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Approximate Probability Distributions of the Master Equation

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Approximate Probability Distributions of the Master Equation Approximate probability distributions of the master equation Philipp Thomas∗ School of Mathematics and School of Biological Sciences, University of Edinburgh Ramon Grimay School of Biological Sciences, University of Edinburgh Master equations are common descriptions of mesoscopic systems. Analytical solutions to these equations can rarely be obtained. We here derive an analytical approximation of the time-dependent probability distribution of the master equation using orthogonal polynomials. The solution is given in two alternative formulations: a series with continuous and a series with discrete support both of which can be systematically truncated. While both approximations satisfy the system size expansion of the master equation, the continuous distribution approximations become increasingly negative and tend to oscillations with increasing truncation order. In contrast, the discrete approximations rapidly converge to the underlying non-Gaussian distributions. The theory is shown to lead to particularly simple analytical expressions for the probability distributions of molecule numbers in metabolic reactions and gene expression systems. I. INTRODUCTION first few moments [12{14]; alternative methods are based on moment closure [15]. It is however the case that the Master equations are commonly used to describe fluc- knowledge of a limited number of moments does not al- tuations of particulate systems. In most instances, how- low to uniquely determine the underlying distribution ever, the number of reachable states is so large that their functions. Reconstruction of the probability distribution combinatorial complexity prevents one from obtaining therefore requires additional approximations such as the analytical solutions to these equations. Explicit solutions maximum entropy principle [16] or the truncated moment are known only for certain classes of linear birth-death generating function [17] which generally yield different processes [1], under detailed balance conditions [2], or results. While the accuracy of these repeated approxi- for particularly simple examples in stationary conditions mations remains unknown, analytical expressions for the [3]. Considerable effort has been undertaken to approx- probability density can rarely be obtained, or might not imate the solution of the master equation under more even exist [18]. A systematic investigation of the distri- general conditions including time-dependence and condi- butions implied by the higher order terms in the SSE, tions lacking detailed balance [4, 5]. without resorting to moments, is therefore still missing. A common technique addressing this issue was given We here analytically derive, for the first time, a closed- by van Kampen in terms of the system size expansion form series expansion of the probability distribution un- (SSE) [6]. The method assumes the existence of a spe- derlying the master equation. We proceed by outlining cific parameter, termed the system size, for which the the expansion of the master equation in Section I and master equation approaches a deterministic limit as its briefly review the solution of the leading order terms value is taken to infinity. The leading order term of this given by the LNA in Section II. While commonly the expansion describes small fluctuations about this limit SSE is truncated at this point, we show that the higher in terms of a Gaussian probability density, called the order terms can be obtained using an asymptotic expan- linear noise approximation (LNA). This approximation sion of the continuous probability density. The resulting has been widely applied in biochemical kinetics [7], but series is given in terms orthogonal polynomials and can also in the theory of polymer assembly [8], epidemics [9], be truncated systematically to any desired order in the economics [10], and machine learning [11]. The benefit inverse system size. Analytical expressions are given for of the LNA is that it yields generic expressions for the the expansion coefficients. probability density. Its deficiency lies in the fact that, Thereby we establish two alternative formulations of strictly speaking, it is valid only in the limit of infinite this expansion: a continuous and a discrete one both sat- system size. Hence one generally suspects that its pre- isfying the expansion of the master equation. We show arXiv:1411.3551v2 [cond-mat.stat-mech] 2 Oct 2015 dictions become inaccurate when one studies fluctuations that for linear birth-death processes, the continuous ap- that are not too small compared to the mean and there- proximation often fails to converge reasonably fast. In fore implying non-Gaussian statistics. contrast, the discrete approximation introduced in Sec- Higher order terms in the SSE have been employed to tion III accurately converges to the true distribution with calculate non-Gaussian corrections to the LNA for the increasing truncation order. In Section IV, we show that for nonlinear birth-death processes, renormalization is required for achieving rapid convergence of the series. Our analysis is motivated by the use of simple examples ∗ [email protected] throughout. Using a common model of gene expression, y [email protected] we conclude in Section VI that the new method allows to 2 predict the full time-dependence of the molecule number explicitly on Ω. We assume that the propensity possesses distribution. a power series in the inverse volume 1 X γ (Ω[X]; Ω) = Ω Ω−sf (s) ([X]) : (6) II. SYSTEM SIZE EXPANSION r r s=0 As a starting point, we focus on the master equation For mass-action kinetics, for instance, the propensity is 1−`r n given by γr(n; Ω) = Ω kr`r! , where `r is the re- formulation of biochemical kinetics. We therefore con- `r sider a set of R chemical reactions involving a single action order of the rth reaction. Using the Taylor ex- (0) species confined in a well-mixed volume Ω. Note that pansion of the binomial coefficient, we have fr ([X]) = (s) (s) for chemical systems the system size coincides with the `r `r −s kr[X] , fr ([X]) = kr[X] S`r ;`r −s, and fr = 0 for reaction volume. We denote by Sr the net-change in the s ≥ `r, where S denotes the Stirling numbers of the first th molecule numbers in the r reaction and by γr(n; Ω) the kind. Note also that effective propensities being deduced probability per unit time for this reaction to occur. The from mass action kinetics have an expansion similar to probability of finding n molecules in the volume Ω at time Eq. (6). The Michaelis-Menten propensity γr(n; Ω) = t, denoted by P (n; t), then obeys the master equation n (0) [X] Ωkr n+KΩ [19], for instance, has fr ([X]) = kr [X]+K (s) and fr ([X]) = 0 for s > 0. R dP (n; t) X Substituting now Eqs. (3-6) into Eq. (1) and rearrang- = E−Sr − 1 γ (n; Ω) P (n; t); (1) dt r ing the result in powers of Ω−1=2, we find r=1 where E−Sr is the step operator defined as E−Sr g(n) = @ d[X] @ − Ω1=2 Π(, t) g(n − Sr) for any function g(n) of the molecule numbers @t dt @ [6]. Note that throughout the article, deterministic initial R N conditions are assumed. The system size expansion now X @ X = −Ω1=2 S f (0)([X]) + Ω−k=2L Π(, t) proceeds by separating the instantaneous concentration r r @ k into a deterministic part, given by the solution of the rate r=1 k=0 −(N+1)=2 equations [X], and a fluctuating part , + O(Ω ): (7) Equating terms to order Ω1=2 yields the deterministic n = [X] + Ω−1=2, (2) rate equation Ω which is van Kampen's ansatz. The expansion of the R d[X] X master equation can be summarized in three steps: = S f (0)([X]): (8) dt r r (i) Using Eq. (2) one expands the step operator r=1 −Sr The higher order terms in the expansion of the master E γr (n; Ω) P (n; t) = γr (n − Sr; Ω) P (n − Sr; t) −1=2 equation can be written out explicitly −Ω Sr @ 1=2 1=2 = e γr(Ω[X] + Ω , Ω)P (Ω[X] + Ω , t); dk=2e k−2(s−1) k−p−2(s−1) (3) X X Dp;s L = (−@ )pk−p−2(s−1); k p!(k − p − 2(s − 1))! @ s=0 p=1 where @ denotes @ . (ii) Next, the probability for the molecule numbers is (9) cast into a probability density Π(, t) for the fluctuations where d·e denotes the ceiling value and the coefficients using van Kampen's ansatz, are given by 1=2 1=2 Π(, t) = Ω P (Ω[X] + Ω , t); (4) R X Dq = (S )p@q f (s)([X]); (10) which is essentially a change of variables. Note that p;s r [X] r this step implicitly assumes a continuous approximation r=1 Π(, t) of the probability distribution as thought in the which depend explicitly on the solution of the rate equa- original derivation of van Kampen [6]. tion (8). Note that in the following the abbreviation q q (iii) It remains to expand the propensity about the Dp = Dp;0 is used. deterministic limit 1 k k X @ γr(Ω[X]; Ω) III. EXPANSION OF THE CONTINUOUS γ (Ω[X] + Ω1=2, Ω) = Ω−k=2 : (5) r k! @[X]k PROBABILITY DENSITY k=0 Note that γr(Ω[X]; Ω) is just the propensity evaluated at We here study the time-dependent solution of the par- the macroscopic concentration and hence it must depend tial differential equation approximation of the master 3 equation, Eq. (7). We therefore expand the probabil- ity density of Eq. (4), @ − L Ψ = λ Ψ ; (16) @t 0 m m m N X −j=2 −(N+1)=2 Π(, t) = Ω πj(, t) + O(Ω ); (11) which is solved by λm = −mJ and Ψm = m(, t)π0(, t) j=0 with which also allows the expansion of the time-dependent moments to be deduced in closed-form.
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