Biochemical Engineering Division, GBF National Research Centre for Biotechnology, Mascheroder Weg 1, 38124 Braunschweig, Germany
Correspondence
Ursula Rinas
URI{at}gbf.de
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ABSTRACT |
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Present address: DSM Biologics, 6000 av. Royalmount, Montréal, Quebec, Canada H4P 2T1.
Present address: Aventis Pharma Deutschland GmbH (ein Unternehmen der sanofi-aventis Gruppe), 65926 Frankfurt, Germany.
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INTRODUCTION |
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As protein synthesis, or more specifically the polymerization of amino acids, is the biggest energy-consuming process in the cell, with more than 50 % of the ATP consumption for biosynthetic purposes (Stouthamer, 1973), energy generation may become critical in recombinant-protein-overproducing cells. For example, the metabolic burden of recombinant protein production is manifested in an increased respiratory activity, i.e. in an increased activity of the energy-generating pathway (e.g. Hoffmann & Rinas, 2001
; Weber et al., 2002
). To understand and control the producing cell better, it is important to have a more profound knowledge of how the cell couples energy generation and carbon supply in the catabolic pathways with the anabolic requirements at different growth rates under non-producing conditions.
It appears reasonable to assume that, in a balanced growing cell, fuelling and biosynthetic reactions are fine-tuned in such a way that cells grow at the highest possible rate under given conditions. This assumption is supported by the good agreement of calculated flux distributions with experimental data using maximize growth as an objective function in a linear programming environment (e.g. Varma & Palsson, 1993). Maximize growth implies optimum energy generation in catabolic reaction sequences and an optimum drain of biosynthetic precursors for anabolic demands.
Information on metabolic fluxes can be obtained from a metabolic model and a set of measured fluxes, typically the uptake rates of substrates and secretion rates of metabolites (Vallino & Stephanopoulos, 1990). The analysis relies on the stoichiometry of cellular pathways, the metabolic demand for growth and optimization principles to estimate the intracellular carbon flux within the defined network. These stoichiometry-based metabolic models have been applied to a variety of organisms, including E. coli (Varma & Palsson, 1993
; Pramanik & Keasling, 1997
). The calculated flux distributions are based on a pseudo-steady state assumption for intracellular metabolites, which is best verified at steady state in continuous culture.
In this study, the effect of growth rate on catabolic and anabolic fluxes and energetic efficiency of the E. coli K-12 strain TG1 growing in aerobic glucose-limited continuous culture was examined. Moreover, the energetic state of the cells was analysed by measurement of the intracellular adenosine nucleotide pool.
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METHODS |
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Cultivation conditions.
Continuous cultivations were performed at 28 °C, at a stirring speed of 800 r.p.m., and the pH was controlled at pH 6·6 with 2·5 mol NaOH l1. The 2-l bioreactor (model SGI 7F-Set2; Setric) was equipped with standard measuring and control units (temperature, pH, dissolved oxygen, stirrer speed). A constant aeration rate of 1·33 v.v.m. was achieved by a mass flow controller (Brooks). Continuous cultivation was performed for at least eight residence times at a given dilution rate; steady-state conditions were verified by constant optical density and oxygen and carbon dioxide transfer rates. Except for glucose, all other substrates were in excess. The concentration of dissolved oxygen was always maintained above 20 % of air saturation.
Analytical techniques.
Culture liquid was withdrawn from the bioreactor into an ice-cooled beaker. Cell dry mass was determined gravimetrically after centrifugation of a known sample volume. The pellet was washed twice with 0·9 % w/v NaCl and dried at 40 °C under vacuum for 48 h. Elemental biomass composition was analysed with a PE 2400 II elemental analyser (Perkin Elmer). Glucose was measured enzymically by a YSI glucose analyser (model 2700; Yellow Springs Instrument Co.). Organic acids, i.e. acetic acid, formic acid, lactic acid and pyruvic acid, were analysed by HPLC using an HPX-87H Aminex ion-exclusion column (Bio-Rad). The column, connected to a UV detector (=210 nm) and RI detector, was eluted at 50 °C with 5 mmol H2SO4 l1 at a flow rate of 0·5 ml min1.
Ammonium concentrations were analysed by an ammonium electrode (model Orion 95-12; Colora).
The concentrations of oxygen and carbon dioxide in the off-gas were measured by paramagnetic and infrared gas analysis systems, respectively (Maihak).
For determination of the adenosine nucleotides AMP, ADP and ATP, 35 ml cell suspension was taken from the bioreactor using a rapid sampling device (described in Theobald et al., 1997). Since turnover of the adenosine nucleotide pool occurs in a few seconds (Nielsen & Villadsen, 1994
; p. 55ff.), a fast sampling technique was necessary to inactivate metabolic activities immediately. Briefly, samples were removed from the bioreactor through a capillary needle into evacuated sample tubes containing precooled glass beads in 5 mol perchloric acid l1 (25 °C) for immediate inactivation of cell metabolism. One freezethaw cycle between 0 °C and 25 °C was performed. The extract was neutralized on ice by careful addition of 2 mol KOH l1 and insoluble cell debris was removed by centrifugation (14 000 r.p.m., 15 s; Eppendorf model 5417) and subsequent membrane filtration (pore size 0·45 µm). Neutralized samples were analysed by ion-pair HPLC, using a reversed-phase column (Supelcosil LC-18T; Supelco) with tetrabutylammonium hydrogensulfate as pairing agent and methanol pH-gradient elution (Ryll & Wagner, 1991
).
Mass balances, calculations and flux analysis.
Volumetric oxygen and carbon dioxide transfer rates (OTR and CTR, respectively) were calculated from the mass balance of the gas phase as follows:
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Carbon (Crec) and nitrogen (Nrec) recoveries at steady-state conditions were determined from reactor mass balances according to the following equations:
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The adenylate energy charge (AEC) was calculated from the adenosine nucleotide pool measurement according to the definition given by Atkinson (1968):
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Stoichiometrically derived mass balances were used to estimate the carbon flux through the central metabolic pathways (Vallino & Stephanopoulos, 1990; van Gulik & Heijnen, 1995
). The biochemical reactions and considered metabolites form a set of linear equations which can be expressed in matrix notation as:
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Linear programming was carried out using a two-phase simplex algorithm (MATLAB, version 4.2c.1; The MathWorks). The metabolic network was constructed on basis of the E. coli database EcoCyc (Karp et al., 1999; http://BioCyc.org/ecocyc/); anabolic reactions for biomass synthesis including polymerization, biosynthesis, fuelling reactions and transport are based on data given by Ingraham et al. (1983)
. The network contains 102 reactions and 90 metabolites and is listed in the Appendix. It includes reactions of the central metabolic pathways, i.e. the EmbdenMeyerhofParnas (EMP) pathway, the tricarboxylic acid (TCA) cycle, the pentose phosphate (PP) pathway, the methylglyoxal (MG) pathway and gluconeogenesis. Anaplerotic pathways detected in E. coli include reactions catalysed by phosphoenolpyruvate (PEP) carboxylase, PEP carboxykinase, an NAD- and NADP-specific malate enzyme and isocitrate lyase and malate synthase of the glyoxylate shunt. In E. coli, two different types of pyridine dinucleotide transhydrogenase exist for the interconversion of NADPH and NADH: a membrane-bound energy-dependent transhydrogenase (Bragg et al., 1972
; Rydström, 1977
) and a soluble cytoplasmic transhydrogenase involved in the energy-independent transfer of hydride from NADPH to NAD+ (Boonstra et al., 1999
). Oxidative phosphorylation was accounted for by an energy- and non-energy-linked NADH dehydrogenase, an FADH reductase and a formate dehydrogenase reaction. Some reactions in linear metabolic routes were lumped together, i.e. anabolic reactions concerning nucleotide and lipid metabolism and the synthesis of several amino acids. The biomass equation was adapted to the macromolecular composition of E. coli. The values 3·1 % DNA, 9·1 % lipids, 3·4 % lipopolysaccharide, 2·5 % peptidoglycan and 2·5 % glycogen (% w/w) were taken from Ingraham et al. (1983)
. Values for protein, 62 %, and RNA, 11·5 %, were obtained from our own measurements according to the methods described by Herbert et al. (1971)
and Benthin et al. (1991)
, respectively. Metabolic fluxes were calculated on a molar basis and for biomass we have considered the amounts required for the formation of 100 g biomass. The stoichiometries used in the lumped biomass equation (r93; see Appendix, detailed stoichiometric model) were adjusted accordingly. This way, for the convenience of modelling, we virtually consider a molecular mass of the biomass' corresponding to 100 g mol1.
Although the macromolecular biomass composition changes with the growth rate (Shahab et al., 1996), it was considered constant as the impact of these changes on the intracellular flux distributions is negligible, as has been shown by a sensitivity analysis (data not shown; Daae & Ison, 1999
).
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RESULTS AND DISCUSSION |
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At dilution rates below 0·3 h1, the specific uptake rates for glucose, ammonium and oxygen and the specific carbon dioxide evolution rate increased linearly with the dilution rate while acetate formation was not observed (Fig. 2; see also data in Table 2
). At dilution rates between 0·3 h1 and 0·4 h1, a strong deviation from the linear increase to lower specific oxygen uptake and carbon dioxide evolution rates occurred (Fig. 2a
). This strong deviation from the linear increase to lower rates was not observed for the biomass formation rate and the specific uptake rates of glucose and ammonium (Fig. 2a and b
, respectively). In contrast, the biomass formation (Fig. 2a
) and the specific ammonium uptake (Fig. 2b
) rates even revealed a small deviation from the linear increase towards higher rates, while the specific glucose uptake rate (Fig. 2b
) revealed a slightly decreasing deviation from the linear increase with increasing dilution rates. In summary, these results point to a higher efficiency of nitrogen and carbon incorporation into biomass, i.e. more efficient substrate utilization for growth, at dilution rates between 0·3 h1 and 0·4 h1. From the linear range of the specific glucose uptake rate as a function of the dilution rate (rGlucose, D=0
0·347 h1), the growth-associated stoichiometric true yield coefficient
and the non-growth-associated maintenance coefficient mGlucose were determined according to equation 8 as 0·57 g g1 and 0·02 g g1 h1, respectively.
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For calculation of the percentage of glucose carbon flow towards biomass, carbon dioxide and acetate, the data from the reactor mass balance (Table 1) were expressed on a molar basis of carbon atoms, i.e. percentage of carbon atoms from glucose converted to biomass, carbon dioxide and acetate (Fig. 3
). This analysis additionally corroborated the decreasing flux of glucose carbon towards carbon dioxide and an increasing flux towards biomass, suggesting a more energy-efficient utilization of the carbon substrate for biomass formation with increasing growth rates. The trend of decreasing carbon recovery with increasing growth rates considering only the formation of biomass, carbon dioxide and acetate might be caused by the formation of an unidentified by-product(s).
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Again, ATP consumption is ascribed to growth- and non-growth-associated processes, with YX/ATP as the parameter describing the growth-associated ATP requirements (g mol1) and mATP as the maintenance term describing ATP requirement in the absence of growth (mol g1 h1). A simple stoichiometric model including catabolic and anabolic reactions as listed in the Appendix was used to determine the energetic parameters YX/ATP (equation 12) and mATP (equation 13). For the P/O ratio, a value of 1·75 was assumed as determined for enterobacteria (Zeng et al., 1990). Furthermore, the values for the growth-associated stoichiometric true yield coefficient
and the non-growth-associated maintenance coefficient mGlucose determined from the linear range of the glucose uptake rate as described above were used as input parameters (see Appendix). Based on this simple stoichiometric model, the ATP consumption for maintenance requirements in the absence of growth mATP was determined as 2·81 mmol g1 h1 and the growth-associated energy consumption YX/ATP as 11·6 g mol1. The above values are in the range of previously reported values determined from aerobic glucose-limited chemostat cultures for growth-associated ATP requirements YX/ATP (13·9 g mol1 at 30 °C; Farmer & Jones, 1976
) and ATP consumption for maintenance mATP of non-growing cells (2 mmol g1 h1 at 30 °C; Farmer & Jones, 1976
).
Growth energetics and metabolic flux distributions: a linear programming approach using a detailed stoichiometric model
The simple stoichiometric model can give a rough estimate of the growth energetics in the linear range of glucose uptake rates, not taking into account any change in the energetic efficiency of growth-rate-dependent variations of energy requirements for biomass formation. Moreover, it should be noted that the P/O ratio, YX/ATP and mATP can neither be measured directly nor evaluated independently. A feasible approach to tackle this problem is to set two of these variables as constant and adjust the third to meet experimentally determined data. Thus, the P/O ratio was considered constant as P/O=1·75 (Zeng et al., 1990) and mATP was defined as the energy requirement for maintenance in the absence of growth and determined as mATP=2·81 mmol g1 h1 (simple stoichiometric model; this study). The values of YX/ATP were calculated by adjusting their values to reach the experimentally determined biomass yield YX/Glucose (Table 3
) using the detailed stoichiometric model (see Appendix) constructed for estimation of the intracellular carbon flux distribution. Thus, based on the glucose uptake rate, the intracellular fluxes were calculated by linear programming with maximize growth as the objective function, keeping the energetic parameters P/O and mATP constant and adjusting the third energetic parameter YX/ATP (present in the biomass formation reaction; r97) to be compatible with the experimentally determined biomass yield YX/Glucose (Table 3
).
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Based on the detailed stoichiometric model, the metabolic flux distribution was determined at low (D=0·066 h1) and high (D=0·375 h1) dilution rates under conditions of cell-carbon and energy-carbon limitation, respectively (Fig. 5). With increasing dilution rate, the flux through the oxidative PP pathway increased at the expense of the flux through the EMP pathway and the TCA cycle. The excess carbon flux in the PP pathway is channelled back to the EMP pathway via the non-oxidative transaldolase- (r40) and transketolase- (r41) catalysed reactions. At the level of glyceraldehyde 3-phosphate (GAP; r7), 168 % mol mol1 at D=0·066 h1 and 157 % mol mol1 at D=0·375 h1 enter the EMP pathway. Thus, the carbon which is used for biomass and CO2 formation up to the GAP level increases from 16 to 22 %. At the higher dilution rate, less carbon enters the TCA cycle. The resulting decreased NADH production rate decreases the ATP formation rate in the respiratory chain (r33 and r34) by 24 %. Alternative pathways such as the MG pathway (r42) or the glyoxylate shunt (r27 and r28) are not predicted to function in glucose-limited cultures under steady-state conditions. The MG pathway is most likely only activated under conditions of glucose excess (see accompanying paper; Weber et al., 2005
) and the glyoxylate shunt may not function in E. coli K-12 strains, as has been shown experimentally (13C labelling) for the E. coli K-12 strain JM109 (Noronha et al., 2000
; Phue & Shiloach, 2004
), a close relative of the E. coli K-12 strain TG1 (Sambrook et al., 1989
) employed in this study.
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At very high growth rates close to the wash-out point, the energy limitation becomes more severe, leading to the formation of acetate as an alternative route for energy generation through substrate-level phosphorylation. Energy generation through acetate formation yields less ATP compared with complete oxidation of the sugar carbon substrate; however, this route for energy generation appears to be the result of maximized energy generation under conditions of restrictions in the TCA cycle or in respiratory NADH turnover.
Although a detailed mechanism of acetate formation in aerobic cultures remains unknown, different hypotheses have been suggested for its formation by E. coli under carbon-overflow conditions. All attribute acetate formation either to limitations in respiratory NADH turnover and concomitant ATP generation through oxidative phosphorylation (Andersen & von Meyenburg, 1980; Doelle et al., 1982
; Reiling et al., 1985
; Varma & Palsson, 1994
) or to initial limitations in the TCA cycle (Majewski & Domach, 1990
; Han et al., 1992
). Moreover, Han et al. (1992)
concluded that these limitations cause a reorganization of the catabolic flux distribution to meet the anabolic demands at high growth rates by generating the necessary amount of energy by using both oxidative metabolism and acetic acid formation.
Such an reorganization of metabolism was observed in E. coli TG1 with increasing dilution rates. During the shift from cell-carbon to energy-carbon limiting conditions, the anabolic requirements are met first by a more efficient and less energy-consuming way of biomass formation by reduced TCA cycle activity and increased PP pathway flux, and, at even higher growth rates, by the activation of an alternative but less-efficient pathway for energy generation through substrate-level phosphorylation and concomitant formation of acetate.
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APPENDIX |
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cat) CH2O
2 H2+0·667 ATP+CO2 (I)
op) H2+0·5 O2
P/O ATP (II)
µ) Glucose/XCH2O+
N/XNH3+
ATP+
H/XH2
CH1·85O0·574N0·22+
CO2 (III)
mATP) ATPdiss. (IV)
For catabolism, reactions of the EMP pathway and TCA cycle were lumped together. NADH and NADPH were considered as reducing equivalents H2 and account for 10 moles per mole glucose oxidized. FADH was considered to account for 2 moles H2 per mole glucose oxidized. Oxidative phosphorylation was described with the assumption that only NADH, not NADPH, can be oxidized. An energy dissipation reaction (mATP) was included to account for all ATP not consumed for biomass (X) formation.
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From the reaction rates, balances for the metabolites and cofactors can be generated. By eliminating the reaction rates vcat and vop, an equation for the glucose uptake rate rGlucose was obtained which can be coupled to the model of Pirt (1965):
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Parameters:
=0·581 C-mol C-mol1 (0·574 g g1), mGlucose=0·02 C-mol C-mol1 h1 (0·02 g g1 h1); parameter determined from experimental data for the linear range D=0
0·347 h1 (Table 2
).
Glucose/X=1·174 C-mol C-mol1,
H/X=0·159 mol C-mol1; ratio determined by a stoichiometric analysis of the (macromolecular) biomass composition as described in Nielsen & Villadsen (1994)
(p. 106 ff.).
P/O=1·75 (according to Zeng et al., 1990).
Detailed stoichiometric model
Reactions in the biochemical network:
Reactions are indicated as reversible (=) or irreversible ().
Phosphotransferase system
r1) GLC+PEPGLC6P+PYR
EMP pathway
r2) GLC6P=FRU6P
r3) FRU6P+ATPFRU16P
r4) FRU16PFRU6P
r5) FRU16P=GAP+DHAP
r6) DHAP=GAP
r7) GAP=NADH+G3P+ATP
r8) G3P=PEP
r9) PEPATP+PYR
r10) PYR+ATPPEP
PEP carboxykinase and PEP carboxylase
r11) OAA+ATPPEP+CO2
r12) PEP+CO2OAA
By-products
r13) PYR=ACCOA+FORM
r14) ACCOA=AC+ATP
r15) 2 ATP+ACACCOA
r16) ACCOA+2 NADHETOH
r17) PYRACCOA+CO2+NADH
TCA cycle
r18) ACCOA+OAACIT
r19) CIT=ISOCIT
r20) ISOCIT=AKG+NADPH+CO2
r21) AKGSUCCOA+CO2+NADH
r22) SUCCOA=ATP+SUC
r23) SUCFADH+FUM
r24) FUM=MAL
r25) MALOAA+NADH
Glyoxylate shunt
r26) ISOCITSUC+GLX
r27) ACCOA+GLXMAL
r28) MALPYR+CO2+NADPH
r29) MALPYR+CO2+NADH
Transhydrogenation
r30) NADPHNADH
r31) NADH+PO ATPNADPH
Oxidative phosphorylation
r32) NADH+0·5 O2PO ATP
r33) NADH+0·5 O22 PO ATP
r34) FADH+0·5 O2PO ATP
r35) FORM+0·5 O2PO ATP+CO2
PP pathway
r36) GLC6PRIBU5P+CO2+2 NADPH
r37) RIBU5P=RIB5P
r38) RIBU5P=XYL5P
r39) XYL5P+RIB5P=SED7P+GAP
r40) SED7P+GAP=FRU6P+E4P
r41) XYL5P+E4P=FRU6P+GAP
MG pathway
r42) DHAPMG
r43) MG+NADPH=LACALD
r44) MG+GLUTH=LACTGL
r45) LACTGL=DLAC+GLUTH
r46) MGDLAC
r47) PYR+NADHDLAC
r48) DLACPYR+FADH
r49) LACALDLLAC+NADH
r50) PYR+NADHLLAC
r51) LLACPYR+FADH
Ammonium, glutamate and glutamine
r52) NH3EX+0·5 ATP=NH3
r53) NH3+AKG+NADPH=GLUT
r54) GLUT+NH3+ATP=GLUM
Amino acids
r55) NADPH+ATP+2 GLUT+ACCOAAKG+AC+ORN
r56) CO2+GLUM+2 ATP=GLUT+CAP
r57) CAP+ORN=CR
r58) ATP+ASP+CR=FUM+ARG
r59) GLUT+G3P=NADH+AKG+SER
r60) SER+ACCOA+H2S=CYS+AC
r61) SER=NNMTHF+GLY
r62) OAA+GLUT=ASP+AKG
r63) 2 ATP+GLUM+ASP=ASN+GLUT
r64) 2 NADPH+ATP+ASP=HSER
r65) SUCCOA+CYS+HSER=SUC+PYR+NH3+HCYS
r66) NMTHF+HCYS=MET
r67) ATP+HSER=THR
r68) NADPH+2 PYR=CO2+AKI
r69) AKI+GLUT=AKG+VAL
r70) AKI+ACCOA+GLUT=LEU+AKG+NADH+CO2
r71) GLUT+PYR=AKG+ALA
r72) NADPH+PYR+GLUT+THR=CO2+NH3+AKG+ILE
r73) ATP+NADPH+E4P+2 PEPCHOR
r74) CHOR+GLUM+SER+PRPPTRP+GLUT+PYR+GAP+CO2
r75) CHOR=PREPH
r76) PREPH+GLUTCO2+AKG+TYR+NADH
r77) GLUT+PREPHCO2+AKG+PHE
r78) GLUM+ATP'+PRPP2 NADH+AICAR+AKG+HIS
r79) GLUT+ATP+2 NADPHPRO
r80) ASP+PYR+2 NADPH+SUCCOA+GLUT+ATPSUC+AKG+CO2+LYS
Protein
r81) 1·18 ATP+0·042 VAL+0·005 TRP+0·013 TYR+0·024 THR+0·021 SER+0·021 PRO+0·018 PHE+0·015 MET+0·033 LYS+0·043 LEU+0·028 ILE+0·009 HIS+0·059 GLY+0·025 GLUM+0·025 GLUT+0·009 CYS+0·023 ASP+0·023 ASN+0·028 ARG+0·049 ALA=PROTEIN
Nucleotides
r82) NFTHF+AICAR=IMP
r83) IMP+ASP+3 ATP=ATP'+MAL
r84) IMP+GLUM+4 ATP=GTP+GLUT+NADH
r85) 4 ATP+GLUM+ASP+PRPP=UTP+GLUT+NADH
r86) ATP+GLUM+UTP=GLUT+CTP
RNA
r87) 0·0165 ATP'+0·0203 GTP+0·0136 UTP+0·0126 CTP+0·0256 ATP=RNA
DNA
r88) 0·00247 ATP'+0·00247 UTP+0·00254 GTP+0·00254 CTP+0·015 ATP=DNA
Lipids
r89) 0·0129 PAL+0·0129 OL+0·0129 GAP+0·0129 SER+0·0258 ATP=LIPID
r90) 8 ACCOA+7 ATP+13 NADPH=PAL
r91) 9 ACCOA+9 ATP+15 NADPH=OL
Lipopolysaccharide
r92) 0·00509 GLC6P+0·0481 ATP+0·0376 NADPH+0·033 ACCOA+0·0023 RIB5P+0·0023 PEP+0·00392 GLUT+0·00235 G3P=LPS+0·0023 NADH+0·00392 AKG
Peptidoglycan
r93) 0·00276 FRU6P+0·0055 ACCOA+0·00276 PEP+0·00276 PYR+0·00276 OAA+0·02484 ATP+0·0193 GLUT+0·0193 NADPH=PG+0·0138 AKG
Glycogen
r94) 0·0154 GLC6P+0·0154 ATP=GLYC
One-carbon units and polyamine
r95) 0·00485 SER=C1
r96) 0·0119 ATP+0·01779 NADPH+0·0119 GLUT=PA+0·01397 AKG
Biomass
r97) 1·12 PROTEIN+0·56 RNA+LIPID+LPS+GLYC+PG+DNA+C1+PA+ ATP=BIOMASS
Miscellaneous
r98) NNMTHF+NADH=NMTHF
r99) GLY=NADH+CO2+NH3+NNMTHF
r100) 2 ATP+RIB5P=PRPP
r101) PRPP+GLY+ASP+NFTHF+CO2+4 ATP=AICAR+MAL+2 GLUT
r102) NNMTHF=NADPH+NFTHF
Objective
r103) BIOMASS
Metabolite accumulation rate vector:
1) AC Acetate
2) ACCOA Acetyl coenzyme A
3) AICAR Aminoimidazole carboxamide ribonucleotide
4) AKG -Ketoglutarate
5) AKI -Ketoisovalerate
6) ALA Alanine
7) ARG Arginine
8) ASN Asparagine
9) ASP Aspartate
10) ATP Adenosine 5'-triphosphate (energy source)
11) ATP' Adenosine 5'-triphosphate (nucleotide for RNA and DNA)
12) BIOMASS Biomass
13) C1 One-carbon unit
14) CAP Carbamoyl phosphate
15) CHOR Chorismate
16) CIT Citrate
17) CO2 Carbon dioxide
18) CR Citrulline
19) CTP Cytidine 5'-triphosphate
20) CYS Cysteine
21) DHAP Dihydroxyacetone phosphate
22) DLAC D-Lactate
23) DNA Deoxyribonucleic acid
24) E4P Erythrose 4-phosphate
25) ETOH Ethanol
26) FADH Flavin adenine dinucleotide, reduced
27) FORM Formate
28) FRU16P Fructose 1,6-bisphosphate
29) FRU6P Fructose 6-phosphate
30) FUM Fumarate
31) G3P 3-Phosphoglycerate
32) GAP Glyceraldehyde 3-phosphate
33) GLC Glucose
34) GLC6P Glucose 6-phosphate
35) GLUM Glutamine
36) GLUT Glutamate
37) GLX Glyoxylate
38) GLY Glycine
39) GLYC Glycogen
40) GTP Guanidine 5'-triphosphate
41) HCYS Homocysteine
42) HIS Histidine
43) HSER Homoserine
44) ILE Isoleucine
45) IMP Inosine 5'-monophosphate
46) ISOCIT Isocitrate
47) LACALD Lactaldehyde
48) LACTGL Lactoylglutathione
49) LEU Leucine
50) LIPID Lipid
51) LLAC L-Lactate
52) LPS Lipopolysaccharide
53) LYS Lysine
54) MAL Malate
55) MET Methionine
56) MG Methylglyoxal
57) NADH Nicotinamide adenine dinucleotide, reduced
58) NADPH Nicotinamide adenine dinucleotide phosphate, reduced
59) NFTHF N10-formyl tetrahydrofolate
60) NH3 Ammonia
61) NH3EX Ammonia, extracellular
62) NMTHF N5-methenylformyl tetrahydrofolate
63) NNMTHF N5-N10-methyleneformyl tetrahydrofolate
64) O2 Oxygen
65) OAA Oxaloacetate
66) OL Oleate
67) ORN Ornithine
68) PA Polyamine
69) PAL Palmitoleate
70) PEP Phosphoenolpyruvate
71) PG Peptidoglycan
72) PHE Phenylalanine
73) PREPH Prephenate
74) PRO Proline
75) PROTEIN Protein
76) PRPP 5-Phosphoribosyl 1-pyrophosphate
77) PYR Pyruvate
78) RIB5P Ribose 5-phosphate
79) RIBU5P Ribulose 5-phosphate
80) RNA Ribonucleic acid
81) SED7P Sedoheptulose 7-phosphate
82) SER Serine
83) SUC Succinate
84) SUCCOA Succinyl CoA
85) THR Threonine
86) TRP Tryptophan
87) TYR Tyrosine
88) UTP Uridine 5'-triphosphate
89) VAL Valine
90) XYL5P Xylulose 5-phosphate
For simplicity, the oxidized forms of the cofactors NADP, NAD and FAD and also ADP are not shown in the reactions of the biochemical network. Moreover, these compounds are not included in the metabolite balance because of lack of information gain. Also, glutathione (GLUTH, r44 and r45) and H2S (r60) are shown in the biochemical network but were not included in the metabolite balance. Moreover, uridine 5'-triphosphate (UTP) is considered to be equivalent to thymidine 5'-triphosphate (TTP) in the biochemical network.
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ACKNOWLEDGEMENTS |
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REFERENCES |
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Received 12 July 2004;
revised 3 December 2004;
accepted 6 December 2004.
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