, methanol somewhat
facilitates acetate utilisation, whilst at higher growth rates
it seems to compete with acetate. In the case of formate,
almost in all cases, its utilisation is lower in the methanol-
free media. Hydrogen is, on the other hand, not well
assimilated in the presence of methanol. It means that
methanol competes with carbon dioxide and hydrogen
utilisation. Despite the significant differences in the uptake
fluxes of other substrates in the presence of methanol,
methanol itself is utilised with an approximately same
uptake flux between 15.4 and 15.8 mmol g X-1 h-1
(Fig.7).
Fig. 5 Fluxes of the excreted
metabolites (in
mmol g X-1
h-1
) at varying
rate of biomass growth obtained
with the use of an optimisation
procedure for the network of
C. butyricum; experimental data
from Solomon et al. are
depicted as line and symbol
(square) curves, whilst the
values of simulated fluxes are
depicted with pentagons
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It is seen in Fig.8 that even more than 35% of the
acetate excreted byC. butyricummay remain in the system
in some cases. It would be a more satisfactory result, if
more acetate were utilised by M. mazei. So the question
arises, what strategy should be used to maximise the
scavenging of by-products by M. mazei.
Therefore, the competition between all available sub-
strates and various strategies to decrease the excretion of
acetate and formate were tested in various scenarios. There
is a common constraint set used in all these scenarios. First
of all, the specific growth rate of C. butyricum was set
constant at P58_qX = 0.1 h-1. This choice is justified by
Table 2 Comparison of literature and simulated data for M. mazei growth on various substrates
Reaction rate or
yield coefficient
Calculated
valuesa
Literature
valuesa
Literature and comments Constraints used
in calculationsb
Methylotrophic growth
lMM 0.0636 0.0470.084 Rajoka et al. [25] for the range of initial
methanol concentrations from 5 to 15 g l-1
M_maintenance = 030
M_X_growth = 00.09
M_CO2_demand = 0M3_AC = 0
M_FORM_demand = 0
M_H2_demand = 0
M_HAc_demand = 0
M09_nitrogenase = 0
qCH4 14.45 13.3816.79
qCO2 2.69 4.685.46qCH3OH 19.29 12.0519.71
YCH4=X 227 232241
YCH4=CH3OH 0.75 0.390.88
Aceticlastic growth
lMM 0.0776 0.0590.1 Rajoka et al. [25] for the range of initial
acetate concentrations from 5 to 30 g l-1M_maintenance = 030
M_X_growth = 00.09
M_CO2_demand = 0
M_FORM_demand = 0
M_H2_demand = 0
M_101_CO2 = 0
M09_nitrogenase = 0
M_CH3OH = 0
qCH4 10.8 5.310.9
qCO2 14.89 5.311.0
qCH3COOH 14.15 8.1714.5
YCH4=X 139 90122
YCH4=CH3COOH 0.76 0.650.77
Hydrogenotrophic growth (H2 ? CO2)
lMM 0.0555 0.058 Weimer und Zeikus [26] for M. barkeri M_maintenance = 030
M_X_growth = 00.06
M_FORM_demand = 0
M3_AC = 0
M09_nitrogenase = 0
M_CH3OH = 0
M_CO2_excretion_from_
methanogen = 0
qCH4 15.14
qCO2 17.01
qH2 77.24
YCH4=X 273 115156
YCH4=CO2 0.89 1c
0.81
YCO2=H2
0.22 0.25c
Growth on formate
lMM 0.0896 0.0410.069 Schauer and Ferry for Methanobacterium
formicicum[27]
M_maintenance = 030
M_X_growth = 00.09
M3_AC = 0
M_CO2_demand = 0
M09_nitrogenase = 0
M_CH3OH = 0
M_CO2_excretion_from_
methanogen = 0
qCH4 11.29 618
qHCOOH 63.36 3664
qCO2 49.05
YCH4=X 126 208
YCH4=HCOOH 0.18 0.25d
YCO2=HCOOH 0.77 0.75d
aThe units for l, q and Y are h-
1, mmol g X-
1h-
1and mmol mmol-
1, respectively, whilst for YCH4=X is mmol g X
-1
b Other constraints for optimisation were set in the range 0100 for the irreversible reactions and -100 to 100 for the reversible onesc
The value found from the Stoichiometric equation: CO2 ? 4H2 = CH4 ? H2Od
The value found from the Stoichiometric equation: 4HCOOH = 3CO2 ? CH4 ?H2O
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the fact that the specific growth rate ofM. mazei does not
exceed 0.1 h-1, and the continuous (chemostat) culture
should be kept at a dilution rate such that none of the
organisms are washed out.
Three other fluxes from C. butyricum were set constant
too. These were acetate excretion flux set at 1.5 mmol g
X-1 h-1, butyrate flux at 3 mmol X-1 h-1 and formate
flux at 0.8 mmol g X-1 h-1, if formate formation was
included in the scenario. The range constraint for methanol
uptake was set at M_CH3OH = 1219 mmol g X-1 h-1.
These simulations were aimed at finding such physio-
logical conditions of the two-species system as to maxi-
mally utilise acetate and formate produced by
C. butyricum. It is proposed to be achieved by maximising
either the methane production inM. mazeior the growth of
M. mazei. These two approaches are biologically most
Fig. 6 Variations in catabolic
fluxes in M. mazei in the
utilisation of various carbon
substrates; the fluxes are shown
from top to bottom as follows:
for methanol use (in red), for
carbon dioxide and hydrogen (in
green), for acetate (in blue) and
for formate (in magenta)
Fig. 7 Influence of methanol
on the excretion fluxes
of 1,3-propanediol and methane
in the two-species system
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relevant because the two-species system can be physio-
logically controlled by the medium composition or theprocess conditions.
The calculations of scenarios #1#5 presented in Fig. 9
were performed only with the use of the above-mentioned
constraints. In scenario #1, all the exchanged substrates, i.e.
formate, acetate and hydrogen, are utilised by M. mazeiand
so is methanol. In other cases, the modifications of
C. butyricum pathways are considered. Therefore, in sce-
nario #2, formate excretion byC. butyricum is blocked. In
scenario #3, formate dehydrogenase from C. butyricum is
deactivated, which results in the lack of hydrogen in the
system. Scenario #4 is a combination of scenarios #2 and
#3 and thus neither formate nor hydrogen is available for
M. mazei. Finally, scenario #5 evolves from scenario #4 by
subtracting methanol from the medium. It results in a
system with acetate as the sole carbon source forM. mazei.
As mentioned above, the acetate flux fromC. butyricum
was set at 1.5 mmol g X-1 h-1. In scenario #1, acetate flux
of 0.59 mmol g X-1 h-1 is found to be excreted to the
medium. It means that 61% of the acetate is utilised by
M. mazei. Following the same reasoning, when formate
excretion flux is equal to 0.26 mmol g X-1 h-1, it means
that 68% of formate is utilised by M. mazei. If no formate is
available for M. mazei (scenario #2), hardly any changesare seen. Acetate excretion flux is the same and only the
hydrogen excretion flux increases slightly from 1.44 to
1.66 mmol g X-1 h-1.
In scenario #3, no hydrogen is available in the system,
but formate, acetate and methanol can be utilised by
M. mazei. In this case, the lack of hydrogen contributes
to the increase of acetate utilisation, as its excretion
flux is lower than in scenario #1 and equal to
0.4 mmol g X-1 h-1. It corresponds to 73% of acetate
utilisation. Compared to scenario #1, no effect is observed
with regard to formate utilisation. Also, less methanol is
assimilated as its flux decreases from 13.73 (scenario #1)
to 12.09 mmol g X-1 h-1. The significant change in
acetate scavenging in the absence of hydrogen is in
agreement with the thermodynamic data. Methanogenesis
from carbon dioxide and hydrogen is highly preferred
over other substrates, as its Gibbs energy is the lowest
and acetate strongly falls behind the other carbon sub-
strates as the Gibbs energy of its transformation into
methane is on average almost three times higher than for
the other three discussed substrates [19].
Fig. 8 Influence of methanol
on the uptake of acetate and
formate by M. mazei; the flux
ratios are defined as the net
excretion flux of acetate
M_HAc_excretion, formate
M_FORM_excretion and
hydrogen M_H2_excretion into
the medium divided by the
production fluxes of these
metabolites from C. butyricum
alone (P54_qHAc, P83_qFORM
and P53_qH2, respectively)
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When only acetate and methanol are available for
M. mazei(scenario #4), acetate excretion flux is not lower
than in scenario #3 as one may have expected. It is equal to
0.46 mmol g X-1 h-1. The best results are obtained in
scenario #5, in which acetate is the sole carbon source
(methanol-free medium) forM. mazei. Acetate flux is thenequal to 0.26 mmol g X-1 h-1, which means that 83% of
acetate is utilised by M. mazei. At the same time, methane
flux is extremely low in this case and equal to
0.22 mmol g X-1 h-1. It shows again that methanol con-
tributes to methane formation in the tested two-species
system to the highest extent.
Another issue addressed in the simulations for the two-
species system is the effects of maximising eitherM. mazei
growth or methanogenesis flux. Here, simulations are
performed with the use of, apart from the aforementioned
constraints, a linear objective function to maximise the
biomass flux ofM. mazei. Its maximum value was, how-ever, limited to 0.1 h-1, as this is a physiologically relevant
value for this microorganism as mentioned by Rajoka et al.
[25].
Methanosarcina mazei biomass flux is maximised to
0.1 h-1 in scenarios #6#10 for the same substrate sets as
in scenarios #1#5 (Fig.9). Maximising the M. mazei
biomass flux does not decrease significantly acetate
excretion flux in scenarios #6, #7 and #8. It is slightly
lower (0.55 mmol g X-1 h-1) in scenarios #6 and # 7 as
compared to the reference scenarios #1 and #2
(0.59 mmol g X-1 h-1). In scenario #8, it is even higher
than in scenario 3 (Fig. 9). So is the formate flux, which is
equal to 0.33 mmol g X-1 h-1 in scenario #8, as compared
to 0.25 mmol g X-1 h-1 in scenario #1. A positive
effect of M. mazei biomass flux maximisation is foundonly in scenario #9. Acetate flux is then equal to
0.28 mmol g X-1 h-1. When acetate is the sole carbon
source (scenario #10), the set acetate flux (1.5 mmol g
X-1 h-1) fromC. butyricum is not sufficient to assure such
a high (0.1 h-1)M. mazeibiomass flux. As a result, acetate
is completely utilised and M_X_growth is equal only to
0.0889 h-1.
The simulations of methanogenesis maximisation were
performed as follows. A linear optimisation function to
maximise the methane flux is used and the upper
boundary of range constraint for methane flux is set each
time at different values from 4 to 18 mmol g X-1
h-1
for the cases with methanol present in the system
(Fig.10ad) and from 0.2 to 5 mmol g X-1 h-1 for
methanol-free systems (Fig.10e, f). The same substrate
sets are tested as above (Fig. 9). Additionally, a new set
including acetate, formate and hydrogen as substrates for
M. mazei is included.
It turned out that if the upper boundary of the range
constraint is set closer to the maximum methane flux,
irrespective of the set of substrates, a better utilisation of
Fig. 9 The effect ofM. mazei biomass flux maximisation on acetate and formate utilisation in the two-species culture
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acetate and formate is achieved (Fig.10af). Analysing the
effects of various substrate sets, it is well seen that there isnot much difference between Fig.10a, b. The lack of
formate does not have an impact on the decrease of acetate
excretion flux. If there is no hydrogen in the system, the
situation is more complicated. There is no decreasing trend
for acetate excretion flux. A maximum value for the flux
M_HAc_excretion is observed when the flux
M_CH4_excretion is equal to about 10 mmol g X-1 h-1
(Fig.10c, d). The situation is the same for the flux
M_FORM_excretion (Fig.10c).
In the methanol-free systems (Fig. 10e, f), lower meth-
ane fluxes are again observed, exactly as in the simulationsshown in Figs. 7 and 9. In contrast to Fig.10c , d , a
monotonically decreasing trend is observed with the
increase of methane flux and thus the lowest acetate
excretion fluxes are found at the highest methane fluxes.
Comparing these two approaches, i.e. the maximisation
of M. mazei growth and the maximisation of methano-
genesis, it is clear that an increase in methane production
results in a better scavenging of the two by-products:
acetate and formate.
Fig. 10 Methanogenesis maximisation in the two-species culture; the
simulations are performed for various maximum methane fluxes from
4 to 18 mmol g X-1 h-1 for systems with methanol and from 0.2 to
5 mmol g X-1
h-1
for methanol-free systems and various substrate
mixtures: acetate, formate, methanol and hydrogen (a), acetate,
methanol and hydrogen (b), acetate, formate and methanol (c), acetate
and methanol (d), acetate, formate and hydrogen (e), and solely
acetate (f)
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Conclusions
In this study, a metabolic model is elaborated for the
industrially important bacterium, C. butyricum, and the
methanogenic archeon, M. mazei, and a mixed culture
comprising them. The mixed culture is intended to be used
for a more efficient degradation of glycerol and production
of PDO, especially with raw glycerol from biodieselproduction. The metabolic model was first examined for
the individual organisms. The metabolic fluxes calculated
agree well with available experimental data for both
microorganisms. For the first time, a two-species metabolic
model is used to analyse different scenarios, which might
be encountered in the mixed culture system. The model
calculations suggest that the following conditions are
preferable for the removal of the toxic by-products, such as
acetate and formate, from the glycerol fermentation in the
mixed culture: (1) switching of M. mazei metabolism
towards a maximal methanogenesis, (2) avoiding extensive
biomass growth of M. mazei. The latter condition can bebest realised in a bioreactor system with cell recycle.
Furthermore, the analysis reveals that if C. butyricum
produced no hydrogen, it would be preferable for acetate
scavenging. This is exactly the ideal case for an optimal
PDO production [15]. Thus, this conceptual study is useful
to guide the ongoing experimental study in our laboratory.
Acknowledgments Marcin Bizukojc wishes to express his gratitude
to Deutscher Akademischer Austausch Dienst (DAAD) for the
financial support during his stay at the Hamburg University of
Technology (special scholarship programme Modern Applications
of Biotechnology PKZ no. A/07/97472). This work was also sup-
ported by the German Research Foundation (DFG project no. ZE 542/2-1) and the European 7 Framework Research Programme (project
no. 212671-Propanergy).
Abbreviations used in the graphs, tables and text (for
protein amino acids, standard abbreviations were used)
3-HPA 3-Hydroxypropionaldehyde
3PG 3-Phosphoglycerate
AcCoA, CH3COCoA Acetyl-CoA
AKG a-Ketoglutarate
Asa L-Aspartate 4-semialdehydeCH2H4MPT Methylenetetrahy
dromethanopterin
CH3CoM Methyl coenzyme M
CH3H4MPT Methyltetrahydromethanopterin
CH3H4SPT Methyltetrahydrosarcinopterin
CHR Chorismate
CO Carbon monoxide
DHA Dihydroxyacetone
DHAP Dihydroxyacetonephosphate
E4P Erythrose-4-phosphate
EtOH Ethanol
F420(H), F420(red) Coenzyme F420(reduced)
F6P Fructose-6-phosphate
FBP Fructose-1,6-biphosphate
Fd(H), FdH Ferredoxin (reduced)
FORM Formic acid (formate)
FUM Fumarate
G6P Glucose-6-phosphate
H4MPT Tetrahydromethanopterin
H4SPT Tetrahydrosarcinopterin
HAc Acetic acid (acetate)
HBu Butyric acid (butyrate)
HCH4MPT Methenyltetrahydro
methanopterin
HCOH4MPT Formyltetrahydro
methanopterin
Hse Homoserine
ICT Isocitrate
Ind Indole
kiV a-Ketoisovalerate
LAC Lactic acid (lactate)
MeOH Methanol
MethPhen(H), dHMePhe Methanophenazine (reduced)
OAA Oxalacetate
PEP Phosphoenolpyruvate
PPA Prephenate
PRPP Phosphoribosyl pyrophosphate
PYR Pyruvate
qCH3COOH Specific acetate uptake rate
qCH3OH Specific methanol uptake rate
qCH4 Specific methane production
rate
qCO2 Specific carbon dioxide
production/uptake rate
qH2 Specific hydrogen uptake rate
qHCOOH Specific formate uptake rate
RIB5P Ribose-5-phosphate
S7P Sedoheptulose-7-phosphate
SKA Shikimate
SUC-CoA Succinyl-CoA
X5P Xylose-5-phosphate
YCH4=CH3COOH
Methane to acetate yield
coefficient
YCH4=CH3OH Methane to methanol yield
coefficient
YCH4=CO2 Methane to carbon dioxide
yield coefficient
YCH4=HCOOH Methane to formate yield
coefficient
YCH4=X Methane to biomass yield
coefficient
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YCO2=H2 Carbon dioxide to hydrogen
yield coefficient
YCO2=HCOOH Carbon dioxide to formate
yield coefficient
lMM M. mazei specific biomass
growth rate
Abbreviations used in the formulation of metabolic
network only (in the stoichiometric equations)1:
exchanged metabolites
CO2(exch) Carbon dioxide (the exchanged pool)
FORM(exch) Formate (the exchanged pool)
H2(exch) Hydrogen (the exchanged pool)
HAc(exch) Acetate (the exchanged pool)
Intracellular metabolites ofM. mazei
M_3PG 3-Phosphoglycerate
M_AcCoA Acetyl coenzyme A
M_AcP Acetophosphate
M_aIVA a-Ketovalerate
M_AKG Aconitate
M_Ala Alanine
M_Arg Arginine
M_ASA L-Aspartate 4-semialdehyde
M_Asn Asparagine
M_Asp Aspartate
M_ATP Adenosinetriphosphate
M_CH3CoM Methyl coenzyme M
M_CH3H4MPT Methyltetrahydromethanopterin
M_CH3H4SPT Methyltetrahydrosarcinopterin
M_CH3OH Methanol
M_CH4 Methane
M_CHR Chorismate
M_CO Carbon monoxide
M_CO2 Carbon dioxide
M_Cys Cysteine
M_dHMethphen Methanophenazine (reduced)
M_DNA DNA
M_E4P Erythrose-4-phosphate
M_F420red Coenzyme F420(reduced)
M_F6P Fructose-6-phosphate
M_FBP Fructose-1,6-diphosphate
M_FdH Ferredoxin (reduced)
M_FORM Formate
M_FORMH4MPT Formyltetrahydromethanopterin
M_FUM Fumarate
M_G1P Glucose
M_G6P Glucose-6-phosphate
M_GA3P Glyceraldehyde phosphate
M_Gln Glutamine
M_Glu Glutamate
M_Gly Glycine
M_gly Glycogen
M_H? Hydrogen ions
M_H2 Hydrogen
M_HAc Acetic acid
M_His Histidine
M_Hse Homoserine
M_Ile Isoleucine
M_IND Indole
M_Leu Leucine
M_Lys Lysine
M_MeH4MPT Methylenetetrahydromethanopterin
M_Met Methionine
M_MthH4MPT Methenyltetrahydromethanopterin
M_N2 Nitrogen
M_NADH NADH
M_NADPH NADPH
M_NH4? Ammonium ions
M_OAA Oxalacetate
M_PEP Phosphoenolpyruvate
M_Phe Phenylalanine
M_PLIP Phospholipids
M_PPA Prephenate
M_Pro Proline
M_PROT Protein
M_PRPP Phosphoribosyl pyrophosphate
M_PYR Pyruvate
M_RIB5P Ribose-5-phosphate
M_RNA RNA
M_SED7P Sedoheptulose-7-phosphate
M_Ser Serine
M_SKA Shikimate
M_SUCC_CoA Succinyl coenzyme A
M_Thr Threonine
M_Trp Tryptophane
M_Tyr Tyrosine
M_Val Valine
M_X Biomass
M_XYL5P Xylose-5-phosphate
Intracellular metabolites ofC. butyricum
P_13PD 1,3-Propanediol
P_3HPA 3-Hydroxypropionealdehyde
P_AcCoA Acetyl coenzyme A
1The notation was so designed that the names of all metabolites,
which belong to C. butyricum start with letter P and so do the
names of reaction rates listed down in Tables 1and2. ForM. mazei, it
is the letter M.
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P_AKG a-ketoglutarate
P_ATP ATP
P_CO2 Dioxide
P_DHA Dihydroxyacetone
P_DHAP Dihydroxyacetonephosphate
P_E4P Erythrose-4-phosphate
P_EtOH Ethanol
P_FdH Ferredoxin (reduced)
P_FORM Formate
P_FRU6P Fructose-6-phosphate
P_GA3P Glyceraldehyde-3-phosphate
P_GLC Glycerol
P_GLU6P Glucose-6-phosphate
P_H2 Hydrogen
P_HAc Acetic acid
P_HBu Butyric acid
P_ISOCIT Isocitrate
P_LAC Lactate
P_NADH NADH
P_NADPH NADPH
P_NH4? Ammonium ions
P_OAA Oxalacetate
P_PEP Phosphoenolpyruvate
P_PYR Pyruvate
P_RIBOS5P Ribose-5-phosphate
P_RIBUL5P Ribulose-5-phosphate
P_S7P Sedoheptulose-7-phosphate
P_X Biomass
P_XYL5P Xylose-5-phosphate
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