Introduction
The knowledge of a complete genome sequence holds the
potential to reveal the 'blueprints' for cellular life. The
genome sequence contains the information to propagate the
living system, and this information exists as open reading
frames (ORFs) and regulatory information. Computational
approaches have been developed (and are continuously being
improved) to decipher the information encoded in the DNA [
1, 2, 3, 4, 5, 6, 7]. However, it is becoming evident that
cellular functions are intricate and the integrated
function of biological systems involves many complex
interactions among the molecular components within the
cell. To understand the complexity inherent in cellular
networks, approaches that focus on the systemic properties
of the network are also required.
The complexity of integrated cellular systems leads to
an important point, namely that the properties of complex
biological processes cannot be analyzed or predicted based
solely on a description of the individual components, and
integrated systems based approaches must be applied [ 8].
The focus of such research represents a departure from the
classical
reductionist approach in the
biological sciences, and moves toward the
integrated approach to understanding
the interrelatedness of gene function and the role of each
gene in the context of multi genetic cellular functions or
genetic circuits [ 8, 9, 10].
The engineering approach to analysis and design of
complex systems is to have a mathematical or computer
model; e.g. a dynamic simulator of a cellular process that
is based on fundamental physicochemical laws and
principles. Herein, we will analyze the integrated function
of the metabolic pathways, and there has been a long
history of mathematical modeling of metabolic networks in
cellular systems, which dates back to the 1960s [ 11, 12].
While the ultimate goal is the development of dynamic
models for the complete simulation of cellular metabolism,
the success of such approaches has been severely hampered
by the lack of kinetic information on the dynamics and
regulation of metabolism. However, in the absence of
kinetic information it is still possible to assess the
theoretical capabilities and operative modes of metabolism
using flux balance analysis (FBA) [ 10, 13, 14, 15, 16,
17].
We have developed an
in silico representation of
Escherichia coli (E. coli in
silico) to describe the bacterium's metabolic
capabilities [ 18].
E. coli in silico was derived based
on the annotated genetic sequence [ 19], biochemical
literature [ 20], and the online bioinformatic databases [
21, 22, 23]. The properties of
E. coli in silico were analyzed and
compared to the
in vivo properties of
E. coli, and it was shown that
E. coli in silico can be used to
interpret the metabolic phenotype of many
E. coli mutants [ 18]. However, the
utilization of the metabolic genes is dependent on the
carbon source and the substrate availability [ 24, 25].
Thus, the mutant phenotype is also dependent on specific
environmental parameters. Therefore, herein we have
utilized
E. coli in silico to computationally
examine the condition dependent optimal metabolic pathway
utilization, and we will show that the FBA can be used to
analyze and interpret the metabolic behavior of wildtype
and mutant
E. coli strains.
Materials and Methods
Flux balance analysis
All biological processes are subjected to physico
chemical constraints (such as mass balance, osmotic
pressure, electro neutrality, thermodynamic, and other
constraints). As a result of decades of metabolic
research and the recent genome sequencing projects, the
mass balance constraints on cellular metabolism can be
assigned on a genome scale for a number of organisms.
Methods have been developed to analyze the metabolic
capabilities of a cellular system based on the mass
balance constraints and this approach is known as flux
balance analysis (FBA) [ 13, 14, 16] (see the
supplementary information for an FBA primer). The mass
balance constraints in a metabolic network can be
represented mathematically by a matrix equation:
S • v = 0 Equation 1
The matrix
S is the
mxn stoichiometric matrix, where
m is the number of metabolites and
n is the number of reactions in the
network (The
E. coli stoichiometric matrix is
available in matrix format in the supplementary
information and in a reaction list in Appendices 1-3).
The vector
v represents all fluxes in the
metabolic network, including the internal fluxes,
transport fluxes and the growth flux.
For the
E. coli metabolic network
represented by Eqn. 1, the number of fluxes was greater
than the number of mass balance constraints; thus, there
were multiple feasible flux distributions that satisfied
the mass balance constraints, and the solutions (or
feasible metabolic flux distributions) were confined to
the nullspace of the matrix
S .
In addition to the mass balance constraints, we
imposed constraints on the magnitude of individual
metabolic fluxes.
α
i
≤ v
i
≤ β
i
Equation 2
The linear inequality constraints were used to enforce
the reversibility of each metabolic reaction and the
maximal flux in the transport reactions. The
reversibility constraints for each reaction are indicated
online. The transport flux for inorganic phosphate,
ammonia, carbon dioxide, sulfate, potassium, and sodium
was unrestrained (α
i
= -∞ and β
i
= ∞). The transport flux for the other metabolites,
when available in the
in silico medium, was constrained
between zero and the maximal level (0 ≤ v
i
≤ v
i
max). The v
i
max values used in the simulations
are noted for each simulation (Fig. 1). When a metabolite
was not available in the medium, the transport flux was
constrained to zero. The transport flux for metabolites
capable of leaving the metabolic network (i.e. acetate,
ethanol, lactate, succinate, formate, and pyruvate) was
always unconstrained in the net outward direction.
The intersection of the nullspace and the region
defined by the linear inequalities defined a region in
flux space that we will refer to as the feasible set, and
the feasible set defined the capabilities of the
metabolic network subject to the imposed cellular
constraints. It should be noted that every vector
v within the feasible set is not
reachable by the cell under a given condition due to
other constraints not considered in the analysis (i.e.
maximal internal fluxes and gene regulation). The
feasible set can be further reduced by imposing
additional constraints (i.e. kinetic or gene regulatory
constraints), and in the limiting condition where all
constraints are known, the feasible set may reduce to a
single point.
A particular metabolic flux distribution within the
feasible set (vector
v which satisfies the constraints in
Eqns. 1 and 2) was found using linear programming (LP). A
commercially available LP package was used (LINDO, Lindo
Systems Inc., Chicago, II). LP identified a solution that
minimized a metabolic objective function (subject to the
imposed constraints- Eqns. 1 and 2) [ 16, 48, 49], and
was formulated as shown below:
Minimize -Z
where
Z = Σ
c
i
v
i
= <c • v> Equation 3
The vector
c was used to select a linear
combination of metabolic fluxes to include in the
objective function [ 50]. Herein,
c was defined as the unit vector in
the direction of the growth flux, and the growth flux was
defined in terms of the biosynthetic requirements:
(Equation 4)
where
d
m
is the biomass composition of metabolite X
m
(we used a constant biomass composition defined from
the literature [ 51] (see Appendix 4)), and the growth
flux was modeled as a single reaction that converts all
the biosynthetic precursors into biomass.
Phenotype Phase Plane Analysis
All feasible
E. coli in silico metabolic flux
distributions are mathematically confined to the feasible
set, which is a region in flux space ( n), where each
solution in this space corresponds to a feasible
metabolic flux distribution.
Phenotype Phase Plane (PhPP): A PhPP is a
two-dimensional projection of the feasible set, and below
we will briefly discuss the formalism for constructing
the PhPP. Two parameters that describe the growth
conditions (such as substrate and oxygen uptake rates)
were defined as the two axes of the two dimensional
space. The optimal flux distribution was calculated
(using LP) for all points in this plane by solving the LP
problem while adjusting the exchange flux constraints
(defining the two-dimensional space). A finite number of
qualitatively different patterns of metabolic pathway
utilization were identified in such a plane, and lines
were drawn to demarcate these regions. Each region is
denoted by Pn
x, y , where 'P' indicates that the
region was defined by a phenotype phase plane analysis,
'n' denotes the number of the demarcated phase (as shown
in a particular PhPP figure), and 'x, y' denotes the two
uptake rates on the axis of the PhPP. PhPPs were also
generated for mutant genotypes; represented as P genen
x, y .
One demarcation line in the PhPP was defined as the
line of optimality (LO). The LO represents the optimal
relation between exchange fluxes defined on the axes of
the PhPP.
Alterations of the genotype
FBA and
E. coli in silico were used to
examine the systemic effects of in
silico gene deletions. The genes
involved in the central metabolic pathways (glycolysis,
pentose phosphate pathway, TCA cycle, electron transport)
were subjected to removal from
E. coli in silico. To simulate a
gene deletion, all metabolic reactions catalyzed by a
given gene product were simultaneously constrained to
zero. Some metabolic reactions were catalyzed by more
than one enzyme, and all genes that code for enzymes that
catalyze a given reaction were simultaneously removed
(i.e.
rpiAB ). Furthermore, all genes
that make up an enzyme complex were also simultaneously
removed (i.e.
sdhABCD ).
The optimal metabolic flux distribution for the
generation of biomass was calculated for each
in silico deletion strain. The
in silico gene deletion analysis
was performed with the transport flux constraints defined
by the wild-type PhPP. The constraints imposed for each
simulation are noted in Fig. 1.
For each
in silico deletion strain, the
optimal production of the twelve biosynthetic precursors
and the metabolic cofactors was also calculated to
identify auxotrophic requirements and impaired functions
in the metabolic network (Table 1). The optimal
production of the biosynthetic precursors was calculated
by setting the objective function to the drain of a
single metabolite (i.e., ATP → ADP + P
i , or PEP →). The numerical value of
the objective function for each
in silico deletion strain was
reported as a fraction of the wild-type optimal value
(Table 1).
Results
We have previously described the construction of
phenotype phase planes (PhPPs) (see materials and methods)
and the analysis of the glucose-oxygen PhPP. We have
previously described the effect of
in silico 'gene deletions' on the
ability of
E. coli in silico to 'grow' under a
single condition [ 18]. Since the utilization of the
metabolic pathways is condition dependent, herein, we have
investigated the link between the environmental conditions
and the optimal metabolic pathway utilization
in silico by: 1. studying the effects
of gene deletions in all phases of the glucose-oxygen PhPP,
and 2. broadening the analysis of
in silico deletion strains by
comparing PhPPs from isogenic
in silico strains.
Gene Deletions : A point within each
phase of the glucose-oxygen PhPP was chosen to define the
transport flux constraints (indicated in Fig. 1) for the
FBA simulations. At each point, the growth characteristics
of all
in silico gene deletion strains (of
central metabolic pathway genes) were examined. Based on
the results, the genes were categorized as;
essential (growth under the defined
condition requires the activity of the corresponding gene
product),
critical (growth at a reduced yield
(< 95% of wild-type)), or
non-essential (growth at near
wild-type yield (> 95%)). The effects of the
in silico gene deletions were
phase-dependent, allowing us to identify optimal growth
phenotypes for each growth condition. Additionally, the
optimal production of the 12 biosynthetic precursors,
high-energy phosphate bonds, and redox potential was
calculated for each
in silico deletion strain (Table 1)
to determine the specific effect of the gene deletion on
the metabolic capabilities. For instance, the
in silico acnAB' strain was unable to
synthesize α-ketoglutarate under all simulated growth
conditions, and thus,
acnAB was defined as essential for
growth in a glucose minimal media (Table 1).
The optimal utilization of the metabolic pathways was
dependent on the specific transport flux constraints, and
the qualitative shifts in optimal metabolic behavior as a
function of two transport fluxes are shown in the PhPP. The
optimal biomass yield and biosynthetic precursor production
capabilities were calculated for each
E. coli in silico deletion strain for
a point within each region of the PhPP, and the optimal
values were normalized to the wild-type (Fig. 1). The
condition dependent metabolic phenotypes were
computationally analyzed, and the results are organized by
the overall metabolic phenotype;
essential, conditionally
essential, or
non-essential genes.
Essential genes : The gene products
that were essential for growth with conditions defined by
the line of optimality (LO) (see materials and methods)
were also identified as essential within all other phases (
acnAB, gapAC, gltA, icdA, pgk, rpiAB,
tktAB ). Specifically, the
gltA -,
icdA -, and
acnAB -
in silico deletion strains were
unable to produce one biosynthetic precursor
(α-ketoglutarate, Table 1), and retained the capability to
synthesize the remaining biosynthetic precursors and
cofactors nearly equivalent to the wild-type. This
prediction is consistent with the defined media required
for the cultivation of
aglt -
E. coli mutant strain (glucose
minimal media supplemented with glutamine or proline) [
26]. Furthermore, the essential glycolytic gene products (
pgk, gapAC ) were required for the
synthesis of oxaloacetate, succinyl-CoA, α-ketoglutarate,
pyruvate, phosphoenolpyruvate (PEP), and 3-phosphoglycerate
within all conditions, and were unable to synthesize all
biosynthetic precursors under anaerobic growth conditions.
The remaining two essential gene products were in the
pentosephosphate pathway (
tktAB, rpiAB ). The
tktAB and
rpiAB gene products were required for
the synthesis of erythrose 4-phosphate in all phases
(aromatic amino acid supplement required for the
cultivation of
tkt -
E. coli mutant strains [ 27]).
Additionally,
rpiAB -strains were identified as
ribose auxotrophs by the
in silico analysis, which was
consistent with experimental data [ 28].
Conditionally essential genes :
During the growth simulations with external parameters
defined by the LO, there were genes defined as critical for
growth; however, many of these genes were essential for
cellular growth upon oxygen limitations (
fba, pfkAB, tpiA, eno, gpmAB ). These
genes were termed conditionally essential. The
fba -,
pfkAB -, and
tpiA -
in silico deletion strains had a
limited capability to synthesize glyceraldehyde
3-phosphate, 3-phosphoglycerate, phosphoenolpyruvate,
pyruvate, acetyl-CoA, α-ketoglutarate, succinyl-CoA,
oxaloacetate, and high-energy phosphate bonds in all
phases, and were completely unable to synthesize many of
the biosynthetic precursors in phases 46 (Table 1) (
tpi -
in silico strain discussed below).
The growth potential of the
eno -and
gpmAB -
in silico deletion strains was
theoretically maintained under aerobic conditions by the
synthesis and degradation of serine, and without the serine
degradation pathway, the
eno -and
gpmAB -gene products were defined as
essential. However, the
eno -and
gpmAB -
in silico deletion strains were
limited in their production capability of high-energy
phosphate bonds under all conditions, and were unable to
produce any of the biosynthetic precursors in phase 6 even
with the serine degradation pathway.
Additionally, several LO non-essential gene products
were essential (
sdhABCD, ppc, frdABCD ) for growth
within other phases. The
in silico analysis suggested that the
sdhABCD and
frdABCD gene products were required
for anaerobic pyrimidine biosynthesis. Additionally, the
frdABCD gene products were essential
for the anaerobic synthesis of the NAD cofactor. However,
these
in silico results could be due to
inaccurate stoichiometric information with respect to
cofactor utilization and should be critically examined.
Finally, the
ppc gene product was required for the
anaerobic synthesis of oxaloacetate and α-ketoglutarate,
but the
in silico analysis suggests that this
gene product is not essential for growth in aerobic
conditions where the glyoxylate by-pass has the potential
to replenish the biosynthetic precursors [ 29].
Non-essential genes : Several genes
that are critical for growth in conditions defined by the
LO were non-essential for growth in other phases (
nuo ,
cyoABCD, fumABC ). The
in silico nuo -and
cyoABCD -deletion strains were
limited in their production capabilities of high-energy
phosphate bonds for aerobic growth; however, under
anaerobic conditions high-energy phosphate bonds were
produced by substrate level phosphorylation. The production
capabilities of the
fumABC -
in silico deletion strain was not
limited with respect to the biosynthetic precursors shown
in the table (other than a slight limitation of ATP
production in P1
glucose, oxygen ). However, the
fumABC -
in silico deletion strain was limited
in its production capabilities of several amino acids (arg,
gly, his- not shown in table), but under anaerobic
conditions, these capabilities were not limited with
respect to the wild-type.
Several LO non-essential gene products were critical (
pgi, pta, ackAB ) for growth within
other phases. The
in silico pgi deletion strain had a
reduced capacity to produce all the biosynthetic precursors
under oxygen limitation, and this resulted in a decreased
normalized growth yield of this
in silico deletion strain. The
pta and
ackAB gene products participate in
the metabolic pathway leading to the formation of acetate.
Acetate was predicted as a metabolic by-product upon oxygen
limitations (all phases below the LO). Under conditions
defined by P5-6
glucose, oxygen , the production
capabilities of several of the biosynthetic precursors
(glucose 6-phosphate, fructose 6-phosphate, ribose
5-phosphate, erythrose 5-phosphate, glyceraldehyde
3-phosphate) were limited in the
pta and
ackAB in silico deletion strains (
pta -
In silico deletion strain discussed
below).
This sub-section illustrated the condition-dependent
effect of gene deletions on the metabolic
genotype-phenotype relation. The results covered the range
of substrate uptake rates and defined the optimal metabolic
pathway utilization of isogenic strains
in silico under different
combinations of environmental parameters. The optimal
utilization of the metabolic pathways was dependent on the
metabolic genotype; thus, different metabolic genotypes are
characterized by different PhPPs. The results presented
above provide insight into the genotype phenotype relation.
Next, we will compare the PhPPs from
in silico deletion strains to the
wild-type to provide a more complete definition of optimal
phenotypes.
in silico Deletion Strain Phenotype Phase
Plane Analysis : Comparative analysis of the phase
planes for several mutant strains (
tpi -
, pta -
, and
zwf ) were performed. These case
studies were chosen to further investigate the metabolic
genotype-phenotype relation
in silico and to demonstrate the use
of FBA to interpret and analyze cellular metabolism.
tpi : The
tpi -PhPP showed 3 distinct optimal
metabolic phenotypes- one glucose limited phase (P
tpi 2
glucose, oxygen ), and two futile phases
(Fig. 2A). Futile phases are characterized by a negative
effect of one of the substrates on the objective function.
One of the futile phases was due to excess oxygen (P
tpi 1
glucose, oxygen ) and the other was due
to excess glucose (P
tpi 3
glucose, oxygen ). Although the
tpi -
in silico metabolic genotype
theoretically supported biomass production, the feasible
steady states were restricted to a limited phase of the
phase plane and the flexibility of the metabolic network
was reduced to one dimension.
The optimal utilization of
the tpi -metabolic network under
environmental conditions defined by the LO
tpi was characterized by increased
PPP fluxes to bypass the TPI block. The PPP operated
cyclically; thus, leading to a high production of NADPH.
Due to the high NADPH production in the PPP, the TCA cycle
flux was optimally reduced and functioned only to produce
the biosynthetic precursors.
The
in silico analysis suggests that the
tpi -metabolic network was restricted
by the ability to regenerate phosphoenolpyruvate (PE) for
the PTS, and the
in silico analysis identified 3
metabolic 'routes' for the regeneration of PEP. Two of the
'routes' were equivalent (alternate optimal solutions), (1)
The PEP was regenerated by the phosphoenolpyruvate synthase
(PPS), or (2) the glactose transporter was used for the
transport of glucose which was subsequently phosphorylated
by the glucokinase reaction. These two routes were
equivalent with respect to the objective function (although
they were structurally different). The third PEP
regeneration route involved the glyoxylate bypass and the
phosphoenolpyruvate carboxykinase, and this route was
characterized by a 38% reduction in the optimal biomass
yield. Furthermore, experimentally it was shown that
constitutive expression of the glyoxylate bypass suppressed
the PEP deficient phenotype [ 30, 31]. The PEP regeneration
routes (discussed above) theoretically allow the
tpi -to grow, and one of these
solutions was required for the growth of the
tpi -
in silico strain.
zwf :
zwf codes for glucose-6-phosphate
dehydrogenase (G6PDH), the first enzyme in the oxidative
branch of the PPP.
zwf has been shown to be a
non-essential gene for the growth of
E. coli in glucose minimal media, and
zwf strains grow at near wild-type
growth rates [ 32].
zwf was predicted by FBA to be a
non-essential gene for growth in glucose minimal media
(Fig. 1). We conducted a phenotype phase plane analysis of
the
zwf strain and examined the systemic
metabolic function of
zwf and its relation to the
environmental conditions
in silico (Fig. 2B). The slope of the
LO
zwf slightly increased (relative to
the wild-type), indicating a higher oxygen:glucose ratio
for optimal growth. Removing the G6PDH from the metabolic
network eliminated all metabolic pathways that utilized the
oxidative branch of the PPP. Therefore, the
zwf PhPP was significantly changed in
the phases that utilized the oxidative branch of the PPP (P
zwf 2
glucose, oxygen and P
zwf 3
glucose, oxygen ) but was unchanged in
phases that did not optimally utilize the
zwf gene product (P
zwf 4
glucose, oxygen ).
pta : Acetate excretion is a common
characteristic of
E. coli metabolism and several
approaches have been applied to reduce acetate production
to improve the productivity of engineering strains [ 33,
34, 35]. Acetate production can be interpreted using FBA [
36, 37], and we have used a phase plane analysis to
quantitatively analyze the conditions for which acetate
excretion optimally occurs. Acetate was optimally excreted
from the cell within all phases of the glucose-oxygen PhPP
below the LO. We have generated the
pta -PhPP and analyzed the metabolic
characteristics of the in
silico pta -strain (Fig. 2C). The
pta -PhPP indicated that this mutant
strain maintained the potential to support growth (both
aerobically and anaerobically). Experimentally,
the pta -
E. coli strain has been shown to grow
aerobically and anaerobically on glucose minimal media [
38]. The
in silico analysis predicted that
the pta -strain optimally shifted the
carbon flux from acetate to ethanol in P
pta 3. However, in P
pta 4, the optimal metabolic
by-products included lactate, ethanol, and pyruvate, and
under completely anaerobic conditions, succinate was also
optimally produced as a metabolic byproduct. These
metabolic byproducts were qualitatively consistent with
experimental observations in the
pta -strain [ 38].
Discussion
The rapid development of bioinformatic databases is
resulting in extensive information about the molecular
composition and function of several single cellular
organisms. These genetic and biochemical databases [ 21,
23, 39] have now been developed to the point where the
methods of systems science need to be used to analyze,
interpret, and predict the integrated behavior of complex
multigeneic biological processes. Herein, we have utilized
an
in silico representation of
E. coli to study the condition
dependent phenotype of
E. coli and central metabolism gene
deletion strains. We have shown that a computational
analysis of the metabolic behavior can provide valuable
insight into cellular metabolism. The results presented
herein address a pressing question in the post-genome era;
how can genome sequence information be
used to analyze integrated cellular functions? Given
the central importance of this question, we will discuss
the general applicability, limitations, and future
prospects for FBA and functional genomics.
The FBA metabolic modeling framework is different than
other well-known metabolic modeling approaches. FBA can
more accurately be defined as a metabolic constraining
approach, this is because FBA defines the 'best' the cell
can do, rather than predicting the metabolic behavior. To
accomplish this, we have constrained metabolic function
based on the most reliable information, the metabolic
stoichiometry (the stoichiometry is well known for the vast
majority of the metabolic processes). However, FBA does
have predictive capabilities when a physiologically
meaningful objective function can be defined, and the
E. coli FBA results, with maximal
growth rate as the objective function, have been shown to
be consistent with experimental data under nutritionally
rich conditions [ 40]. It should be mentioned that FBA does
not directly consider regulation, or the regulatory
constraints on the metabolic network, but rather FBA
assumes that the regulation is such that metabolic behavior
is optimal. This assumption produces results that are
generally consistent with experimental data, however, this
assumption is only valid for a system that has evolved
toward optimality. In mutant strains, the regulation of the
metabolic network has not evolved to operate in an optimal
fashion. Therefore, the optimal utilization of the mutant
metabolic network does not necessarily correspond to the
in vivo utilization of the metabolic
network. Computational analysis of metabolic processes,
coupled to an experimental program may provide valuable
information regarding the regulatory structure of metabolic
networks, and will provide a challenge for future
computational studies coupled to highly parallel
experimental programs, such as large-scale mutation studies
[ 41].
Currently, about one-third of the
E. coli open reading frames do not
have a functional assignment. Thus, the metabolic network
studied here is incomplete and does not account for all the
metabolic processes carried out by
E. coli. However, we have used the
biochemical literature to refine the
in silico metabolic genotype and
given the long history of
E. coli metabolic research [ 20], a
large percentage of the
E. coli metabolic capabilities have
likely been identified. However, when additional metabolic
capabilities are discovered [ 42], the
E. coli stoichiometric matrix can be
updated, leading to an iterative model building process.
Furthermore, inconsistencies between the model and
experimental data may help point to unidentified metabolic
functions. Additionally, the
in silico analysis can help identify
missing or incorrect functional assignments; for example,
by identifying sets of metabolic reactions that are not
connected to the metabolic network by the mass balance
constraints.
The study presented herein is an example of the rapidly
growing field of
in silico biology. It is clear that
computer modeling and simulations must be used iteratively
with an experimental program to continually improve
in silico models and to develop
systemic understanding of cellular functions. Thus, an
in silico analysis can be used to
define an experimental program. For example, the ability to
construct well-defined knockout strains of
E. coli [ 43] opens the possibility
to critically evaluate the relation between the
in silico representation of mutant
behavior and the
in vivo metabolic network under
well-defined genetic and environmental conditions for
strategically chosen genes. This possibility is
particularly timely, given the increasing number of genome
scale measurements that are now possible, through 2D gels [
44, 45] and DNA array technology [ 46, 47].
Conclusions
Herein, we have utilized an
in silico representation of
E. coli to study the condition
dependent phenotype of
E. coli and central metabolism gene
deletion strains. We have shown that a computational
analysis of the metabolic behavior can provide valuable
insight into cellular metabolism. The present
in silico study builds on the ability
to define metabolic genotypes in bacteria and mathematical
methods to analyze the possible and optimal phenotypes that
they can express. These capabilities open the possibility
to perform
in silico deletion studies to help
sort out the complexities of
E. coli mutant phenotypes.
