V22 Current metabolomics

1
V22 Current metabolomics
Review (1) recent work on metabolic networks
required revising the picture of separate
biochemical pathways into a densely-woven
metabolic network (2) The connectivity of
substrates in this network follows a
power-law. (3) Constraint-based modeling
approaches (FBA) were successful in analyzing the
capabilities of cellular metabolism including -
its capacity to predict deletion phenotypes -
the ability to calculate the relative flux values
of metabolic reactions, and - the capability to
identify properties of alternate optimal growth
states in a wide range of simulated
environmental conditions Open questions - what
parts of metabolism are involved in adaptation to
environmental conditions? - is there a central
essential metabolic core? - what role does
transcriptional regulation play?
2
Distribution of fluxes in E.coli
Aim understand principles that govern the use of
individual reactions under different growth
conditions.
Nature 427, 839 (2004)
Stoichiometric matrix for E.coli strain MG1655
containing 537 metabolites and 739 reactions
taken from Palsson et al. Apply flux balance
analysis to characterize solution space (all
possible flux states under a given condition).
vj is the flux of reaction j and Sij is the
stoichiometric coefficient of reaction j.
3
Optimal states
Denote the mass carried by reaction j producing
(consuming) metabolite i by Fluxes vary
widely e.g. dimensionless flux of succinyl
coenzyme A synthetase reaction is 0.185, whereas
the flux of the aspartate oxidase reaction is
10.000 times smaller, 2.2 ? 10-5.
Using linear programming and adapting constraints
for each reaction flux vi of the form ?imin vi
?imax, the flux states were calculated that
optimize cell growth on various substrates. Plot
the flux distribution for active (non-zero flux)
reactions of E.coli grown in a glutamate- or
succinate-rich substrate.
4
Overall flux organization of E.coli metabolic
network
a, Flux distribution for optimized biomass
production on succinate (black) and glutamate
(red) substrates. The solid line corresponds to
the power-law fit that a reaction has flux
v P(v) ? (v v0)-? , with v0 0.0003 and ?
1.5. d, The distribution of experimentally
determined fluxes from the central metabolism of
E. coli shows power-law behaviour as well, with
a best fit to P(v)? v-? with ? 1. Both
computed and experimental flux distribution show
wide spectrum of fluxes.
Almaar et al., Nature 427, 839 (2004)
5
Response to different environmental conditions
Is the flux distribution independent of
environmental conditions? b, Flux distribution
for optimized biomass on succinate (black)
substrate with an additional 10 (red), 50
(green) and 80 (blue) randomly chosen subsets of
the 96 input channels (substrates) turned on.
The flux distribution was averaged over 5,000
independent random choices of uptake metabolites.
? the flux distribution is independent of the
external conditions.
Is the wide flux distribution also present in
non-optimal conditions? c, Flux distribution from
the non-optimized hit-and-run sampling method of
the E. coli solution space. The solid line is the
best fit, with v0 0.003 and ? 2. Inset shows
the flux distribution in four randomly chosen
sample points. Many individual non-optimal
states are consistent with an exponent ? 1.
Almaar et al., Nature 427, 839 (2004)
6
Use scaling behavior to determine local
connectivity
The observed flux distribution is compatible with
two different potential local flux
structures (a) a homogenous local organization
would imply that all reactions producing
(consuming) a given metabolite have comparable
fluxes (b) a more delocalized high-flux backbone
(HFB) is expected if the local flux organisation
is heterogenous such that each metabolite has a
dominant source (consuming) reaction. Schemat
ic illustration of the hypothetical scenario in
which (a) all fluxes have comparable activity,
in which case we expect kY(k) ? 1 and (b) the
majority of the flux is carried by a single
incoming or outgoing reaction, for which we
should have kY(k) ? k .
Almaar et al., Nature 427, 839 (2004)
7
Measuring the importance of individual reactions
To distinguish between these 2 schemes for each
metabolite i produced (consumed) by k reactions,
define
where vij is the mass carried by reaction j which
produces (consumes) metabolite i. If all
reactions producing (consuming) metabolite i have
comparable vij values, Y(k,i) scales as
1/k. If, however, the activity of a single
reaction dominates we expect Y(k,i) ?1
(independent of k).
Almaar et al., Nature 427, 839 (2004)
8
Characterizing the local inhomogeneity of the
flux net
a, Measured kY(k) shown as a function of k for
incoming and outgoing reactions, averaged over
all metabolites, indicates that Y(k) ? k-0.27.
Inset shows non-zero mass flows, vij,
producing (consuming) FAD on a glutamate-rich
substrate. ? an intermediate behavior is found
between the two extreme cases. ? the large-scale
inhomogeneity observed in the overall flux
distribution is also increasingly valid at the
level of the individual metabolites. The more
reactions that consume (produce) a given
metabolite, the more likely it is that a single
reaction carries most of the flux, see FAD.
Almaar et al., Nature 427, 839 (2004)
9
Clean up metabolic network
Simple algorithm removes for each metabolite
systematically all reactions but the one
providing the largest incoming (outgoing) flux
distribution. The algorithm uncovers the
high-flux-backbone of the metabolism, a
distinct structure of linked reactions that form
a giant component with a star-like topology.
Almaar et al., Nature 427, 839 (2004)
10
Maximal flow networks
glutamate rich succinate rich
substrates Directed links Two metabolites (e.g.
A and B) are connected with a directed link
pointing from A to B only if the reaction with
maximal flux consuming A is the reaction with
maximal flux producing B. Shown are all
metabolites that have at least one neighbour
after completing this procedure. The background
colours denote different known biochemical
pathways.
Almaar et al., Nature 427, 839 (2004)
11
FBA-optimized network on glutamate-rich substrate
High-flux backbone for FBA-optimized metabolic
network of E. coli on a glutamate-rich substrate.
Metabolites (vertices) coloured blue have at
least one neighbour in common in glutamate- and
succinate-rich substrates, and those coloured red
have none. Reactions (lines) are coloured blue if
they are identical in glutamate- and
succinate-rich substrates, green if a different
reaction connects the same neighbour pair, and
red if this is a new neighbour pair. Black dotted
lines indicate where the disconnected pathways,
for example, folate biosynthesis, would connect
to the cluster through a link that is not part of
the HFB. Thus, the red nodes and links highlight
the predicted changes in the HFB when shifting E.
coli from glutamate- to succinate-rich media.
Dashed lines indicate links to the biomass growth
reaction.
(1) Pentose Phospate (11) Respiration (2)
Purine Biosynthesis (12) Glutamate Biosynthesis
(20) Histidine Biosynthesis (3) Aromatic Amino
Acids (13) NAD Biosynthesis (21) Pyrimidine
Biosynthesis (4) Folate Biosynthesis (14)
Threonine, Lysine and Methionine
Biosynthesis (5) Serine Biosynthesis (15)
Branched Chain Amino Acid Biosynthesis (6)
Cysteine Biosynthesis (16) Spermidine
Biosynthesis (22) Membrane Lipid
Biosynthesis (7) Riboflavin Biosynthesis (17)
Salvage Pathways (23) Arginine Biosynthesis (8)
Vitamin B6 Biosynthesis (18) Murein
Biosynthesis (24) Pyruvate Metabolism (9)
Coenzyme A Biosynthesis (19) Cell Envelope
Biosynthesis (25) Glycolysis (10) TCA Cycle
Almaar et al., Nature 427, 839 (2004)
12
Interpretation
Only a few pathways appear disconnected
indicating that although these pathways are part
of the HFB, their end product is only the
second-most important source for another HFB
metabolite. Groups of individual HFB reactions
largely overlap with traditional biochemical
partitioning of cellular metabolism.
Almaar et al., Nature 427, 839 (2004)
13
How sensitive is the HFB to changes in the
environment?
b, Fluxes of individual reactions for
glutamate-rich and succinate-rich conditions.
Reactions with negligible flux changes follow the
diagonal (solid line). Some reactions are turned
off in only one of the conditions (shown close to
the coordinate axes). Reactions belonging to the
HFB are indicated by black squares, the rest are
indicated by blue dots. Reactions in which the
direction of the flux is reversed are coloured
green.
Only the reactions in the high-flux territory
undergo noticeable differences! Type I
reactions turned on in one conditions and off in
the other (symbols). Type II reactions remain
active but show an orders-in-magnitude shift in
flux under the two different growth conditions.
Almaar et al., Nature 427, 839 (2004)
14
Flux distributions for individual reactions
Shown is the flux distribution for four selected
E. coli reactions in a 50 random environment. a
Triosphosphate isomerase b carbon dioxide
transport c NAD kinase d guanosine kinase.
Reactions on the ? ? v curve (small fluxes)
have unimodal/gaussian distributions (a and c).
Shifts in growth-conditions only lead to small
changes of their flux values. Reactions off this
curve have multimodal distributions (b and d),
showing several discrete flux values under
diverse conditions. Under different growth
conditions they show several discrete and
distinct flux values.
Almaar et al., Nature 427, 839 (2004)
15
Summary
Metabolic network use is highly uneven (power-law
distribution) at the global level and at the
level of the individual metabolites. Whereas
most metabolic reactions have low fluxes, the
overall activity of the metabolism is dominated
by several reactions with very high fluxes. E.
coli responds to changes in growth conditions by
reorganizing the rates of selected fluxes
predominantly within this high-flux
backbone. Apart from minor changes, the use of
the other pathways remains unaltered. These
reorganizations result in large, discrete changes
in the fluxes of the HFB reactions.
16
The same authors as before used Flux Balance
Analysis to examine utilization and relative flux
rate of each metabolite in a wide range of
simulated environmental conditions for E.coli, H.
pylori and S. cerevisae consider in each case
30.000 randomly chosen combinations where each
uptake reaction is a assigned a random value
between 0 and 20 mmol/g/h. ? adaptation to
different conditions occurs by 2 mechanisms (a)
flux plasticity changes in the fluxes of already
active reactions. E.g. changing from glucose- to
succinate-rich conditions alters the flux of 264
E.coli reactions by more than 20 (b) less
commonly, adaptation includes structural
plasticity, turning on previously zero-flux
reactions or switching off active pathways.
17
Emergence of the Metabolic Core
The two adaptation method mechanisms allow for
the possibility of a group of reactions not
subject to structural plasticity being active
under all environmental conditions. Assume that
active reactions were randomly distributed. If
typically a q fraction of the metabolic reactions
are active under a specific growth condition, we
expect for n distinct conditions an overlap of at
least qn reactions. This converges quickly to 0.
18
Emergence of the Metabolic Core
(AC) The average relative size of the number of
reactions that are always active as a function of
the number of sampled conditions (black line) for
(A) H. pylori, (B) E. coli, and (C) S.
cerevisiae. (D and E) The number of metabolic
reactions (D) and the number of metabolic core
reactions (E) in the three studied organisms.
However, as the number of conditions increases,
the curve converges to a constant enoted by the
dashed line, identifying the metabolic core of an
organism. Red line number of reactions that
are always active if activity is randomly
distributed in the metabolic network. The fact
that it converges to zero indicates that the real
core represents a collective network effect,
forcing a group of reactions to be active in all
conditions.
19
Metabolic Core of E.coli
The constantly active reactions form a tightly
connected cluster! All reactions that are found
to be active in each of the 30,000 investigated
external conditions are shown. Metabolites that
contribute directly to biomass formation are
colored blue, while core reactions (links)
catalyzed by essential (or nonessential) enzymes
are colored red (or green). (Black-colored links
denote enzymes with unknown deletion phenotype.)
Blue dashed lines indicate multiple appearances
of a metabolite, while links with arrows denote
unidirectional reactions. Note that 20 out of
the 51 metabolites necessary for biomass
synthesis are not present in the core, indicating
that they are produced (or consumed) in a
growth-condition-specific manner. Blue and brown
shading folate and peptidoglycan biosynthesis
pathways White numbered arrows denote current
antibiotic targets inhibited by (1)
sulfonamides, (2) trimethoprim, (3) cycloserine,
and (4) fosfomycin. A few reactions appear
disconnected since we have omitted the drawing of
cofactors.
20
Metabolic Core Reactions
The metabolic cores contain 2 types of
reactions (a) reactions that are essential for
biomass production under all environment
conditions (81 of 90 in E.coli) (b) reactions
that assure optimal metabolic performance.
21
Characterizing the Metabolic Cores
(A) The number of overlapping metabolic reactions
in the metabolic core of H. pylori, E. coli, and
S. cerevisiae. The metabolic cores of simple
organisms (H. pylori and E.coli) overlap to a
large extent. The largest organism (S.cerevisae)
has a much larger reaction network that allows
more flexbility ? the relative size of the
metabolic core is much lower. (B) The fraction
of metabolic reactions catalyzed by essential
enzymes in the cores (black) and outside the core
in E. coli and S. cerevisiae. ? Reactions of the
metabolic core are mostly essential ones. (C)
One could assume that the core represents a
subset of high-flux reactions. This is apparently
not the case. The distributions of average
metabolic fluxes for the core and the noncore
reactions in E. coli are very similar.
22
Correlation among E.coli Metabolic Reactions
Pearson correlation using flux values from 30,000
conditions for each reaction pair before grouping
the reactions according to a hierarchical
average-linkage clustering algorithm. The values
of the flux-correlation matrix range from -1
(red) through 0 (white) to 1 (blue). The
horizontal color bar denotes if a reaction is a
member of the core (green), and the vertical
color bar denotes whether the enzymes catalyzing
the reaction are essential (red). ? group of
highly correlated reactions significantly
overlaps with metabolic core. (B) Distribution
of Pearson correlation in mRNA copy numbers from
41 experiments. The correlations of the core
reactions are clearly shifted towards higher
values.
23
Summary
- Adaptation to environmental conditions occurs
via structural plasticity and/or flux
plasticity. Here identification of a
surprisingly stable metabolic core of reactions
that are tightly connected to eachother. - the
reactions belonging to this core represent
potential targets for antimicrobial intervention.
24
Integrated Analysis of Metabolic and Regulatory
Networks
Sofar, studies of large-scale cellular networks
have focused on their connectivities. The
emerging picture shows a densely-woven web where
almost everything is connected to everything. In
the cells metabolic network, hundreds of
substrates are interconnected through biochemical
reactions. Although this could in principle lead
to the simultaneous flow of substrates in
numerous directions, in practice metabolic fluxes
pass through specific pathways (? high flux
backbone).
Topological studies sofar did not consider how
the modulation of this connectivity might also
determine network properties.
Therefore it is important to correlate the
network topology with the expression of enzymes
in the cell.
25
Analyze transcriptional control in metabolic
networks
Regulatory and metabolic functions of cells are
mediated by networks of interacting biochemical
components. Metabolic flux is optimized to
maximize metabolic efficiency under different
conditions. Control of metabolic flow -
allosteric interactions - covalent modifications
involving enzymatic activity - transcription
(revealed by genome-wide expression
studies) Here N. Barkai and colleagues analyzed
published experimental expression data of
Saccharomyces cerevisae.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
26
Recurrence signature algorithm
Aim identify transcription modules (TMs). ? a
set of randomly selected genes is unlikely to be
identical to the genes of any TM. Yet many such
sets do have some overlap with a specific TM. In
particular, sets of genes that are compiled
according to existing knowledge of their
functional (or regulatory) sequence similarity
may have a significant overlap with a
transcription module. Algorithm receives a gene
set that partially overlaps a TM and then
provides the complete module as output.
Therefore this algorithm is referred to as
signature algorithm.
Ihmels et al. Nat Genetics 31, 370 (2002)
27
Recurrence signature algorithm
normalization of data
identify modules
classify genes into modules
a, The signature algorithm. b , Recurrence as a
reliability measure. The signature algorithm is
applied to distinct input sets containing
different subsets of the postulated transcription
module. If the different input sets give rise to
the same module, it is considered reliable. c,
General application of the recurrent signature
method.
Ihmels et al. Nat Genetics 31, 370 (2002)
28
Correlation between genes of the same metabolic
pathway
Distribution of the average correlation between
genes assigned to the same metabolic pathway in
the KEGG database. The distribution
corresponding to random assignment of genes to
metabolic pathways of the same size is shown for
comparison. Importantly, only genes coding for
enzymes were used in the random control.
Interpretation pairs of genes associated with
the same metabolic pathway show a similar
expression pattern.
However, typically only a set of the genes
assigned to a given pathway are coregulated.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
29
Correlation between genes of the same metabolic
pathway
Genes of the glycolysis pathway (according KEGG)
were clustered and ordered based on the
correlation in their expression profiles. Shown
here is the matrix of their pair-wise
correlations. The cluster of highly correlated
genes (orange frame) corresponds to genes that
encode the central glycolysis enzymes. The
linear arrangement of these genes along the
pathway is shown at right.
Of the 46 genes assigned to the glycolysis
pathway in the KEGG database, only 24 show a
correlated expression pattern. In general, the
coregulated genes belong to the central pieces of
pathways.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
30
Coexpressed enzymes often catalyze linear chain
of reactions
Coregulation between enzymes associated with
central metabolic pathways. Each branch
corresponds to several enzymes. In the cases
shown, only one of the branches downstream of the
junction point is coregulated with upstream
genes. Interpretation coexpressed enzymes are
often arranged in a linear order, corresponding
to a metabolic flow that is directed in a
particular direction.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
31
Co-regulation at branch points
To examine more systematically whether
coregulation enhances the linearity of metabolic
flow, analyze the coregulation of enzymes at
metabolic branch-points. Search KEGG for
metabolic compounds that are involved in exactly
3 reactions. Only consider reactions that exist
in S.cerevisae. 3-junctions can integrate
metabolic flow (convergent junction) or allow
the flow to diverge in 2 directions (divergent
junction). In the cases where several reactions
are catalyzed by the same enzymes, choose one
representative so that all junctions considered
are composed of precisely 3 reactions catalyzed
by distinct enzymes. Each 3-junction is
categorized according to the correlation pattern
found between enzymes catalyzing its branches.
Correlation coefficients gt 0.25 are considered
significant.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
32
Coregulation pattern in three-point junctions
All junctions corresponding to metabolites that
participate in exactly 3 reactions (according to
KEGG) were identified and the correlations
between the genes associated with each such
junction were calculated. The junctions were
grouped according to the directionality of the
reactions, as shown. Divergent junctions, which
allow the flow of metabolites in two alternative
directions, predominantly show a linear
coregulation pattern, where one of the emanating
reaction is correlated with the incoming reaction
(linear regulatory pattern) or the two
alternative outgoing reactions are correlated in
a context-dependent manner with a distinct
isozyme catalyzing the incoming reaction (linear
switch). By contrast, the linear regulatory
pattern is significantly less abundant in
convergent junctions, where the outgoing flow
follows a unique direction, and in conflicting
junctions that do not support metabolic flow.
Most of the reversible junctions comply with
linear regulatory patterns. Indeed, similar to
divergent junctions, reversible junctions allow
metabolites to flow in two alternative
directions. Reactions were counted as coexpressed
if at least two of the associated genes were
significantly correlated (correlation coefficient
gt0.25). As a random control, we randomized the
identity of all metabolic genes and repeated the
analysis.
In the majority of divergent junctions, only one
of the emanating branches is significantly
coregulated with the incoming reaction that
synthesizes the metabolite.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
33
Co-regulation at branch points conclusions
The observed co-regulation patterns correspond to
a linear metabolic flow, whose directionality can
be switched in a condition-specific manner. When
analyzing junctions that allow metabolic flow in
a larger number of directions, there also only a
few important branches are coregulated with the
incoming branch. Therefore transcription
regulation is used to enhance the linearity of
metabolic flow, by biasing the flow toward only a
few of the possible routes.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
34
Connectivity of metabolites
The connectivity of a given metabolite is defined
as the number of reactions connecting it to other
metabolites. Shown are the distributions of
connectivity between metabolites in an
unrestricted network (?) and in a network where
only correlated reactions are considered (?). In
accordance with previous results (Jeong et al.
2000) , the connectivity distribution between
metabolites follows a power law (log-log plot).
In contrast, when coexpression is used as a
criterion to distinguish functional links, the
connectivity distribution becomes exponential
(log-linear plot).
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2004)
35
Differential regulation of isozymes
Observe that isozymes at junction points are
often preferentially coexpressed with alternative
reactions. ? investigate their role in the
metabolic network more systematically. Two
possible functions of isozymes associated with
the same metabolic reaction. An isozyme pair
could provide redundancy which may be needed for
buffering genetic mutations or for amplifying
metabolite production. Redundant isozymes are
expected to be coregulated. Alternatively,
distinct isozymes could be dedicated to separate
biochemical pathways using the associated
reaction. Such isozymes are expected to be
differentially expressed with the two alternative
processes.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
36
Differential regulation of isozymes in central
metabolic PW
Arrows represent metabolic pathways composed of a
sequence of enzymes. Coregulation is indicated
with the same color (e.g., the isozyme
represented by the green arrow is coregulated
with the metabolic pathway represented by the
green arrow). ? Most members of isozyme pairs
are separately coregulated with alternative
processes.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
37
Differential regulation of isozymes
Regulatory pattern of all gene pairs associated
with a common metabolic reaction (according to
KEGG). All such pairs were classified into
several classes (1) parallel, where each gene
is correlated with a distinct connected reaction
(a reaction that shares a metabolite with the
reaction catalyzed by the respective gene pair)
(2) selective, where only one of the enzymes
shows a significant correlation with a connected
reaction and (3) converging, where both enzymes
were correlated with the same reaction.
Correlations coefficients gt0.25 were considered
significant. To be counted as parallel, rather
than converging, we demanded that the correlation
with the alternative reaction be lt80 of the
correlation with the preferred reaction.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
38
Differential regulation of isozymes
interpretation
The primary role of isozyme multiplicity is to
allow for differential regulation of reactions
that are shared by separated processes. Dedicatin
g a specific enzyme to each pathway may offer a
way of independently controlling the associated
reaction in response to pathway-specific
requirements, at both the transcriptional and the
post-transcriptional levels.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
39
Genes coexpressed with metabolic pathways
Identify the coregulated subparts of each
metabolic pathway and identify relevant
experimental conditions that induce or repress
the expression of the pathway genes. Also
associate additional genes showing similar
expression profiles with each pathway using the
signature algorithm. Input set of genes, some of
which are expected to be coregulated. Output
coregulated part of the input and additional
coregulated genes together with the set of
conditions where the coregulation is
realized. Numerous genes were found that are not
directly involved in enzymatic steps -
transporters - transcription factors
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
40
Co-expression of transporters
Transporter genes are co-expressed with the
relevant metabolic pathways providing the
pathways with its metabolites. Co-expression is
marked in green.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
41
Co-regulation of transcription factors
Transcription factors are often co-regulated with
their regulated pathways. Shown here are
transcription factors which were found to be
co-regulated in the analysis. Co-regulation is
shown by color-coding such that the transcription
factor and the associated pathways are of the
same color.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
42
Hierarchical modularity in the metabolic network
Sofar co-expression analysis revealed a strong
tendency toward coordinated regulation of genes
involved in individual metabolic pathways.
Does transcription regulation also define a
higher-order metabolic organization, by
coordinated expression of distinct metabolic
pathways?
Based on observation that feeder pathways (which
synthesize metabolites) are frequently
coexpressed with pathways using the synthesized
metabolites.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
43
Feeder-pathways/enzymes
Feeder pathways or genes co-expressed with the
pathways they fuel. The feeder pathways (light
blue) provide the main pathway (dark blue) with
metabolites in order to assist the main pathway,
indicating that co-expression extends beyond the
level of individual pathways. These results can
be interpreted in the following way the organism
will produce those enzymes that are needed.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
44
Hierarchical modularity in the metabolic network
Derive hierarchy by applying an iterative
signature algorithm to the metabolic pathways,
and decreasing the resolution parameter
(coregulation stringency) in small steps. Each
box contains a group of coregulated genes
(transcription module). Strongly associated genes
(left) can be associated with a specific
function, whereas moderately correlated modules
(right) are larger and their function is less
coherent. The merging of 2 branches indicates
that the associated modules are induced by
similar conditions. All pathways converge to one
of 3 low-resolution modules amino acid
biosynthesis, protein synthesis, and stress.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
45
Hierarchical modularity in the metabolic network
Although amino acids serve as building blocks for
proteins, the expression of genes mediating these
2 processes is clearly uncoupled! This may
reflect the association of rapid cell growth
(which triggers enhanced protein synthesis) with
rich growth conditions, where amino acids are
readily available and do not need to be
synthesized. Amino acid biosynthesis genes are
only required when external amino acids are
scarce. In support of this view, a group of
amino acid transporters converged to the protein
synthesis module, together with other pathways
required for rapid cell growth (glucose
fermentation, nucleotide synthesis and fatty acid
synthesis).
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
46
Global network properties
Jeong et al. showed that the structural
connectivity between metabolites imposes a
hierarchical organization of the metabolic
network. That analysis was based on connectivity
between substrates, considering all potential
connections. Here, analysis is based on
coexpression of enzymes. In both approaches,
related metabolic pathways were clustered
together!
There are, however, some differences in the
particular groupings (not discussed here), and
importantly, when including expression data the
connectivity pattern of metabolites changes from
a power-law dependence to an exponential one
corresponding to a network structure with a
defined scale of connectivity. This reflects the
reduction in the complexity of the network.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
47
Summary

• Transcription regulation is prominently involved
  in shaping the metabolic network of S. cerevisae.
  
• Transcription leads the metabolic flow toward
  linearity.
• Individual isozymes are often separately
  coregulated with distinct processes, providing a
  means of reducing crosstalk between pathways
  using a common reaction.
• Transcription regulation entails a higher-order
  structure of the metabolic network.
• It exists a hierarchical organization of
  metabolic pathways into groups of decreasing
  expression coherence.

Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)