i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 6 ( 2 0 1 1 ) 1 4 1 4 1e1 4 1 5 2

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Flux balance analysis of mixed anaerobic microbialcommunities: Effects of linoleic acid (LA) and pH onbiohydrogen production

Subba Rao Chaganti a, Dong-Hoon Kim b, Jerald A. Lalman a,*aDepartment of Civil and Environmental Engineering, University of Windsor, 401 Sunset Ave., Essex Hall,

Windsor, Ontario, Canada N9B 3P4bKorea Institute of Energy Research, Renewable Energy Division, 102 Gajeong-ro, Yuseong-gu, Daejeon 305-343, Republic of Korea

a r t i c l e i n f o

Article history:

Received 30 December 2010

Received in revised form

20 April 2011

Accepted 21 April 2011

Available online 2 June 2011

Keywords:

Acetogenic H2-consumers

Flux balance analysis

Mixed culture

Anaerobic

Hydrogen fermentation

Universal bacterium

* Corresponding author. Tel.: þ1 519 253 300E-mail addresses: chaganti@uwindsor.ca

0360-3199/$ e see front matter Copyright ªdoi:10.1016/j.ijhydene.2011.04.161

a b s t r a c t

The internal fluxes of mixed anaerobic cultures fed 2000 mg l�1 linoleic acid (LA) plus

glucose at 6 initial pH conditions and maintained at 37 �C were estimated using a flux

balanced analysis (FBA). In cultures fed LA at pH 7, less than 8% of the flux was diverted to

CH4. At an initial pH � 5.5, the quantity of glucose removed was greater than 95%; however,

at pH 4.5 and 5.0 the quantity consumed were 38% and 75%, respectively. The FBA output

showed that the acetogenic H2-consumers were responsible for more than 20% of the H2

consumed. Adding LA and decreasing the pH was ineffective in reducing the activity of

acetogenic H2-consumers. As the initial pH decreased, the acetogenic H2-consuming flux

decreased in the presence of 2000 mg l�1 LA. A maximumH2 yield of 1.55 mol mol�1 glucose

consumed (peak hydrogenase flux (R12)) was attained when the acetogenic H2-consuming

flux reached 0.42 mol at a pH of 5.5.

Copyright ª 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights

reserved.

1. Introduction H2-consumers and H2-producers. Hydrogen consumers such

During sugar fermentation, hydrogen (H2) plus end products

such as volatile fatty acids (VFAs) and alcohols are produced

as intermediates by acidogens and acetogens. In the first

reaction step, sugars are degraded into simple volatile fatty

acids plus alcohols by acidogens. In the next series of reac-

tions, acidogenic reaction byproducts are converted into

acetate, H2 and formate during acetogenesis. In a thermody-

namically stable anaerobic reactor, H2 does not accumulate to

elevated levels because of a syntrophic association between

0x2519; fax: þ1 519 971 36(S.R. Chaganti), dhkim772011, Hydrogen Energy P

as aceticlastic and hydrogenotrophic methanogens produced

methane (CH4), a terminal end product to maintain H2 partial

pressures between 0.1 and 10 Pa. In communities where H2

accumulates, the growth of H2-consumers is controlled by

applying a stressing agent.

Mixed anaerobic cultures utilized for producing H2 or CH4

production share two common features. In both cases,

a gaseous byproduct is generated and they essentially contain

similar microbial populations. However, one major difference

is that successful biological H2 production requires inhibition

86.@kier.re.kr (D.-H. Kim), lalman@uwindsor.ca (J.A. Lalman).ublications, LLC. Published by Elsevier Ltd. All rights reserved.

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 6 ( 2 0 1 1 ) 1 4 1 4 1e1 4 1 5 214142

of H2-consumers such as homoacetogens and hydro-

genotrophicmethanogens. A considerable amount of research

work has been conducted to understand conditions under

which H2 production is maximized in mixed anaerobic

communities. Microbial growth is affected by varying envi-

ronmental conditions (temperature and pH), changing biore-

actor engineering design variables (hydraulic retention time

(HRT)) and adding chemical agents such as BES, acetylene and

LCFAs [1e6].

Different microbial populations are affected to a certain

extent by a wide variety of stressing agents. The type and

intensity of the stress condition causes varying quantities of

electron fluxes to produce an assortment of products in

a metabolic network involving many microorganisms. The

byproduct distribution pattern is dependent upon the stress

condition. Stressing agents (physical or chemical) are applied

at a threshold value to kill, inhibit or control methanogenic

growth while enhancing growth of the H2 producing pop-

ulations [3,4]. Chemical stresses are imposed by end products

or by using chemicals which are added to control the growth

of selected microbial populations. Suppressing the growth of

H2-consuming microorganisms such as methanogens is also

accomplished by applying physical stress such as heat

(102 �C for 2 h) [5]. Under thermal stress conditions, H2

accumulation is observed together with a number of major

end products (acetate, butyrate, propionate and ethanol) at

pH 6.2 and 7.5 [5]. According to Oh et al. [5], simultaneous

accumulation of acetate and H2 clearly indicate that heat

stress was effective in inhibiting methanogens is; however,

H2 loss via acetogenesis was not prevented. In mixed

anaerobic cultures, when a stress condition is applied, the

growth of selected H2-consumers are affected and

hydrogenase enzymes and electron carriers assist with the

disposal of excess electron equivalents via H2 production

[4e6].

Under non-optimal pH conditions, organisms are forced to

survive in stressful environments by adjusting their metabo-

lism. In the low pH regime, excess electron equivalences are

not utilized by methanogens but instead, they are converted

into H2 plus reduced carbon compounds such as butyrate,

ethanol and butanol. Organisms affected by pH include acid-

producing bacteria (acidogens) and CH4-producing bacteria

(methanogens). The preferred operating pH range for acid-

ogens is 5.5e6.5 while for methanogens the range is 7.8e8.2

[7]. In an environment where both acid-producers (acidogens)

and CH4-producers (methanogens) coexist, the optimal pH

range is 6.8e7.4. Methanogenesis is considered a rate-

limiting step and if both populations are present, it is

necessary to maintain neutral pH conditions such that

methanogenic growth is unaffected [8].

In addition to pH changes, microbial stresses can be

induced by adding long chain fatty acids (LCFAs). LCFAs such

as linoleic acid (LA) and oleic acid (OA) are inhibitory to

microorganisms such as acidogens, acetogens, aceticlastic

methanogens and hydrogenotrophic methanogens [9e12].For

example, methanogens (hydrogenotrophic and aceticlastic

methanogens) and butyrate degraders are affected by

threshold LA, OA and lauric (LaA) acid levels [9,10,12,13]. In the

case of LA, increasing levels to 2000 mg l�1 LA can cause

significant H2 accumulation [14].

Developing strategies to impose stresses on H2-consumers

and redirecting electron fluxes to H2 is of critical importance

for increasing the H2 yield. To date, a significant amount of

work describing the impact of inhibitory stresses upon

hydrogenotrophic methanogens has been reported; however,

H2 consumption of via homoacetogenesis has not been

examined very extensively. The presence of acetogenic H2-

consumers (homoacetogens) is a major cause for low experi-

mental yields (reaction 1). Applying thermal stresses has not

been a very viable approach to inactivate acetogenic H2-

consumers because they can survive thermal stresses (104 �Cfor 2 h) [5]. Adding chemicals such as LCFAs could impair the

growth of acetogenic H2-consumers; however, to date

evidence using this approach has not been documented in

any study. The present work is focused on assessing the

effects of pH and LA on H2 fermentation using a flux

2CO2 þ 4H2/acetate� þHþ þ 2H2O�DG

� ¼ �95 kJ=mol�

(1)

balanced analysis (FBA). FBA is a useful tool for analyzing

electron or carbon flux distribution patterns and maximizing

the yield of products such as organic acids, amino acids,

polysaccharide and antibiotics [15e17]. FBA is also useful in

analyzing the interaction and control of metabolic pathways.

According to Varma and Palsson [18], FBA is useful tool in

quantifying metabolic physiology, simulating and interpret

experimental data, analyzing metabolic pathways for

metabolic engineering, optimizing cell culture medium and

designing and optimizing bioprocesses.

The objectives of this work are to develop and utilize an

FBA model for a mixed culture H2 producing metabolic reac-

tion network and to explain the impact of pH and LA on

experimental H2 yields using the model.

2. Material and methods

2.1. Inocula sources

The granulated anaerobic cultures utilized in this study were

acquired from wastewater facilities treating industrial efflu-

ents. The cultures (designated as A and B) were obtained from

upflow anaerobic sludge blanket (UASB) reactors located at

a brewery facility (Guelph, ON) and an ethanol manufacturing

facility (Chatham, ON). The inocula (20,000 mg l�1 VSS and

8000 mg l�1 VSS in 4 l semi-continuous reactors A and B,

respectively) were maintained at 37 �C and between pH 7.5 to

8.2. Both reactors (A and B) were fed 5000 mg l�1 glucose

(Spectrum Chemicals, CA) every 6e7 days. The quantity of

volatile fatty acids (VFAs) produced and the amount of gas

liberated for every 5e6 days was measured to establish when

all the VFA byproducts were consumed [14,19].

2.2. Experimental design

An inocula characterization study to establish the time for

converting 5000 mg l�1 glucose to CH4 was conducted at 37 �Cand at a pH between pH 7.5 to 8.2. During the H2 production

study, the total reactor volume of 50 ml contained 5000 mg l�1

glucose plus 2000 mg l�1 LA and 2000 mg l�1 VSS inocula. The

Fig. 1 e Simplified metabolic pathway of glucose

degradation by Clostridium sp. (Enzymes are indicated by

the following notation: (A) hydrogenase; (B) pyruvate-

ferredoxin oxidoreductase; (C) NADH-ferredoxin

oxidoreductase; (D) phosphate acetyltransferase; (E)

acetate kinase; (F) acetaldehyde dehydrogenase; (G)

ethanol dehydrogenase; (H) thiolase; (I) acetoacetate

decarboxylase; (J) isopropanol dehydrogenase; (K) 3-

hydroxybutyryl-CoA dehydrogenase; (L) butyryl-CoA

dehydrogenase; (M) phosphate butyryltransferase; (N)

butyrate kinase; (O) butyaldehyde dehydrogenase; (P)

butanol dehydrogenase, (Q) lactic dehydrogenase (R)

Propionate dehydrogenase (S) Pyruvate:formatelyase). This

figure is adopted and modified from Jones and Woods [29].

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 6 ( 2 0 1 1 ) 1 4 1 4 1e1 4 1 5 2 14143

reactor wasmaintained at 37 �C and the initial pH values were

adjusted from 4.5 to 7.0 in 0.5 increments. Control cultures

were prepared with only LA (no glucose) and only glucose (no

LA). All experimental conditions were examined in triplicate

[14,19].

2.3. Chemical analyses

Headspace (H2 and CH4) and liquid samples (volatile fatty

acids (VFAs), alcohols and glucose) were analyzed using gas

chromatography (GC) and ion chromatography (IC), respec-

tively [14,19]. Details of the GC and ICmethods are provided in

work reported by Ray et al. [14] and Chowdhury et al. [19]. The

detection limit for formate, acetate, propionate and butyrate

was 0.5 mg l�1 while the limits for glucose and alcohols were

1.0 and 5.0 mg l�1, respectively. The detection limits for H2

and CH4 were 0.0032 kPa [5 ml/bottle (160 ml)] and 0.0064 kPa

[5 ml/bottle (160 ml)], respectively.

2.4. Developing the concept of a universal bacterium

The basis for the FBA method is computation of in-vivo fluxes

from substrate and product data using a system of linear

equations. The system of linear equations is developed using

the metabolic reaction stoichiometry [20e23]. The method is

applicable to mixed culture systems; however, it is necessary

to introduce the concept of a universal organism [24].

According to Rodrı́guez et al. [24] the universal organism

produces all the metabolites which are observed during H2

fermentation. The universal bacterium concept is based on

a thermodynamic definition of an open system which

continuously interacts with its environment. This interaction

can take the form of energy or material transfers into or out

of the system boundary. Microorganisms acquire nutrients

from their surrounding environment and expel waste and

other products. In this concept, sequential reactions operate

close to their equilibrium conditions [25].

Understanding the complex metabolic network among

organisms which mediate the different reactions is the main

challenge in developing the FBA model for microbial H2

production application [26]. In this analysis, microorganisms

synthesize and allocate metabolic capability in a manner to

optimally utilize the electron donors and electron acceptors.

A flux balancemodel was first reported by Stolyar et al. [27]

for a twomicroorganismmicrobial system. This simplemodel

for a mixed system represented a step towards a larger

modeling effort although it consists of only 170 reactions and

147 metabolites. The proposed metabolic network is based on

a multitude of byproducts (acids and solvents) produced by

Clostridium sp, Lactobacillus and Seleomanas sp. These

organisms can produce a variety of metabolites and they

mediate many microbial reactions which are involved in H2

production (Fig. 1, Appendices A and B) [23,28,29]. Clostridium

sp. produces lactate, propionate, formate, acetate, butyrate,

ethanol, butanol, acetone, propanol, H2 and CO2 [30e33]. Even

though formate, lactate and propionate are produced by

Clostridium sp., these production routes are not major

pathways. The metabolic pathways for lactic acid bacteria

(LAB) (R4 and R6, Table 1 and Appendix B) and propionic acid

bacteria (PAB) (R4, R6, and R8, Table 1 and Appendix B) are

included in the proposed scheme (Fig. 2 and Table 1). For

example, the pathways for LAB and PAB are based on the

heterofermentative pathway of Lactobacillus sp. and

Selenomonas sp., respectively [34,35]. For formate production,

the CO2 reduction pathway (R5, Fig. 2 and Appendix B) of

Selenomonas sp. is included. Valerate (R14, Fig. 2 and

Appendix B) and caproate (R26, Fig. 2 and Appendix B)

production are also included in the network because these

medium chain fatty acids have been observed by several

researchers during H2 production [36,37]. Additionally,

acetate production via acetogenic H2-consumers (R17, Fig. 2

and Appendix B) is included in the analysis. Because the

reactions observed in pure anaerobic cultures are also

observed in mixed culture communities, they serve a basis

for developing a universal bacterium metabolic network.

Hence, the principal byproducts produced by the proposed

metabolic network for the universal bacterium includes H2,

CO2, acetate, butyrate, lactate, formate, propionate, valerate,

caproate, ethanol, butanol, acetone and propanol [4,6].

2.5. Metabolic model and basic reactions for a universalbacterium

The metabolic network of a H2 producing community is

essentially a series of interconnected redox microbial

Table 1 e Stoichiometric matrix model for the universal bacterium.

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30

Intra-cellular GLC 1 �1 �1 �1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

PYR 0 0 0 2 0 �1 0 0 0 �1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

NADH 0 0 0 2 �1 �1 0 �1 0 0 �1 0 0 0 0 0 0 �2 0 0 0 0 �2 0 0 0 �1 0 0 0

ACCOA 0 0 0 0 0 0 0 0 0 1 0 0 0 0 �1 0 0 �1 �2 0 0 0 0 0 0 0 0 0 0 0

Fdþ 0 0 0 0 0 0 0 0 0 2 2 �2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

ACACCOA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 �1 0 0 �1 0 0 0 0 0 0 0

BTCOA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 �1 0 0 �1 0 0 0

H2 0 0 0 0 0 0 0 0 0 0 0 1 �1 �6 0 0 �4 0 0 0 �1 0 0 0 0 �6 0 0 �4 0

HAc 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 �1 1 0 0 0 0 0 0 0 0 0 0 �1 0 0

HPr 0 0 0 0 0 0 0 1 �1 0 0 0 0 �1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

HBu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 �1 �1 0 0 0 0

HLa 0 0 0 0 0 1 �1 �1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Act 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 �1 �1 0 0 0 0 0 0 0 0

CH4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 �1

Extra-cellular GLC (ext) �1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Biomass 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Res GLC 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

H2 (ext) 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

HBu (ext) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

HAc (ext) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

HLa (ext) 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

HPr (ext) 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

HCa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

HVa 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

HFo 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

EtOH 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

PrOH 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

BuOH 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

Act (ext) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

CH4 (ext) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

internatio

naljo

urnalofhydrogen

energy

36

(2011)14141e14152

14144

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 6 ( 2 0 1 1 ) 1 4 1 4 1e1 4 1 5 2 14145

reactions. For the flux calculations, 14 intra-cellular and 16

extracellular compounds (Table 1) were considered in

configuring the network (Appendix A). The stoichiometric

reactions associated with each compound are listed in Table

1 and Appendix B. In the flux calculations, many reactions

as possible were considered in developing the proposed

generalized metabolic scheme (Fig. 2). Intermediates not

affecting the flux were not included in the metabolic

network. For example, reactions such as for example,

glucose to glucose 6-phosphate (G6P), G6P to fructose 6-

phosphate (F6P), F6P to fructose 1,6-biphosphate, etc were

excluded [38] and the conversion of GLC to pyruvate was

condensed into a single step.

Compounds which are involved in the metabolic network

but not originating from glucose degradation cannot

contribute to the flux (Table 1). For example, CoA and electron

acceptors/donors such as NADþ/NADH and Fd2þ/Fd1þ are

recycled and associated with mediating many reactions

(R10, R15, R18, R19, R20, R24, and R27). In the FBA analysis,

the reaction network takes into account every metabolite

and cofactor. The electron equivalents per mol of substrate

is listed in Appendix A for glucose and many of its

metabolites. In Appendix B, except for R1, R2, R3, R7, R9, R13

and R22, the remaining reactions are a couple between an

oxidation and a reduction half-reaction. For example, 1 mol

of NADH generates 2 mol electrons and according to

equation R11 (Appendix B), 2 mol of Fdþ2 are reduced to

produce 2 mol of Fdþ þ 1 mol of oxidized NADþ. The

anaerobic biomass yield is assumed to be relatively low.

According to several reports, the biomass yield can range

from 5 to 20% of the electron donor in anaerobic cultures fed

glucose [39e41]. In this study, the biomass yield ranged from

11 to 22% (w/w) of the electron donor.

Many metabolites might be present as intra-cellular or

extracellular intermediates. Hence, two expressions were

defined to describe the function of compounds such as acetic

acid (HAc), lactic acid (HLa) and butyric acid (HBu) in the

metabolic network. In order to distinguish the function of

Fig. 2 e Metabolic flux analysis of the proposed universal bacter

stoichiometry for each reaction is given in Appendix B.)

several compounds, the notation ‘ext’ is used to denote the

final extracellular product.

2.6. Flux based models

A 30 � 30 matrix (Table 1) was developed to describe the

metabolic reaction network for the proposed universal

bacterium. The first 14 rows in Table 1 correspond to intra-

cellular compounds while the remaining 16 rows refer to

extracellular compounds. Multiplication of the 30 � 30

matrix by a 30 � 1 flux vector (R1-R30) produces a vector for

all the intra-cellular and extracellular compounds in

equation (2). The stoichiometric matrix of the metabolic

network ðjSijjÞ is 30 � 30, nj is the reaction flux or rate vector

matrix is 30 � 1 and n̂ij is a 30 � 1 net metabolic output

vector. Equation (2) is normally undetermined since the

number of fluxes exceeds the number of metabolites [43].

Because of the large number of solutions which exists,

a particular solution to equation (2) can be determined using

linear optimization and stating an objective. An optimal

solution can be determined within a defined stoichiometric

domain.

A flux based model typically involves optimizing a set of

fluxes such that a particular cellular objective is achieved. For

the mixed culture system under consideration, the stoichio-

metric matrix consists of 30 metabolites and 30 reactions. The

steady-state metabolite concentrations are given by the

following equation:

n̂ij ¼ SjSijnj (2)

where Sij is the stoichiometric coefficient of metabolite Ai in

reaction j and nj is the flux of reaction j or the reaction vector.

The convention used for assigning values to Sij is as follows: 1.

If metaboliteAi is a substrate in reaction j, then Sij < 0 and 2. If

Ai is a product then Sij> 0. Any positive fluxes vector {nj} which

satisfies equation (2) corresponds to a state of the metabolic

network and hence, a potential state of operation of the cell.

ia in a mixed culture H2 and acetate producing culture (The

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 6 ( 2 0 1 1 ) 1 4 1 4 1e1 4 1 5 214146

This study is restricted to the subspace of solutions for which

all components of n satisfy the constraint nj > 0 [42].

2.7. Flux balance analysis (FBA) and metabolic fluxanalysis (MFA)

Many researchers have used the FBA or MFA approach to

predict internal and external fluxes. Explaining the differ-

ences between the FBA and MFA techniques is important

because they are based on steady-state assumptions which

are focused on predicting metabolic fluxes [43]. Selecting

between the FBA and MFA method depends upon the

number of measurements which are available. In the MFA

technique, the number of flux measurements exceeds the

rank of Sij, so that all fluxes are estimated by the residual

minimization criteria. In comparison, the FBA technique

computes all unknown fluxes when Sij is under-determined

[44]. In this study, the FBA approach was selected to

determine the fluxes.

2.8. Objective functions

Determining the objective function that living systems follow

and constraints causing them to diverge from this function

are the subject of many reports [45,46]. In the case of the flux

based model for living systems, the objective functions for

prokaryotic metabolism can include energy production

[47,48], biomass growth [43,49e53]. The objective function is

expressed in terms of the system variables which can be

manipulated. These variables are denoted as the decision

variables. Maximization or minimization of the objective

function with respect to the decision variables leads to opti-

mization of the system. The rationale behind selecting

a particular objective function is based on the fact that the

organism will maximize its performance under conditions to

which it is adapted [54].

2.9. Thermodynamic constraints

Besides constraints on themass balance equations, additional

constraints on the biochemical reactions are based on ther-

modynamic considerations. For a chemical or transport

process to become favorable, the Gibbs free energy change

must be negative.

2.10. Model development

Many studies have reported using the MetaFluxNet optimiza-

tionprocedure to solvesystemsof linearequations foravariety

ofmetabolic networks [25,50,54e56]. In this study, acetate and

acetone production were selected as the objective functions

and the linear optimization program, MetaFluxNet (Version

1.8.6.2), was used to solve the system of linear equations.

Although 16 items were identified as extracellular

compounds, the value for glucose uptake (GLC (ext)) was

neglected from the flux calculation in order to avoid redun-

dancy. Calculating the degrees of freedom (DOF) is based on

the difference between the number of fluxes and intra-cellular

compounds. In this case, theDOF is 16 (30e14). If 16 itemswere

input into the model, then this would cause the system of

equations to become over specified. Outputs from the FBA

were developed for the different pH conditions (4.5e7.0) in

presenceof LAandwithout LA for pH5.5 (control). In each case,

the mathematically calculated value for GLC (ext) was similar

to the experimental value.

2.11. Maximizing the H2 yield using the FBA model

Hydrogen production (R12), reutilization of H2 for acetate

production, that is, the acetogenic H2-consuming reaction

(R17) and the net H2 production is shown in Fig. 3 (A and B).

Hydrogen consumption also proceeds by reactions denoted

as R14, R21, and R26. However, valerate, propanol, and

caproate were not detected in the liquid metabolite and the

net H2 produced was calculated by considering R12, R15, R24

and R17. According to the proposed model, acetate can be

derived from R15 (production from acetyl CoA (ACCOA) and

R17 (production from CO2 and H2). The net H2 formation

during acetate production is R15 � 2 mol H2 mol�1 hexose -

R17 � 4 mol H2 mol�1 hexose. The experimental net H2

produced per mol hexose was calculated as follows: R13 ¼(R24 � 2 mol H2 mol�1 hexose) þ (R15 � 2 mol H2 mol�1

hexose) - (R17 � 4 mol H2 mol�1 hexose). A maximum H2

yield (4.0 mol H2 mol�1 glucose) is theoretically feasible with

the acetate (R16) or acetone (R22) production. Hence, these

reactions are considers as the objective functions. Hydrogen

production (R13) was not selected because an unlimited

number of solutions are possible by selecting R13. According

to Jones and Wood [29], a maximum H2 yield of

4.0 mol mol�1 glucose is possible if acetate and/or acetone

are metabolites and none of the electron donor is used to

produce new cells. The flux distribution for conditions under

which acetate (R16) or acetone (R22) is the only metabolite

with a theoretical H2 yield of 4.0 mol mol�1 glucose

according to equations (3) and (4).

C6H12O6 þ 2H2O/2CH3COOHþ 2CO2 þ 4H2 (3)

C6H12O6 þH2O/CH3CðOÞCH3 þ 3CO2 þ 4H2 (4)

The flux analysis suggests that the R11 reaction should be

dominant during the production of acetate and acetone. This

reversible reaction is unfavorable in the presence of NADH [57].

The FBA analysis shows that R11 proceeded in the opposite

direction in one of the six experimental conditions. Based on

this observation, R11 was indicated as reversible in the list of

reactions for the MetaFluxNet program (Appendix B).

Inmixed anaerobic communities, differentmetabolites are

produced under a wide range of pH conditions and this has

a major impact on the theoretically maximumH2 yield as well

as the flux distribution. The experimental data used in the FBA

model and the flux value for each reaction including the

acetogenic H2-consuming reaction is discussed in the Results

and Discussion section.

2.12. Metabolic flux and enzyme activity

Explaining the relationship between flux and enzyme activity

is essential because the experimental results clearly show

a correlation between flux and pH. According to the

Fig. 3 e Molar fluxes for maximum H2 production at different pH values (A: Initial pH [ 4.5, 5.0 and 5.5; B: Initial pH [ 6.0,

6.5 and 7.0; Rn (n [ 1 to 30) is used to denote the reaction number; The FBA analysis is based on 1 mol of glucose.)

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 6 ( 2 0 1 1 ) 1 4 1 4 1e1 4 1 5 2 14147

summation theorem for flux control coefficients [58],

increasing the metabolic flux causes an increase in the

enzyme activity. Altering the enzyme activity to effect

a change in the metabolic flux is linked to changes in one or

more metabolite concentration.This is anticipated, since

increasing the enzyme activity in a linear section of

a metabolic network decreases the metabolite concentration

on the reactant side of the equation and increases them on

the product side [59]. This linear relationship is valid when

the flux control coefficient is close to 1. According to Fell

[60,61], the relationship between the flux and the amount of

enzyme is approximately hyperbolic in many cases.

However, the hyperbolic relationship is not guaranteed and

significant deviations are possible under selected conditions

[62].

3. Results and discussion

3.1. Optimal flux distributions

The optimum metabolic pathway for H2 production predicted

in silico is shown in Fig. 2. Glucose is converted to pyruvate and

finally to H2 plus acetate by a sequence of reactions. However,

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 6 ( 2 0 1 1 ) 1 4 1 4 1e1 4 1 5 214148

the pathway in which lower yields are observed is different

from that shown in Fig. 2 because the mixture contains

a variety of carbon byproducts (Fig. 3 (A and B)). The H2 yield

is calculated based on the H2 flux divided by the glucose flux

(quantity consumed) [63].

Fig. 5 e Flux versus pH. (A: Hydrogen and VFAs; B:

Hydrogen and alcohols).

3.2. Analyzing the impact of pH and LCFA using FBA

Under oxidizing conditions, the formation of acetate and

butyrate accompanies the production of reducing equivalents

while the consumption of reducing equivalents is associated

with the production of lactate, propionate and ethanol. A

dominant microbial population controls the distribution of

fermentation products. For example, microbial communities

containing elevated levels of acetate producers are associated

with high H2 yields [64].

The activity of two major H2-consumers, homoacetogens

and hydrogenotrophic methanogens was a function of pH

(Fig. 4). Chemical addition and decreasing pH and are among

the various factors which are effective in decoupling the

syntrophic interaction between H2-producers and H2-

consumers. According to Ray et al. [14] using a combination

of pH and LA is more effective than applying either factor

alone. In this work, the metabolic flux for various reactions

associated with H2 production were assessed using mixed

anaerobic cultures fed with a constant amount of LA plus

glucose at varying initial pH levels. At pH 5.5, the magnitude

of the fluxes indicates that the levels of acetate and ethanol

produced were greater than that for butyrate, propionate,

lactate and propanol. Producing large quantities of short

chain carbon compounds suggest a larger flux of electron

equivalents was diverted to H2. The H2 flux (1.42 mol) and

yield (1.55 mol mol�1 glucose consumed) reached

a maximum at an initial pH of 5.5 (Fig. 5). When

methanogenesis was inhibited, the excess electron

equivalents were converted to H2 via reaction R12 under low

pH conditions. The FBA predicted the H2 flux attained a peak

value when the acetate, butyrate and ethanol fluxes reached

threshold levels at an initial pH of 5.5 (Fig. 5 (A and B)).

Increasing acetate and ethanol fluxes with increasing pH

(from 4.5 to 5.5) indicate both pathways were active over this

Fig. 4 e Homoacetogenic (4 3 R17) and hydrogenotrophic

(4 3 R29) fluxes under different initial pH conditions.

range while with increasing pH beyond 6.0, the production

of acetate and ethanol remained constant. The inactivity of

acetate and ethanol producers and increasing activity of

propionate producers at pH values greater 6.0 are linked to

decreasing H2 levels (Fig. 5 (A and B)). Increasing acetate and

ethanol levels below pH 6.0 is consistent with observations

by Ren et al. [65]. They reported H2 production in a pH range

of 4.0e4.5 during ethanol-type fermentation using

acidophilic bacteria. In this study, beyond a pH of 5.5, the

acetate and ethanol fluxes reached a constant value while

the butyrate flux gradually decreased. Under these

conditions, the H2 flux decreased from 1.42 mol (pH 5.5) to

0.36 mol (pH 7.0).

Production of acetate, butyrate plus ethanol could be due to

switching from acidogenesis to solventogenesis as the pH

decreased over the duration of the experiment. The H2 yield

reached a maximum during acetate and butyrate production.

However, as the pH decreased to a threshold value due to VFA

production, the pathway switched to ethanol production. In

this pathway, acetate is used as a H2 acceptor and it is

subsequently reduced to ethanol.

3.3. Hydrogen producing and hydrogen consumingreactions e Ferredoxin reduction (R13) and net production(R12), acetogenic (R17), methanogenic (R29) and acetonereduction (R21)

Themaximumtheoretical (R12)andnet (R13)H2yieldattainedat

an initial pH 5.5were 1.96mol and 1.42mol (Fig. 6), respectively.

Although a minimum (R12-R13 ¼ 4 � R17 þ 4

� R29 þ R21 ¼ 0.48 mol) quantity of H2 diverted to

homoacetogenesis (4 � R17), hydrogenotrophic methaogenesis

(4 � R29) and acetone reduction (R21) was observed at pH 5.0,

Fig. 6 e H2-producers (R12) and H2-consumers

(4 3 R17 D 4 3 R29 D R21) fluxes under different pH

conditions.

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 6 ( 2 0 1 1 ) 1 4 1 4 1e1 4 1 5 2 14149

a maximum net flux was attained at pH 5.5 with a loss of

0.54 mol to the H2-consumers. A major reduction in the H2

yields were observed at pH 6.5 to 7.0 (0.81e0.91 mol) and at pH

4.5 (0.81 mol). The metabolic flux was assumed to correlate

with the enzyme activity and in this study, increasing

acetogenic H2-consuming activities were observed in cultures

fed 2000 mg l�1 LA plus increasing initial pH values. Kim et al.

[66] observed homoacetogenic activity at pH values < 5.0 and

according to Calli et al. [67], increasing activity was correlated

with increasing pH from 5.0 to 6.0. In other studies, Oh et al. [5]

reported optimum homoacetogenic activity at pH 6.0.

The FBA demonstrated that homoacetogenesis was the

major mechanism accounting for H2 losses at initial pH

values between 4.5 and 7.0 while at pH values �6.5, hydro-

genotrophicmethanogenesis was the dominant H2-consuming

mechanism (Fig. 4). At pH � 6.0, no hydrogenotrophic

methanogenic activity was observed while homoacetogenic

activity was reduced by approximately 50% when the initial

pH increased from 4.5 to 5.0. Hydrogen losses due to

hydrogenotrophic methanogenesis reached a minimum when

the initial pH values were <6.5. In this study, suppressing

hydro-genotrophic methanogenesis by simultaneously adding

LA and reducing the pH increased the H2 yield [14]. Notice the

H2-consuming acetogenic (homoacetogenic) activity was

unaffected by pH even when the pH was adjusted to 5.0

(Fig. 4). Although several studies have provided evidence of H2

consumption by homoacetogens, strategic methods to inhibit

the growth of these organisms have not been reported. Hence,

understanding stress mechanisms to inhibit the growth of

H2-consuming acetogens is a major research priority if

increasingH2 yields are to be attained inmixedculture systems.

Hydrogenotrophic methanogens and homoacetogens are

unique because they reduce C1 carbon compounds by

consuming electron equivalents. The theoretical (R12) and net

H2 fluxes predicted by the FBA are depicted in Fig. 6. Notice

losses due to H2 consumption (30%e75% (R12eR13)) were

mediated by homoacetogens (R17 and reaction (1)) and

hydrogenotrophic methanogens (R29). The hydrogenotrophic

methanogenic reaction (ΔG ¼ �130.7 kJ mol�1) is

thermodynamically more feasible in comparison to the

homoacetogenic reaction (ΔG ¼ �95 kJ mol �1) and hence, CH4

production from H2 plus CO2 proceeds preferentially. If

a stress condition such as decreasing the pH is imposed upon

the hydrogenotrophic methanogenic population, CH4

production from CO2 reduction is inhibited and H2 production

is expected to increase. However, in the presence of

homoacetogens and hydrogenotrophic methanogens, low H2

yields are observed because the H2-consuming activity is not

completely inhibited under low pH conditions and in the

presence of LA.

4. Conclusions

In this study, an FBA was used to describe the metabolic

fluxes as a function of initial pH in presence of LA for a mixed

anaerobic community. Hydrogen production using mixed

anaerobic microorganisms is accompanied with the

production of various acids and solvents. The sum of acetate

and butyrate levels or acetate to butyrate ratio has been used

as an indicator for high or low H2 yields. However, there has

been no attempt to describe how eachmetabolite contributes

to the H2 yield. Although the maximum H2 flux was observed

at an initial pH of 5.5, the minimum quantity of H2 lost to H2-

consumers was observed at pH 5.0. Increasing acetate and

ethanol levels with increasing pH to 5.5 correlated with

a maximum H2 flux of 1.42 mol (maximum H2 yield of

1.55 mol mol�1 glucose consumed). Hydrogenotrophic

methanogenic activity was completed inhibited with

a combination of LA plus pH adjustment (4.5e6.0). However,

the activity gradually increased with increasing pH values

(6.0e7.0). In the case of homoacetogens, lowering the pH

from 5.0 to 4.5 caused a small increase in the acetogenic H2-

consuming flux. Although the growth of H2-consumers such

as hydrogenotrophic methanogens can be controlled by

adjusting the pH, control of homoacetogens is more difficult

because their growth is unaffected by pH adjustment in the

presence of LA. Hence, increasing H2 yields in mixed micro-

bial communities must rely on strategies other than pH and

LA addition to inhibit the growth of acetogenic H2-

consumers.

The FBA approach provides a means of increasing our

understanding of the complex metabolic reactions involved

in mixed culture H2 fermentation systems. The acetogenic

H2-consuming activity was estimated using the FBA proce-

dure. Adding 2000 mg l�1 LA and reducing the pH was

ineffective in reducing the growth of acetogenic H2-

consumers. This analysis could be applied to many H2

fermentation systems; however, the acetogenic activity

must be carefully examined because it can be affected by

operation parameters such as culture source pH and

temperature.

Acknowledgements

Financial support for this work was provided by the Natural

Sciences and Engineering Research of Canada (NSERC), the

Appendix B (continued)

Reaction number Reaction

R13 H2 / H2 (ext)

R14 HPr þ 6H2 / HVa [68]

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 6 ( 2 0 1 1 ) 1 4 1 4 1e1 4 1 5 214150

Canada Research Chair program and the University of Wind-

sor. The FBA software was provided by Dr. Sang Yup Lee,

Department of Chemical & Biomolecular Engineering, Korea

Advanced Institute of Science andTechnology, 373-1Guseong-

dong, Yuseong-gu, Daejeon 305-701, Republic of Korea.

R15 ACCOA / HAc þ CoA [29,38]

R16 HAc / HAc (ext)

R17 4H2 þ CO2 / HAc [38]þ

Appendix A

Compound nomenclature and their molar electronequivalent per mol of substrate.

Compound Abbreviation e-equivalence/mol

Acetic acid HAc 8

Acetic acid (ext) HAc (ext) 8

Acetoacetyl-CoA ACACCOA 17

Acetone Act 16

Acetyl-CoA ACCOA 9

Butanol BuOH 24

Butyric acid HBu 20

Butyric acid (ext) HBu (ext) 20

Butyryl-CoA BTCOA 21

Caproic acid HCa 32

Ethanol EtOH 12

Ferredoxin (oxidized) Fdþ 1

Formic acid HFo 2

Glucose GLC 24

Hydrogen H2 2

Hydrogen (ext) H2 (ext) 2

Lactic acid HLa 12

Lactic acid (ext) HLa (ext) 12

Methane CH4 8

Methane (ext) CH4 (ext) 8

Nicotinamide adenine NADH 2

dinucleotide (reduced)

Propanol PrOH 18

Propionic acid HPr 14

Propionic acid (ext) HPr (ext) 14

Pyruvate PYR 10

Residual glucose ResGLC 24

Valeric acid HVa 26

R18 ACCOA þ 2NADH/ EtOH þ 2NAD þ CoA

[29,38]

R19 2 ACCOA / ACACCOA þ CoA [29,38]

R20 ACACCOA / Act þ CoA þ CO2 [29,38]

R21 Act þ H2 / PrOH [38]

R22 Act / Act (ext)

R23 ACACCOA þ 2NADH / BTCOA þ 2NADþ

[29,38]

R24 BTCOA / HBu þ CoA [29,38]

R25 HBu / HBu (ext)

R26 HBu þ 6H2 / HCa [68]

R27 BTCOAþ 2NADH/ BuOHþ 2NADþ þ CoA

[29,38]

R28 HAc / CO2 þ CH4 [38]

R29 CO2 þ 4H2 / CH4 þ 2H2O [38]

R30 CH4 / CH4 (ext)

ext. ¼ external to the cell.

Appendix B

List of reactions in the proposed metabolic reactionnetwork.

Reaction number Reaction

R1 GLC (ext) / GLC

R2 GLC / Biomass

R3 GLC / Res GLC

R4 GLC þ 2NADþ / 2PYR þ 2NADH [29,38]

R5 NADH þ CO2 / NADþ þ HFo [29,38]

R6 PYR þ NADH / HLa þ NADþ [29,38]

R7 HLa / HLa (ext)

R8 HLa þ NADH / HPr þ NADþ [38]

R9 HPr / HPr (ext)

R10 PYRþCoAþ 2Fd2þ/ACCOAþCO2þ 2Fdþ

[29,38]

R11 NADH þ 2Fd2þ 4 NADþ þ 2Fdþ [29,38]

R12 2Fdþ þ 2Hþ / 2Fd2þ þ H2 [29,38]

r e f e r e n c e s

[1] Sparling RD, Risbey D, Poggi-Varaldo HM. Hydrogenproduction from inhibited anaerobic composters. Int JHydrogen Energy 1997;22:563e6.

[2] Ray S, Saady NMC, Lalman JA. Diverting electron fluxes tohydrogen in mixed anaerobic communities fed with glucoseand unsaturated C18 long chain fatty acids. ASCE J EnvirEngrg 2010;136:568e75.

[3] Hawkes FR, Hussy I, Kyazze G, Dinsdale R, Hawkes DL.Continuous dark fermentative hydrogen production bymesophilic microflora: Principles and progress. Int JHydrogen Energy 2007;32:172e84.

[4] Li C, Fang HHP. Fermentative hydrogen production fromwastewater and solid wastes by mixed cultures. Crit RevEnviron Sci Technol 2007;37:1e39.

[5] Oh SE, van Ginkel S, Logan BE. The relative effectiveness ofpH control and heat treatment for enhancing biohydrogengas production. Environ Sci Technol 2003;37:5186e90.

[6] Hawkes FR, Dinsdale R, Hawkes DL, Hussy I. Sustainablefermentative hydrogen production: Challenges for processoptimization. Int J Hydrogen Energy 2002;27:1339e47.

[7] Khanal SK. Overview of anaerobic biotechnology. In:Khanal SK, editor. Anaerobic biotechnology for bioenergyproduction: Principles and applications. Oxford, UK: Wiley-Blackwell; 2009. p. 1e27.

[8] Noike T, Endo G, Chang J-E, Yaguchi J-I, Matsumoto JI.Characteristics of carbohydrate degradation and the rate-limiting step in anaerobic digestion. Biotechnol Bioeng 1985;27:1482e9.

[9] Mykhaylovin O, Roy JM, Jing N, Lalman JA. Influence of C18

long chain fatty acids on butyrate degradation by a mixedculture. J Chem Technol Biotechnol 2005;80:169e75.

[10] Lalman JA, Bagley DM. Anaerobic degradation and inhibitoryeffects of linoleic acid. Water Res 2000;34:4220e8.

[11] Hwu C-S, Lettinga G. Acute toxicity of oleate toacetate-utilizing methanogens in mesophilic andthermophilic anaerobic sludges. Enzym Microb Technol1997;21:297e301.

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 6 ( 2 0 1 1 ) 1 4 1 4 1e1 4 1 5 2 14151

[12] Koster IW, Cramer A. Inhibition of methanogenesis fromacetate in granular sludge by long-chain fatty acids. ApplEnviron Microbiol 1987;1987(53):403e9.

[13] Lalman JA, Bagley DM. Effects of C18 long chain fatty acidson glucose, butyrate and hydrogen degradation. Water Res2002;36:3307e13.

[14] Ray S, Chowdhury N, Lalman JA, Seth R, Biswas N. Impact ofinitial pH and linoleic acid (C18:2) on hydrogen production bya mesophilic anaerobic mixed culture. ASCE J Environ Eng2008;134:110e7.

[15] Edwards JS, Covert M, Palsson BØ. Metabolic modeling ofmicrobes: the flux-balance approach. Environ Microbiol 2002;4:133e40.

[16] Kim HB, Smith CP, Micklefield J, Mavituna F. Metabolic fluxanalysis for calcium dependent antibiotic (CDA) productionin Streptomyces coelicolor. Metab Eng 2004;6:313e25.

[17] Stephanopoulos GN, Aristidou AA, Nielsen J. Metabolicengineering principles and methodologies. San Diego:Academic Press; 1998.

[18] Varma A, Palsson BO. Metabolic flux balancing: Basicconcepts, scientific and practical use. Nat Biotechnol 1994;12(10):994e8.

[19] Chowdhury N, Lalman JA, Seth R, Ndegwa P. Biohydrogenproduction by mesophilic anaerobic fermentation of glucosein the presence of linoleic acid. ASCE J Environ Eng 2007;133:1145e52.

[20] Papoutsakis ET. Equations and calculations forfermentations of butyric acid bacteria. Biotechnol Bioeng1984;26:174e87.

[21] Manish S, Venkatesh KV, Banerjee R. Metabolic flux analysisof biological hydrogen production by Escherichia coli. Int JHydrogen Energy 2007;32:3820e30.

[22] Oh YK, Kim HJ, Park S, Kim MS, Ryu DDY. Metabolic-fluxanalysis of hydrogen production pathway in Citrobacteramalonaticus Y19. Int J Hydrogen Energy 2008;33:1471e82.

[23] Cai G, Jin B, Saint C, Paul M. Metabolic flux analysis ofhydrogen production network by Clostridium butyricum W5:effect of pH and glucose concentrations. Int J HydrogenEnergy 2010;35:6681e90.

[24] Rodriguez J, Kleerebezem R, Lema JM, van Loosdrecht MCM.Modeling product formation in anaerobic mixed culturefermentation. Biotechnol Bioeng 2006;93:592e605.

[25] Morris JG. Thermodynamics of biological systems. Br JAnaesth 1974;46:210e6.

[26] Dias JML, OehmenA, SerafimLS, Lemos PC, ReisMAM,Olive R.Metabolic modelling of polyhydroxyalkanoate copolymersproduction bymixedmicrobial cultures. BMC Syst Biol 2008;2:59, http://www.biomedcentral.com/1752-0509/2/59.

[27] Stolyar S, Van Dien S, Hillesland KL, Pinel N, Lie TJ, Leigh JA,et al. Metabolic modeling of a mutualistic microbialcommunity. Mol Syst Biol 2007;3:1e14.

[28] Desai RP, Harris LM, Welker NE, Papoutsakis ET. Metabolicflux analysis elucidates the importance of the acid-formationpathways in regulating solvent production by Clostridiumacetobutylicum. Met Eng 1999;1:206e13.

[29] Jones DT, Woods DR. Acetone-butanol fermentationrevisited. Microbiol Rev 1986;50:484e524.

[30] Hussy I, Hawkes FR, Dinsdale R, Hawkes DL. Continuousfermentative hydrogen production from a wheat starch co-product by mixed microflora. Biotechnol Bioeng 2003;84:619e26.

[31] KotsopoulosTA,ZengRJ, Angelidaki I. Biohydrogenproductionin granular up-flow anaerobic sludge blanket (UASB) reactorswith mixed cultures under hyper-thermophilic temperature(70�C). Biotechnol Bioeng 2006;94:296e302.

[32] Kim DH, Kim SH, Ko IB, Lee CY, Shin HS. Start-up strategy forcontinuous fermentative hydrogen production: earlyswitchover from batch to continuous operation. Int JHydrogen Energy 2008;33:1532e41.

[33] Kim DH, Kim SH, Shin HS. Sodium inhibition of fermentativehydrogen production. Int J Hydrogen Energ 2009;34:3295e304.

[34] Stiles ME, Holzapfel WH. Lactic acid bacteria of foods andtheir current taxonomy. Int J Food Microbiol 1997;36:1e29.

[35] Paynter LJB, Elsden SR. Mechanism of propionate formationby Selenomonas ruminantium, a rumen microorganism. J GenMicrobiol 1970;60:1e7.

[36] Wang Y, Mu Y, Yu HQ. Comparative performance of twoupflow anaerobic biohydrogen-producing reactors seededwithdifferent sludges. Int JHydrogenEnergy 2007;32:1086e94.

[37] Mu Yang, Yu Han-Qing, Wang Yi. The role of pH in thefermentative H2 production from an acidogenic granule-based reactor. Chemosphere 2006;64:350e8.

[38] Gottschalk G. Bacterial metabolism. 2nd ed. New York:Springer-Verlag; 1985. p. 17.

[39] Kim DH, Han SK, Kim SH, Shin HS. Effect of gas sparging oncontinuous fermentative hydrogen production. Int JHydrogen Energy 2006;31:2158e69.

[40] Jo JH, Lee DS, Park JM. The effects of pH on carbon materialand energy balances in hydrogen-producing Clostridiumtyrobutyricum JM1. Bioresour Technol 2008;99:8485e91.

[41] Lee HS, Salerno MB, Rittmann BE. Thermodynamicevaluation on H2 production in glucose fermentation.Environ Sci Technol 2008;42:2401e7.

[42] Varma A, Palsson BØ. Metabolic capabilities of Escherichia coli:I. Synthesis of biosynthetic precursors and cofactors. J TheorBiol 1993;165:477e502.

[43] Rodrı́guez A, Infante D. Network models in the study ofmetabolism. Electronic J Biotechnol 2009;12:1e19.

[44] Stephanopoulos GN, Aristidou AA, Nielsen J. Metabolicengineering: principles and methodologies. San Diego:Academic Press; 1998.

[45] Vallino JJ. Modeling microbial consortiums as distributedmetabolic networks. Biol Bull 2003;204:174e9.

[46] Price ND, Reed JL, Palsson BØ. Genome-scale models ofmicrobial cells: evaluating the consequences of constraints.Nat Rev Microbiol 2004;2:886e97.

[47] Edwards JS, Palsson BØ. The Escherichia coli MG1655 in silicometabolic genotype: its definition, characteristics, andcapabilities. Proc Natl Acad Sci USA 2000;97:5528e33.

[48] Varma A, Palsson BØ. Stoichiometric flux balance modelsquantitatively predict growth and metabolic by-productsecretion in wildtype Escherichia coli W3110. Appl EnvironMicrobiol 1994;60:3724e31.

[49] Edwards JS, Ibarra RU, Palsson BØ. In silico predictions ofEscherichia coli metabolic capabilities are consistent withexperimental data. Nat Biotechnol 2001;19:125e30.

[50] Kim S, Seol E, Oh Y-K, Wang GY, Park S. Hydrogen productionand metabolic flux analysis of metabolically engineeredEscherichia coli strains. Int J Hydrogen Energy 2009;34:7417e27.

[51] Ramakrishna R, Edwards JS, McCulloch A, Palsson BO. Flux-balance analysis of mitochondrial energy metabolism:consequences of systemic stoichiometric constraints. Am JPhysiol Regul Integr Comp Physiol 2001;280:R695e704.

[52] Varma A, Boesch BW, Palsson BO. Stoichiometricinterpretation of Escherichia coli glucose catabolism undervarious oxygenation rates. Appl Environ Microbiol 1993;59:2465e73.

[53] Henriksen CM, Christensen LH, Nielsen J, Villadsen J. Growthenergetics and metabolic fluxes in continuous cultures ofPenicillium chrysogenum. J Biotechnol 1996;45:149e64.

[54] Lee JM, Gianchandani EP, Papin JA. Flux balance analysis inthe era of metabolomics. Brief Bioinform 2006;7:140e50.

[55] Yoon SH, Lee SY. Comparison of transcript levels by DNAmicroarray and metabolic flux based on flux analysis for theproduction of poly-g-glutamic acid in recombinantEscherichia coli. Genome Inform 2002;13:587e8.

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 6 ( 2 0 1 1 ) 1 4 1 4 1e1 4 1 5 214152

[56] Lee DY, Yun HS, Lee SY, Park SW. MetaFluxNet: themanagement of metabolic reaction information andquantitative metabolic flux analysis. Bioinform 2003;19:2144e6.

[57] Hong SH, Moon SY, Lee SY. Prediction of maximum yields ofmetabolites and optimal pathways for their production bymetabolic flux analysis. J Microbiol Biotechnol 2003;13:571e7.

[58] Kacser H, Burns JA. The control of flux. Symp Soc Exp Biol1973;27:65e104.

[59] Fell DA. Enzymes, metabolites and fluxes. J Exp Bot 2005;56(410):267e72.

[60] Fell DA. Metabolic control analysis: a survey of its theoreticaland experimental developments. Biochem J 1992;286:313e30.

[61] Fell DA. Understanding the control of metabolism. London:Portland Press; 1996.

[62] Kholodenko BN, Brown GC. Paradoxical control properties ofenzymes within pathways: can activation cause an enzymeto have increased control? Biochem J 1996;314:753e60.

[63] Stephanopoulos G, Vallino JJ. Network rigidity and metabolicengineering in metabolite overproduction. Science 1991;252:1675e81.

[64] Ueno Y, Otsuka S, Morimoto M. Hydrogen production fromindustrial wastewater by anaerobic microflora in chemostatculture. J Ferment Bioeng 1996;82:194e7.

[65] Ren N, Rittmann BE, Zhao L, Xie T, Zhao X. Microbialcommunity structure of ethanol type fermentation inbiohydrogen production. Environ Microbiol 2007;9:1112e25.

[66] KimS,HwangMH, JangNJ,HyunSH, LeeST. Effect of lowpHontheactivityofhydrogenutilizingmethanogen inbio-hydrogenprocess. Int J Hydrogen Energy 2004;29(11):1133e40.

[67] Calli B, Zhao J, Nijssen E, Vanbroekhoven K. Significance ofacetogenic H2 consumption in dark fermentation andeffectiveness of pH. Water Sci Technol 2008;57(6):809e14.

[68] Ding H-B, Tan G-YA, Wang J-Y. Caproate formation in mixed-culture fermentative hydrogen production. BioresourceTechnol 2010;10:9550e9.