1 Introduction
Population ContinuousTime Markov Chains (PCTMCs) provide a widely used framework to capture stochastic interactions between groups of identical agents. This subclass of ContinuousTime Markov Chains (CTMCs) is used to describe the stochastic dynamics of systems in various domains. Prominent applications are chemical reaction networks in quantitative biology [50], epidemic spreading [42], performance analysis of technical and information systems [9, 20] as well as the behavior of collective adaptive systems [7].
For the quantitative analysis of CTMCs, many approaches have been developed, where properties of interest are often expressed in terms of temporal logics such as CSL [1, 4, 3], MTL [12], and timedautomata specifications [13, 37]. In addition, there exist efficient software tools [28, 34, 15]
. A central problem in this context is the computation of reachability probabilities.
Popular exact methods for CMTCs rely on numerical approaches that explicitly consider each system state individually. A major problem is that these methods cannot scale in the context of population models with large copy numbers of agents. A popular alternative to tackle this problem is statistical model checking, which is based on stochastic simulation [14]. For PCTMCs arising in the context of chemical reaction networks, trajectories of the process are usually generated using the Stochastic Simulation Algorithm (SSA) [23]
. However, since the number of possible interactions grows with the number of agents, stochastic simulations of PCTMCs are timeconsuming. Moreover, they are subject to inherent statistical uncertainty and give only statistically estimated bounds.
Recent work concentrates on numerical methods for PCTMCs that approximate the statistical moments of the system without the need to consider the probability of each state. For groups of identically behaving agents, it is possible to derive systems of differential equations for the evolution of the statistical population moments
[8, 47, 10, 19, 46, 20]. However, as the system of exact moment equations is not closed, approximation schemes typically rely on certain assumptions about the underlying probability distribution. For example, one might employ a “low dispersion closure” which assumes that higherorder moments are the same as those of a normal distribution
[27]. Such approximations are, by nature, adhoc and do typically not come with any guarantees. Here, we do not need such closure schemes and retain guaranteed results up to the numerical accuracy of the computations.Momentbased methods often scale well in terms of population sizes. However, it is not possible to control the effects of such approximations, which in some cases can lead to large errors [46]. This issue reverberates on the application of these methods to compute reachability probabilities and mean first passage times [25, 10, 11]. Moreover, they can suffer from numerical instabilities, in particular, when the maximum order of the considered moments has to be increased to more appropriately describe the underlying distribution.
Here, we build on recent work on moment bounds [43, 18] to propose a method to compute bounds for Mean First Passage Times (MFPTs) in PCTMCs. For a set of states, the MFPT within a fixed time horizon directly characterizes the probability of reaching that set within time units. Thus, safe upper and lower bounds on MFPTs can constitute a core component for the verification of properties in PCTMCs. Our approach is based on a martingale formulation of the stopped process that we derive from the exact moment equations. From this formalization, we deduce a set of linear moment constraints from which we derive upper and lower moment bounds using semidefinite programming (SDP). Monotone sequences of both upper and lower bounds can be obtained by increasing the order of the relaxation. Crucially, no closure approximations are introduced. Therefore the bounds are strict up to the numerical accuracy of the SDP solver. For our numerical investigations, we concentrate on examples from biology and find encouraging results already for a small number of moments. For instance, in one of our case studies
SSA runs are necessary to achieve a relative width of 0.9% for the MFPT confidence interval. The SDP solver, however, returns a guaranteed interval with a relative width of 0.3%.
In summary, this paper presents the following novel contributions:

the derivation of moment constraints for bounding first passage times and reachability probabilities using a convex programming scheme

the extension of this scheme to stochastic hybrid systems exhibiting multimodal behavior

a scaling strategy for improved robustness during optimization
The paper is structured as follows: Section 2 covers work related to the analysis of first passage times in PCTMCs and recent work on moment bounds. Section 3 introduces the PCTMC framework and its semantics. In Section 4 we derive a martingale from the moment dynamics of a PCTMC. Based on this process, in Section 5 we formulate linear and semidefinite constraints to state a semidefinite program to compute bounds on the MFPT and reachability probabilities. In Section 6, we discuss the practical considerations of the SDP implementation and provide results on a set of case studies. Finally, in Section 7 we provide concluding remarks and directions of future work.
2 Related Work
Considerable effort has been directed at the analysis of first passage time distributions in PCTMCs. Most works can either focus on an explicit statespace analysis [5, 39, 33, 32] or employ approximation techniques for which, in general, no error bounds can be given [45, 25, 11]. For some model classes such as kinetic proofreading, analytic solutions are possible [39, 6, 29].
Barzel and Biham [5] propose a recursive scheme that consists of one equation for each state, expressing the average time the system needs to transition from that state to the target state. Kuntz et al. [32]
propose to employ moment bounds in a linear programming approach to compute exit time distribution using statespace truncation schemes. In Ref.
[33] the authors propose a finite statespace projection scheme to bound first passage time distributionsHayden et al. [25] use moment closure approximations and Chebychev’s inequality to gain an understanding of first passage time dynamics. Schnoerr et al. [45] also employ a moment closure approximation and further approximate threshold functions to derive an approximate first passage time distribution. Bortolussi and Lanciani [11] use a meanfield approximation which is required to reach the target region.
Recently, several groups independently suggested the use of semidefinite optimization for the computation of moment bounds for the limiting distribution [21, 17, 31, 43]. In this approach, the differential equations describing the moment dynamics are set to zero and form linear constraints. Alongside, semidefinite constraints can be placed on the moment matrices. These give a semidefinite program that can be solved efficiently.
This approach has been extended to the transient case [18, 44]. The approach is similar in both works and is a cornerstone of the MFPT analysis presented here. They differ mainly by the fact that Sakurai and Hori apply a polynomial timeweighting [44], while Dowdy and Barton use an exponential one [18]. We adopt the former approach because it can be naturally adapted to the description of densities over time. The resulting forms can also be adapted to statistical estimation problems [2].
Semidefinite programming has been applied to a wide range of problems, including stochastic processes in the context of financial mathematics [36, 30]. For good introductions and overviews of application areas, we refer the reader to Parrilo [41] and, more recently, Lasserre [35].
Particularly relevant for this work is the application of convex optimization to first passage times. Helmes et al. [26] formulated a linear program using the Hausdorff moment conditions to bound moments of the first passage time distribution in Markovian processes. Semidefinite optimization has been successfully applied in financial mathematics by Kashima and Kawai [30], as well as Lasserre et al. [36] to bound prices of exotic options. Here, the approach by Lasserre is adapted to PCTMCs.
3 Preliminaries
A Population ContinuousTime Markov Chain (PCTMC) describes the interactions among a set of species in a wellstirred reactor^{1}^{1}1In the sequel, we will also use other letters than as species names.. Since we assume that all reactant molecules are equally distributed in space, we only keep track of the overall copy number of molecules of each species. Therefore the statespace is . The interactions are expressed as reactions
with a certain gain and loss of molecules, given by the nonnegative integer vectors
and for some reaction , respectively. Such a reaction is denoted as(1) 
The reaction rate constant determines the propensity function of the reaction. If just a constant is given, massaction propensities are assumed, where for we define
(2) 
The system’s behavior is described by a stochastic process . We denote the abundance of a given species in by . The propensity gives the infinitesimal probability of a reaction occurring, given a state . That is, for and a small time step ,
(3) 
Therefore, given a system of reactions, the semantics of is given by a continuoustime Markov chain (CTMC) on with infinitesimal generator matrix with entries
(4) 
Accordingly, given an initial distribution on , the timeevolution of the process’ distribution is given by the Kolmogorov forward equation. For a single state, it is commonly referred to as the chemical master equation (CME)
(5) 
where and .
In this work, we are interested in first passage times of such processes. That is the time, the process first enters a set of target states . Naturally, the analysis of first passage times is equivalent to the analysis of times at which the process exits the complement . More formally, the first passage time for some target set
is defined as the random variable
(6) 
Consider the following simple nonlinear PCTMC as an example.
Model 3.1 (Dimerization)
We first examine a simple dimerization model on an unbounded statespace with reactions
and initial condition . The semantics is given by a CTMC , where . The reaction propensities according to (2) are and . The change vectors , , , and . Consequently, and .
In this example, we are interested in the time at which exceeds some threshold . With the framework presented in the sequel, one can bound the expected value of this time. Further, it is possible to impose a timehorizon , and find bounds on the probability of for some . The employed framework is centered around semidefinite relaxations of the generalized moment problem [35]. These require linear constraints on the moments of measures. In the following section, we derive such constraints.
4 Martingale Formulation
Next, we will discuss equations for the evolution of the statistical moments of the process and a related martingale formulation. This is later used to derive linear constraints on the moments of appropriate measures that can be used to bound MFPTs.
In particular, we consider the dynamics of raw moments for and a fixed probability measure. The order of a moment is given by its exponent sum, i.e. . We can derive the time evolution of the raw moments directly from the CME in (5). Note, that the notion of the expected value can be generalized to any measure on a Borelmeasurable space . There the th raw moment is .
Let be a polynomial function,
. We can easily derive ordinary differential equations (ODEs) to describe the dynamics of
. Specifically,(7) 
When choosing and and , for Model 3.1, for example, we get the following system of ODEs for the change of the first and second statistical moment of species
(8)  
(9) 
where we let for ease of notation. These ODEs cannot be integrated because the system is not closed. The righthand side for moment always contains . To solve an initial value problem, one typically resorts to adhoc approximations of the highest order moments to close the system. Here we do not need such approximations because we do not numerically integrate such equations.
Multiplying (7) with some polynomial function and integrating on yields [18, 44]
(10) 
If we now assume that and remain finite for all , we can interchange summation and integral of a monomial and pull all expectation operators outside, i.e. for a polynomial
Hence, for (10) pulling the expectation operator outside yields a martingale , where
(11) 
with respect to . When choosing with and it takes the form
(12) 
where , , and are finite sequences resulting from the substitution of and and expansion of (11). We will use this martingale in the following section to derive linear constraints for the semidefinite program used to bound MFPTs.
5 Bounds for Mean First Passage Times
We now turn to the analysis of first passage times within some timebound . Given some set the first passage time is given by the random variable
(13) 
For this work, we only look at threshold hitting times, i.e. we set a threshold for species and thus .^{2}^{2}2Note, that this framework allows for a more general class of target sets, which are discussed in Section 5.4. In the sequel, we will use as a stopping time in our martingale formulation and consider instead of . Since (12) defines a martingale, remains a martingale by Doob’s optional sampling theorem [22]. In particular, this implies that .
5.1 Linear Moment Constraints
To simplify our presentation, we fix an initial state , i.e. . Using and the form (12) for yields the following linear constraint on expected values.
(14) 
where . For the ease of exposition, we now turn to first passage times of onedimensional processes w.r.t. an upper threshold . In particular, we will consider moments of a onedimensional process for . The approach proposed in the sequel, however, can be straightforwardly extended to multidimensional processes and more complex target sets .
Consider again Model 3.1 and assume that we are interested in the time at which species exceeds threshold . Since the abundance of does not influence , we can ignore species and treat the process as onedimensional. Figure 1 shows three example trajectories: Two reach an upper threshold , while one reaches the final time horizon .
We notice, that (14) expresses a relationship between the process dynamics up to the hitting time via expected values of the timeintegrals and the final process state at the hitting time via . In particular, we can distinguish between the following two positive measures [35, Chapter 9.2]:

Expected Occupation Measure supported on :
(15) 
Exit Location Probability supported on :
(16)
where is a measurable set, i.e. and are elements of the Borel algebras on and , respectively.
Using Figure 1, one can gain an intuition for these two measures. The expected occupation measure is shaded in blue. As the name implies tells us how much time the process spends in up to restricting to the time instants belonging to . In particular, . The exit location probability , while being a twodimensional distribution, can be viewed as a composition of a density describing the time at which the process reaches (if it does) and a probability mass function on the states of the process if the timehorizon is reached without exceeding . We split the measure into and by conditioning on . Thus, and . To refer to the moments of these measures, we define partial moments
for some polynomial and some indicator function . Then
Therefore the linear moment constraints have the form
(17) 
Next, we consider infinite sequences of partial moments by letting and range over the natural numbers. Let , , and denote the moment sequences of , , and , respectively.
Thus, the variable corresponding to becomes the objective of the optimization problem that we describe in the sequel.
5.2 SemiDefinite Constraints
It is a necessary condition for a positive measure that the moment matrices are positive semidefinite. A matrix is positive semidefinite, denoted by if and only if
As an example, let us consider a onedimensional random variable with moment sequence . For moment order , the entries of the moment matrix are given by the raw moments. In particular, for where and the maximum order in the matrix is . For instance,
(18) 
needs to be positive semidefinite. By Sylvester’s criterion this means and . We can easily see, that this entails
This restriction is natural since the variance is always nonnegative.
It is crucial to restrict the measures , , and to their supports. This can be done, by defining polynomials that are nonnegative on the intended support of the measure. For example, has support . We can now define
as a polynomial that is nonnegative on . Using such polynomials, we can construct localizing matrices, which have to be positive semidefinite [35]. Applying to the moment matrix in (18) we obtain
with the constraint , where the application of a polynomial such as to a moment matrix is formally defined for the multidimensional case in Section 5.4. Similarly, let to restrict to .
5.3 A semidefinite program to bound MFPTs
With the linear constraints on the measures (15) and (16) and the semidefinite constraints discussed in the previous sections, we can now formulate a semidefinite program (SDP). An SDP is an optimization over the cone of positive semidefinite matrices under linear constraints:
(19) 
with constant matrices , and constants , . Such a problem is convex and can be solved efficiently [51].
The derived linear equations and linear matrix inequalities can now be used to formulate an SDP. The full optimization problem has infinitely many constraints because there are infinitely many moments. We relax this problem by constructing the SDP using by choosing a finite order for the moment matrices . With each moment sequence we associate a sequence proxy variables used in the optimization problem. Now we can state the SDP relaxation to the MFPT problem for any order
(20) 
This problem can be solved using offtheshelf SDP solvers such as MOSEK [38], CVXOPT [51], or SCS [40].
5.4 MultiDimensional Generalization
For a general multidimensional moment sequence , the moment matrix is [35]
where row and column indices, and , are ordered according to the canonical basis
(21) 
Equivalently, . For a moment sequence the semidefinite restriction must hold.
Measures can be restricted to semialgebraic sets , where , are polynomials [35]. This is done by placing restrictions on the localizing matrices. For each polynomial with coefficient vector , i.e. , the localizing matrix is
Requiring that this matrix is positive semidefinite restricts the measure to . This way we can, for example, restrict the moment sequence to measures that are positive w.r.t. dimension . Simply letting and requiring for gives us this restriction.
6 Implementation and Evaluation
The main challenge of finding a solution to the SDP problem in (20) is numerical stability. Usually, the moment sequences vary by many orders of magnitude. For an SDP solver to work, the moment matrices need to be rescaled [17] such that moments only vary by few orders of magnitude. In other scenarios such as the bounding of general transient or steadystate moments, the scaling can be particularly difficult, because the magnitude of moments is generally not known a priori. However, for the MFPT problem, we propose the following moment scaling.
6.1 Moment Scaling
Using the fact that is often finite, it is possible to derive trivial bounds, which can be used to scale moments. If, for example, we have a onedimensional process with a.s. and are interested in the hitting time of an upper threshold until time for
Thus, we fix a scaling vector with entries in the same order as the canonical base vector (21). Using this scaling vector, we can define a scaling matrix . Clearly, . Now we can formulate the optimization (20) over a scaled version instead of . The moment matrices of the exit location probabilities are scaled in the same way. Alternatively, one can use approximations such as moment closures or bounds obtained by lowerorder relaxations.
6.2 Case Studies
We implemented and solved the SDP programs described above using MOSEK [38] (version 9.1.2) via the CVXPY interface [16] (version 1.0.24).
As a first case study, we use Model 3.1 with parameters and . In this model, we are interested in the time at which the number of agents of type surpasses a threshold of 25 before some timehorizon , i.e. . First, we set no finite time horizon , i.e. . This is achieved by dropping the moments of measure in the linear constraints (20). The empirical FPT distribution based on 100,000 SSA simulations is given in Figure 2a and the bounds, given different moment orders, are given in Figure 2b. As we can see in Figure 2b, the bounds capture the MFPT precisely for orders 5, 6. The difference between upper and lower bound decreases roughly exponentially with increasing relaxation order . We found that this trend was consistent among the case studies presented here (cf. Figure 4).
Next, we look at first passage times within a finite timehorizon . In Figure 3a we summarize the bounds obtained for the MFPT over . While loworder relaxations (light) give rather loose bounds, the bounds are already fairly tight when using . In many cases, hitting probabilities, that is, the probability of reaching the threshold before time , are of particular interest. This is done by switching the optimization objective in (20) from the mass of the expected occupation measure to the mass of . In terms of moments, the objective changes from to . The need for such a scenario often arises in the context of model checking, where one might be interested in the probability of a population exceeding a critical threshold. By varying the time horizon, we are able to recover bounds on the cumulative density of the first passage time (Fig. 3b).
As a second study, we consider a 2dimensional model by combining two independent dimerizations.
Model 6.1 (Parallel independent dimerizations)
As a FPT we consider the time at which either or surpasses a threshold of 200 or a time horizon of is reached, i.e.
As before, we ignore the products and since they do not influence . Still, the possible statespace reaches a size of . The SSA (using runs) gives the estimate which is captured tightly by the SDP bounds (cf. Table 1). For higher relaxation orders numerical issues prevented the solution of the corresponding SDPs.
6.3 Hybrid Models and MultiModal Behaviour
The analysis of switching times is a particularly interesting case of FPTs that arises in many contexts. Often mode switching in such systems can be described a modulating Markov process whose switching rates may depend on the system state (e.g. the population sizes). In biological applications, mode switching often describes a change of the DNA state [24, 49] and the analysis of switching time distribution is of particular interest [48, 5]. In the context of PCTMCs, the statespace of such models can be given as
This state is modeled by a population variables with binary domains. Therefore, at each time point, the state of these modulator variables is given by a set of Bernoulli random variables. When considering the moments of such a variable , clearly for all .
We apply a split of into the high count part and the binary part to the expectations in (7). Similarly, we split and with a case distinction over the mode variable, we arrive at a similar result as in [24]:
(22) 
Similarly to the general moment case, we can derive a constraint, by multiplying with a timeweighting factor and integrating.
Model  Relaxation Order  

1  2  3  4  5  
Double Dim. (Model 6.1)  lower  0.0010  0.0250  0.0275  0.0280  — 
upper  10.0000  0.0575  0.0323  0.0299  —  
Gene Expression (Model 6.2)  lower  4.0000  6.0028  6.2207  6.3377  6.3772 
upper  10.7179  6.4619  6.4079  6.4004  6.3835 
For simplicity, here we assume . Fixing appropriate sequences , , , and the constraint has the following form.
(23) 
This way we can decompose the moment matrices such that for each mode , we have moment matrices composed of the respective partial moments. To this end, let be the partial moment w.r.t. . The moment constraint over the partial moments has a linear structure:
(24) 
As an instance of a multimodal system, we consider a simple gene expression with selfregulating negative feedback which is a common pattern in many genetic circuits [49].
Model 6.2 (Negative selfregulated gene expression)
This model consists of a gene state that is either on or off, i.e. , . Therefore the system has two modes.
The model parameters are and , a.s.
As a first passage time we consider
The results are summarized in Table 1. The estimated MFPT based on SSA samples is at confidence level. Note that our SDP solution for yields tighter moment bounds than the statistical estimation.
In Fig. 4 we summarize our results about the decrease of the interval widths for increasing relaxation order by plotting them on a logscale. We see an approximately exponential decrease in . The semidefinite programs above were all solved within at most a few seconds.
7 Conclusion
Statebased methods to compute reachability probabilities and first passage times for continuoustime Markov chains are not scalable due to statespace explosion, an issue exacerbated in population models. Momentbased methods offer an alternative for PCTMCs, which scales with the number of different populations in the system, but are approximate methods with little or no control of the error. In this paper, we bridge this gap by proposing a rigorous approach to derive bounds on first passage times and reachability probabilities, leveraging a semidefinite programming formulation based on appropriate moment constraints.
Our proposed scaling mitigates numerical instabilities of the SDP solvers, which are caused by the fact that moments typically span several orders of magnitude. However, the scaling only addresses the moment matrices but not the linear constraints which still contain values with varying orders of magnitudes. We, therefore, plan as future work to introduce an appropriate scaling for the linear constraints or to redefine the moment constraints (e.g. using an exponential time weighting [18]). Based on this investigation, we expect to make this approach applicable to more problems including, for example, the computation of bounds of rare event probabilities. Numerical instabilities due to moment values of largely differing orders of magnitudes are a current limitation of all momentbased methods. We expect that the development of more sophisticated scaling techniques will improve approximate momentbased methods, as well.
7.0.1 Acknowledgements
We would like to thank Andreas Karrenbauer for helpful comments on the usage of SDP solvers and Gerrit Großmann for the valuable comments on this manuscript. This work is supported by the DFG project “MULTIMODE”, and partially supported by the italian PRIN project “SEDUCE” n. 2017TWRCNB.
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