Metabolic Modelling of Syntrophic-like Growth of a 1,3-Propanediol Producer, Clostridium Butyricum,...

7/26/2019 Metabolic Modelling of Syntrophic-like Growth of a 1,3-Propanediol Producer, Clostridium Butyricum, And a Metha

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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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DOI 10.1007/s00449-009-0359-0
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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/

508 Bioprocess Biosyst Eng (2010) 33:507523

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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

Bioprocess Biosyst Eng (2010) 33:507523 509

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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


1 3


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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


1 3


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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


1 3


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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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