Stochastic fluctuations in metabolic pathways
Erel Levine and Terence Hwa
Center for Theoretical Biological Physics and Department of Physics, University of
California at San Diego, La Jolla, CA 92093-0374
Abstract. Fluctuations in the abundance of molecules in the living cell may
affect its growth and well being. For regulatory molecules (e.g., signaling proteins
or transcription factors), fluctuations in their expression can affect the levels of
downstream targets in a network. Here, we develop an analytic framework to
investigate the phenomenon of noise correlation in molecular networks. Specifically,
we focus on the metabolic network, which is highly inter-linked, and noise properties
may constrain its structure and function. Motivated by the analogy between the
dynamics of a linear metabolic pathway and that of the exactly soluable linear
queueing network or, alternatively, a mass transfer system, we derive a plethora
of results concerning fluctuations in the abundance of intermediate metabolites in
various common motifs of the metabolic network. For all but one case examined, we
find the steady-state fluctuation in different nodes of the pathways to be effectively
uncorrelated. Consequently, fluctuations in enzyme levels only affect local properties
and do not propagate elsewhere into metabolic networks, and intermediate metabolites
can be freely shared by different reactions. Our approach may be applicable to study
metabolic networks with more complex topologies, or protein signaling networks which
are governed by similar biochemical reactions. Possible implications for bioinformatic
analysis of metabolimic data are discussed.
http://arxiv.org/abs/0704.1667v1
Stochastic fluctuations in metabolic pathways 2
Due to the limited number of molecules for typical molecular species in microbial
cells, random fluctuations in molecular networks are common place and may play
important roles in vital cellular processes. For example, noise in sensory signals
can result in pattern formation and collective dynamics [1], and noise in signaling
pathways can lead to cell-to-cell variability [2]. Also, stochasticity in gene expression
has implications on cellular regulation [3, 4] and may lead to phenotypic diversity [5, 6],
while fluctuations in the levels of (toxic) metabolic intermediates may reduce metabolic
efficiency [7] and impede cell growth.
In the past several years, a great deal of experimental and theoretical efforts have
focused on the stochastic expression of individual genes, at both the translational and
transcriptional levels [8, 9, 10]. The effect of stochasticity on networks has been studied
in the context of small, ultra-sensitivie genetic circuits, where noise at a circuit node (i.e.,
a gene) was shown to either attenuate or amplify output noise in the steady state [11, 12].
This phenomenon — termed ‘noise propagation’ — make the steady-state fluctuations
at one node of a gene network dependent in a complex manner on fluctuations at other
nodes, making it difficult for the cell to control the noisiness of individual genes of
interest [13]. Several key questions which arise from these studies of genetic noise
include (i) whether stochastic gene expression could further propagate into signaling
and metabolic networks through fluctuations in the levels of key proteins controlling
those circuits, and (ii) whether noise propagation occurs also in those circuits.
Recently, a number of approximate analytical methods have been applied to
analyze small genetic and signaling circuits; these include the independent noise
approximation [14, 15, 16], the linear noise approximation [14, 17], and the self-consistent
field approximation [18]. Due perhaps to the different approximation schemes used,
conflicting conclusions have been obtained regarding the extent of noise propagation
in various networks (see, e.g., [17].) Moreover, it is difficult to extend these studies to
investigate the dependences of noise correlations on network properties, e.g., circuit
topology, nature of feedback, catalytic properties of the nodes, and the parameter
dependences (e.g., the phase diagram). It is of course also difficult to elucidate these
dependences using numerical simulations alone, due to the very large degrees of freedoms
involved for a network with even a modest number of nodes and links.
In this study, we describe an analytic approach to characterize the probability
distribution for all nodes of a class of molecular networks in the steady state. Specifically,
we apply the method to analyze fluctuations and their correlations in metabolite
concentrations for various core motifs of the metabolic network. The metabolic network
consists of nodes which are the metabolites, linked to each other by enzymatic reactions
that convert one metabolite to another. The predominant motif in the metabolic
network is a linear array of nodes linked in a given direction (the directed pathway),
which are connected to each other via converging pathways and diverging branch points
[19]. The activities of the key enzymes are regulated allosterically by metabolites
from other parts of the network, while the levels of many enzymes are controlled
transcriptionally and are hence subject to deterministic as well as stochastic variations
Stochastic fluctuations in metabolic pathways 3
in their expressions [20]. To understand the control of metabolic network, it is important
to know how changes in one node of the network affect properties elsewhere.
Applying our analysis to directed linear metabolic pathways, we predict that
the distribution of molecule number of the metabolites at intermediate nodes to
be statistically independent in the steady state, i.e., the noise does not propagate.
Moreover, given the properties of the enzymes in the pathway and the input flux, we
provide a recipe which specifies the exact metabolite distribution function at each node.
We then show that the method can be extended to linear pathways with reversible
links, with feedback control, to cyclic and certain converging pathways, and even to
pathways in which flux conservation is violated (e.g., when metabolites leak out of
the cell). We find that in these cases correlations between nodes are negligable or
vanish completely, although nontrivial fluctuation and correlation do dominate for a
special type of converging pathways. Our results suggest that for vast parts of the
metabolic network, different pathways can be coupled to each other without generating
complex correlations, so that properties of one node (e.g., enzyme level) can be changed
over a broad range without affecting behaviors at other nodes. We expect that the
realization of this remarkable property will shape our understanding of the operation
of the metabolic network, its control, as well as its evolution. For example, our results
suggest that correlations between steady-state fluctuations in different metabolites bare
no information on the network structure. In contrast, temporal propagation of the
response to an external perturbation should capture - at least locally - the morphology
of the network. Thus, the topology of the metabolic network should be studied during
transient periods of relaxation towards a steady-state, and not at steady-state.
Our method is motivated by the analogy between the dynamics of biochemical
reactions in metabolic pathways and that of the exactly solvable queueing systems [46]
or, alternatively, as mass transfer systems [22, 47]. Our approach may be applicable
also to analyzing fluctuations in signaling networks, due to the close analogy between
the molecular processes underlying the metabolic and signaling networks. To make our
approach accessible to a broad class of circuit modelers and bioengineers who may not
be familiar with nonequilibrium statistical mechanics, we will present in the main text
only the mathematical results supported by stochastic simulations, and defer derivations
and illustrative calculations to the Supporting Materials. While our analysis is general,
all examples are taken from amino-acid biosynthesis pathways in E. Coli [24].
1. Individual Nodes
1.1. A molecular Michaelis-Menton model
In order to set up the grounds for analyzing a reaction pathway and to introduce our
notation, we start by analyzing fluctuations in a single metabolic reaction catalyzed by
an enzyme.
Recent advances in experimental techniques have made it possible to track the
Stochastic fluctuations in metabolic pathways 4
enzymatic turnover of a substrate to product at the single-molecule level [26, 27], and
to study instantaneous metabolite concentration in the living cell [28]. To describe
this fluctuation mathematically, we model the cell as a reaction vessel of volume V ,
containing m substrate molecules (S) and NE enzymes (E). A single molecule of S can
bind to a single enzyme E with rate k+ per volume, and form a complex, SE. This
complex, in turn, can unbind (at rate k−) or convert S into a product form, P , at rate
k2. This set of reactions is summarized by
S + E
k2→P + E . (1)
Analyzing these reactions within a mass-action framework — keeping the substrate
concentration fixed, and assuming fast equilibration between the substrate and the
enzymes (k± ≫ k2) — leads to the Michaelis-Menten (MM) relation between the
macroscopic flux c and the substrate concentration [S] = m/V :
c = vmax[S]/([S] +KM) , (2)
where KM = k−/k+ is the dissociation constant of the substrate and the enzyme,
and vmax = k2[E] is the maximal flux, with [E] = NE/V being the total enzyme
concentration.
Our main interest is in noise properties, resulting from the discreteness of molecules.
We therefore need to track individual turnover events. These are described by the
turnover rate wm, defined as the inverse of the mean waiting time per volume between
the (uncorrelated‡) synthesis of one product molecule to the next. Assuming again
fast equilibration between the substrate and the enzymes, the probability of having
NSE complexes given m substrate molecules and NE enzymes is simply given by the
Boltzmann distribution,
p(NSE|m,NE) =
K−NSE
Zm,NE
m!NE !
NSE!(m−NSE)!(NE −NSE)!
for NSE < NE and m. Here K
−1 = V k+/k− is the Boltzmann factor associated
with the formation of an SE complex, and the Zm,NE takes care of normalization (i.e.,
chosen such that
p(NSE|m,NE) = 1.) Under this condition, the turnover rate
NSE · p(NSE |m,NE) is given approximately by
wm = vmax
m+ (K +NE − 1)
+O(K−3) , (4)
with vmax = k2NE/V ; see Supp. Mat. We note that for a single enzyme (NE = 1), one
has wm = vmaxm/(m+K), which was derived and verified experimentally [27, 29].
1.2. Probability distribution of a single node
In a metabolic pathway, the number of substrate molecules is not kept fixed; rather,
these molecules are synthesized or imported from the environment, and at the same time
‡ We note in passing that some correlations do exist – but not dominate – in the presence of “dynamical
disorder” [27], or if turnover is a multi-step process [29, 30].
Stochastic fluctuations in metabolic pathways 5
turned over into products. We consider the influx of substrate molecules to be a Poisson
process with rate c. These molecules are turned into product molecules with rate wm
given by Eq. (4). The number of substrate molecules is now fluctuating, and one can ask
what is the probability π(m) of finding m substrate molecules at the steady-state. This
probability can be found by solving the steady-state Master equation for this process
(see Supp. Mat.), yielding
π(m) =
m+K + (NE − 1)
(1− z)K+NEzm , (5)
where z = c/vmax [31]. The form of this distribution is plotted in supporting figure 1
(solid black line). As expected, a steady state exists only when c ≤ vmax. Denoting the
steady-state average by angular brackets, i.e., 〈xm〉 ≡
m xm π(m), the condition that
the incoming flux equals the outgoing flux is written as
c = 〈wm〉 = vmax
s+ (K +NE)
, (6)
where s ≡ 〈m〉.
Comparing this microscopically-derived flux-density relation with the MM relation
(2) using the obvious correspondence [S] = s/V , we see that the two are equivalent
with KM = (K +NE)/V . Note that this microscopically-derived form of MM constant
is different by the amount [E] from the commonly used (but approximate form)
KM = K/V , derived from mass-action. However, for typical metabolic reactions,
KM ∼ 10 − 1000µM [24] while [E] is not more than 1000 molecules in a bacterium
cell (∼ 1µM); so the numerical values of the two expressions may not be very different.
We will characterize the variation of substrate concentration in the steady-state by
the noise index
η2s ≡
c · (K +NE)
, (7)
where σ2s is the variance of the distribution π(m). Since c ≤ vmax and increases with
s towards 1 (see Eq. 6), ηs decreases with the average occupancy s as expected. It
is bound from below by 1/
K +NE , which can easily be several percent. Generally,
large noise is obtained when the reaction is catalyzed by a samll number of high-affinity
enzymes (i.e., for low K and NE).
2. Linear pathways
2.1. Directed pathways
We now turn to a directed metabolic pathway, where an incoming flux of substrate
molecules is converted, through a series of enzymatic reactions, into a product flux [19].
Typically, such a pathway involves the order of 10 reactions, each takes as precursor the
product of the preceding reaction, and frequently involves an additional side-reactant
(such as a water molecule or ATP) that is abundant in the cell (and whose fluctuations
can be neglected). As a concrete example, we show in figure 1(a) the tryptophan
Stochastic fluctuations in metabolic pathways 6
Figure 1. Linear biosynthesis pathway. (a) Tryptophan biosynthesis pathway in E.
Coli. (b) Model for a directed pathway. Dashed lines depict end-product inhibition.
Abbreviations: CPAD5P, 1-O-Carboxyphenylamino 1-deoxyribulose-5-phosphate;
NPRAN, N-5-phosphoribosyl-anthranilate; IGP, Indole glycerol phosphate; PPI,
Pyrophosphate; PRPP, 5-Phosphoribosyl-1-pyrophosphate; T3P1, Glyceraldehyde 3-
phosphate.
biosynthesis pathway of E. Coli [24], where an incoming flux of chorismate is converted
through 6 directed reactions into an outgoing flux of tryptophan, making use of several
side-reactants. Our description of a linear pathway includes an incoming flux c of
substrates of type S1 along with a set of reactions that convert substrate type Si to
Si+1 by enzyme Ei (see figure 1(b)) with rate w
mi = vimi/(mi +Ki − 1) according to
Eq. (4). We denote the number of molecules of intermediate Si by mi, with m1 for the
substrate and mL for the end-product. The superscript (i) indicates explicitly that the
parameters vi = k
E /V and Ki = (K
(i) + N
E ) describing the enzymatic reaction
Si → Si+1 are expected to be different for different reactions.
The steady-state of the pathway is fully described by the joint probability
distribution π(m1, m2, . . . , mL) of having mi molecules of intermediate substrate type
Si. Surprisingly, this steady-state distribution is given exactly by a product measure,
π(m1, m2, . . . , mL) =
πi(mi) , (8)
where πi(m) is as given in Eq. (5) (with K +NE replaced by Ki and z by zi = c/vi),
as we show in Supp. Mat. This result indicates that in the steady state, the number of
molecules of one intermediate is statistically independent of the number of molecules of
any other substrate§. The result has been derived previously in the context of queueing
networks [46], and of mass-transport systems [47]. Either may serve as a useful analogy
for a metabolic pathway.
Since the different metabolites in a pathway are statistically decoupled in the steady
state, the mean si = 〈mi〉 and the noise index η2si = c
−1vi/Ki can be determined by
Eq. (7) individually for each node of the pathway. It is an interesting consequence
of the decoupling property of this model that both the mean concentration of each
substrate and the fluctuations depend only on the properties of the enzyme immediately
downstream. While the steady-state flux c is a constant throughout the pathway, the
§ We note, however, that short-time correlations between metabolites can still exist, and may be probed
for example by measuring two-time cross-correlations; see discussion at the end of the text.
Stochastic fluctuations in metabolic pathways 7
1 2 3 4 5 6
1 2 3 4 5 6
1 2 3 4 5 6
1 2 3 4 5 6
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6
7 8 9
Figure 2. Noise in metabolite molecular number (ηs = σs/s) for different pathways.
Monte-Carlo simulations (bars) are compared with the analytic prediction (symbols)
obtained by assuming decorrelation for different nodes of the pathways. The structure
of each pathway is depicted under each panel. Parameter values were chosen randomly
such that 103 < Ki < 10
4 and c < vi < 10c. SImilar decorrelation was obtained for
100 different random choices of parameters, and for 100 different sets with Ki 10-fold
smaller (data not shown). The effect on the different metabolites of a change in the
velocity of the first reaction, v1 = 1.1c (dark gray)→ 5c (light gray), is demonstrated.
Similar results are obtained for changes in K1 (data not shown.) (a) Directed pathway.
Here the decorrelation property is exact. (b) Directed pathway with two reversible
reactions. For these reactions, v+
3,4 = 8.4, 6.9c; v
3,4 = 1.6, 3.7, c;K
3,4 = 2500, 8000
and K−
3,4 = 7700, 3700. (c) Linear dilution of metabolites. Here β/c = 1/100. (d)
End-product inhibition,where the influx rate is given by α = c0 [1 + (mL/KI)]
KI = 1000. (e) Diverging pathways. Here metabolite 4 is being processed by two
enzymes (with different affinities, KI = 810,KII = 370) into metabolites 5 and 7,
resp. (f) Converging pathways. Here two independent 3-reaction pathways , with
fluxes c and c′ = c/2, produce the same product, S4.
parameters vi and Ki can be set separately for each reaction by the copy-number and
kinetic properties of the enzymes (provided that c < vi). Hence, for example, in a case
where a specific intermediate may be toxic, tuning the enzyme properties may serve to
decrease fluctuations in its concentration, at the price of a larger mean. To illustrate
the decorrelation between different metabolites, we examine the response of steady-state
fluctuations to a 5-fold increase in the enzyme level [E1]. Typical time scale for changes
in enzyme level much exceeds those of the enzymatic reactions. Hence, the enzyme level
changes may be considered as quasi-steady state. In figure 2(a) we plot the noise indices
of the different metabolites. While noise in the first node is significantly reduced upon
a 5-fold increase in [E1], fluctuations at the other nodes are not affected at all.
2.2. Reversible reactions
The simple form of the steady-state distribution (8) for the directed pathways may
serve as a starting point to obtain additional results for metabolic networks with more
elaborate features. We demonstrate such applications of the method by some examples
below. In many pathways, some of the reactions are in fact reversible. Thus, a
Stochastic fluctuations in metabolic pathways 8
metabolite Si may be converted to metabolite Si+1 with rate v
maxmi/(mi + K
i ) or
to substrate Si−1 with rate v
maxmi/(mi + K
i ). One can show — in a way similar to
Ref. [47] — that the decoupling property (5) holds exactly only if the ratio of the two
rates is a constant independent of mi, i.e. when K
i = K
i . In this case the steady state
probability is still given by (5), with the local currents obeying
v+i zi − v−i+1zi+1 = c . (9)
This is nothing but the simple fact that the overall flux is the difference between the
local current in the direction of the pathway and that in the opposite direction.
In general, of course, K+i 6= K−i . However, we expect the distribution to be given
approximately by the product measure in the following situations: (a) K+i ≃ K−i ; (b)
the two reactions are in the zeroth-order regime, s ≫ K±i ; (c) the two reactions are in
the linear regime, s ≪ K±i . In the latter case Eq. (9) is replaced by
zk+1 = c .
Taken together, it is only for a narrow region (i.e., si ∼ Ki) where the product measure
may not be applicable. This prediction is tested numerically, again by comparing two
pathways (now containing reversible reactions) with 5-fold difference in the level of
the first enzyme. From figure2(b), we see again that the difference in noise indices
exist only in the first node, and the computed value of the noise index at each node
is in excellent agreement with predictions based on the product measure (symbols).
SImilar decorrelation was obtained for 100 different random choices of parameters, and
for 100 different sets with Ki 10-fold smaller (data not shown).
2.3. Dilution of intermediates
In the description so far, we have ignored possible catabolism of intermediates or dilution
due to growth. This makes the flux a conserved quantity throughout the pathway, and
is the basis of the flux-balance analysis [32]. One can generalize our framework for the
case where flux is not conserved, by allowing particles to be degraded with rate um.
Suppose, for example, that on top of the enzymatic reaction a substrate is subjected
to an effective linear degradation, um = βm. This includes the effect of dilution due
to growth, in which case β = ln(2)/(mean cell division time), and the effect of leakage
out of the cell. As before, we first consider the dynamics at a single node, where the
metabolite is randomly produced (or transported) at a rate c0. It is straightforward to
generalize the Master equation for the microscopic process to include um, and solve it
in the same way. With wm as before, the steady state distribution of the substrate pool
size is then found to be
π(m) =
m+K − 1
(c0/β)
(v/β +K)m
, (10)
where (a)m ≡ a(a+ 1) · · · (a+m− 1). This form of π(m) allows one to easily calculate
moments of the molecule number from the partition function Z as in equilibrium
statistical mechanics, e.g. s = 〈m〉 = c0dZ/dc0, and thence the outgoing flux, c = c0−βs.
Using the fact that Z can be written explicitly in terms of hypergeometric functions,
Stochastic fluctuations in metabolic pathways 9
we find that the noise index grows with β as η2s ≃ v/(Kc0) + β/c0. The distribution
function is given in supporting figure 1 for several values of β.
Generalizing the above to a directed pathway, we allow for β, as well as for vmax
and K, to be i-dependent. The decoupling property (8) does not generally hold in the
non-conserving case [33]. However, in this case the stationary distribution still seems
to be well approximated by a product of the single-metabolite functions πi(m) of the
form (10), with c0/β → ci−1/βi. This is supported again by the excellent agreement
between noise indices obtained by numerical simulations and analytic calculations using
the product measure Ansatz, for linear pathways with dilution of intermediates; see
figure 2(c). In this case, change in the level of the first enzyme does ”propagate” to
the downstream nodes. But this is not a “noise propagation” effect, as the mean fluxes
〈ci〉 at the different nodes are already affected. (To illustrate the effect of leakage, the
simulation used parameters that corresponded to a huge leakage current which is 20%
of the flux. This is substantially larger than typical leakage encountered, say due to
growth-mediated dilution, and we do not expect propagation effects due to leakage to
be significant in practice.)
3. Interacting pathways
The metabolic network in a cell is composed of pathways of different topologies. While
linear pathways are abundant, one can also find circular pathways (such as the TCA
cycle), converging pathways and diverging ones. Many of these can be thought of as a
composition of interacting linear pathways. Another layer of interaction is imposed
on the system due to the allosteric regulation of enzyme activity by intermediate
metabolites or end products. To what extent can our results for a linear pathway
be applied to these more complex networks? Below we address this question for a few of
the frequently encountered cases. To simplify the analysis, we will consider only directed
pathways and suppress the dilution/leakage effect.
3.1. Cyclic pathways
We first address the cyclic pathway, in which the metabolite SL is converted into S1
by the enzyme EL. Borrowing a celebrated result for queueing networks [34] and mass
transfer models [35], we note that the decoupling property (8) described above for the
linear directed pathway also holds exactly even for the cyclic pathways‖. This result is
surprising mainly because the Poissonian nature of the “incoming” flux assumed in the
analysis so far is lost, replaced in this case by a complex expression, e.g., w
mL · πL(mL).
In an isolated cycle the total concentration of the metabolites, stot – and not the
‖ In fact, the decoupling property holds for a general network of directed single-substrate reactions,
even if the network contains cycles.
Stochastic fluctuations in metabolic pathways 10
flux – is predetermined. In this case, the flux c is give by the solution to the equation
stot =
si(c) =
vi − c
. (11)
Note that this equation can always be satisfied by some positive c that is smaller than
all vi’s. In a cycle that is coupled to other branches of the network, flux may be governed
by metabolites going into the cycle or taken from it. In this case, flux balance analysis
will enable determination of the variables zi which specify the probability distribution
3.2. End-product inhibition
Many biosynthesis pathways couple between supply and demand by a negative feedback
[24, 19], where the end-product inhibits the first reaction in the pathway or the transport
of its precursor; see, e.g., the dashed lines in figure 1. In this way, flux is reduced when
the end-product builds up. In branched pathways this may be done by regulating
an enzyme immediately downstream from the branch-point, directing some of the flux
towards another pathway.
To study the effect of end-product inhibition, we consider inhibition of the inflow
into the pathway. Specifically, we model the probability at which substrate molecules
arrive at the pathway by a stochastic process with exponentially-distributed waiting
time, characterized by the rate α(mL) = c0
1 + (mL/KI)
, where c0 is the maximal
influx (determined by availability of the substrate either in the medium or in the
cytoplasm), mL is the number of molecules of the end-product (SL), KI is the
dissociation constant of the interaction between the first enzyme E0 and SL, and h is a
Hill coefficient describing the cooperativity of interaction between E0 and SL. Because
mL is a stochastic variable itself, the incoming flux is described by a nontrivial stochastic
process which is manifestly non-Poissonian.
The steady-state flux is now
c = 〈α(mL)〉 = c0 ·
1 + (mL/KI)
. (12)
This is an implicit equation for the flux c, which also appears in the right-hand side of
the equation through the distribution π(m1, ..., mL).
By drawing an analogy between feedback-regulated pathway and a cyclic pathway,
we conjecture that metabolites in the former should be effectively uncorrelated. The
quality of this approximation is expected to become better in cases where the ration
between the influx rate α(mL) and the outflux rate wmL is typically mL idependent.
Under this assumption, we approximate the distribution function by the product
measure (8), with the form of the single node distributions given by (5). Note that the
conserved flux then depends on the properties of the enzyme processing the last reaction,
and in general should be influenced by the fluctuations in the controlling metabolite. In
this sense, these fluctuations propagate throughout the pathway at the level of the mean
Stochastic fluctuations in metabolic pathways 11
flux. This should be expected from any node characterized by a high control coefficient
Using this approximate form, Eq. (12) can be solved self-consistently to yield c(c0),
as is shown explicitly in Supp. Mat. for h = 1. The solution obtained is found to be
in excellent agreement with numerical simulation (Supporting figure 2a). The quality
of the product measure approximation is further scrutinized by comparing the noise
index of each node upon increasing the enzyme level of the first node 5-fold. Figure 2(c)
shows clearly that the effect of changing enzyme level does not propagate to other nodes.
While being able to accurately predict the flux and mean metabolite level at each node,
the predictions based on the product measure are found to be under-estimating the
noise index by up to 10% (compare bars and symbols). We conclude that in this case
correlations between metabolites do exist, but not dominate. Thus analytic expressions
dervied from the decorrelation assumption can be useful even in this case (see supporting
figure 2b).
3.3. Diverging pathways
Many metabolites serve as substrates for several different pathways. In such cases,
different enzymes can bind to the substrate, each catabolizes a first raction in a different
pathway. Within our scheme, this can be modeled by allowing for a metabolite Si to be
converted to metabolite SI1 with rate w
= vImi/(mi +K
I − 1) or to metabolite SII1
with rate wImi = v
IImi/(mi +K
II − 1). The paramters vI,II and KI,II characterize the
two different enzymes.
Similar to the case of reversible reactions, the steady-state distribution is given
exactly by a product measure only if wImi/w
is a constant, independent of mi (namely
when KI = KII). Otherwise, we expect it to hold in a range of alternative scenarios,
as described for reversible pathways.
Considering a directed pathway with a single branch point, the distribution (5)
describes exactly all nodes upstream of that point. At the branchpoint, one replaces
wm by wm = w
m + w
m , to obtain the distribution function
π(m) =
(KI)m(K
m!((KIvII +KIIvII)/(vI + vII))m
. (13)
From this distribution one can obtain the fluxes going down each one of the two
branching pathway, cI,II =
wI,IIm π(m). Both fluxes depend on the properties of
both enzymes, as can be seen from (13), and thus at the branch-point the two pathways
influence each other [36]. Moreover, fluctuations at the branch point to propagate into
the branching pathways already at the level of the mean flux. This is consistent with the
fact that the branch node is expected to be characterized by a high control coefficient
While different metabolite upstream and including the branch point are
uncorrelated, this is not exactly true for metabolites of the two branches. Nevertheless,
since these pathways are still directed, we further conjecture that metabolites in the two
Stochastic fluctuations in metabolic pathways 12
carbamoyl−phosphate L−ornithine
citrulline
L−arginino−succinate
L−arginine
L−threonine
L−serine
glycine
(a) (b)
Figure 3. Converging pathways. (a) Glycine is synthesized in two independent
pathways. (b) Citrulline is synthesized from products of two pathways. Abbreviations:
2A3O, 2-Amino-3-oxobutanoate.
branching pathways can still be described, independently, by the probability distribution
(5), with c given by the flux in the relevant branch, as calculated from (13). Indeed, the
numerical results of figure 2(e) strongly support this conjecture. We find that changing
the noise properties of a metabolite in the upstream pathway do not propagte to those
of the branching pathways.
3.4. Converging pathways – combined fluxes
We next examine the case where two independent pathways result in synthesis of the
same product, P . For example, the amino acid glycine is the product of two (very short)
pathways, one using threonine and the other serine as precursors (figure 3(a)) [24]. With
only directed reactions, the different metabolites in the combined pathway – namely,
the two pathways producing P and a pathway catabolizing P – remain decoupled.
The simplest way to see this is to note that the process describing the synthesis of P ,
being the sum of two Poisson processes, is still a Poisson process. The pathway which
catabolizes P is therefore statistically identical to an isolated pathway, with an incoming
flux that is the sum of the fluxes of the two upstream pathways. More generally, the
Poissonian nature of this process allows for different pathways to dump or take from
common metabolite pools, without generating complex correlations among them.
3.5. Converging pathways – reaction with two fluctuating substrates
As mentioned above, some reactions in a biosynthesis pathway involve side-reactants,
which are assumed to be abundant (and hence at a constant level). Let us now discuss
briefly a case where this approach fails. Suppose that the two products of two linear
pathways serve as precursors for one reaction. This, for example, is the case in the
arginine biosynthesis pathway, where L-ornithine is combined with carbamoyl-phosphate
by ornithine-carbamoyltransferase to create citrulline (figure 3(b)) [24]. Within a flux
balance model, the net fluxes of both substrates must be equal to achieve steady state,
in which case the macroscopic Michaelis-Menten flux takes the form
c = vmax
[S1][S2]
(KM1 + [S1])(KM2 + [S2])
Stochastic fluctuations in metabolic pathways 13
0 2 4 6 8 10
Metabolite 1
Metabolite 2
Figure 4. Time course of a two-substrate enzymatic reaction, as obtained by a
Gillespie simulation [44]. Here c = 3t−1, k+ = 5t
−1 and k− = 2t
−1 for both substrates,
t being an arbitrary time unit.
Here [S1,2] are the steady-state concentrations of the two substrates, and KM1,2 the
corresponding MM-constants. However, flux balance provides only one constraint to a
system with two degrees of freedom.
In fact, this reaction exhibits no steady state. To see why, consider a typical time
evolution of the two substrate pools (figure 4). Suppose that at a certain time one of
the two substrates, say S1, is of high molecule-number compared with its equilibrium
constant, m1 ≫ K1. In this case, the product synthesis rate is unaffected by the precise
value of m1, and is given approximately by vmaxm2/(m2+K2). Thus, the number m2 of
S2 molecules can be described by the single-substrate reaction analyzed above, while m1
performs a random walk (under the influence of a weak logarithmic potential), which is
bound to return, after some time τ , to values comparable with K1. Then, after a short
transient, one of the two substrates will become unlimiting again, and the system will
be back in the scenario described above, perhaps with the two substrates changing roles
(depending on the ratio between K1 and K2).
Importantly, the probability for the time τ during which one of the substrates is
at saturating concentration scales as τ−3/2 for large τ . During this time the substrate
pool may increase to the order
τ . The fact that τ has no finite mean implies that this
reaction has no steady state. Since accumulation of any substrate is most likely toxic,
the cell must provide some other mechanism to limit these fluctuations. This may be one
interpretation for the fact that within the arginine biosynthesis pathway, L-ornithine is
an enhancer of carbamoyl-phosphate synthesis (dashed line in figure 3(b)).
In contrast, a steady-state always exists if the two metabolites experience linear
degradation, as this process prevents indefinite accumulation. However, in general one
expects enzymatic reactions to dominate over futile degradation. In this case, equal
in-fluxes of the two substrates result in large fluctuations, similar to the ones described
above [31].
Stochastic fluctuations in metabolic pathways 14
4. Discussion
In this work we have characterized stochastic fluctuations of metabolites for dominant
simple motifs of the metabolic network in the steady state. Motivated by the analogy
between the directed biochemical pathway and the mass transfer model or, equivalently,
as the queueing network, we show that the intermediate metabolites in a linear pawthway
– the key motif of the biochemical netrowk – are statistically independent. We then
extend this result to a wide range of pathway structures. Some of the results (e.g.,
the directed linear, diverging and cyclic pathways) have been proven previously in
other contexts. In other cases (e.g., for reversible reaction, diverging pathway or with
leakage/dilution), the product measure is not exact. Nevertheless, based on insights
from the exactly solvable models, we conjecture that it still describes faithfuly the
statistics of the pathway. Using the product measure as an Ansatz, we obtained
quantitative predictions which turned out to be in excellent agreement with the numerics
(figure 2). These results suggest that the product measure may be an effective starting
point for quantitative, non-perturbative analysis of (the stochastic properties) of these
circuit/networks. We hope this study will stimulate further analytical studies of
the large variety of pathway topologies in metabolic networks, as well as in-depth
mathematical analysis of the conjectured results. Moreover, it will be interesting to
explore the applicability of the present approach to other cellular networks, in particular,
stochasticity in protein signaling networks [2], whose basic mathematical structure is also
a set of interlinked Michaelis-Menton reactions.
Our main conclusion, that the steady-state fluctuations in each metabolite depends
only on the properties of the reactions consuming that metabolite and not on fluctuations
in other upstream metabolites, is qualitatively different from conclusions obtained for
gene networks in recent studies, e.g., the “noise addition rule” [14, 15] derived from the
Independent Noise Approximation, and its extension to cases where the singnals and
the processing units interact [17]. The detailed analysis of [17], based on the Linear
Noise Approximation found certain anti-correlation effects which reduced the extent of
noise propagation from those expected by “noise addition” alone [14, 15]. While the
specific biological systems studied in [17] were taken from protein signaling systems,
rather than metabolic networks, a number of systems studied there are identical in
mathematical structure to those considered in this work. It is reassuring to find that
reduction of noise propagation becomes complete (i.e., no noise propagation) according
to the analysis of [17], also, for Poissonian input noise where direct comparisons can
be made to our work (ten Wolde, private communication). The cases in which residue
noise propagation remained in [17], corresponded to certain “bursty” noises which is non-
Poissonian. While bursty noise is not expected for metabolic and signaling reactions, it
is nevertheless important to address the extent to which the main finding of this work
is robust to the nature of stochasticity in the input and the individual reactions. The
exact result on the cyclic pathways and the numerical result on the directed pathway
with feedback inhibition suggest that our main conclusion on statistical independence
Stochastic fluctuations in metabolic pathways 15
of the different nodes extends significantly beyond strict Poisson processes. Indeed,
generalization that preserve this property include classes of transport rules and extended
topologies [37, 38].
The absence of noise propagation for a large part of the metabolic network allows
intermediate metabolites to be shared freely by multiple reactions in multiple pathways,
without the need of installing elaborate control mechanisms. In these systems, dynamic
fluctuations (e.g., stochasticity in enzyme expression which occurs at a much longer
time scale) stay local to the node, and are shielded from triggering system-level failures
(e.g., grid-locks). Conversely, this property allows convenient implementation of controls
on specific node of pathways, e.g., to limit the pool of a specific toxic intermediate,
without the concern of elevating fluctuations in other nodes. We expect this to make
the evolution of metabolic network less constrained, so that the system can modify its
local properties nearly freely in order to adapt to environmental or cellular changes. The
optimized pathways can then be meshed smoothly into the overall metabolic network,
except for junctions between pathways where complex fluctuations not constrained by
flux conservation.
In recent years, metabolomics, i.e., global metabolite profiling, has been suggested
as a tool to decipher the structure of the metabolic network [39, 40]. Our results suggest
that in many cases, steady-state fluctuations do not bare information about the pathway
structure. Rather, correlations between metabolite fluctuations may be, for example, the
result of fluctuation of a common enzyme or coenzyme, or reflect dynamical disorder [27].
Indeed, a bioinformatic study found no straightforward connection between observed
correlation and the underlying reaction network [41]. Instead, the response to external
perturbation [28, 39, 42] may be much more effective in shedding light on the underlying
structure of the network, and may be used to study the morphing of the network under
different conditions. It is important to note that all results described here are applicable
only to systems in the steady state; transient responses such as the establishment of
the steady state and the response to external perturbations will likely exhibit complex
temporal as well as spatial correlations. Nevertheless, it is possible that some aspects of
the response function may be attainable from the steady-state fluctuations through non-
trivial fluctuation-dissipation relations as was shown for other related nonequilibrium
systems [22, 43].
Acknowledgments
We are grateful to Peter Lenz and Pieter Rein ten Wolde for discussions. This work
was supported by NSF through the PFC-sponsored Center for Theoretical Biological
Physics (Grants No. PHY-0216576 and PHY-0225630). TH acknowledges additional
support by NSF Grant No. DMR-0211308.
Stochastic fluctuations in metabolic pathways 16
Supporting Material
Appendix A. Microscopic model
Under the assumption of fast equlibration between the substrate and the enzyme, the
probability of having NSE complexes given m substrate molecules and NE enzymes is
given by equation (3) of the main text. To write the partition function explicitly, we
define u(x) = U(x, 1 −m − NE;−K), where U denotes the Confluent Hypergeometric
function [45]. One can then write the partition sum as Zm,NE = (−K)−NEu(−m).
The turnover rate is then given by wm =
[−mu(1 − m)]/[u(−m)], which can be
approximated by Equation (4).
Appendix B. Influx of metabolites
Ametabolic reaction in vivo can be described as turnover of an incoming flux of substrate
molecules, characterized by a Possion process with rate c, into an outgoing flux. To find
the probability of having m substrate molecules we write down the Master equation,
π(m) = [c(a− 1) + (â− 1)wm]π(m) = c[π(m−1)−π(m)]+[wm+1π(m+1)−wmπ(m)] , (B.1)
where we took the opportunity to define the lowering and raising operators a and â,
which – for any function h(n) – satisfy ah(n) = h(n−1), ah(0) = 0, and âh(n) = h(n+1).
The first term in this equation is the influx, and the second is the biochemical reaction.
The solution of this steady state equation is of the form π(m) ∼ cm/
k=1wk (up to a
normalization constant), as can be verified by plugging it into the equation,
π(m− 1)
π(m+ 1)
wm+1 − wm
wm+1 − wm
= 0.(B.2)
Using the approximate form of wm, as given in (4), the probability π(m) takes the
form,
π(m) =
m+K + (NE − 1)
(1− z)K+NEzm , (B.3)
as given in equation (5) of the main text.
Appendix C. Directed linear pathway
We now derive our key results, equation (8) (The result has been derived previously in
the context of queueing networks [46], and of mass-transport systems [47]). To this end
we write the Master equation for the joint probability function π ≡ π(m1, m2, · · · , mL),
c(a1 − 1) +
(âiai+1 − 1)w(i)mi + (âL − 1)w
π , (C.1)
which generalizes (B.1). As above, ai and âi are lowering and raising operators, acting
on the number of Si molecules. The first term in this equation is the incoming flux c
Stochastic fluctuations in metabolic pathways 17
of the substrate, and the last term is the flux of end product. Let us try to solve the
steady-state equation by plugging a solution of the form π(m1, m2, · · · , mL) =
gi(mi),
yielding
gi(m1 − 1)
g1(m1)
gi(mi + 1)gi+1(mi+1 − 1)
gi(mi)gi+1(mi+1)
−w(i)mi ]+[w
gL(mL + 1)
gL(mL)
−w(L)mL ] = 0 .(C.2)
Motivated by the solution to (B.1), we try to satisfy this equation by choosing
gi(m) = c
k . With this choice we have g(m + 1)/g(m) = c/wm+1 and
g(m− 1)/g(m) = wm/c. It is now straightforward to verify that indeed
w(i+1)mi+1 − w
c− w(L)mL
= 0 . (C.3)
Finally, in our choice of gi(m) we replace w
m by the MM- rate vimi/(mi+Ki), and find
that in fact gi(m) = πi(m), namely
π(m1, m2, . . . , mL) =
πi(mi) , (C.4)
as stated in (8).
Appendix D. End-product inhibition
Equation (13) of the main text is a self-consistent equation for the steady- state flux c
through a pathway regulated via end-product inhibition. Using considerations analogous
to what led to the exact result on the product measure distribution for the cyclic
pathways, we conjecture that even for the present case of end-product inhibition, the
distribution function can still be approximated by the product measure (C.4) with the
form of the single node distributions given by (B.3). The flux c enters the calculation of
the average on the right-hand side through the probability function π(m). Solving this
equation for c yields the steady state current, and consequently determines the mean
occupancy and standard deviation of all intermediates.
To verify the validity of this conjecture, and to demonstrate its application, we
consider the case h = 1. In this case one can carry the sum, and find
1 + (mL/KI)
πL(mL) (D.1)
= c0(1− z)KL2F1(KI , KL;KI + 1; z)
with z = c/vL and 2F1 the hypergeometric function [45]. This equation was solved
numerically, and plotted in supporting figure D2(a) for some values of KI and KL. Note
that predictions based on the product measure (lines) are in excellent agreement with
the results of numerical simulation (circles) for the different sets of parameters tried.
Stochastic fluctuations in metabolic pathways 18
0 50 100 150
Figure D1. The steady-state distribution π(m) of a metabolite, that experiences
enzymatic reaction (with rate wm = vm/(m +K − 1)) and linear degradation (with
rate βm), as given by equation (10) of the main text. Here K = 100 and v = 2c0.
Results obtained from equation (D.1) can be used, for example, to compare the
flux that flows through the noisy pathway with the mean-field flux cMF, obtained when
one ignores fluctuations in mL, i.e.,
cMF =
1 + (sL/KI)h
. (D.2)
The fractional difference δc = (c− cMF)/cMF is plotted in supporting figure D2(b). The
results show that number fluctuations in the end-product always increase the flux in the
pathway since δc > 0 always. Quantitatively, this increase can easily be several percent.
For large c0, a simplifying expression can be derived by using an asymptotic expansion
of the hypergeometric function [45]. For example, when KI < KL,
(1− z)KL2F1(KI , KL;KI + 1; z) ∼
1 +KL −KI
, (D.3)
which yields
c− cMF
. (D.4)
Thus the effect of end-product fluctuations on the current is enhanced by stronger
binding of the inhibitor (smaller KI), as one would expect. We note that obtaining
these predictions from Monte-Carlo simulation is rather difficult, given the fact that
one is interested here in sub-leading quantities.
Stochastic fluctuations in metabolic pathways 19
0.2 0.4 0.6 0.8 1 1.2 1.4
0.2 0.4 0.6 0.8 1 1.2 1.4
=10, K
=100, K
=10, K
=100, K
Figure D2. Pathway with end-product inhibition. The influx rate is taken to be
c0/(1+mL/KI), and thus the steady-state flux is given by equation (12) of the main
text, with h = 1. (a) Assuming that different metabolites in the pathway remain
decoupled even in the presence of feedback regulation, (12) can be approximated
by (D.1). Numerical solutions of equation (D.1) (lines) are compared with Monte-
Carlo simulations (symbols). Values of parameters are chosen randomly such that
100 < Ki < 1000 and c < vi < 10c. For the data presented here, vL = 2.4c. We
find that (D.1) yields excellent prediction for the steady-state flux. (b) Neglecting
fluctuations altogether yields a mean-field approximation for the flux, cMF, given in (
D.2). For the same data of (a), we plot the fractional difference δc = (c− cMF)/c. We
find that steady-state flux is increased by fluctuations, and thus taking fluctuations
into account (even in an approximate manner) better predicts the steady-state flux.
Stochastic fluctuations in metabolic pathways 20
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[43] Forster, D. , Nelson, D., & Stephens, M. (1977) Phys. Rev. A 16, 732749
Stochastic fluctuations in metabolic pathways 21
[44] Gillespie, D.T. (1977). J. Phys. Chem 81, 2340.
[45] M. Abramowitz, Handbook of Mathematical Functions (Dover, New York, 1972).
[46] Taylor, H.M. & Karlin, S. (1998) An Introduction to Stochastic Modeling, 3rd edition (Academic
Press); Ross, S.M. (1983) Stochastic Processes (John Wiley & Sons).
[47] Levine, E. , Mukamel, D. & Schütz, G.M. (2005) J. Stat. Phys. 120, 759.
	Individual Nodes
	A molecular Michaelis-Menton model
	Probability distribution of a single node
	Linear pathways
	Directed pathways
	Reversible reactions
	Dilution of intermediates
	Interacting pathways
	Cyclic pathways
	End-product inhibition
	Diverging pathways
	Converging pathways – combined fluxes
	Converging pathways – reaction with two fluctuating substrates
	Discussion
	Microscopic model
	Influx of metabolites
	Directed linear pathway
	End-product inhibition

  Fluctuations in the abundance of molecules in the living cell may affect its
growth and well being. For regulatory molecules (e.g., signaling proteins or
transcription factors), fluctuations in their expression can affect the levels
of downstream targets in a network. Here, we develop an analytic framework to
investigate the phenomenon of noise correlation in molecular networks.
Specifically, we focus on the metabolic network, which is highly inter-linked,
and noise properties may constrain its structure and function. Motivated by the
analogy between the dynamics of a linear metabolic pathway and that of the
exactly soluable linear queueing network or, alternatively, a mass transfer
system, we derive a plethora of results concerning fluctuations in the
abundance of intermediate metabolites in various common motifs of the metabolic
network. For all but one case examined, we find the steady-state fluctuation in
different nodes of the pathways to be effectively uncorrelated. Consequently,
fluctuations in enzyme levels only affect local properties and do not propagate
elsewhere into metabolic networks, and intermediate metabolites can be freely
shared by different reactions. Our approach may be applicable to study
metabolic networks with more complex topologies, or protein signaling networks
which are governed by similar biochemical reactions. Possible implications for
bioinformatic analysis of metabolimic data are discussed.

Introduction to Stochastic Modeling, 3rd edition (Academic
Press); Ross, S.M. (1983) Stochastic Processes (John Wiley & Sons).
[22] Liggett, T.M. (1985) Interacting Particle Systems (Springer-Verlag, New York).
[23] Levine, E. , Mukamel, D. & Schütz, G.M. (2005) J. Stat. Phys. 120, 759.
[24] Neidhardt, F.C. et al, eds. (1996) Escherichia coli and Salmonella: Cellular and Molecular Biology,
2nd ed. (Am. Soc. Microbiol., Washington, DC).
[25] McAdams, H.H. & Arkin, A. (1997) Proc. Natl. Acad. Sci. USA 94, 814.
[26] Xie, X.S. & Lu, H.P. (1999) J Biol. Chem. 274, 15967.
[27] English, B.P., Min, W., van Oijen, A.M., Lee, K.T., Luo, G., Sun, H., Cherayil, B.J., Kou, S.C.
& Xie., X.S. (2005) Nat. Chem. Bio. 2, 87.
[28] Arkin, A. Shen, P. & Ross, J. (1997) from measurements, Science 29, 1275.
[29] Kou, S.C., Cherayil, B.J., Min, W., English, B.P. & Xie, X.S. (2005) J. Phys. Chem. B 109, 19068.
[30] Qiana, H. & Elson, E.L. (2002) Biophys. Chem. 101-102, 565.
[31] Elf, J., Paulsson, J., Berg, O.G. & Ehrenberg, M. (2003) Biophys. J 84, 154.
[32] Edwards, J.S., Covert, M. & Palsson B.O. (2002) Environ Microbiol. 4, 133.
[33] Evans, M. R. & Hanney, T. (2005) J. Phys. A 38, R195.
[34] Jackson, J.R. (1957) Operations Research 5, 58.
[35] Spitzer, F. (1970) Adv. Math. 5, 246.
[36] LaPorte D.C., Walsh K., & Koshland, Jr D.E (1984) J. Biochem. 259 14068.
[37] Evans M.R., Majumdar S.N., & Zia R.K.P (2004) J. Phys. A: Math. Gen. 37 L275.
[38] Greenblatt, R.L., & Lebowitz, J.L. (2006), J. Phys. A: Math. Gen. 39 15651573.
[39] Arkin, A. & Ross, J. (1995) Measured Time-Series, J. Phys. Chem. 99, 970.
[40] Weckwerth, W. & Fiehn, O. (2002) Curr. Opin. Biotech. 13, 156.
[41] Steuer, R., Kurths, J., Fiehn, O. & Weckwerth, W.(2003) Bioinformatics 19, 1019.
[42] Vance, W., Arkin, A. & Ross, J. (2002) networks, Proc. Natl. Acad. Sci. USA 99, 5816.
[43] Forster, D. , Nelson, D., & Stephens, M. (1977) Phys. Rev. A 16, 732749
Stochastic fluctuations in metabolic pathways 21
[44] Gillespie, D.T. (1977). J. Phys. Chem 81, 2340.
[45] M. Abramowitz, Handbook of Mathematical Functions (Dover, New York, 1972).
[46] Taylor, H.M. & Karlin, S. (1998) An Introduction to Stochastic Modeling, 3rd edition (Academic
Press); Ross, S.M. (1983) Stochastic Processes (John Wiley & Sons).
[47] Levine, E. , Mukamel, D. & Schütz, G.M. (2005) J. Stat. Phys. 120, 759.
	Individual Nodes
	A molecular Michaelis-Menton model
	Probability distribution of a single node
	Linear pathways
	Directed pathways
	Reversible reactions
	Dilution of intermediates
	Interacting pathways
	Cyclic pathways
	End-product inhibition
	Diverging pathways
	Converging pathways – combined fluxes
	Converging pathways – reaction with two fluctuating substrates
	Discussion
	Microscopic model
	Influx of metabolites
	Directed linear pathway
	End-product inhibition
