Abstract
Biochemical reactions are often subject to a complex fluctuating environment, which means that the corresponding reaction rates may themselves be time-varying and stochastic. If the environmental noise is common to a population of downstream processes, then the resulting rate fluctuations will induce statistical correlations between them. In this paper we investigate how such correlations depend on the form of environmental noise by considering a simple birth–death process with dynamical disorder in the birth rate. In particular, we derive expressions for the second-order statistics of two birth–death processes evolving in the same noisy environment. We find that these statistics not only depend on the second-order statistics of the environment, but the full generator of the process describing it, thus providing useful information about the environment. We illustrate our theory by considering applications to stochastic gene transcription and cell sensing.
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Acknowledgements
PCB was supported by the National Science Foundation (DMS-1613048). EL was supported by the National Science Foundation (DMS-RTG 1148230).
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Appendix: Continuous Environmental Noise
Appendix: Continuous Environmental Noise
One of the useful features of the method developed in our paper is that a similar analysis can be applied to the case of the environmental variable \(\xi (t)\) being a continuous stochastic process \(X(t)\in \varOmega \subset {{\mathbb {R}}}\), satisfying the SDE
with W(t) a Wiener process:
As a further simplification, we will assume that X(t) is a stationary process with mean \({\overline{X}}\) and covariance
where \(\tau _c\) is the correlation time. For example, if X(t) evolves according to an Ornstein-Uhlnebeck process for which \(F(X)=-\eta X\) in Eq. (58), then \({\overline{X}}=0\), \(\tau _c=1/\eta \) and \(\varSigma _0=D/\eta \) (Gardiner 2009). The discrete probability distribution \(q_j(t)\) evolving according to the master Eq. (15) is replaced by the probability density \(q(x,t)dx={{\mathbb {P}}}[x<X(t)<x+dx]\), which satisfies the Fokker–Planck equation
where \(L_x\) is the generator of the forward Fokker–Planck equation for the SDE (58), which is given by the differential operator
which is supplemented by appropriate boundary conditions on \(\partial \varOmega \). The analysis now proceeds along similar lines to Sects. 3 and 4, except that the matrix generator \(\mathbf{Q}\) is replaced by the differential operator \({{\mathbb {L}}}_x\).
The first step is to consider a single BD process and introduce the joint probability density
which evolves according to the CK equation (see also Eq. 24)
where \(V_m\) is given by Eq. (21). Introduce the first-order moments of \({{\mathcal {P}}}\),
Multiplying both sides of (62) by \(p_m\) and then integrating with respect to \({{\mathbf {p}}}\) gives the effective differential CK equation
We can now derive an equation for the first-order moments
Multiplying both sides of (63) by m and summing over m yields
where \(\rho (x)\) is the stationary density of the stochastic process X(t). Taking the limit \(t\rightarrow \infty \) then yields the time-independent equation
This can be inverted to give the integral solution
where \(G_k(x,x')\) is the Green’s function defined according to the equation
together with boundary conditions on \(\partial \varOmega \).
Now consider two identical BD processes \(Z_1(t)\) and \(Z_2(t)\) as in Sect. 4. Introduce the joint probability density
The differential CK Eq. (37) becomes
where \(V_m\) is given by Eq. (21). Define the second-order cross-moments of the density \({{\mathcal {P}}}_n\) according to
These satisfy the CK equation
We can now derive equations for the second-order moments
by multiplying both sides of (70) by \(m_1m_2\) and summing over \(m_1\) and \(m_2\). This yields
Taking the limit \(t\rightarrow \infty \), we obtain the time-independent equation
This can be inverted to give the integral solution
Finally, substituting for \(z^{\infty }\) using Eq. (66), we obtain the integral solution
In the special case \(\alpha _1=0\),
where
Equations (75) and (76) are the analogs of Eqs. (44) and (45).
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Bressloff, P.C., Levien, E. Propagation of Extrinsic Fluctuations in Biochemical Birth–Death Processes. Bull Math Biol 81, 800–829 (2019). https://doi.org/10.1007/s11538-018-00538-0
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DOI: https://doi.org/10.1007/s11538-018-00538-0