Abstract
Mesoscopic oscillatory reaction systems, for example in cell biology, can exhibit stochastic oscillations in the form of cyclic random walks even if the corresponding macroscopic system does not oscillate. We study how the intrinsic noise from molecular discreteness influences the frequency spectrum of mesoscopic oscillators using as a model system a cascade of coupled Brusselators away from the Hopf bifurcation. The results show that the spectrum of an oscillator depends on the level of noise. In particular, the peak frequency of the oscillator is reduced by increasing noise and the bandwidth increased. Along a cascade of coupled oscillators, the peak frequency is further reduced with every stage and also the bandwidth is reduced. These effects can help understand the role of noise in chemical oscillators and provide fingerprints for more reliable parameter identification and volume measurement from experimental spectra.
Introduction
In mesoscopic reaction systems the copy numbers of the reacting molecules are low enough for molecular discreteness to become relevant. This induces intrinsic noise and replaces the deterministic kinetics by a stochastic model, potentially leading to quantitative and qualitative differences in the system behavior.
Oscillatory chemical reaction networks are appealing systems to study as they can exhibit a wide range of complex behaviors, such as bifurcations, limit cycles and chaos in different parts of their phase spaces. Consequently, they have been shown to be involved in a number of fundamental phenomena, including pattern formation^{1}, turbulence^{2,3}, chemical waves^{3} and vortex dynamics^{4}. Chemical oscillators also play important roles in biological systems, ranging from circadian clocks^{5,6} to rhythmic gene expression and metabolism^{7}, glycolytic oscillators^{8}, embryonic segmentation clocks^{9} and celldivision control in both space and time^{10,11,12}.
Oscillatory chemical reaction networks have traditionally been studied using deterministic, macroscopic reaction rate equations (RRE) in the form of ordinary differential equations. While this enables the application of a wealth of bifurcation and stability analysis tools from dynamical systems theory, it is only valid in the limit of large numbers of molecules, which typically requires that the reactions progress in a reactor of large (macroscopic) volume^{13,14,15}. If the reactions are confined to smaller (mesoscopic) volumes, such as intracellular organelles, nanoreactors, or porous foams, the number of reactive molecules within any wellmixed subspace is typically too small for RRE to be generally valid. In these regimes, molecular discreteness and hence intrinsic noise, needs to be accounted for. It has been shown in numerous studies that intrinsic noise can lead to nontrivial chemical kinetics that cannot be predicted by RRE^{15,16,17,18,19,20,21}.
The effect of intrinsic noise manifests itself differently in different types of chemical reaction networks: In linear reaction networks RRE predictions of the mean concentrations are always correct, regardless of the reactor volume^{14}. In nonlinear reaction networks, however, noise induces quantitative differences from the concentrations predicted by RRE^{14,15}. A fingerprint of these differences is the relaxation kinetics of the steadystate concentration fluctuations^{22}. In monostable nonlinear systems, the relaxation kinetics of the concentration fluctuations around a nonequilibrium steady state is altered by intrinsic noise through an increase in the lifetimes of species that are reactants in any nonlinear reaction^{22}. In frequency space, this corresponds to an increase in the bandwidth of the concentration fluctuation spectrum with increasing intrinsic noise. This quantitative difference can become large enough to render RRE invalid in certain regimes^{20}. In multistable systems, intrinsic noise can lead to switching behavior between the multiple fixed points of the system^{14,15}. This phenomenon has been used to explain spontaneous switching behavior in biochemical systems^{23,24,25} and the switching of geneexpression patterns in response to environmental changes^{26}. More remarkably, intrinsic noise can induce oscillatory behavior at steady state, even when the corresponding RRE are away from Hopf bifurcation and hence do not exhibit oscillatory behavior^{27,21}. This has, e.g., been used to explain circadian rhythms in biological organisms^{28,5,6}.
Analysis and prediction of noiseinduced effects in multistable and oscillatory systems is impeded by the fact that many analytical methods, such as van Kampen's systemvolume expansion^{15} or the effective mesoscopic rate equations (EMRE)^{29}, are limited to asymptotically (in a Lyapunov sense) monostable systems^{15}. Consequently, understanding mesoscopic oscillatory systems requires other theoretical approaches, such as the stochastic normal form equations^{30}, Gaussian approximation methods^{31}, the MoriZwanzig projection method^{32}, or the HamiltonJacobi method^{33,34}. These methods have been used to understand stochastic fluctuations around a limit cycle in the weaknoise limit and they have led to a wealth of results about the radius of the limit cycle, correlation times and the minimum number of molecules required for sustained timecorrelated oscillations.
All these approaches are based on approximations of the chemical master equation (CME)^{35}, which governs the stochastic kinetics of chemical reaction networks in mesoscopic and macroscopic volumes. While analytically intractable, the CME renders these systems amenable to simulation studies by numerically sampling trajectories from the exact solution of the CME using an exact stochastic simulation algorithm (SSA)^{36}. These algorithms are exact in the sense that they reproduce the correct fluctuation spectrum of the species concentrations to arbitrarily high order.
In this paper, we use an exact SSA and a mesoscopic oscillatory model system away from Hopf bifurcation to characterize the effect of intrinsic noise on the frequency spectrum of the steadystate concentration fluctuations. Extending previous work on monostable systems^{22}, we study the fingerprints of noise in the frequency spectrum of the stochastic chemical oscillations as a function of the magnitude of intrinsic noise. For this, we study the fluctuation–relaxation kinetics of the concentrations of species in an open, mesoscopic chemical model system. We use Gillespie's original exact SSA^{36} to sample trajectories governed by the corresponding CME. The impact of noise is quantified by changes in the power spectral density (PSD) of the concentration fluctuations at a nonequilibrium steady state. Larger intrinsic noise is realized by decreasing the reactor volume Ω at constant mean concentration, hence decreasing the total number of reactive molecules in the system. We observe that the PSD has a Lorentzianlike form, confirming an earlier study^{30}. Further, we show that the frequency at which the PSD is maximum depends on the reactor volume and hence the noise magnitude. We observe that the peak shifts toward lower frequencies with increasing intrinsic noise. This shift is also accompanied by an increase in bandwidth of the fluctuation spectrum, similar to the observation in monostable systems^{22}.
Interactions between several oscillatory systems may lead to nontrivial effects^{37}. We hence also study cascades of downstreamcoupled mesoscopic chemical systems and compare the results to a singlestage system and to the linearnoise regime^{38,15}. We show that due to intrinsic noise, each cascade stage further amplifies the shift in the peak of the PSD toward lower frequencies. For a fixed volume, the bandwidth decreases along the cascade, rendering the peak sharper and more relevant with every additional stage. In biology, downstreamcoupled cascades of reaction networks are found, e.g., as cascades of autocatalytic reactions or in signaling cascades. This includes the up to six downstreamcoupled autocatalytic stages of MAPK signaling cascades^{39,40}, which can exhibit oscillatory behavior under global feedback^{41}. Another example of an oscillatory autocatalytic biochemical system is found in cell cycle control^{11,12}.
We believe that our findings shed light on the effect of intrinsic noise on mesoscopic oscillatory chemical reaction systems. We show that intrinsic noise can not only induce oscillatory behavior in systems away from Hopf bifurcation^{27,21}, but that it also alters the frequency spectrum of the oscillations. The key novelty in our findings is the shift of the peak of the Lorentzianlike PSD with increasing intrinsic noise. Our findings can be used to understand the fundamental effects of intrinsic noise in (cascades of) mesoscopic chemical reaction networks. Furthermore, it has been shown that steadystate fluctuation spectra can aid parameter identification of stochastic chemical reaction networks^{42}. Under this premise, our results can be used to more reliably identify the parameters of mesoscopic chemical reaction networks by using the corrected fluctuation–relaxation kinetics as an additional fingerprint of the effects of intrinsic noise, where the time series of steadystate fluctuations can, e.g., be obtained using fluorescence correlation spectroscopy (FCS)^{43,44,45}.
Results
Model
We consider a chain of dowstreamcoupled Brusselators^{38} in a reactor of volume Ω (see Fig. 1) as a model system. The Brusselator is a model system for autocatalytic reactions. Examples of autocatalytic reactions include the BelousovZhabotinsky reaction, MAPK signaling cascades^{39,40} and activation of the Mphase promoting factor in cellcycle control^{11,12}. The reaction network of our model system is:
where n denotes the stage of the cascade and N ≥ 1 the total number of stages. The k's are the macroscopic reaction rates. Each stage n ≥ 1 involves reactions between two species, X _{n} and Y _{n} . The first stage of the cascade is driven by a buffer (species X_{0}) whose concentration is fixed at all times. Every subsequent stage of the cascade is driven by species X _{n}_{−1} of the previous stage. Therefore, the first stage n = 1 is independent of the subsequent downstream stages (n > 1). In general, any stage n = q is independent of all the subsequent downstream stages (n > q).
The CME corresponding to the reaction system in Eq. 1 is given by
where X_{n} and Y_{n} are the populations (copy numbers) of species X _{n} and Y _{n} , respectively, x_{0} is the concentration of the buffer X_{0}, P(X_{n} ,Y_{n} ) is the probability of having X_{n} molecules of species X _{n} and Y_{n} molecules of species Y _{n} and E_{X} is a step operator defined as for any function f(·). The corresponding RRE describing the deterministic time evolution of the concentrations x_{n} = X_{n} /Ω and y_{n} = Y_{n} /Ω is given in Eq. 6 (see Methods section).
For simplicity, we set k_{2} = k_{4} = 1. We also enforce the concentration x_{0} of the buffer X_{0} to be 1 at all times. Under these conditions, the fixed point of the deterministic RRE (Eq. 6) is given by x_{n} (t → ∞) = k_{1} and y_{n} (t → ∞) = k_{3}/k_{1} for all n ≥ 1. This fixed point is exponentially stable if , undergoes a Hopf bifurcation at and becomes unstable for . In the latter case, x_{n} (t) and y_{n} (t) exhibit oscillations for each n = 1,…,N. In the deterministic RRE description, the condition for a limit cycle has to be strictly fulfilled in order to observe oscillatory behavior, whereas in the stochastic description oscillations can be observed even when the limitcycle condition is not fulfilled^{27,5}.
We numerically sample trajectories from the CME (Eq. 2) using the direct method^{46}, an exact formulation of Gillespie's stochastic simulation algorithm^{36}, for different reactor volumes Ω. The reactor volume determines the magnitude of intrinsic noise, as smaller Ω decrease the total number of reactive molecules in the system and hence increase the intrinsic noise magnitude. We set N = 20, thus simulating a cascade of 20 Brusselator stages, which includes the singlestage case when looking at the concentrations in stage 1, since they are independent of any subsequent stages. This value for N is chosen arbitrarily and the results would not change if a different N were chosen. The concentrations of all species are 0 at time t = 0. We also set k_{1} = 1 and k_{3} = 1. For these parameters, the fixed point of the system is stable and the deterministic system does hence not exhibit limitcycle oscillations, reaching the fixed point (x_{n} , y_{n} ) = (1,1) for all n = 1,…,N. The mesoscopic system, however, shows oscillations due to the concentration fluctuations from intrinsic noise. This can be seen in Fig. 2, where the deterministic RRE trajectories from the first and last cascade stages are shown in panels (b) and (d), respectively and a single trajectory sampled from the CME in panels (a) and (c). Starting from the above initial condition, the stochastic trajectory shows sustained oscillations, whereas the deterministic trajectory reaches the stable fixed point.
We study the normalized steadystate PSD S_{n} (ω) of the concentration fluctuations of species X _{n} (species X of the nth stage) for n = 1,…,N as a function of the angular frequency ω for different reactor volumes Ω. The normalized steadystate PSD S_{n} (ω) is defined as the Fourier transform of the timeautocorrelation function C_{n} (τ):
where the time autocorrelation in function of the time lag τ is given by
The subscript s denotes quantities computed at steady state, and the variance at steady state . The normalization of C_{n} (τ) with factors out the energy of the fluctuations, so that . We hence call S_{n} (ω) the normalized steadystate PSD. It quantifies the fraction of energy of the fluctuations around a specific frequency, namely, S_{n} (ω)dω gives the fraction of energy of the fluctuations between ω and ω + dω. We compute S_{n} (ω) by recording a single long trajectory of x_{n} (t) at steady state. We sample 8 404 992 (2^{23} + 2^{14}) data points of x_{n} (t) starting from t = 2000 with a time resolution of δt = 0.1. We then compute the timeautocorrelation function (Eq. 4) from a minimum lag of τ = 0.1 up to a maximum lag of τ = 2^{14}δt. S_{n} (ω) is obtained by fast Fourier transform (Eq. 3).
We quantify the effect of intrinsic noise by the PSD's peak frequency and bandwidth. The peak frequency is defined as the angular frequency at which S_{n} (ω) is maximum, hence
Since S_{n}(ω) is generated by a stochastic process and hence is noisy, we smooth S_{n} (ω) before computing . Smoothing is done using a movingaverage filter with a window diameter of 10 data points, corresponding to a frequencyspace resolution of δω = 2 · 10^{−2} (data points in frequency space are uniformly spaced with a distance of 2 · 10^{−3}).
The bandwidth of the steadystate PSD is defined as the difference between the two frequencies (ω _{1}, ω _{2}) where the steadystate PSD drops to half of its maximum value, i.e., so that with ω _{2} > ω _{1}. Also is computed on the smoothed PSD.
In the linearnoise (large volume) regime, the quantities S_{n} (ω) and can be calculated analytically^{38,15} (see Methods section). We use the results from the linearnoise regime as a baseline to understand the effect of intrinsic noise on our model system.
Effect of noise
We present the normalized steadystate PSD S_{n} (ω), the peak frequency and the bandwidth for different reactor volumes Ω of our model system. These quantities are numerically computed from exact SSA trajectories^{46,35} as described above. In the linearnoise limit, the quantities are analytically computed as described in the Methods section.
First, we assess S_{n} (ω) as a function of reactor volume Ω for the stage n = 1 alone and then extend our results to cascades of coupled Brusselators. Figure 3(a) shows S_{1}(ω) for three different reactor volumes Ω = 1, 5, 50 and in the linearnoise limit. It can be seen that the steadystate PSD is indeed a function of Ω and that it has a Lorentzianlike form with a peak frequency and an associated bandwidth. This form of the steadystate PSD is expected, since the timeautocorrelation function is . As the volume Ω is reduced, the peak shifts to lower frequencies. The linearnoise regime is the weaknoise limit for large Ω. For Ω = 50 the peak frequency is . This reduces to and for Ω = 5 and Ω = 1, respectively.
The bandwidth for Ω = 50 is . For Ω = 5, it increases to and for Ω = 1 further to . The bandwidth of the steadystate PSD hence increases with decreasing reactor volume Ω.
We now consider how these results change along a cascade of downstreamcoupled Brusselators. Figure 3(b) shows S_{20}(ω) at stage n = 20 for the same three reactor volumes Ω = 1, 5, 50 and in the linearnoise limit. Similar to what is observed in the first stage, the peak also shifts toward lower frequencies as Ω decreases. For Ω = 50 the peak frequency is , which reduces to and for Ω = 5 and Ω = 1, respectively. For a given Ω, the peak frequency is successively reduced by each cascase stage, hence .
The bandwidth at stage n = 20 also increases with decreasing volume, just as it did for the first stage (see Fig. 3(b)). For Ω = 50, 5 and 1, we find , and , respectively. Comparing and , we observe that for fixed reactor volume Ω the bandwidth decreases with every stage along the cascade.
These results are summarized in Table 1 and shown also for intermediate cascade stages in Fig. 4. We observe that for a given cascade stage n the peak frequency decreases with decreasing reactor volume and that for a given reactor volume the peak frequency also decreases with every stage along the cascade. In the linearized approximation (valid for large volumes), the peak frequency is independent of the reactor volume and also of the cascade stage (see Eq. 14). This indicates that the effects observed for smaller volumes are indeed caused by intrinsic noise in the system.
Discussion
We have studied the effect of intrinsic noise due to molecular discreteness on mesoscopic oscillatory chemical reaction networks. We considered the model system of a Brusselator away from the Hopf bifurcation, where the deterministic reaction rate equations (RRE) do not exhibit oscillatory behavior. Oscillations can nevertheless be induced by intrinsic noise, the magnitude of which was tuned by changing the reactor volume. We also studied propagation of noiseinduced effects along a downstreamcoupled cascade of Brusselators. Noiseinduced effects were quantified using the frequency spectrum of concentration fluctuations, given by the normalized power spectral density (PSD), at a nonequilibrium steady state. Specifically, we used the peak frequency and the bandwidth of the PSD as fingerprints of noiseinduced effects. We used an exact stochastic simulation algorithm^{46,35} to study the kinetics of the system as governed by the corresponding chemical master equation.
The results have shown that the frequency at which the steadystate PSD is maximal decreases with decreasing reactor volume. This effect is further amplified by every stage of a cascade of coupled Brusselators, leading to a further decrease along the cascade. This is in contrast to the linearized approximation, which does not predict any effect of intrinsic noise on the normalized steadystate PSD. We also showed that for a given cascade stage the bandwidth of concentration fluctuations decreases with increasing reactor volume, which is in agreement with observations in nonoscillatory monostable^{22} and oscillatory^{34,30} chemical reaction systems. For a fixed volume, the bandwidth also decreases along the cascade, rendering the peak sharper and more pronounced with every stage. One may hence speculate whether intrinsic noise plays any role in “tuning” the output frequency of biochemical oscillators. This tuned output frequency could in turn drive further downstream reaction networks, qualitatively changing their behavior. It is for example known that the behavior of glycolytic oscillators can change from periodic to quasiperiodic to chaotic upon small changes in the driving input frequency (see p. 33, Fig. 2.24, in Ref.^{47}, or Refs.^{48,49}).
We believe that our study sheds light on the role of intrinsic noise in chemical oscillators. Even though we studied a simple model system, the results show how intrinsic noise qualitatively influences the frequency spectrum of the oscillations. We have shown that mesoscopic chemical reaction networks and cascades thereof, exhibit different output spectra depending on the magnitude of intrinsic noise. We expect this effect to be present also in mesoscopic oscillatory reaction systems where the RRE show oscillatory behavior. This is because there is no qualitative difference in the concentration trajectories of mesoscopic systems just before and after a Hopf bifurcation^{27}.
Our findings are relevant for identifying the reactor volumes of (cascades of) mesoscopic reaction networks when the concentration fluctuations are measured experimentally, e.g., using fluorescence correction spectroscopy. In addition, we believe that the effects reported here can be used as fingerprints to more reliably identify parameters of stochastic chemical reaction networks in systems biology models. Finally, the results presented here might contribute towards developing a general understanding of how noise influences the kinetics of different chemical systems, when deterministic RRE predictions are valid and what deviations are to be expected otherwise.
Methods
Flutuations around the linearized RRE
We analytically derive the normalized steadystate PSD S_{n} (ω) and its peak frequency for each cascade stage n in the linearnoise regime. The derivation follows that of Shibata [^{5}] and is valid in the weaknoise limit at large (macroscopic) volumes Ω.
The deterministic RRE for the reaction system given in Eq. 1 are
where x_{n} and y_{n} are the concentrations of species X _{n} and Y _{n} , respectively. As in the numerical study for mesoscopic volumes, we set k_{2} = k_{4} = x_{0} = 1. Let δx_{n} and δy_{n} be small perturbations around the steady state (x_{n} , y_{n} ) = (k_{1}, k_{3}/k_{1}). The linear equations for the perturbations around the steady state are given by
This expression for the perturbations around the fixed point of the RRE is the same as the one that can be obtained from the linearnoise approximation using vanKampen expansion.
Taking the Fourier transform on both sides, we obtain
where and are the Fourier transforms of x_{n} (t) and y_{n} (t), respectively. Simplifying the above equation leads to
where
Since we consider a linearized version of the macroscopic system, the normalized PSD is invariant to the input noise excitation used to quantify the correlations introduced by the system. We here use the simple input noise δx_{0} = ε _{0}(t), which is uncorrelated white noise with 〈ε _{0}(t)〉 = 0 and , where δ(τ) is the Dirac delta distribution. The PSD P_{0}(ω) of the input buffer to the first stage is then given by
The PSD P_{n} (ω) of the output of cascade stage n ≥ 1 is given by
Substituting Eqs. 9 and 11 into Eq. 12 we get
The normalized steadystate PSD S_{n} (ω) is then
Given the expression for F(ω) (Eq. 10), the normalized steadystate PSD S_{n} (ω) has a Lorentzianlike form with peak frequency
It is therefore evident that in the linearnoise regime the peak frequency is independent of Ω and of the stage number n. The bandwidth in the linearnoise limit is computed directly from Eq. 13.
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Acknowledgements
This project was supported with two grants from the Swiss SystemsX.ch initiative (grants “WingX” and “LipidX”), evaluated by the Swiss National Science Foundation.
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RR and IFS conceived the study and RR implemented and ran the simulations. RR and IFS wrote the manuscript text and RR prepared the figures. Both authors reviewed and edited the manuscript.
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Ramaswamy, R., Sbalzarini, I. Intrinsic noise alters the frequency spectrum of mesoscopic oscillatory chemical reaction systems. Sci Rep 1, 154 (2011) doi:10.1038/srep00154
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