Metabolic flux analysis of *Shewanella spp* central carbon metabolism
reveals evolutionary, genetic and environmental robustness

Yinjie J. Tang^1,2^ , Hector Garcia Martin^3^ , Paramvir S Dehal^1,2^,
Adam Deutschbauer^1,2^, Xavier Llora^4^, Adam Meadows^5^, Phil
Hugenholtz^3^, Adam Arkin^1,2,6^, Jay. D. Keasling^1,2,6,7,#^

^1^Virtual Institute of Microbial Stress and Survival, Lawrence Berkeley
National Laboratory, Berkeley, USA. ^2^Physical Biosciences Division,
Lawrence Berkeley National Laboratory, Berkeley, USA. ^3^Joint Genome
Institute, Walnut Creek, CA. ^4^National Center for Supercomputing
Applications, University of Illinois at Urbana-Champaign, 1205 W. Clark
Street, Urbana, IL 61801, USA. ^5^Genentech, Inc., South San Francisco,
CA. ^6^Department of Bioengineering, University of California, Berkeley,
USA. ^7^Department of Chemical Engineering, University of California,
Berkeley, USA

\# Corresponding author:

Berkeley Center for Synthetic Biology

University of California

Berkeley, CA 94720

Phone: (510) 642-4862; Fax: (510) 495-2630; Email: Keasling@berkeley.edu

**[Abstract Paragraph]{.underline}**:

Metabolite fluxes link genes, proteins, and metabolites to macroscopic
biological functions^1^. In spite of its importance, only a few, not
thoroughly tested, general principles have been proposed to predict and
understand the flux configuration of an organism^2^ ^3^ ^4^. Among those
general principles, robustness of central metabolism has been
reported^4^ with respect to genetic perturbation. Here we show that the
relative metabolic flux distributions are very similar in a
phylogenetically and environmentally diverse set of organisms in
*Shewanella spp*. This phylogenetic robustness suggests understanding
microbial fluxomics in terms of metabolic types (or metabotypes), as
opposed to phylotypes. In addition to phylogenetic, environmental, and
genetic robustness, *Shewanella spp* shows the metabolic preference to
maximize the utilization of carbon source coupled to the secretion of
progressively less energetically favorable metabolites for later
consumption. (Comments: Unlike E.coli, all Shewanella strains produce
pyruvate and acetate when lactate is sufficient. The order of carbon
utilization is in the order: lactatepyruvateacetate)

Robustness, as defined by Stelling et al.^5^, is the ability to maintain
performance in the face of perturbations and uncertainty, and is a
long-recognized key property of biological systems. For microorganisms,
robustness has been recently studied in terms of the distribution of
metabolic fluxes, which are widely recognized as the most direct measure
of the phenotype because they represent the functional outcome of the
lower levels of cellular functions^4,6-8^. Those studies have addressed
robustness of central metabolic fluxes to perturbations in the form of
mutations and environmental conditions. A relatively unstudied type of
robustness, however, involves the change of relative metabolic fluxes
across a clade of related species under similar environmental
conditions. Through the diversification of the *Shewanella* lineage, a
single ancestral species evolved into multiple species that inhabit
diverse environments through changes in gene content, gene function,
regulation, etc. The recent sequencing of several of these *Shewanella*
species gives us the opportunity to use the well-characterized
*Shewanella Oneidensis* MR1 as a basis for a robustness analysis of
central metabolic fluxes across wide phylogenetic differences.
(Comments: the robustness here only focuses on central metabolic fluxes.
We do not study chemotaxis or motility. *E. coli* does not produce
pyruvate, however, many fluxes in *E.coli* are similar to those in MR-1.
I think it is because that their central metabolisms share similar
regulation mechanism since both species have almost same genes for the
central metabolism).

***Flux distribution robust to phylotypes.*** We calculated the fluxomes
in central metabolism for the eight *Shewanella* strains able to grow in
a complete minimal lactate medium and one *E. coli* strain at the stage
of maximum batch growth when lactate was in excess (Supplementary Fig.
S1 and Table S1). Despite different growth kinetics among those
*Shewanella* strains (Supplementary Fig. S2), the results show very
similar relative intracellular flux profiles for all the *Shewanella*
species and *E. coli* (Fig. 1). Although all *Shewanella* strains
studied belong to the same genus, they show as a large degree of
divergence as do all enterobacteria, which separated from *Shewanella*
approximately a billion years ago^9^ (Fig. 2). Out of a total of 11,214
genes for the pangenome of these eight species, 4983 are uniquely found
in only one species with only 1,419 present across all lineages.
Furthermore, in addition to the large number of genetic difference,
these species are found in distinct environments ranging from soil, to
marine and freshwater, also occupying a wide range of salinity,
temperature, and nutrient resources (Supplementary, Table S-2). Hence,
this result is an unprecedented example of flux profile robustness in
light of significant evolutionary and environmental differences. This
phenomenon, if widespread in nature, implies that microbial communities
of different phylotypes share a similar relative flux distribution in
central metabolism under same growth condition (metabotype). The concept
of metabotype allows us to understand central metabolism of many close
species (whose genome may not even be sequenced yet) by only studying
one representative strain.

***Flux distribution robust to growth conditions.*** Flux analysis is
often conducted in strict minimal medium (amino acid free) because of
the concern of un-labeled carbons confounding isotopomer labeling
analysis. However, after a proper correction of amino acid labeling
data, the addition of unlabeled amino acids can produce information on
the relative rates of endogenous and exogenous amino acid
utilization^10^. In a defined rich medium containing an amino acid mix
(17 amino acids, 25 M each), the labeling patterns of Ala, Asp, Glu,
Gly, Ser (group 1, Fig. 3a) did not change (\<2%); Val, Leu, Iso, Pro,
Thr, and Lys (group 2) changed moderately (\<10%); and Met, Phe, His,
Tyr (group 3) labeling changed significantly (\>10%). The three groups
respectively correlate to an increasing number of biochemical steps away
from a TCA cycle metabolite and the amount of energy required for
synthesis. Hence, the first group is mostly endogenously synthesized,
and the last group is mainly imported from the medium. After proper
correction, (Fig. 3a, see methods), the labeling data from the amino
acid-supplemented medium were very close to those from a completely
minimal medium (Fig. 3b). The relative flux distribution through the
central pathways was not significantly changed even though the addition
of amino acids doubled the growth and lactate uptake rates
(Supplementary data Fig. S1-c).

Salt stress reduced the specific growth rate of cells (\> two fold) but
did not change the relative flux distribution (Table S1). This stability
does not correlate with microarray data obtained from elevated salt
conditions^11^, which indicated that the expression of most of the TCA
cycle-related genes were up-regulated under salt stress. A similar lack
of correlation between fluxome and transcriptome data was also recently
reported for *E. coli* grown on lactate^12^. These results indicate that
a large part of the activity of the regulatory complexity (e.g., genetic
regulation, transcription, and protein interactions) is devoted to
maintaining a stable flux profile, as has been hypothesized to be a
general biological principle^13^. The rigidity of metabotype indicates
that the functional output of combined regulations among genome,
transcription and proteomics in *Shewanella oneidensis* MR-1 lead
towards a stable intra-cellular metabolic status under various growth
conditions.

***Flux distribution robust to random genetic perturbations.*** To
examine whether genetic perturbations affected the *Shewanella* relative
flux distribution, ten random transposon mutants of MR-1 were created
and cultured individually in the \[3-^13^C\] lactate medium (see
Supplementary Table S-3 for details of MR-1 mutants). The resulting
isotopomer data were mostly identical (Fig. 4), implying that the
relative flux through central carbon metabolism is very robust to random
genetic perturbation (Comments: If necessary, we can try more mutants. I
think the central flux will be still same). However, perturbation of
certain biosynthesis pathways was observed, while the flux through the
TCA cycle remained stable. For example, the SO0781 mutant (lacking
glycine dehydrogenase: glycineC1+CO~2~) showed increased ^13^C abundance
in histidine. This is because the pool of C1 used in histidine
biosynthesis mainly comes from two reactions: serine and glycine
degradation^14^. Since glycine displays a relatively low level of ^13^C
labeling, by knocking out the glycine degradation pathway, an increase
in labeled C1 was observed from serine degradation, which translates
into a higher abundance of ^13^C in histidine as indicated in Fig. 4.
This invariability in the relative flux distributions to genetic
perturbations has also been observed in *Bacillus subtilis* and *E.
coli* ^4,8^, which provides a major hurdle for rational metabolic
engineering of microorganism: although it is easy to change cell's
secondary metabolism or biosynthesis pathways (e.g., MR-1 glycine
dehydrogenase mutant), a significant alteration of central metabolism is
difficult.

***Flux distribution flexible to utilization of energetically favorable
carbon sources.*** The flux profiles for this *Shewanella* metabotype
showed that the TCA cycle was not the primary carbon metabolism route
(fluxes \<40% of lactate uptake), with the "overflow" flux toward
pyruvate and acetate being larger (\>50% of lactate uptake). Despite
growth under aeration (see methods), this flux profile showed limited
TCA cycle usage such that lactate was not be fully oxidized^15^,
resulting in acetate accumulation, which is known to inhibit growth^16^.
Growth rate maximization would imply full acetyl-CoA oxidation through
the TCA cycle instead of acetate secretion or glyoxylate shunt
utilization, since the former would produce a higher yield of
ATP-equivalents per acetyl-CoA. Furthermore, when comparing with the
stoichiometric flux balance (FBA) model of *E. coli* where growth rate
is optimized, no acetate accumulation or anapleurotic activity (e.g.
glyoxylate shunt) and a higher growth rate than observed are predicted
for batch growth on lactate if there were no preconditioning constraints
(Model constructed via Simpheny, Genomatica, San Diego, CA, see
supplementary file for FBA flux results). This is consistent with recent
report that linear maximization of biomass yields in FBA model achieved
the highest predictive accuracy only under carbon scarcity in continuous
cultures, in contrast, metabolic status under batch cultures prefers
nonlinear maximization of the ATP yield through central pathways ^17^,
i.e., generation of ATP via acetate secretion when favorable carbon
source is not limited. Hence, the evolutionary selection of metabolic
flux states is closely related to the availability of carbon sources.

The late phase MR1 flux profile shows an increase in the activity of TCA
cycle and anapleurotic reactions in MR1. The increased flux through the
TCA cycle results from the depletion of lactate and subsequent
substitution by the previously excreted acetate as the primary carbon
source (Supplementary Fig. 2S-b). To achieve a balance between
biosynthesis and energy production, an increase in glyoxlate shunt flux
reduces the amount of acetyl-CoA being completely oxidized by the TCA
cycle and saves carbon substrate for biosynthesis^18^. Observation of
high activity in anapleurotic reactions is consistent with previous flux
analysis studies on *Shewanella* (MR-1) and *E. coli* under chemostat
conditions when cells grew under severe carbon-limited conditions
^8,14,19^. In the late exponential phase, the Entner-Doudoroff (ED)
pathway (necessary for biosynthesis of nucleotides and amino acids) also
increases. These results indicate that there is a shift from early
primarily metabolite (i.e., pyruvate and acetate) secretion and
energy-producing phase towards the late phase where biosynthesis becomes
more prominent (based on per carbon substrate utilization). Taken
together, this demonstrates the high flexibility of *Shewanella* species
metabolism to alter its growth strategy in accordance with carbon source
availability, i.e., *Shewanella* *spp.* central metabolism tends to
maximize conversion of favorable carbon substrate (lactate) to two less
energetically favorable substrates (pyruvate and acetate) which can be
utilized later (*E.coli* secreted acetate only in the batch culture).
From the evolutionary view, such metabolism under unlimited growth may
benefit cells for competing with other organism by quickly consuming
favorable carbon substrates and secretion of inhibitory products.

Finally, we need to mention that the lack of constancy over time in the
amino acid labeling profiles attests to a change in flux distribution
from the early to the late phase of exponential growth. The amino acid
labeling profiles from the final phase is an average of the results of
both phases. Thus, the flux results based on the isotopomer data in the
late exponential phase must be interpreted as the "apparent" or
"averaged" flux distribution through the multiple growth stages.

***Conclusion*.** Our study shows that the relative flux distribution is
robust with respect to amino acid addition, salt stress, genetic
perturbation, gene content, and phylogenetic distance. The latter
suggests the introduction of the concept of metabotype, or metabolic
type for *Shewanella spp.*, which provides a more natural classification
of organisms than phylotypes regarding the characterization of the
metabolic activity in a microbial community. *Shewanella* show
suboptimal performance under the studied batch conditions, which
provides further evidence^4^ beyond genetic perturbations that microbial
metabolism is not geared towards growth rate maximization. Finally, in
addition to phylogenetic, environmental, and genetic robustness,
*Shewanella* *spp.* display a flexible relative metabolic flux
distribution aimed towards the progressive utilization of diverse carbon
sources. (Comments: suboptimal growth for *Ecoli* has been reported
before. Here we add more evidence using Shewanella).

# Materials and methods

***Culture conditions and analytical methods for metabolites***. Eight
*Shewanella* strains in this study are from the Shewanella Federation.
*E. coli* K12 W3110 (as a comparison) is from The American Type Culture
Collection (ATCC 27325). Ten MR-1 mutants were obtained by Tn5
transposon mutagenesis using plasmid pRL27^20^ (gift of Dr. William
Metcalf from Department of Microbiology, University of Illinois at
Urbana-Champaign). Mutagenesis was carried out by conjugation of the *E.
coli* donor strain WM3064 harboring pRL27 with *Shewanella oneidensis*
MR-1. Transposon mutants were selected on LB medium with kanamycin (10
g/ml). Mapping the insertion location of the ten transposon mutants was
carried out via a two-step degenerate PCR protocol as described
previously ^21^ (Supplementary Table S-3). All strains (including
mutants) were cultured in the modified MR-1 defined medium^22^
(supplementary materials) in shaking glass tubes (12 mL) at 30C. The
carbon source was \[3-^13^C\] labeled sodium L-lactate (98%, Cambridge
Isotope, USA). For salt stress experiments, extra NaCl (260 mM or 330
mM) was present in the medium. To enhance growth, a standard amino acid
mix (containing 17 amino acids, without tryptophan, glutamine, and
asparagine, Cat#AA-S-18, Sigma, USA) was added to the medium to a final
concentration of 25 M for each amino acid. The inoculum was prepared in
LB medium overnight (optical density at a wavelength of 600 nm,
OD~600~\>1.5); the cultures were started with a 0.09% inoculation
volume. Total biomass growth was monitored by measuring the optical
density at a wavelength of 600 nm (OD~600~), based on the correlation
between dried biomass and its corresponding OD~600~. The concentrations
of lactate, acetate and pyruvate in the medium were measured using
enzyme kits (r-Biopharm, Darmstadt, Germany). Biomass constituents
(fatty acid, protein and its amino acid composition, carbohydrates, RNA,
and DNA) were taken from previous reports^23-25^.

***Gas chromatograph-mass spectrometry (GC-MS)***. The GC-MS protocol
for isotopomer measurement has been described previously^26^. In brief,
a 12-mL culture was harvested and centrifuged at 10000 × g. The cell
pellets were sonicated, and the resulting lysates were precipitated
using trichloroacetic acid and washed with cold acetone; then hydrolyzed
in 6 M HCl at 100°C for 24 hours. GC-MS samples were prepared in 100 l
tetrahydrofuran (THF) and 100 l
N-(tert-butyldimethylsilyl)-N-methyl-trifluoroacetamide (Sigma-Aldrich,
USA) at 65-80°C for 1 hour. GC-MS was carried out using a gas
chromatograph (DB5 column, HP6890 series, Agilent Inc, USA) equipped
with a mass spectrometer (5973 Network, Agilent Inc, USA). Two types of
positively charged species were used in model simulation: unfragmented
amino acids, \[M-57\]^+^, and fragmented amino acids that have lost
their α carboxyl group, \[M-159\]^+^. Corrections due to the natural
abundance of isotopes were applied using the publicly available
tool^27^.

***Algorithm for flux calculation and isotopomer modeling***.
Biochemical pathways for the central carbon metabolism of *Shewanella*
and *E. coli* were obtained from the genome data base MicrobesOnline
^28^ (see Fig S1 in supplementary material). The metabolic network
includes the tricarboxylic acid (TCA) cycle (including the glyoxylate
shunt), C~1~ metabolism, the Entner-Doudoroff (ED) pathway, and the
pentose phosphate (PP) pathway. Only a few reactions (the coupled
reactions: 8-42, 4-26, 22-25 and 21-25, supplementary Fig. S1) were
considered as reversible due to their significant impact on the
isotopomer distribution^18^. Reversible reactions in the
pentose-phosphate pathway were not included, since the total flux
through the PP pathway in this study was very low (\< 5% of total carbon
flow) and mainly for biomass production ^29^ ^18^.

Metabolic fluxes for all reactions in Fig. S1 were calculated by
minimizing the error (or objective) function:

$\varepsilon(v) = \sum_{i = 1}^{m}{w_{i}\varepsilon_{i}}/m$ (1)

$\varepsilon_{i}(v) = \sqrt{\sum_{j = 1}^{k_{j}}\left( \frac{M_{\text{ij}} - N_{\text{ij}}(v)}{\delta_{\text{ij}}} \right)^{2}}$
(2)

where the vector *v* are the unknown fluxes, *M~ij~* are the measured
GC-MS data for the fraction of molecules with *j* labeled carbons for
the *i*th amino acid or fragmented amino acid, *N~ij~* are the
corresponding model-simulated MS data (see below), *~ij~* are the
corresponding data measurement errors, *m* is the total of MS
measurements. Different weights *w~i~* are used for each amino acid
depending on their relative importance for the fit. For example,
histidine (including his^-^) was given a weight of 0.0025 since exchange
dynamics not captured in the model are expected to distort the measured
values. The rest of the amino acids and fractionated amino acids were
given a weight of 0.25 except glutamate, asparagine, alanine, and
phenyalanine, which were given a weight of 1. The error functions *~i~*
can hence be interpreted as the weighted average difference between the
calculated and the experimental data measured in units of the
experimental error.

The carbon labeling expected from a flux profile ***v*** was calculated
via the cumomer method^30^. The vector ***v~ind~*** is formed by the 16
independent fluxes, obtained automatically from the reaction network
(Table S1) through Gaussian elimination, plus the unlabeled fractions of
CO~2~ and C1, for a total of 18 independent variables. Fluxes were
loosely constrained by cell biomass composition and previously reported
fluxes for similar microorganisms.

For each strain, the minimization of the error function was performed
through genetic algorithms (GA)^31,32^. Variables being optimized were
encoded using floating point numbers with the additional constraint that
their values should belong to the \[0, 1\] interval. The adapted
operators used were: tournament selection without replacement
(s=4)^33,34^, simulated binary crossover (SBX)^35,36^ with ηc = 10,
crossover probability pc = 0.9, a polynomial mutation^37^ with η= 20 and
mutation probability p= 0.1. The GA was run for 325 iterations. In
tournament selection without replacement with tournament size s, s
chromosomes (solutions to the optimization problem) are chosen at random
without replacement and entered into a tournament against each other.
The best (low error) individual in the group of s chromosomes wins the
tournament and is selected into a mating pool for evolving new
solutions. In SBX, individuals in the mating pool are divided into
random pairs and each pair undergoes recombination with a probability
pc. For each pair participating in the crossover, each gene (or
variable) undergoes contracting or expanding crossover operation with a
probability 0.5. Therefore, for each pair of chromosomes undergoing
recombination on average half of the genes are modified using either
contracting or expanding crossover operations. The operations are
designed to mimic crossover operator behavior on binary domains. The
polynomial mutation is similar to SBX, and the only difference is in the
computation of the polynomial probability. Instead of using genotypic
distance between two parents as in SBX, the distance between a gene and
its corresponding upper or lower bound, whichever is closer, is
considered in computing the contracting and expanding probability
distributions. In polynomial mutation, each gene (or variable) undergoes
contracting or expanding operation with a probability p.

The robustness of the results was gauged by changing the GC-MS data
randomly within the error measurement and running simulated annealing
processes^38^ until the error function did not decrease any further.
Each process performed 10,000 error function evaluations with an initial
temperature of T~ini~ = 3.0 and a final one of T~fin~ = 0.05 (scheme
which showed best results) and K=100 cooling steps following an
exponentially decreasing process for T~i~. The initial independent flux
vector ***v~ind~*** was chosen to be that of the solution ***v*** for
each strain found through genetic algorithms with a random noise of
maximum value 0.05 added to each flux component. This procedure was
repeated 100 times, and the standard deviation for fluxes obtained
(***v~i~***) was determined:

![](media/image1.wmf){width="1.2916666666666667in"
height="0.7083333333333334in"} (3)

**Aminoacid isotopomer noise correction.** The "isotopomer noise" from
unlabeled amino acids in rich medium culture can be "cleaned" using the
formula below by assuming that M~0~ of the GC-MS data was mainly from
unlabeled amino acids (see supplementary material for details):

$M_{O}^{'} = 0\text{.}\text{02}$;
$M_{i}^{'} = \frac{0\text{.}\text{98} \cdot M_{i}}{1 - M_{O}}\forall i \neq 0$
(4)

where M is the original GC-MS data, M' is the corrected GC-MS data, *i*
is the number of labeled carbon atoms (0,1, 2, 3...), and 0.02 refers to
2% non-labeled carbon substrate (i.e., 2% of lactate is not labeled) in
the medium.

**Acknowledgements**

We thank J. Chu for help with experiments and isotopomer analysis. We
also thank D. Wemmer and J. Hwang for discussion on MR-1 pathways. This
work is part of the Virtual Institute for Microbial Stress and Survival
([http://VIMSS.lbl.gov](http://VIMSS.lbl.gov/)) supported by the U.S.
Department of Energy, Office of Science, Office of Biological and
Environmental Research, Genomics:GTL Program through contract
DE-AC02-05CH11231 between the Lawrence Berkeley National Laboratory and
the US Department of Energy.

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binary crossover: Studies on multimodal and multiob jective problems.
*Complex Systems* **9** 431-454 (1995).

37\. Deb, K. *Multi-objective optimization using evolutionary
algorithms*, (John Wiley & Sons, Chichester, UK, 2001).

38\. Press, W.H., Flannery, B.P. & Teukolsky, S.A. *Numerical Recipes in
C: the art of scientific computing.*, (Cambridge University Press,
Cambridge, 1992).

# Figures

### Figure 1

![](media/image2.jpeg){width="5.0055555555555555in"
height="3.0381944444444446in"}![](media/image3.jpeg){width="5.0055555555555555in"
height="3.245138888888889in"}

### Figure 1. Calculated flux profiles for each of the considered phenotypes. (a) Profiles for the phylogenetically diverse *Shewanella* species (see Fig. 2) and the average of these profiles with the average confidence intervals, which defines the metabotype (data for each case can be seen in Table S1). As can be seen, most of profiles fall within this metabolic type and can be represented as this profile plus a small correction. (b) Profiles for mutated and stressed MR1 strains, *E. coli*, and late exponential phase profiles for both *E. coli* and MR1. The metabotype from panel (a) is plotted as well for reference. Although late exponential phase profiles (fluxomes based on biomass sampled from late exponential phase) differ from the metabotype, neither the mutated nor stressed MR1 profiles differ significantly. Numbering has been altered from that found in Fig. S1 to allow for grouping of related fluxes (see Table S-1). Abbreviations: TCA cycle (TCA); Gly shunt (GSH); C1 metabolism (RN); Reversible exchange (RE); Glycolysis (Gly); Pentose Phosphate + ED pathway (PPP); Amino acids and external (AM+ext); Av (standard deviation); strain abbreviation follows supplementary Table S1.

### 

### Figure 2

![](media/image4.pct){width="6.131889763779528in"
height="3.251968503937008in"}

**Figure 2.** Phylogenetic relatedness of 11 sequenced *Shewanella*
genomes. A maximum likelihood tree (PHYML) showing the evolutionary
relationship between the *Shewanella* species is illustrated. The tree
is based on a concatenated multiple sequence alignment of 100 single
copy genes present in all of the species. All nodes were consistent with
100 bootstraps. *E. coli* and *Vibrio cholerae* were used as the
outgroups. Eight *Shewanella* strains were studied, and the parenthesis
includes the source of strain, number of genes, and percent of unique
genes not found by sequence homology in the other 10 *Shewanella*
genomes.

###  Figure 3

![](media/image6.wmf){width="4.127777777777778in"
height="2.9479166666666665in"}
![](media/image7.wmf){width="4.332638888888889in"
height="3.204861111111111in"}

**Figure 3.** Effect of addition of non-labeled amino acids on central
metabolism in MR-1 (illustrated by change of GC-MS data in key amino
acids). (a) Comparison of GC-MS data for proteinogenic amino acids
between cells grown in minimal medium (control) and rich medium
(addition of amino acid mixtures, 17 amino acids, 25 M each). ♦: Ala,
Asp, Glu, Gly, Ser; □: Val, Leu, Iso, Pro, Thr, Lys; ○: Met, Phe, His,
Tyr. (b) GC-MS data from rich medium culture were corrected for
non-labeled amino acid effect by equation 4.

### Figure 4

![](media/image8.wmf){width="4.746527777777778in"
height="3.4493055555555556in"}

**Figure 4.** Effect of transposon mutation on central metabolism. The
GC-MS data were from ten different mutants. Nine key amino acids were
included (Ala, Asp, Glu, Gly, Ser, Val, Leu, Phe, His). The outliers
were GC-MS data from SO0781 (lack of glycine dehydrogenase).

# 

# 

> **[Supplementary material]{.underline}**

### Figure S1

![](media/image9.pct){width="5.638194444444444in"
height="7.427083333333333in"}

**Figure S1.** Pathways of lactate metabolism in *S. oneidensis* MR-1.
The amino acids used for isotopomer models are boxed. Numbers denote the
arbitrary flux indices used in modeling the pathways. Abbreviations:
ACCOA, acetyl-coenzyme A; CIT, citrate; E4P, erythrose-4-phosphate; C1,
5,10-Me-THF; F6P, fructose-6-phosphate; G6P, glucose-6-phosphate; 6PG,
6-phosphogluconate; ICT, isocitrate; MAL, malate; OA, oxaloacetate; OGA,
2-oxoglutarate; PEP, phosphoenolpyruvate; PGA, 3-phosphoglycerate; R5P,
ribose-5-phosphate; Ru5P, ribulose-5-phosphate; Xu5P,
xylulose-5-phosphate; S7P, sedoheptulose-7-phosphate; SUCC, succinate;
T3P, triose-3-phosphate; PYR, pyruvate.

### Figure S2

![](media/image10.wmf){width="3.752083333333333in"
height="2.8430555555555554in"}![](media/image11.wmf){width="3.9993055555555554in"
height="3.0368055555555555in"}

**Figure S2.** Growth kinetics of *Shewanella oneidensis* MR-1 and other
strains. (a) *Shewanella* and *E. coli* growth kinetics ■: MR-4, ▲:
MR-7, ●: ANA-3, ◊: PV-4, ♦: SB2B, □: W3-18-1, ○: CN-32, Δ: MR-1, ×: *E.
coli* W3110. (b) Substrate consumption and product secretion kinetics in
MR-1 and W3110 strain. ■: Lactate (MR-1), ♦: Acetate (MR-1), ●: Pyruvate
(MR-1), □: Lactate (*E. coli* W3110), ◊: Acetate (*E. coli* W3110). (c)
*Shewanella oneidensis* MR-1 growth under different growth medium
conditions ♦: MR-1 medium with amino acids supplement; ◊: MR-1 medium
with low salt concentration (0.26 M); ■: MR-1 medium with high salt
concentration (0.33 M) and amino acids supplement (17 amino acids and 25
μM each), □: MR-1 medium with high salt concentration (0.33 M).

### Figure S3

![](media/image12.wmf){width="3.8743055555555554in" height="2.875in"}

![](media/image13.wmf){width="3.497916666666667in"
height="2.723611111111111in"}

![](media/image14.wmf){width="3.345138888888889in"
height="2.578472222222222in"}

**Figure S3.** Labeling fraction of most abundant ion changes as the
function of time shows the isotopomer shifting in proteinogenic amino
acids during MR-1 and *E.coli* W3110 growth. M0, M1, M2... represent
unlabeled, singly ^13^C and doubly ^13^C labeled mass ions,
respectively, of a given amino acid. (a) Amino acids (precursors from
gluconeogenesis and serine metabolism pathway) ■: Ser (M1 \[-57\],
MR-1), ●: Gly (M1 \[-57\], MR-1), ♦: Ala (M1 \[-57\], MR-1), □: Ser (M1
\[-57\], *E. coli*), ○: Gly (M1 \[-57\], *E. coli*), ◊: Ala (M1 \[-57\],
*E. coli*). (b) Amino acids (precursors from TCA cycle). ■: Glu (M3
\[-57\], MR-1), ●: Asp (M2 \[-57\], MR-1), ♦: Leu (M3 \[-159\], MR-1),
□: Glu (M3 \[-57\], *E. coli*), ○: Asp (M2 \[-57\], *E. coli*), ◊: Leu
(M3 \[-159\], *E. coli*). (c) Amino acids (precursors from
gluconeogenesis and pentose phosphate pathway). ■: His (M1 \[-57\],
MR-1), ♦: Phe (M1 \[-57\], MR-1), □: His (M1 \[-57\], *E. coli*), ◊: Phe
(M1 \[-57\], *E. coli*).

**Table S1.** Comparison of fluxomes in central metabolism (lactate
uptake flux, v~1~, is normalized to 1). Abbreviation: MR1mut (mutant
SO0781); MR1st (MR1 under high concentration of salt stress)

  --------------------------------------------------------------------------------------------------------------------------------------------------------
  **Flux No**    **MR1**   **MR4**   **MR7**   **SB**   **CN**   **W3**   **ANA**   **PV4**   **MR1mut**   **MR1st**   **Ec**   **MR1late**   **Eclate**
  -------------- --------- --------- --------- -------- -------- -------- --------- --------- ------------ ----------- -------- ------------- ------------
  **TCA cycle                                                                                                                                 
  (TCA)**                                                                                                                                     

  5              0.72 +/-  0.90 +/-  0.90 +/-  0.82 +/- 0.74 +/- 0.86 +/- 0.83 +/-  0.89 +/-  0.73 +/-     0.69 +/-    0.94 +/- 0.86 +/- 0.18 1.07 +/-
                 0.15      0.15      0.12      0.18     0.12     0.15     0.14      0.13      0.10         0.12        0.16                   0.09

  7              0.36 +/-  0.34 +/-  0.30 +/-  0.53 +/- 0.42 +/- 0.43 +/- 0.39 +/-  0.32 +/-  0.33 +/-     0.37 +/-    0.39 +/- 0.76 +/- 0.18 0.75 +/-
                 0.13      0.11      0.09      0.18     0.14     0.15     0.12      0.09      0.11         0.12        0.14                   0.20

  9              0.36 +/-  0.34 +/-  0.30 +/-  0.53 +/- 0.42 +/- 0.43 +/- 0.39 +/-  0.32 +/-  0.33 +/-     0.37 +/-    0.39 +/- 0.76 +/- 0.18 0.75 +/-
                 0.13      0.11      0.09      0.18     0.14     0.15     0.12      0.09      0.11         0.12        0.14                   0.20

  10             0.30 +/-  0.24 +/-  0.20 +/-  0.46 +/- 0.39 +/- 0.38 +/- 0.32 +/-  0.21 +/-  0.30 +/-     0.33 +/-    0.32 +/- 0.66 +/- 0.15 0.57 +/-
                 0.10      0.07      0.08      0.16     0.13     0.12     0.09      0.06      0.10         0.11        0.09                   0.21

  11             0.28 +/-  0.23 +/-  0.19 +/-  0.45 +/- 0.38 +/- 0.36 +/- 0.31 +/-  0.20 +/-  0.28 +/-     0.31 +/-    0.30 +/- 0.65 +/- 0.16 0.56 +/-
                 0.09      0.07      0.08      0.17     0.13     0.13     0.10      0.06      0.10         0.10        0.09                   0.21

  13             0.34 +/-  0.32 +/-  0.29 +/-  0.52 +/- 0.40 +/- 0.42 +/- 0.38 +/-  0.31 +/-  0.31 +/-     0.35 +/-    0.37 +/- 0.75 +/- 0.19 0.74 +/-
                 0.13      0.11      0.10      0.19     0.13     0.15     0.13      0.10      0.11         0.12        0.14                   0.20

  14             0.34 +/-  0.32 +/-  0.29 +/-  0.52 +/- 0.40 +/- 0.42 +/- 0.38 +/-  0.31 +/-  0.31 +/-     0.35 +/-    0.37 +/- 0.75 +/- 0.19 0.74 +/-
                 0.13      0.11      0.10      0.19     0.13     0.15     0.13      0.10      0.11         0.12        0.14                   0.20

  16             0.29 +/-  0.25 +/-  0.18 +/-  0.38 +/- 0.33 +/- 0.31 +/- 0.28 +/-  0.22 +/-  0.26 +/-     0.29 +/-    0.30 +/- 0.65 +/- 0.13 0.63 +/-
                 0.08      0.07      0.04      0.11     0.08     0.08     0.06      0.05      0.06         0.07        0.08                   0.15

  **Gly shunt                                                                                                                                 
  (GSH)**                                                                                                                                     

  12             0.06 +/-  0.10 +/-  0.10 +/-  0.07 +/- 0.03 +/- 0.05 +/- 0.07 +/-  0.11 +/-  0.03 +/-     0.03 +/-    0.07 +/- 0.10 +/- 0.04 0.17 +/-
                 0.04      0.07      0.06      0.04     0.02     0.03     0.05      0.06      0.02         0.02        0.05                   0.06

  19             0.06 +/-  0.10 +/-  0.10 +/-  0.07 +/- 0.03 +/- 0.05 +/- 0.07 +/-  0.11 +/-  0.03 +/-     0.03 +/-    0.07 +/- 0.10 +/- 0.04 0.17 +/-
                 0.04      0.07      0.06      0.04     0.02     0.03     0.05      0.06      0.02         0.02        0.05                   0.06

  **Reversible                                                                                                                                
  C1 metabolism                                                                                                                               
  (RN)**                                                                                                                                      

  20             0.02 +/-  0.01 +/-  0.01 +/-  0.02 +/- 0.02 +/- 0.02 +/- 0.02 +/-  0.02 +/-  0.01 +/-     0.02 +/-    0.02 +/- 0.03 +/- 0.02 0.02 +/-
                 0.03      0.03      0.03      0.02     0.03     0.03     0.03      0.02      0.02         0.03        0.03                   0.02

  21             0.51 +/-  0.51 +/-  0.51 +/-  0.51 +/- 0.51 +/- 0.51 +/- 0.51 +/-  0.51 +/-  0.51 +/-     0.51 +/-    0.51 +/- 0.52 +/- 0.01 0.51 +/-
                 0.01      0.02      0.01      0.01     0.02     0.02     0.01      0.01      0.01         0.02        0.01                   0.01

  24             0.01 +/-  0.00 +/-  0.00 +/-  0.04 +/- 0.00 +/- 0.00 +/- 0.01 +/-  0.01 +/-  0.26 +/-     0.00 +/-    0.02 +/- 0.06 +/- 0.06 0.09 +/-
                 0.04      0.03      0.03      0.05     0.05     0.01     0.03      0.05      0.10         0.02        0.06                   0.13

  22             0.51 +/-  0.51 +/-  0.51 +/-  0.51 +/- 0.51 +/- 0.51 +/- 0.51 +/-  0.51 +/-  0.01 +/-     0.51 +/-    0.51 +/- 0.52 +/- 0.01 0.51 +/-
                 0.01      0.02      0.01      0.01     0.02     0.02     0.01      0.01      0.02         0.02        0.01                   0.01

  25             0.01 +/-  0.00 +/-  0.00 +/-  0.01 +/- 0.01 +/- 0.01 +/- 0.01 +/-  0.00 +/-  0.00 +/-     0.01 +/-    0.00 +/- 0.02 +/- 0.02 0.01 +/-
                 0.02      0.03      0.04      0.01     0.01     0.02     0.01      0.01      0.00         0.02        0.01                   0.03

  **Reversible                                                                                                                                
  exchange                                                                                                                                    
  (RE)**                                                                                                                                      

  4              0.46 +/-  0.45 +/-  0.43 +/-  0.42 +/- 0.45 +/- 0.43 +/- 0.44 +/-  0.44 +/-  0.46 +/-     0.45 +/-    0.45 +/- 0.44 +/- 0.02 0.43 +/-
                 0.03      0.03      0.04      0.03     0.03     0.03     0.03      0.03      0.02         0.03        0.03                   0.03

  26             0.00 +/-  0.00 +/-  0.00 +/-  0.00 +/- 0.00 +/- 0.00 +/- 0.00 +/-  0.00 +/-  0.01 +/-     0.00 +/-    0.03 +/- 0.00 +/- 0.00 0.03 +/-
                 0.01      0.01      0.03      0.03     0.01     0.01     0.01      0.02      0.01         0.01        0.02                   0.04

  8              0.45 +/-  0.41 +/-  0.39 +/-  0.40 +/- 0.45 +/- 0.42 +/- 0.41 +/-  0.40 +/-  0.46 +/-     0.45 +/-    0.43 +/- 0.40 +/- 0.05 0.36 +/-
                 0.05      0.06      0.06      0.06     0.03     0.05     0.06      0.06      0.03         0.04        0.05                   0.04

  42             0.00 +/-  0.00 +/-  0.00 +/-  0.00 +/- 0.00 +/- 0.00 +/- 0.00 +/-  0.00 +/-  0.00 +/-     0.00 +/-    0.00 +/- 0.00 +/- 0.03 0.00 +/-
                 0.03      0.04      0.04      0.02     0.02     0.03     0.04      0.04      0.04         0.03        0.03                   0.04

  **Glycolysis                                                                                                                                
  (Gly)**                                                                                                                                     

  2              0.16 +/-  0.17 +/-  0.19 +/-  0.22 +/- 0.19 +/- 0.21 +/- 0.21 +/-  0.19 +/-  0.14 +/-     0.18 +/-    0.16 +/- 0.25 +/- 0.05 0.18 +/-
                 0.04      0.04      0.04      0.04     0.04     0.04     0.03      0.04      0.03         0.04        0.03                   0.06

  3              0.05 +/-  0.05 +/-  0.03 +/-  0.04 +/- 0.06 +/- 0.05 +/- 0.06 +/-  0.06 +/-  0.04 +/-     0.07 +/-    0.04 +/- 0.11 +/- 0.05 0.03 +/-
                 0.06      0.05      0.04      0.05     0.06     0.06     0.04      0.03      0.05         0.05        0.05                   0.04

  27             0.03 +/-  0.04 +/-  0.02 +/-  0.02 +/- 0.04 +/- 0.03 +/- 0.05 +/-  0.04 +/-  0.03 +/-     0.05 +/-    0.02 +/- 0.07 +/- 0.04 0.01 +/-
                 0.04      0.03      0.02      0.04     0.04     0.04     0.03      0.02      0.03         0.03        0.03                   0.04

  41             0.01 +/-  0.02 +/-  0.01 +/-  0.00 +/- 0.02 +/- 0.01 +/- 0.02 +/-  0.01 +/-  0.01 +/-     0.02 +/-    0.01 +/- 0.03 +/- 0.01 0.00 +/-
                 0.01      0.01      0.01      0.01     0.01     0.01     0.01      0.01      0.01         0.01        0.01                   0.02

  28             0.01 +/-  0.02 +/-  0.01 +/-  0.00 +/- 0.02 +/- 0.01 +/- 0.02 +/-  0.01 +/-  0.01 +/-     0.02 +/-    0.01 +/- 0.03 +/- 0.01 0.00 +/-
                 0.01      0.01      0.01      0.01     0.01     0.01     0.01      0.01      0.01         0.01        0.01                   0.02

  29             0.01 +/-  0.02 +/-  0.01 +/-  0.00 +/- 0.02 +/- 0.01 +/- 0.02 +/-  0.01 +/-  0.01 +/-     0.02 +/-    0.01 +/- 0.03 +/- 0.02 0.00 +/-
                 0.01      0.01      0.01      0.01     0.01     0.01     0.01      0.01      0.01         0.02        0.00                   0.03

  **Pentose                                                                                                                                   
  Phosphate + ED                                                                                                                              
  pathway                                                                                                                                     
  (PPP)**                                                                                                                                     

  30             0.01 +/-  0.02 +/-  0.01 +/-  0.00 +/- 0.02 +/- 0.01 +/- 0.02 +/-  0.01 +/-  0.01 +/-     0.02 +/-    0.01 +/- 0.03 +/- 0.02 0.00 +/-
                 0.01      0.01      0.01      0.01     0.01     0.01     0.01      0.01      0.01         0.02        0.00                   0.03

  31             0.00 +/-  0.00 +/-  0.00 +/-  0.01 +/- 0.00 +/- 0.00 +/- 0.01 +/-  0.01 +/-  0.00 +/-     0.00 +/-    0.00 +/- 0.00 +/- 0.01 0.00 +/-
                 0.01      0.01      0.01      0.02     0.01     0.01     0.01      0.01      0.01         0.01        0.01                   0.01

  32             0.02 +/-  0.02 +/-  0.01 +/-  0.01 +/- 0.02 +/- 0.01 +/- 0.02 +/-  0.02 +/-  0.01 +/-     0.02 +/-    0.01 +/- 0.04 +/- 0.02 0.01 +/-
                 0.02      0.01      0.01      0.02     0.02     0.02     0.02      0.01      0.01         0.02        0.01                   0.02

  33             0.01 +/-  0.01 +/-  0.01 +/-  0.01 +/- 0.01 +/- 0.01 +/- 0.01 +/-  0.01 +/-  0.01 +/-     0.01 +/-    0.01 +/- 0.01 +/- 0.02 0.01 +/-
                 0.01      0.01      0.01      0.01     0.01     0.01     0.01      0.01      0.01         0.01        0.01                   0.01

  34             0.01 +/-  0.01 +/-  0.01 +/-  0.01 +/- 0.01 +/- 0.01 +/- 0.01 +/-  0.01 +/-  0.01 +/-     0.01 +/-    0.01 +/- 0.01 +/- 0.01 0.01 +/-
                 0.00      0.00      0.00      0.01     0.00     0.00     0.00      0.00      0.00         0.00        0.00                   0.01

  35             0.01 +/-  0.01 +/-  0.01 +/-  0.01 +/- 0.01 +/- 0.01 +/- 0.01 +/-  0.01 +/-  0.01 +/-     0.01 +/-    0.01 +/- 0.01 +/- 0.01 0.01 +/-
                 0.00      0.00      0.00      0.01     0.00     0.00     0.00      0.00      0.00         0.00        0.00                   0.01

  37             0.02 +/-  0.02 +/-  0.01 +/-  0.01 +/- 0.02 +/- 0.01 +/- 0.02 +/-  0.02 +/-  0.01 +/-     0.02 +/-    0.01 +/- 0.04 +/- 0.02 0.01 +/-
                 0.02      0.01      0.01      0.02     0.02     0.02     0.02      0.01      0.02         0.02        0.02                   0.02

  38             0.00 +/-  0.00 +/-  0.00 +/-  0.00 +/- 0.00 +/- 0.00 +/- 0.00 +/-  0.00 +/-  0.00 +/-     0.00 +/-    0.00 +/- 0.00 +/- 0.01 0.00 +/-
                 0.01      0.01      0.01      0.01     0.01     0.01     0.01      0.01      0.01         0.01        0.01                   0.01

  **Amino acids                                                                                                                               
  and external                                                                                                                                
  (AM+ext)**                                                                                                                                  

  1              1.00 +/-  1.00 +/-  1.00 +/-  1.00 +/- 1.00 +/- 1.00 +/- 1.00 +/-  1.00 +/-  1.00 +/-     1.00 +/-    1.00 +/- 1.00 +/- 0.00 1.00 +/-
                 0.00      0.00      0.00      0.00     0.00     0.00     0.00      0.00      0.00         0.00        0.00                   0.00

  6              0.01 +/-  0.01 +/-  0.01 +/-  0.01 +/- 0.01 +/- 0.01 +/- 0.01 +/-  0.01 +/-  0.01 +/-     0.01 +/-    0.01 +/- 0.01 +/- 0.02 0.01 +/-
                 0.01      0.01      0.01      0.01     0.01     0.01     0.01      0.01      0.01         0.01        0.01                   0.01

  15             0.02 +/-  0.01 +/-  0.01 +/-  0.01 +/- 0.02 +/- 0.01 +/- 0.01 +/-  0.01 +/-  0.02 +/-     0.02 +/-    0.01 +/- 0.01 +/- 0.01 0.01 +/-
                 0.01      0.00      0.00      0.01     0.01     0.01     0.01      0.01      0.01         0.01        0.01                   0.00

  17             0.20 +/-  0.08 +/-  0.11 +/-  0.13 +/- 0.14 +/- 0.06 +/- 0.11 +/-  0.08 +/-  0.18 +/-     0.19 +/-    0.02 +/- 0.06 +/- 0.03 0.01 +/-
                 0.03      0.03      0.02      0.03     0.02     0.03     0.02      0.02      0.02         0.02        0.02                   0.04

  18             0.01 +/-  0.01 +/-  0.01 +/-  0.01 +/- 0.01 +/- 0.01 +/- 0.01 +/-  0.01 +/-  0.01 +/-     0.01 +/-    0.01 +/- 0.01 +/- 0.02 0.01 +/-
                 0.02      0.02      0.01      0.02     0.02     0.01     0.02      0.02      0.01         0.02        0.01                   0.02

  23             0.01 +/-  0.01 +/-  0.01 +/-  0.01 +/- 0.01 +/- 0.01 +/- 0.01 +/-  0.01 +/-  0.01 +/-     0.01 +/-    0.01 +/- 0.01 +/- 0.02 0.01 +/-
                 0.01      0.01      0.01      0.01     0.01     0.01     0.01      0.01      0.01         0.01        0.01                   0.01

  36             0.01 +/-  0.01 +/-  0.00 +/-  0.00 +/- 0.01 +/- 0.01 +/- 0.02 +/-  0.01 +/-  0.01 +/-     0.02 +/-    0.00 +/- 0.03 +/- 0.02 0.00 +/-
                 0.02      0.02      0.01      0.02     0.02     0.02     0.02      0.01      0.02         0.02        0.01                   0.02

  39             0.01 +/-  0.01 +/-  0.01 +/-  0.01 +/- 0.01 +/- 0.01 +/- 0.01 +/-  0.01 +/-  0.01 +/-     0.01 +/-    0.01 +/- 0.01 +/- 0.02 0.01 +/-
                 0.01      0.01      0.01      0.02     0.01     0.01     0.01      0.01      0.01         0.01        0.01                   0.02

  40             0.01 +/-  0.01 +/-  0.01 +/-  0.01 +/- 0.01 +/- 0.01 +/- 0.01 +/-  0.01 +/-  0.01 +/-     0.01 +/-    0.01 +/- 0.01 +/- 0.02 0.01 +/-
                 0.01      0.01      0.01      0.02     0.01     0.01     0.01      0.01      0.01         0.01        0.01                   0.02

  43             0.29 +/-  0.46 +/-  0.48 +/-  0.21 +/- 0.29 +/- 0.37 +/- 0.35 +/-  0.45 +/-  0.36 +/-     0.28 +/-    0.47 +/- 0.00 +/- 0.03 0.14 +/-
                 0.02      0.03      0.02      0.03     0.03     0.03     0.03      0.02      0.02         0.02        0.02                   0.16
  --------------------------------------------------------------------------------------------------------------------------------------------------------

> **Aminoacid isotopomer noise correction.** Let us assume that, for a
> given aminoacid of *c* carbons, the number of molecules with *i*
> (*=0..c*) labeled carbons is *X~i~*, for a given *shewanella* *spp* or
> *E. coli* in the labeling experiments. The addition of unlabeled
> aminoacids to the feed would change only the unlabeled fraction by a
> factor *k*:
>
> *X~0\ ~*= *X~0~'+k*
>
> *X~i\ ~*= *X~i~' (i=1..c)*
>
> The labeled fractions of amionacids are then:
>
> $$M_{i} = X_{i}\sum_{i = 0}^{c}X_{i}$$
>
> $$M'_{i} = X_{i}'\sum_{i = 0}^{c}{X'_{i}}$$
>
> Assuming the fraction *M~0~=0.02* to be the same as the unlabeled
> fraction in the feed, we get:
>
> $$\sum_{i = 1}^{c}{M'_{i} = 1 - 0\text{.}\text{02} = 0\text{.}\text{98}} = \frac{\sum_{i = 1}^{c}{X'_{i}}}{\sum_{i = o}^{c}{X'_{i}}} = \frac{\sum_{i = 1}^{c}X_{i}}{k + \sum_{i = o}^{c}X_{i}}$$
>
> Whence the relationship between *M~i~* and *M'~i~* can be derived:
>
> $$$M'_{i} = \frac{X_{i}}{k + \sum_{i = 0}^{c}X_{i}} = 0\text{.}\text{98}\frac{X_{i}}{\sum_{i = 1}^{c}X_{i}} = 0\text{.}\text{98}\frac{X_{i}}{\sum_{i = 0}^{c}{X_{i} - X_{0}}} = \frac{0\text{.}\text{98}}{1 - M_{0}}\frac{X_{i}}{\sum_{i = 0}^{c}X_{i}} = \frac{0\text{.}\text{98}}{1 - M_{0}}M_{i}$
>
> where the definition of *M~0~* has been used:
>
> $$M_{0} = \frac{X_{0}}{\sum_{i = o}^{c}X_{i}}$$
>
> **Reaction lists:**

\% Metabolites excluded for flux balances

excludedmets 17 18

\% Lactate to pyruvate

1: ABC(1) -\> ABC(2)

\% Pyruvate to phosphoenolpyruvate

2: ABC(2) -\> ABC(14)

\% Phosphoenolpyrutate to 3-phospho-D-glycerate

3: ABC(14) -\> ABC(15)

\% Oxaloacetate to phosphoenolpyruvate

4: ABCD(11) -\> ABC(14) + D(17)

\% Pyruvate to Acetyl-CoA

5: ABC(2) -\> BC(3) + A(17)

\% Leu synthesis step 1

6: AB(3) + CDE(2) -\> ABDE(4) + C(17)

\% Oxaloacetate to citrate

7: AB(3) + abcd(11) -\> dcbBAa(5)

\% Pyruvate to malate

8: ABC(2) + D(17)-\> ABCD(10)

\% Citrate to isocitrate

9: ABCDEF(5) -\> ABCDEF(6)

\% Isocitrate to 2-oxoglutarate

10: ABCDEF(6) -\> ABCDE(7) + F(17)

\% 2-oxoglutarate to succinyl-CoA

11: ABCDE(7) -\> BCDE(8) + A(17) :: ABCDE(7) -\> EDCB(8) + A(17)

\% Isocitrate to succinate

12: ABCDEF(6) -\> FCDE(8) + AB(12) :: ABCDEF(6) -\> EDCF(8) + AB(12)

\% Succinate to Malate

13: ABCD(8) -\> ABCD(9)

\% Fumarate -\> malate

14: ABCD(9) -\> ABCD(10)

\% Glu synthesis

15: ABCDE(7) -\> ABCDE(29)

\% Malate to oxaloacetate

16: ABCD(10) -\> ABCD(11)

\% Alanine synthesis:

17: ABC(2) -\> ABC(28)

\% Asp synthesis

18: ABCD(11) -\> ABCD(13)

\% Glyoxylate to malate

19: AB(12) + CD(3) -\> ABCD(10)

\% Serine production

20: ABC(15) -\> ABC(16)

\% Glycine formation from serine

21: ABC(16) -\> AB(19) + C(18)

\% Glycine to C-1 group

22: AB(19) -\> A(17) + B(18)

\% Leu synthesis (2nd part)

23: ABDE(4) + FGH(2) -\> ABDGHE(40) + F(17)

\% Reaction 21 reversed

24: AB(19) + C(18) -\> ABC(16)

\% Reaction 22 reversed

25: A(17) + B(18) -\> AB(19)

\% Reaction 4 reversed

26: ABC(14) + D(17) -\> ABCD(11)

\% 3-phosphoglycerate to triose-3-phosphate

27: ABC(15) -\> ABC(20)

\% triose-3-phosphate to fructose-6-phosphate

28: ABC(20) + abc(34) -\> CBAabc(21)

\% fructose-6-phosphate to glucose-6-phosphate

29: ABCDEF(21) -\> ABCDEF(22)

\% glucose-6-phosphate to 6-phosphogluconate

30: ABCDEF(22) -\> ABCDEF(23)

\% ED pathway

31: ABC(2) + DEF(20) -\> ABCDEF(23)

\% 6-phosphogluconate to Ribulose 5-phosphate

32: ABCDEF(23) -\> A(17) + BCDEF(27)

\% Transketolase: triose-3-phosphate + fructose 6-P -\> xylulose 5-P +
epythrose 4-P

33: ABC(20) + abcdef(21) -\> abABC(25) + cdef(24)

\% fructose-6-P + epythrose 4-P -\> triose-3-phosphate + D-sedoheptulose
7-P

34: ABC(20) + abcdefg(26) -\> defg(24) + abcABC(21)

\% Tranketolase: D-sedoheptulose 7-P + triose-3-phosphate -\> D-Xylulose
5-P + D-Ribose 5-P

35: ABCDE(25) + abcde(32) -\> CDE(20) + ABabcde(26)

\% Histidine biosynthesis:

36: ABCDE(32) + a(18) -\> ABCDEa(33)

\% Ribulose 5-P Epimerase: D-Ribulose 5-P -\> D-Ribose 5-P

37: ABCDE(27) -\> ABCDE(32)

\% Ribulose-P 3-Epimerase: D-Xylulose 5-P -\> D-Ribulose 5-P

38: ABCDE(25) -\> ABCDE(27)

\% Phenyalanine precursor synthesis

39: ABCD(24) + abc(14) -\> abcABCD(30)

\% Phenyalanine synthesis

40: def(14) + abcABCD(30) -\> a(17) + bcABCDfed(31)

\% Extra step in reaction 28

41: ABC(20) -\> ABC(34)

\% Reaction 8 reversed

42: ABCD(10) -\> ABC(2) + D(17)

\% Acetate efflux

43: AB(3) -\> AB(35)

> For MR1mut reaction 25 was eliminated and reaction 22 was substituted
> by:

\% Glycine biomass

22: AB(19) -\> AB(41)

Note:

The separated two files include: The FBA flux analysis results using
Simpheny and the modeling fitting of isotopomer data.