Download Getting to grips with the plant metabolic network

Biochem. J. (2008) 409, 27–41 (Printed in Great Britain)
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doi:10.1042/BJ20071115
REVIEW ARTICLE
Getting to grips with the plant metabolic network
Lee J. SWEETLOVE*1 , David FELL† and Alisdair R. FERNIE‡
*Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, U.K., †School of Life Sciences, Oxford Brookes University, Headington, Oxford OX3 0BP, U.K.,
and ‡Max-Planck-Institut für Molekulare Pflanzenphysiologie, Am Mühlenberg 1, 14476 Potsdam-Golm, Federal Republic of Germany
Research into plant metabolism has a long history, and analytical
approaches of ever-increasing breadth and sophistication have
been brought to bear. We now have access to vast repositories of
data concerning enzymology and regulatory features of enzymes,
as well as large-scale datasets containing profiling information of
transcripts, protein and metabolite levels. Nevertheless, despite
this wealth of data, we remain some way off from being
able to rationally engineer plant metabolism or even to predict
metabolic responses. Within the past 18 months, rapid progress
has been made, with several highly informative plant network
interrogations being discussed in the literature. In the present
review we will appraise the current state of the art regarding
plant metabolic network analysis and attempt to outline what
the necessary steps are in order to further our understanding of
network regulation.
1. INTRODUCTION
than there are of outcomes that matched prediction, and this is
especially true in the more highly connected reactions of primary
metabolism. A good example is the biosynthesis of storage starch
in organs such as tubers and seeds. As an important dietary
carbohydrate and with industrial uses as a food additive, there
is much interest in increasing the yield of starch. Starch is
synthesized in the plastid via a relatively simple linear pathway
that is well characterized. Attempts to directly manipulate starch
synthesis by targeting the enzymes catalysing these reactions have
met with mixed results [8,9], but, surprisingly, manipulation of
several enzymes that reside outside the direct pathway of starch
synthesis have resulted in substantial increases in storage starch
content [10] (Figure 1). These studies exemplify the two main
reasons why we are currently so poor at predicting the outcome
of metabolic-engineering experiments.
The first is a lack of a comprehensive knowledge of the
contribution of individual enzymes to the control of metabolic
flux. Despite the development of a formal mathematical framework for quantification of metabolic control [11,12] and an initial
enthusiastic embracing of the ideas and methods of Metabolic
Control Analysis by the plant community [13], flux control
coefficients have been determined for only a small number of
plant enzymes. In the starch example, analysis of metabolic
control was only done belatedly after attempts to manipulate the
pathway directly had failed. It was found that enzymes such as
ADP-glucose pyrophosphorylase that were considered to be key
control points actually had rather low flux control coefficients
[14]. In fact, control of metabolic pathways is generally shared
among all enzymes of the pathway, meaning that engineering of
a single enzyme is unlikely to bring about significant flux change
[15].
The second factor that contributes to our failure to predict
the outcome of genetic intervention in metabolism is the predominance of the pathway paradigm. Metabolic pathways are
convenient abstractions that allow metabolism to be broken down
Metabolism is perhaps the best characterized of all molecularinteraction networks in biology. From pioneering studies defining
key metabolic pathways such as the Calvin cycle, through
subsequent decades of enzymology characterizing the catalytic
and regulatory properties of enzymes, through to more recent
genetic studies of metabolism, there is an unprecedented density
of both mechanistic and descriptive data relating to metabolic
behaviour. Added to that list is the current accumulation of large
molecular profiling datasets from transcriptomic, proteomic and
metabolomic experiments. Nevertheless, despite this wealth of
information, we still cannot accurately predict metabolic network
behaviour. In the present review we wish to consider how we can
take the next step forward in understanding the plant metabolic
network. We will discuss what new information needs to be
put into place, as well as detailing approaches through which
the available experimental and theoretical information can be
exploited and integrated.
One of the central goals of plant metabolic biology is to
develop a sufficiently detailed view of the control hierarchy of
the metabolic network to permit the rational design of genetic
strategies to engineer metabolism for the overproduction of
desirable end-products. This extends to placing heterologous
enzymes in plants for the generation of novel (generally pharmaceutical) products [1], to altering the composition of crop plants
for nutritional, health or flavour benefits [2] and to increasing the
production of key secondary metabolites of commercial value
[3,4]. Certainly plants have enormous untapped biosynthetic
potential that could be exploited more fully by metabolic
engineering [5], and there is considerable interest in the postgenomic analysis of metabolic networks [6,7]. However, it is
fair to say that, to date, our efforts to engineer plant metabolism
have largely met with failure. There are many more examples of
unexpected or pleiotropic consequences of genetic intervention
Key words: Arabidopsis, metabolic control, metabolic flux,
metabolomics, plant metabolic network, subcellular compartmentation.
Abbreviations used: FRET, fluorescence resonance energy transfer; ID, identification; KEGG, Kyoto Encyclopedia of Genes and Genomes; NADME,
NAD+ -dependent malic enzyme.
1
To whom correspondence should be addressed (email [email protected]).
c The Authors Journal compilation c 2008 Biochemical Society
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Figure 1
L. J. Sweetlove, D. Fell and A. R. Fernie
Effect of various transgenic manipulations on the starch content of potatoes
Reactions shown within the green box are those considered to be involved directly in the starch biosynthesis pathway, whereas those outside are only distally connected to starch metabolism.
The blue boxes indicate the starch content of transgenic potato tubers (as a percentage of the wild-type value) for the indicated target enzymes. Abbreviations: ADK, adenylate kinase; ADPG,
ADP-glucose; AGPase, ADP-glucose pyrophosphorylase; ANT, adenylate nucleotide translocase; ENZ, enzyme; G1P, glucose 1-phosphate; G6P, glucose 6-phosphate; REF, reference; UMPS, uridine
monophosphate synthase. The photograph of the starch grain was kindly provided by Professor Alison Smith (John Innes Centre, Colney, Norwich, U.K.).
into accessible chunks and can justifiably claim to represent
the main routes through the network in specific cases (e.g.
glycolysis in yeast and the tricarboxylic acid cycle in pigeon
muscle). However, in other organisms and tissues, the role of
such pathways may be very different. For example, in developing
oilseed embryos, the main flux through the tricarboxylic acid
cycle is not cyclic at all, but rather follows a predominantly linear
flux mode in which citrate is exported to support lipid biosynthesis [16]. Moreover, in photoautotrophic organisms, such as
plants, in which all carbon-containing metabolites are ultimately
derived from fixed CO2 , it follows that all carbon-containing metabolites must in some way be linked together. Thus, in reality,
metabolic pathways are just smaller subsets of a larger network,
and there is potential for a change in one part of the network
to have implications for any other part, no matter how distal
[17]. This is clearly illustrated in the starch example, in which
c The Authors Journal compilation c 2008 Biochemical Society
alteration of enzymes catalysing reactions in different subcellular
compartments [NADME (NAD+ -dependent malic enzyme) in
the mitochondrion] and reactions only distantly connected to
starch synthesis (uridine monophosphate synthase) nevertheless
had major effects on the accumulation of starch. A final factor
that complicates a predictive understanding of metabolism is the
extent to which metabolism can be ‘rewired’ through altered
expression of genes encoding metabolic enzymes in response to
stress [18,19] or developmental progressions [20]. Ultimately one
has to know which network one is dealing with, a fact that is often
overlooked when dealing with genome-scale reconstructions of
plant metabolism [21].
It is clear, therefore, that if we are to improve our ability to
make rational manipulations of plant metabolism for engineering
purposes, we have to generate a more complete view of metabolism as a network, rather than dealing with individual pathways,
Getting to grips with the plant metabolic network
and that the new holistic view of the metabolic network must also
include a quantification of the control exerted by each enzyme.
Ultimately, it will be necessary to superimpose a gene-regulatory
network on the metabolic network model so that the consequences
of altered environmental or developmental circumstances on the
structure and balance of the metabolic network can be accounted
for. In the present review we consider several aspects of metabolic
biology research that we believe will be central to attaining the
goal of predictive metabolic engineering [22].
2. ESTABLISHING THE STRUCTURE OF THE PLANT METABOLIC
NETWORK
Moving from a pathway to a network perspective immediately
raises the question of how to obtain a description of the metabolic network of a specific plant, or more precisely, the metabolic
network of a particular cell type in the plant at a given developmental stage in a defined environment. There is no ready-made
source of such network descriptions! A traditional approach
would be to assemble a network from consensus plant metabolic
pathways in plant biochemistry textbooks and reviews and then
to attempt to confirm the presence of each enzyme reaction
in the target cell type by a literature search for experimental
evidence. This could be assisted by using the metabolic maps from
KEGG {Kyoto Encyclopedia of Genes and Genomes (http://www.
genome.ad.jp/ [23]), though the only specific maps for higher
plants are for Arabidopsis thaliana (thale cress) and Oryza sativa
(rice)}, and searching for each enzyme in the database BRENDA
{BRaunschweig ENzyme DAtabase (http://www.brenda.unikoeln.de/ [24])} for organism-specific literature. The goal is to
obtain a list of all the stoichiometrically balanced reactions. If
the list also satisfies the other conditions discussed below, this
gives a network description that can be a basis for the modelling
and flux analysis methods described below in sections 3 and 5.
However, this is clearly a large and labour-intensive approach that
is far from guaranteed to result in a network where the presence
of every reaction is fully validated.
In the post-genomic era, it might be imagined that this
process can be automated for those plants whose genome has
been sequenced. Indeed, since we understand exactly how the
metabolic phenotype is coded in the genes, metabolism is a prime
test case for whether we can use genome sequences to predict
and understand phenotypes. Of course, the genome encodes the
total metabolic potential of the organism, and it will still be
necessary to consider what fraction of this is present in particular
locations and circumstances. Genome-scale reconstructions of
metabolism have already been undertaken for micro-organisms
[25–28], and show reasonably good agreement with experiment
in terms of nutritional capabilities and susceptibility to loss of
enzyme activities by mutation. However, except for the Saccharomyces cerevisiae (baker’s yeast) metabolic model, the microbial
metabolic networks do not need to make much allowance for
compartmentation of metabolism, which will be a major issue
for plant metabolism. Additionally, the microbial genomes are
substantially smaller, so the task is somewhat easier than it will
be for Arabidopsis.
The strategy for deriving the metabolic network from the
sequence information is, in outline:
(i) Use the genome annotation to obtain a list of the EC
numbers of all the genes identified as coding for enzymes, either
by experiment or sequence homology.
(ii) Use the EC numbers and a database such as KEGG
[23] or BioCycTM (http://biocyc.org/ [29]) to obtain the reactions
catalysed by each of the enzymes and add them to the reaction
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list. These databases are suggested because the metabolites
participating in the reactions are identified by compound ID
(identification) numbers that link to database entries giving
synonyms, empirical formulae etc.
In principle, this could be automatic; however, networks
obtained in this way exhibit various deficiencies [30], and the
process currently requires manual intervention [25,31]. The problems arise from a number of different areas:
(i) The genome annotation is likely to be incomplete, since
not all potential coding sequences can be assigned a function,
and those that are may not be identified accurately or sufficiently
precisely. Assignment by homology with known enzymes can
be wrong, but in many cases it is incomplete, giving a partial
identification in terms of a partial EC number that specifies
enzyme class and subclass, but not a specific set of reactions.
On the other hand, the annotation can implicate too many
enzymes, since only a subset of the genome will be transcribed
in given circumstances, and some sequences assigned to open
reading frames and annotated may nevertheless be pseudogenes
that are never transcribed. Other sources of incompleteness are
that some metabolic reactions are spontaneous and not linked
to genes, and that the transport reactions between intracellular
compartments must also be included in the network, but the
transporter proteins are often not identified at a molecular level.
The latter is a particular problem with plants; for example, the
Arabidopsis annotation contains numerous putative phosphatelinked translocators of undefined specificity and localization.
(ii) There are database errors, including incorrectly specified
reactions, and chemically unbalanced equations.
(iii) There are systemic errors, where the reactions individually
are described in a self-consistent manner, but do not combine
to generate a consistent network. For example, when the same
compound appears in the list with more than one ‘unique ID’ (see
[30]), routes through the network are broken, since a product of
one reaction is not recognized as the substrate of another.
These issues are discussed more fully by Poolman et al. [30].
The outcome is that models derived directly from the databases,
and to some extent published genome-scale metabolic networks,
exhibit a variety of unlikely and unsatisfactory characteristics,
including separation of the network into a number of disconnected
components, unbalanced reactions that violate conservation of
mass, a significant fraction of ‘orphan’ metabolites that are either
produced or consumed, but not both, and ‘dead’ reactions that
can be shown to be unable to carry any metabolic flux at steady
state.
The problems encountered in microbial genome-scale models
are magnified in models of eukaryotic metabolism, and especially
those of plants, by the additional uncertainty concerning to which
compartment particular gene products are directed, although, for
Arabidopsis at least, this is starting to be addressed (see section
3.1). If these problems can be resolved, the maximal metabolic
network, obtained as described above, needs trimming back to
the set of enzymes being expressed in a particular set of cells. The
measurement of individual enzyme activities is one possibility,
but is not currently automated for high-throughput analysis as
are the other potential methods. Similarly, proteomics screens
provide another set of clues concerning the presence of particular
proteins in their active states, but lack the precision of comparisons
of enzyme activities and could be misleading for the reasons
given below (in section 4.2). At another level, microarrays can
detect which genes are being transcribed, and, therefore, indicate
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Figure 2
L. J. Sweetlove, D. Fell and A. R. Fernie
Methods for modelling metabolic networks
In constraints-based modelling, known properties of the network (network structure, reaction stoichiometry, thermodynamics and the requirement for flux balance), as well as experimentally measured
parameters, are used to constrain the solution space (shown in grey). Within this bounded space, a unique solution is found (shown in green) that optimizes an objective function such as biomass
production. In kinetic modelling, kinetic rate equations are used to describe the relationship between substrate concentration and reaction velocity. Sets of such equations are used as the basis for
numerical simulation of network behaviour. Abbreviations: F1,6P2 , fructose 1,6-diphosphate; F6P, fructose 6-phosphate; G6P, glucose 6-phosphate; HXK, hexokinase; PFK, phosphofructokinase;
PGI, phosphoglucose isomerase; [t ], time.
which activities may be present in particular cells, as discussed
below.
3. CONSTRUCTING MODELS OF PLANT METABOLISM
A system of the complexity of the plant metabolic network will
only be fully encapsulated and understood through the construction of computer models. The ultimate goal of the modelling
exercise is to fully describe the flux through all reactions in
the network and to be able to predict how those fluxes change
in response to different external environments or when the
network is altered through genetic intervention. This is a considerable challenge. However, the convergence of a maturation
of molecular profiling technologies and developments in the
application of computational approaches means that we are now
well poised to meet this challenge. In this section, the main
approaches to modelling metabolism are explained and the
relative advantages and limitations of each described. Examples
of existing models of plant metabolism are used to illustrate the
benefit of the approach in terms of understanding the control
distribution within a set of connected reactions. Finally, we discuss
some of the challenges that must be faced if we are to move
towards a predictive model of the plant metabolic network.
c The Authors Journal compilation c 2008 Biochemical Society
3.1 Kinetic models
It has long been recognized that a knowledge of the kinetic
properties of the enzymes that catalyse a network of reactions
allows the flux through those reactions to be numerically
simulated [32]. The principle can be illustrated by considering a
reaction in which an enzyme catalyses the conversion of a single
substrate into a single product via Michaelis–Menten kinetics
(Figure 2). Given the concentration of substrate and the values
for K m and V max , one can calculate the velocity (v) of the reaction
according to the Michaelis–Menten equation:
v = (Vmax · [S])/(K m + [S])
In reality, relatively few enzymes have such simple kinetics. Most
enzymes catalyse reactions involving more than one substrate,
and factors such as product inhibition and the presence of
effector molecules lead to considerably more complicated rate
equations. Nevertheless, the principle remains: given the reaction
stoichiometry, rate equation and the values for the kinetic
parameters of each enzyme in the metabolic system under
consideration, then the system can be numerically simulated
if one provides starting values for metabolite concentrations.
This type of modelling has a long history, dating back to the
work of Garfinkel and Hess [33], who modelled glycolysis in
Getting to grips with the plant metabolic network
ascites-tumour cells, and several metabolic pathways in plants
have been tackled using this approach. For a review of how this
type of modelling has improved our understanding of several key
pathways of primary carbon metabolism, see [34].
The great attraction of kinetic models is that they are mechanistic models based on differential equations that implicitly encapsulate the relationship between substrate/effector concentrations
and reaction velocity. As such they are predictive. The effect of
altering enzyme properties or amount can easily be simulated
by altering the relevant parameters in the equations. Furthermore,
the information from such in silico analyses can be used to
provide a formal quantification of the extent to which each enzyme
controls pathway flux within the framework of Metabolic Control
Analysis [11,12,35]. This ability to rapidly interrogate the control distribution of a metabolic network and to examine in detail
the consequences of alteration of specific components of that
network means that kinetic models are extremely useful tools for
the design of strategies to engineer plant metabolism. However,
despite the undoubted power of kinetic models, their application
to plant metabolism has been severely hampered by a lack of
data [36]. The quality of a metabolic model is dependent on the
completeness of the enzyme kinetic data used to parameterize
the model. Metabolic systems in which the kinetics of all enzymes
have been comprehensively characterized are the exception rather
than the rule. Even where there is information in the literature for
all enzymes of a system, invariably they are drawn from several
different plant species. Ultimately, the more parameters that have
to be assumed or ‘borrowed’ from other systems, the less reliable
the model will be. Hence, kinetic models of plant metabolism
have been confined to the consideration of specific subsets of
metabolism such as the Calvin cycle or starch synthesis [34].
Traditionally, the values for the kinetic parameters of enzymes
used in such models have been drawn from the enzymology literature. Enzymes are purified from plant tissues or recombinant enzymes are purified from bacteria, are and the kinetics of the purified enzyme are studied in vitro. The time-consuming and
exacting nature of this work has inevitably contributed to the
incomplete picture of enzyme kinetics that we are faced with
today. The development of experimental approaches which allow
enzyme kinetics to be estimated in a less labour-intensive
fashion would clearly be of huge benefit and would allow
an expansion of the scale and scope of kinetic models of
plant metabolism. One promising approach in this regard is the
derivation of enzyme kinetics from in vivo labelling experiments.
An isotopically labelled metabolic precursor is supplied to
plant cells, organs or tissues in culture and a time series
of samples are taken. The kinetics of accumulation of the
label in intermediates of the metabolic reactions under consideration can be determined by analysis of the samples. Generally
13
C-labelled precursors are used, allowing analysis by either NMR
or MS. The enzyme kinetics that lead to the observed labelling
kinetics can then be modelled by data-fitting procedures [37,38].
This parameter-fitting approach has been used extensively in the
modelling of microbial metabolism [39], but is still a relatively
unexplored method for modelling plant metabolism. In fact, the
development of the approach in plants has been undertaken
almost single-handedly by David Rhodes and colleagues at
Purdue University [40,41] and has only recently been taken
up by other groups [42]. To date, the approach has been
exclusively used to model plant secondary metabolism. This
in part reflects the interest in engineering plant secondary
metabolism, but also that secondary pathways are well suited
to kinetic modelling. In particular, the pathways are often
unbranched, making the recapitulation of enzyme kinetics from
labelling curves computationally less demanding. A seminal
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example of the approach was the work by Andrew Hanson’s
group (in collaboration with David Rhodes and Yair Shachar-Hill)
on glycine betaine (1-carboxy-N,N,N-trimethylmethanamimium
inner salt) metabolism [41]. Glycine betaine is a compatible solute
that is important in many plants in protecting against dehydration
stress. Many plants lack the ability to synthesize glycine betaine
and thus it is an established target for metabolic engineering
[43]. By following the time-dependent redistribution of label
from [14 C]choline supplied to young tobacco (Nicotiana tabacum)
leaves, it was possible to collect sufficient labelling information
that the kinetics of the enzymes of glycine betaine synthesis could
be fitted. The resultant enzyme kinetic parameters were used to
numerically simulate glycine betaine metabolism, and a detailed
analysis of metabolic capacities and fluxes was undertaken. The
work revealed that the import of choline into the chloroplast is a
major limiting constraint on glycine betaine synthesis. Transport
of choline across the chloroplast inner membrane was thus
revealed as a key target for engineering glycine betaine synthesis.
A similar approach was used more recently to investigate
phenylpropanoid metabolism in potato (Solanum tuberosum)
tubers in response to fungal elicitors [42]. In this technically and
mathematically sophisticated work, values for initial metabolite
concentrations, rate constants and exponents of power law
equations were fitted to label distribution kinetics determined by
liquid chromatography–MS. Parameter fitting was done by the
least-squares method, and the output was analysed for statistical
reliability. Not only was it possible to reliably estimate 18 fluxes
in the phenylpropanoid pathway and demonstrate that fluxes were
altered by the elicitor treatment, but also the analysis extended
to a quantification of the control distribution within the phenylpropanoid pathway, in both the presence and absence of the
elicitor.
Whether one is modelling flux from the kinetics of metabolite
pool size or labelling change, the method has a number of advantages. In addition to the obvious benefit of less labour-intensive
access to enzyme kinetics, the approach also has the advantage
that the derived kinetic parameters are effectively in vivo kinetics,
rather than in vitro, as is traditional. Numerous factors in the
in vivo environment can influence the behaviour of enzymes.
These include interaction with other proteins, modifications of
post-translational state and the presence of effector metabolites
(see section 5). Given that such factors are often absent or
not faithfully replicated in vitro, it is doubtful as to whether
kinetic parameters determined in vitro are an accurate reflection
of the properties of the enzyme in vivo [44]. Therefore models
based on in vivo kinetic parameters have a greater potential
to successfully encapsulate the true behaviour of metabolic
networks. Nevertheless, there are still some challenges to be
overcome. Without doubt, the most significant of these is to decide
on the form of the rate equation to be applied. In the absence of
a priori knowledge of the kinetic behaviour of an enzyme, it is
necessary to apply a general form of the rate equation. In some
cases, simple first-order kinetics have been used, but saturation
kinetics based on the Michaelis–Menten equation or power laws
are more realistic. Of course, such a general kinetic form will
not be appropriate for all enzymes in the system and it may be
desirable to leave the form of the rate equation unconstrained.
Given sufficient experimental data, a more global modelling
approach can be shown to successfully recapitulate known kinetic
forms and indeed identify putative novel interactions and effectors
[45]. A second issue to deal with in the parameter-fitting approach
is the extent to which there is a unique solution to the parameter
set. In many cases there will be more than one solution, and one
is then faced with the problem of which one predicts the correct
values for enzyme kinetic parameters. This is a critical issue,
c The Authors Journal compilation c 2008 Biochemical Society
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L. J. Sweetlove, D. Fell and A. R. Fernie
because, although multiple solutions may faithfully model the
behaviour of the experimental system, only the ‘correct’ solution
will have genuine predictive power. To avoid this ‘non-uniquesolution’ scenario, it is probably necessary to impose additional
constraints on the values of the kinetic parameters using a priori
knowledge.
3.2 Stoichiometric flux-balance models
In contrast with the kinetic modelling approach, in which
extensive knowledge of enzyme kinetics is required, stoichiometric modelling is based primarily on a knowledge of the
structure of the metabolic network under consideration [46]. In
its simplest form, the structure of the network itself forms the
central aspect of investigation. In a landmark paper, Stelling et al.
[47] demonstrated, through analysis of elementary modes (the
least decomposable metabolic units that support a steady state),
that, when genes are systematically deleted, Escherichia coli
maintains a maximally efficient metabolic network structure for
a given carbon source. More powerful still, is a constraints-based
approach in which known features of the network, such as reaction
stoichiometry, thermodynamics (reversibility of reactions in vivo)
and the demands of maintaining a steady state, impose constraints
on the possible flux solution space [28,48] (Figure 2). Linear
programming algorithms can be used to find a unique solution that
satisfies an objective function, such as maximal growth rate within
the constrained solution space. Although care has to be taken to
ensure that sufficient constraints are used to overdetermine the
network [49], the approach has been used with some success to
model microbial metabolic networks [25–27,50].
These are genuine network-scale models consisting of between
300 and 1100 reactions and give a depth of coverage of metabolic
flux that has not been attained with other methods. In addition,
such models do more than just providing a snapshot of fluxes
throughout the system (which is in itself highly informative). The
models can also be predictive within the limitations of certain
assumptions about the behaviour of the network. For example, if
one assumes that the network is structured to maintain maximal
possible growth, then one can use the linear programming
optimization to explore the possible flux distributions as the
nature and amount of carbon source is varied. This has allowed
the identification of a core set of reactions in different microorganisms that carry a positive flux under all conditions [51]
and that the network contains a high flux backbone (consisting
mainly of primary metabolism), whereas branch fluxes are lower
and more variant [52]. Similarly, if one assumes that the network
reacts to the removal of a gene product by minimizing metabolic
change, then one can predict the effect of gene mutation on
metabolic fluxes [53]. Using this approach, the majority of growth
phenotypes of gene knockouts in E. coli can be successfully
predicted [54].
To date, the constraints-based approach has been mainly
applied to microbial metabolism. However, all the necessary
information is available to use the approach to analyse the metabolic network of model plants such as Arabidopsis. In particular,
there are genome-scale curated reconstructions of the Arabidopsis
metabolic network [21], which form a starting point for defining
the network structure (see section 2). In addition, there is an
extensive archive of transcriptomic datasets which can be mined
for robust transcript-to-transcript correlations that may indicate
the presence of functionally co-expressed enzyme subsets.
Definition of functional subsets can considerably reduce the complexity of the network under consideration. Moreover, there
are well-established Arabidopsis cell-suspension cultures that
facilitate measurement of constraints such as metabolite inputs
c The Authors Journal compilation c 2008 Biochemical Society
and outputs and rate of biomass accumulation. Although the
development of a constraints-based genome-scale model of
Arabidopsis metabolism would represent a major advance, the
aim is an ambitious one and there are many potential hurdles to
be overcome. Not the least of these hurdles is the complexity
and degree of subcellular compartmentation of plant metabolism.
However, on the basis of transcriptomic information [18], only
600–900 metabolic transcripts are expressed in dark-grown
heterotrophic Arabidopsis cells in culture, giving a network of
similar size and complexity to that of yeast. Reconstruction of a
properly compartmented metabolic network requires accurate
and comprehensive information about enzyme subcellular localization. Although there are clearly large gaps in the subcellular
protein catalogue, organellar proteomic efforts are gathering pace
[55,56], and information from a variety of sources is being
gathered into single database points [57]. In addition, there has
been steady progress in our characterization of integral membrane
transporters, particularly at the mitochondrial, plastidic and
vacuolar membranes [58–60].
3.3 Future developments in metabolic modelling
Kinetic models and stoichiometric flux-balance models represent
two extremes of the modelling spectrum. Kinetic models are
detailed mechanistic descriptions of the system that provide
quantitative prediction of network dynamics. However, they demand significant experimental parameterization and are difficult
to scale up to cover the whole network. By contrast, stoichiometric flux-balance models work well at the network scale and
generate quantitative predictions about network behaviour, but
only within a single state of the system. Developments in both
spheres are likely to expand the scope of each approach. The
ability to engineer affinity-tagged enzymes at high throughput
raises the possibility of large-scale analyses of enzyme kinetics,
and analysis of isotope-labelling kinetics may provide an alternative and ready means of accessing enzyme kinetics in vivo.
For stoichiometric modelling, an interesting development is the
inclusion of thermodynamic constraints, thereby providing a link
between metabolite concentration and flux [61], which may allow
aspects of metabolic regulation to be inferred. Another important
development is the addition of a gene-regulatory model to the
stoichiometric model, allowing the response of the system to
different conditions to be encapsulated in what is otherwise a
static modelling approach [62]. The logical extension of this is to
add proteomic and metabolomic data to the mix, and methods are
being developed to allow the incorporation of such heterogenous
and incomplete datasets [63,64].
Finally, an interesting new approach is structural-kinetic
modelling [65,66]. This sits between kinetic modelling and
constraints-based modelling. The approach allows a connection
to be made between network structure and dynamic flux state
without a priori knowledge of enzyme kinetic parameters. The key
concept of the method is that many aspects of network behaviour
(such as the dynamic response to perturbations and the stability
of a metabolic state) do not require an explicit kinetic model,
but instead can be accessed using a local linear approximation
of the system at a given state. Essentially the approach allows an
exploration of the dynamic capability of the system. Although this
does not necessarily define the actual dynamic behaviour of the
system, it does allow novel aspect of dynamic behaviour of metabolic networks to be uncovered. For example, an analysis of
the tricarboxylic acid cycle revealed that feedback mechanisms
present in the network can induce unstable flux behaviour, and
the probability of a transition to an unstable state increases with
increased strength of feedback [67].
Getting to grips with the plant metabolic network
4. MEASURING NETWORK BEHAVIOUR
Although the construction of mathematical models can be seen as
of primary importance in improving our understanding of metabolic network behaviour, that is not to say that direct experimental measurements of metabolism are of diminished importance.
In fact, the experimental information is more important than
ever, and complementary experimental efforts will be vital to
the progress of the computational effort. In particular, all types of
modelling described require highly specific experimental inputs.
In addition, a model is only useful if it successfully recapitulates
the behaviour of the system, and one of the key future uses of
molecular profiling datasets will be in model validation. One
cannot assess the reliability of a mathematical description of
a metabolic network unless one has an empirically determined
analysis of network behaviour to compare it against! And even
before this step, many important aspects of metabolic network
behaviour and regulation can be inferred by analysis of molecular
correlations in large-scale datasets.
Recent advances in analytical technologies have brought us to
the point where a complete description of the metabolic network
at the level of its molecular components can be contemplated.
Although the ’omic analytical platforms individually probe
different levels of the metabolic network, it is apparent that
an integrated view spanning the multiple levels of the cellular
control hierarchy will be most informative. However, to date,
most studies have concentrated at the level of a single type of
molecular entity with a vast number of transcript or gene networks
described in the literature (see, for example, [68–70]), as well as
an increasing number of protein (see, for example, [71–73]) and
metabolite networks (see, for example, [74–76]). In this section
we describe how these global molecular profiling experiments
have contributed to our understanding of metabolism. We outline
the advantages and limitations of the different approaches and
will make particular reference to studies that address metabolism
at the network level.
4.1 Transcriptomic investigations of the metabolic network
Of all the single-entity networks, transcriptomic networks are
probably the most comprehensive, reflecting the status of transcriptomics as a mature technology, in contrast with proteomics and metabolomics [77]. The comprehensive nature of
transcriptomic studies means that information concerning all
nodes of the network can be accessed. Another useful aspect of
transcriptomic information is that it allows conserved upstream
regulatory sequences to be uncovered that define the genetic
response to environmental and developmental cues. Transcript
networks are largely analysed on the basis of co-response of
different transcripts. Such analyses allow the extent to which
metabolic pathways are co-ordinately transcriptionally regulated
to be investigated. They are also useful in defining the function of
unknown genes using the ‘guilt by association’ principle. To date,
novel gene annotations for enzymes of cell wall [78], brassinosteroid [79], flavonoid [80] and anthocyanin [81] metabolism
have been achieved via this strategy. On a more global level, this
approach can be used reveal the targets for transcription factors
and other regulatory elements. For example, overexpression of
the PAP1 (purple acid phosphatase 1) transcription factor in
Arabidopsis combined with broad metabolite profiling allowed
the recognition of novel targets of this transcription factor [82].
Similarly, an analysis of a cultivated tomato (Solanum lycopersicum L.) population containing introgressions from the wild
species S. pennellii revealed an up-regulation in respiratory gene
expression (which coincided with increases in the metabolic
intermediates of this pathway [83,84]).
33
The examples discussed thus far concentrate on only small
subsets within transcriptional networks and do not really explore
the behaviour of the network as a whole. However, four recent
articles do make explicit links between transcriptome datasets
and metabolic-network behaviour. The first of these to be
published, that by Wei et al. [70], analysed the co-expression
network of 1330 genes from AraCyc (a biochemical-pathway
database for Arabidopsis available at http://www.arabidopsis.org/
biocyc/index.jsp) and revealed that genes associated with the
same metabolic pathway are, on average, more highly expressed
than those associated with different pathways. Perhaps more
importantly, however, this study also revealed that the distribution
of co-expression was highly skewed, with very few genes having
numerous co-expression partners, but most having fewer than ten.
Intriguingly, genes with multiple connections tend to be singlecopy, whereas genes with multiple paralogues tend to exhibit coexpression with a relatively small number of genes. Wei et al. [70]
argue that this suggests that the network expands through gene
duplication, but that co-expression links involving duplicate nodes
are subsequently weakened. Interestingly, a similar argument
has been made for protein interaction networks (see subsequent
discussion). Looking at gene network regulation from a slightly
different perspective, Walther and co-workers [85] recently
analysed transcriptional response diversity in Arabidopsis
and quantified the influence that structural genomic properties
had on this parameter. For this purpose they used the AtGenExpress transcriptomic compendium (http://www.uni-tuebingen.de/
plantphys/AFGN/atgenex.htm) and known cis-element motifs
mapped to upstream gene promoter regions and compared
the breadth of response with the architectural properties of the
genome. The most interesting finding to emerge from this analysis
was that greater gene expression regulatory complexity appears
to be encoded by an increased density of cis-regulatory elements
and thus provide additional evidence for evolutionary adaptation
of the regulatory code to that provided previously. Taking a
similar approach, but one directly focused towards metabolism,
Gutiérrez et al. [68] recently produced a list of what they define
as C–N (carbon–nitrogen) responsive machines. They achieved
this by exploring global gene expression responses in plants
exposed transiently to a matrix of C and N treatments This
work revealed that about half of the transcriptome is regulated
by C, N or C–N interactions, with detailed evaluation of the 2620
genes interconnected across the experiment suggestive of protein
complexes or highly connected signalling or metabolic network
such as protein synthesis and degradation, chromatin assembly,
RNA metabolism, transport, actin-cytoskeleton formation and
many aspects of metabolism, including pathways of glycolysis
and the oxidative pentose phosphate pathway. In addition, the
authors hint at involvements of auxin signalling and, potentially,
mitochondrial RNA interactions. Additional directed experiments
are required to substantiate these claims. Finally, a proof-ofconcept analysis of the differences in global gene expression
within a recombinant inbred line population of A. thaliana allowed
the identification of known key elements of the transition to
flowering, as well as highlighting some previously unidentified
genes that may be involved in this process [87]. It would seem
likely that this procedure could be applied to metabolic regulation.
4.2 Protein–protein interaction networks
It has become increasingly apparent that many proteins do not
exist independently in the cell, but rather interact specifically with
other proteins. Comprehensive protein–protein interaction maps
have been established for yeast and E. coli, revealing not only that
is protein–protein interaction the norm rather than the exception,
c The Authors Journal compilation c 2008 Biochemical Society
34
Figure 3
L. J. Sweetlove, D. Fell and A. R. Fernie
Evolution of networks
This schematic diagram is based on a protein interaction network, but could be equally representative of a metabolite network. Gene duplication followed by divergence resulting in gain and loss of
c 2006.
interactions are key processes that shape interaction networks. This Figure is reproduced from [191] with kind permission from Springer Science and Business Media but also that the interactions are highly dynamic and conditionspecific, implying a regulatory role [88]. In plants, far fewer such
studies have been carried out, and analysis of plant protein–protein
interactions is at a preliminary stage [89]. However, several
large-scale yeast two-hybrid projects have been initiated (see,
for example, [71]) and rapid progress can be anticipated. Thus
far only ‘interologues’ have been mapped [72,73], that is to say,
interactions predicted on the basis of sequence similarity to those
proteins demonstrated to interact in other species. However, on a
smaller scale, focusing on central regulators of plant meristematic
function and leaf development, the study of a complex network of
protein interactions led to the identification of a novel family of
proteins involved in this process [90]. In addition, large-scale proteomic investigations are also likely to reveal novel protein–
protein associations, as demonstrated by several small-scale
analysis of several corners of plant metabolism. The developments
both at the global level in microbial and mammalian fields [91,92],
and at the level of specific developmental process in plants
(described above) suggest that these techniques will be powerful
tools in understanding the regulation of plant metabolism in the
future.
A recent review article [93] raises several salient points
concerning both the quality and utility of current protein–protein
interaction data. They list the following as vital approaches:
the two-hybrid system, affinity purification coupled with MS, the
curation of literature reports and the effort to critically assess
confidence levels for each technique [93–97]. They state that, although both yeast two-hybrid and affinity-purification approaches
clearly have their pitfalls, the yeast-hybrid system being
prone to returning non-physiological artefacts and the affinitypurification method yielding little information on the exact
details of binary interactions, useful data for incorporation
into a whole-cell model of protein proximity can be gleaned
from their application. This is illustrated both by the work
of the Goodsell [94] and of Takamori and co-workers [96].
The former researcher has taken an integrative approach
to produce a cellular model of E. coli, blood cells and
HIV-infected blood cells [94], whereas the latter researchers
recently published a structural model of a synaptic vessel [96]. Again, work of this type in plants would be highly
instructive in terms of providing a better understanding of
metabolism and processes of metabolite transport. However, to
quote from Betts and Russell [91], “the true picture of the cell does
not fit well into the blueprint or wiring diagram analogies that are
so common in text book biology”. They suggest the adaptation of
other analogies, such as the recently defined models of crowd
behaviour of Helbing and co-workers [98]. These analogies
c The Authors Journal compilation c 2008 Biochemical Society
necessitate the consideration of an important aspect of metabolism
(and biology in general) that we have not yet tackled, namely
that of cellular dynamism. Interestingly, analysis of the evolution
of complex protein networks suggests that this appears to be
under control of mechanisms similar to those described for gene
networks [99,100]. A two-step model employing gene duplication
and rewiring has been employed to explain the evolution of protein
interaction networks and their topology (Figure 3; [99,101]).
Recent studies suggest that duplication and divergence shape
network architecture both at the level of single interactions as well
as at the higher order of network motifs and modules [102,103],
The high degree of gene duplication in Arabidopsis, and other
plant species, as well as high rates of divergence of paralogue
function (which has been demonstrated to be highly important in
the evolution of plant secondary metabolism [104]), suggest that
we will be able to address many interesting questions once such
information is available for plants.
Another aspect of protein state that has relevance for metabolic
regulation is their interaction with other proteins at the level
of subunit associations, oligomerization, supercomplexes and
metabolons. The association of proteins as a method of metabolic
regulation has been long studied, with many of the best studied
plant enzymes being active only as a complex of subunits [105–
107]. Protein purification and reconstitutions studies have, over
the last few decades, shown that enzymes across a wide breadth
of metabolic pathways exhibit this phenomena, as do several of
the transport mechanisms of plants [108,109]. Recently an
elegant study revealed that the soluble cytosolic C-terminus of
an oligomeric ammonium transporter from Arabidopsis serves
as an allosteric regulator that is essential for function with
phosphorylation of sites in the C-terminus proposed as the cognate
mechanism [108]. This feature of regulation also extends to wellknown multienzyme complexes such as the pyruvate dehydrogenase complex [107] and the functional organization of
the chloroplastic and mitochondrial electron-transport chains
[110,111].
In addition to interactions of proteins within single-enzyme
complexes, it is becoming increasingly apparent that the formation
of multienzyme complexes is widespread and is likely to have
regulatory consequences. For example, it is now well-established
that the electron-transport-chain complexes in the mitochondrial
inner membrane associate together in supercomplexes [111,112].
Nevertheless, the functional significance and regulation of such
supercomplexes remains an open question. Although the close
spatial proximity of proteins of associated function would have
clear benefit in terms of efficiency, and may facilitate direct
enzyme-to-enzyme substrate channelling, this is something that
Getting to grips with the plant metabolic network
needs to be determined on a case-by-case basis. There is now a
growing number of examples in which this phenomenon, often
referred to as ‘metabolon formation’, has been demonstrated
for proteins with metabolic function. These include the Calvin
cycle [113], biosynthesis of the cyanogenic glycoside dhurrin
[114], flavonoid pathways [115], and phenylpropanoid [116] and
polyamine metabolism [117]. Since the presence and function
of metabolons in plants have been described in detail previously
[118,119], we will not dwell on this aspect of regulation here.
Suffice it to say that although the majority of demonstrations of
metabolons in plants occur in secondary metabolism, the recent
findings that the enzymes of glycolysis functionally associate
with Arabidopsis mitochondria [120] and structural aspects of
the crystal structure of sucrose phosphatase [121] provide some
support for their operation in plant primary metabolism as well.
However, far more evidence is required to demonstrate substrate
channelling is indeed occurring in these pathways. Given the
technical challenge of measuring substrate channelling [122], it
may be some time before we have a truly comprehensive picture
of the extent and significance of enzyme–enzyme interactions in
plant metabolism.
4.3 Metabolomic investigations of the metabolic network
Since the metabolic network is defined by metabolite–metabolite
connections, analysis of the change in metabolite concentrations
across the network is a potentially powerful method to gain
insight into metabolic organization and regulation [75,76]. The
vast majority of such metabolomic network studies are based on
an analysis of linear-abundance correlations between metabolite
pairs [123]. Given that current metabolomics methods do
not provide a comprehensive analysis of plant metabolites
[77,124,125], the networks obtained from these studies are illustrative at best. That said, some interesting findings have been
made, and it seems likely that recent (and future) technological
[126] and bioinformatic [127] advances will improve this
situation. Furthermore, when used to study mixed populations
exhibiting considerable natural variance, correlation analysis
reveals which pathways appear to be co-ordinately regulated and
which not. One recent example of this is that amino acid levels
were found to be very highly co-ordinated in a tomato population
in which the wild species S. pennellii was introgressed into the
cultivated variety [84]. However, and interestingly, the content of
phenylalanine-derived volatiles in this population showed little
correlation with their precursor [129]. The first finding is in
keeping with previous observations of concerted regulation of
levels of amino acids [130–133], although subtle differences
are apparent between species and physiological circumstances.
Indeed, as was detailed in previous reviews [134,135], the study
of plant amino acid metabolism provides a clear illustration of
the complexity of regulation exhibited within modules of the
metabolic network.
To date, these networks have largely been analysed on the basis
of the simple correlation of metabolite pairs. Perhaps the most
striking feature of these studies was that a small number of metabolite pairs displayed a remarkably high correlation among
biological replicates, even though the large majority of metabolite
pairs showed little or no correlation [123]. This initial finding has
been generalized by a number of subsequent studies [76,136,137].
Detailed evaluation of this phenomenon has suggested that
metabolite correlations are not necessarily related to proximity
in the biochemical network [74,138]. These analyses described
four different regulatory configurations that are expected to be
the origin of metabolite correlation. Simulations suggest that
when the correlations are very strong, they are most likely due
35
to chemical equilibrium. An interesting prediction, still to be
confirmed, is that metabolites sharing conserved moieties (i.e.
a common fragment of the metabolites that turns over more
slowly than the metabolites themselves, such as the adenine of
the adenine nucleotides), should have high correlations, and at
least one of them being negatively correlated with the others [74].
Most high correlations in these networks are, however, thought to
be due to stronger mutual control by a single enzyme or a much
higher degree of variation of a single enzyme activity in respect
to the others. Although analysis of the set of metabolites forming
correlation cliques [139] can provide a good hint as to the identity
of the enzyme in question, it seems likely that parallel analysis of
protein or enzyme activities is likely to be required to fully exploit
this approach in the identification of novel aspects of metabolic
regulation.
Analysis of non-linear correlations, although challenging, is
also potentially a rich source of such information. In the initial
approach described above, Roessner et al. [123] documented
hyperbolic relationships between the minority of metabolite pairs,
including lysine and isoleucine, that had previously been demonstrated in another species to be interrelated by feedforward/feedback-control circuitry [140]. Perhaps surprisingly, this approach
has not been much followed in the interim, perhaps largely due to
a lack of similar behaviour between other known metabolites in
GC–MS chromatograms. However, given the fact that technological developments will shortly allow us to measure a far
greater number of metabolites, it seems likely that it may find
greater utility in the future. Thus far we have concentrated
this discussion on individual elements rather than the networks
themselves. The study of Weckwerth et al. [76], although lacking
a robust sensitivity analysis, suggests that analysing the networks
of genetically modified plants may reveal differences that were
not detected directly at the level of the metabolite. Comparison
of network changes in various cellular circumstances is likely to
be of increasing importance once a clear framework has been
established that allows a high confidence of analysis. Ultimately
this will also include the dynamic analysis of rewiring of the
metabolic networks which to date has largely been carried out by
describing changes in individual metabolite levels [141] although
network changes have started to be reported [142].
4.4 Integration of ’omic data
The hierarchical nature of metabolic regulation highlights the fact
that the previously described examples, although informative, are
somewhat artificial, since transcripts, proteins or metabolites do
not act independently of one another. With this in mind, several
studies have attempted to integrate data from different analytical
platforms with a view to establishing a more complete assessment
of the metabolic network. Several of these studies are particularly
worthy of mention. The studies of Hirai et al. [143] and Nikiforova
and co-workers [144] both look at transcript–metabolite networks
during sulphur stress, although the datasets they obtain were
analysed from different perspectives. That of Hirai et al. [143]
was evaluated with respect to assigning gene function on the
basis of correlations between metabolite and transcript and
hence of understanding metabolic regulation at the level of the
constitutive pathway. By contrast, that of Nikiforova et al. [144]
was more focused towards understanding the systemic response
to sulfur stress and elucidating the informational processing
networks by which this is achieved [145]. The work of Weckwerth
and co-workers [146] extended this network approach by
correlating both proteins and metabolites during diurnal cycles in
mutant and wild-type Arabidopsis plants. Their study revealed that
the combined approach resulted in a considerable improvement in
c The Authors Journal compilation c 2008 Biochemical Society
36
L. J. Sweetlove, D. Fell and A. R. Fernie
discriminatory power over independent evaluation of the datasets.
As a final example, in our own work, we followed tomato fruit
development by monitoring changes in metabolites, transcripts
and fluxes and evaluated the correlative behaviour between these
factors [147]. That study revealed several interesting features
of metabolic change across the ripening period, with much
concerted regulation apparent at the pathway level and even
between pathways. Generating insight from such datasets into
how metabolic regulation is exerted remains rather challenging.
For example, the fact that key ripening genes correlated with
many sugars and organic acids implies an important role for transcriptional regulation of sugar and organic acid metabolism.
But ultimately, as with any correlation study, follow-up experimentation is required to prove a causal link between the
correlating elements.
Although not directly evaluating networks per se, the work of
the Stitt group is also worth discussing here. In a series of multilevel profiling experiments in Arabidopsis, the effect of extended
darkness, nutrient deficiency, and even global climate change,
were studied, allowing both the confirmation of long-standing
hypotheses and novel insight into the regulation of metabolism
under these conditions [148]. These studies facilitated a detailed
insight into hierarchical control in the regulation of metabolism
of the illuminated leaf.
5. MEASURING METABOLIC FLUX
Although much can be gained from observation of changes in
metabolic transcripts and proteins, ultimately the true behaviour of
the metabolic network can only be gauged by direct measurement
of flux. Even changes in concentration of metabolites are open
to misinterpretation; it is entirely possible for metabolic pathway
flux to change dramatically without perturbation of the concentration of pathway intermediates [15,149], and metabolites can
change in the opposite direction to overall pathway flux. A
precise knowledge of pathway flux is therefore essential to
characterize the extent of metabolic change when plant cells face
different environmental conditions or physiological demands. In
addition, quantification of flux is an essential part of defining the
control distribution within the metabolic network [150]. Finally,
empirically determined flux values are vital data with which to
compare and validate predicted flux values from mathematical
models.
In its simplest form, pathway flux can be measured from the rate
of accumulation of an end-product of the pathway. However, there
are few true end-products and even relatively stable biopolymers,
such as starch and cellulose, are still subject to a synthesis–
degradation cycle. Therefore a more direct measure of movement
of carbon or nitrogen through metabolic pathways is required.
This is usually achieved by measuring the redistribution of label
when an isotopically labelled precursor is fed to plant tissues or
organs. Flux can be determined from such redistributions in two
ways: either the rate of labelling of metabolite pools is considered
or the pattern of labelling at isotopic steady state is analysed. The
latter approach allows determination of relative fluxes on the basis
that carbon arriving at a metabolite pool from different metabolic
pathway branches will have a different fractional enrichment of
label at each mapping atom of the precursor molecule [151].
Improvements in analytical methods [152,153] and computational
developments (see, for example, [154]) have improved the scope
of the steady-state approach, and increasingly complex metabolic
flux networks are being analysed. There is an established literature
on this subject, and several excellent reviews have recently been
published that cover both the theoretical and practical aspects of
flux measurement and review published flux maps [37,155–158].
c The Authors Journal compilation c 2008 Biochemical Society
We do not intend to reiterate the content of these reviews here, but
rather wish to focus on aspects of flux analysis that still require
further development.
5.1 Subcellular compartmentation and flux
Perhaps the biggest challenge in the determination of fluxes in
plant metabolic networks is the extensive subcellular compartmentation of metabolism and the existence of parallel metabolic
reactions in more than one compartment [159]. In the steady-state
approach, subcellular fluxes are unravelled from the labelling
of end-products synthesized uniquely in a single subcellular
compartment. Thus the labelling of the glucose moiety of starch
and sucrose reflect the labelling of plastidic and cytosolic hexose
phosphates respectively [155]. The problem is that the redundant
nature of plant metabolism means that there are precious few
such metabolites that accurately provide compartment-specific information. However, there are many unexplored possibilities,
and progress on this front is still possible [160]. Moreover, in
certain cases it is possible to simplify the compartmentation
issue, because rapid exchange of some metabolites between
compartments effectively renders the compartmentalized metabolite a single homogenous pool [158].
Failure to properly consider subcellular compartmentation of
metabolites can lead to substantial misinterpretations of labelling
patterns. A good example which highlights this issue is the
analysis of sucrose cycling. In many heterotrophic tissues,
sucrose is continuously degraded and resynthesized, creating an
ATP-consuming futile cycle. This process has been accurately
quantified by comparing the rate of sucrose synthesis (determined
from short-term [14 C]glucose experiments) with the rate of sucrose
accumulation, and typically sucrose cycling accounts for approx.
5 % of cellular ATP turnover [161]. However, various estimates
of the same phenomenon using a steady-state 13 C approach have
reported much higher values of between 43 and 82 % [162,163],
leading to the proposal of a novel glucose phosphatase to account
for the high rate of glucose resynthesis [164]. However, the
validity of these high rates of sucrose cycling and glucose
resynthesis have been recently questioned [165]. Instead, it is
argued that the result is an artefact based on a failure to account
for changes of labelling of glucose as a result of passage through
the vacuole. Indeed the vacuolar compartment is rarely, if ever,
considered in metabolic flux maps, but may introduce labelling
variations to many primary metabolites.
Although this example highlights the importance of subcellular
compartmentation in the interpretation of labelling patterns, it is
not immediately obvious how we might make significant advances
in our ability to account for it. Although it is possible to directly
analyse metabolites from different subcellular compartments by
non-aqueous fractionation [166] or by rapid fractionation of
protoplasts through silicone oil [167], these methods remain
rather specialist and have yet to be applied to analysis of stable
isotope labelling. Currently there are practical limitations to both
approaches. The non-aqueous method gives rather poor separation
of different subcellular compartments, and some compartments,
such as the mitochondrion, remain out of reach. By contrast, the
silicone-oil method can give good separation of organelles such
as plastids and mitochondria, but only works with protoplasts,
entities that may have a metabolic state considerably different
from that of intact cells. An alternative approach is to exploit
the chemical shift generated by substantial pH differences
between compartments such as the cytosol and vacuole to
analyse subcellular metabolite labelling patterns by in vivo NMR.
Typically this method is combined with 31 P-NMR to give a
measure of vacuolar and cytosolic pH [168], but 13 C labelling
Getting to grips with the plant metabolic network
patterns of primary metabolites can also be analysed [169]. It is
likely that a combination of refinements to the methodologies of
subcellular fractionation techniques and in vivo NMR approaches
will be necessary to give a more complete picture of the extent to
which compartmentation leads to multiple metabolite pools that
are differentially labelled.
The problem of compartmentation has troubled plant biochemists for many years [170], and the techniques to analyse
metabolic compartmentation that we have described are not
new. The question therefore arises as to whether new molecular
techniques have emerged that might assist with the compartmentation problem. A promising advance in this respect is the
development of genetically encoded metabolite sensors – bacterial
periplasmic binding proteins modified with cyan and yellow
fluorescent proteins such that metabolite binding leads to FRET
(fluorescence resonance energy transfer) [171,172]. In principle,
such sensors can be targeted to different subcellular compartments
and would permit in vivo quantification of subcellular metabolite
concentration. Recently it has been suggested that such sensors
might be useful in the context of flux analysis [173]. Because
the sensors report only on metabolite concentration and not on
labelling state, this use would be restricted to analysis of dynamic
metabolic changes in which perturbations of metabolite pool
sizes are used to reconstruct fluxes. Although it is tempting to
marry the use of metabolite sensors to such an approach, there
are many practical considerations to be overcome, not least of
which is that the basis of such flux reconstructions is the related
changes in linked metabolites. Because of the extent of biological
variation in metabolite concentration, it is likely that accurate
flux reconstruction will only be possible if dynamic changes in
metabolites are measured in the same sample. Given that the
FRET metabolite sensors are genetically encoded, it is unlikely
that more than three or four sensors for different metabolites could
be introduced into the same transgenic plant line, and therefore
the ability of these sensors to report on multiple metabolites
in the same sample is rather limited.
5.2 Measuring flux dynamics
The established method of measuring multiple metabolic fluxes
depends on the system reaching isotopic and metabolic steady
state. However, it is increasingly being recognized that the dynamics of metabolism are perhaps more informative than the steady
state [67]. Not only are complex dynamic behaviours a feature
of metabolism (e.g. the glycolytic oscillations of yeast [174] and
the diurnal regulation of the plant Calvin cycle), but also the
regulation of metabolic flux is based on the dynamic properties
of the system. Transient regulatory interactions (protein–protein
[88], protein–metabolite and protein–lipid) and feedback loops
are essential features of a regulatory system that generates a
homoeostatic state while at the same time allows rapid adaptation
and response to altered physiological demands [134]. It therefore
follows that an analysis of network flux dynamics is necessary
to probe these regulatory features. In addition, an isotopic and
metabolic steady state is often not experimentally achievable or
physiologically relevant [149].
Attention is therefore turning to the analysis of dynamic flux
patterns in biological systems. The problem is an inherently more
complex one than the solution of flux at steady state, because
change in metabolite pool sizes, as well as labelling patterns, need
to be considered. Moreover, a system of non-linear differential
equations of high dimension is needed to mathematically describe
the system. Fitting the parameters of this high-dimension equation
set to the experimental data is a computationally challenging task.
Quantifying dynamic fluxes is also experimentally more intens-
37
ive than at steady state, because a time series of samples
must be analysed. Moreover, the estimation of dynamic fluxes
generally requires information on the labelling and pool size of a
greater number of metabolites than at steady state, because fluxbalancing no longer applies. In general, the larger the network
under consideration, the more challenging the computational and
experimental problem, which is why most studies of dynamic flux
changes have focused on relatively short linear pathways of plant
secondary metabolism [40,176,177]. Experimental biochemists
have sought pragmatic solutions to the matching of available
labelling and pool-size data from standard GC–MS analyses with
possible flux solutions [178,179]. Such GC–MS-based metabolite
profiling focuses mainly on primary metabolism, where extensive
branching and multiple compartmented pools can be confounding
factors in analysing flux. Nevertheless, in certain areas of the
primary metabolic network, it is possible to estimate fluxes [178].
Although we are clearly some way off being able to estimate
non-steady state fluxes in large networks, recent computational
developments have paved the way for analysing large-scale networks at metabolic – but not isotopic – steady state. This half-way
state is particularly relevant for experimental systems where isotopic steady state is not likely to be reached [179] or where the
labelling patterns at isotopic steady state hold no information
about flux. A key example of the latter is when labelled CO2
is supplied to plants. Because only one carbon atom is labelled
and all other carbon atoms in photoautotrophically synthesized
molecules are ultimately derived from that carbon then at isotopic
steady state, all carbon atoms in the system will be labelled to
the same fractional enrichment as the supplied CO2 , irrespective
of metabolic flux. However, the rate of labelling of different
compounds in the dynamic labelling phase is related to flux.
There is much interest in using CO2 as a label source, because it
allows labelling experiments to be done with intact plants rather
than being restricted to cell-suspension cultures or tissues or
organs in cultures [155], and recently experimental and technical
assessments and preliminary modelling have been carried out
[180,181]. Major steps forward in deriving network fluxes in such
situations have been made by Wiechert and colleagues [182,183].
The experimental and data requirements have been assessed [182]
and, importantly, freely available computational software has been
devised that can estimate flux [183]. The software is essentially
an ordinary differential-equation solver that can handle the veryhigh-dimension equation sets specified by the dynamic labelling
state and estimate flux by fitting parameters in the equations to
the experimental data by non-linear regression. The method has
been demonstrated with an estimation of fluxes in central carbon
metabolism of E. coli supplied with [13 C]glucose and sampled
over a subsequent 16 s time period [184].
6. PERSPECTIVES
The recent flood of post-genomic molecular profiling data has
encouraged us to tackle previously untenable organizational
structures, and we now find ourselves standing in the foothills
of a systems-biology mountain. The challenges we face to
bring together all the experimental data and the computational
models are considerable. In particular, we have highlighted the
considerable gaps in the experimental datasets in relation to
providing model parameters. There are also, as ever, numerous
technical challenges to overcome if we are to refine further our
analytical ability to cope with issues such as subcellular compartmentation and the behaviour of different cell types. Although
it may be the case that truly predictive and validated models
of the Arabidopsis metabolic network remain some way off,
it is clear that the systems-level analysis of molecular change
c The Authors Journal compilation c 2008 Biochemical Society
38
L. J. Sweetlove, D. Fell and A. R. Fernie
is revealing new aspects of metabolic network behaviour and
regulation. For example correlation studies have proven highly
effective in uncovering novel gene function. The present review
has highlighted the relative paucity of global network studies at
the protein and metabolite levels, and this is an obvious area
were technological developments are likely to facilitate improved
understanding of network behaviour. Although technological
advances can be anticipated that will improve further the quality of
data that we can access, we should not play down the tremendous
progress that has been made in the understanding of metabolic
networks in the last few years. One field where this is particularly
noticeable is that of flux analysis, and there are further exciting
prospects for additional improvement. For example, recently
developed tools for metabolic profiling [185] have the potential to
dramatically enhance our understanding of metabolic dynamics.
The field of metabolic network biology is very much at the
crossroads of contemporary experimental and theoretical biology,
with its development dependent on effective collaboration of
practitioners in both fields. Reassuringly, despite the manifold
technical obstacles and the sheer complexity of the problem at
hand, it is apparent that there is much interest in unravelling plant
metabolic networks, and we can look forward to continued rapid
progress in years to come.
L. J. S. (grants E20443 and BBE0023231) and D. A. F. (grants CFB17702 and BBE00203X1)
acknowledge support from the Biotechnology and Biological Sciences Research Council,
and A. R. F. acknowledges support from the Deutsche Forschungsgemeinschaft, for their
research in this area.
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Received 14 August 2007/11 September 2007; accepted 14 September 2007
Published on the Internet 11 December 2007, doi:10.1042/BJ20071115
c The Authors Journal compilation c 2008 Biochemical Society