1. Introduction
Reaction networks are used to model the complex behaviour of chemical systems [Reference Anderson and Kurtz3, Reference Feinberg10]. Ordinary differential equations can be used to describe the evolution of species concentrations when the molecule counts in the system are relatively large. However, when the molecule counts are low, random fluctuations become significant, and probabilistic techniques need to be employed [Reference McQuarrie22]. In such situations, continuous-time Markov chains (CTMCs) are commonly used to model the stochastic dynamics of the molecule counts. We refer readers to the monograph by Anderson and Kurtz [Reference Anderson and Kurtz3] for an introduction to stochastic reaction networks, as well as to [Reference Kurtz20, Reference McQuarrie22] for early seminal work.
A first objective in the study of stochastic reaction networks is to investigate the stationary distributions of the CTMC, which describe the long-term behaviour of the system [Reference Anderson and Cotter1, Reference Anderson, Craciun and Kurtz2, Reference Cappelletti and Wiuf8, Reference Hoessly, Wiuf and Xia15, Reference Hong, Hernandez, Kim and Kim16, Reference Hong, Kim, Al-Radhawi, Sontag and Kim17]. In the present paper, we explore conditional and asymptotic properties of stationary distributions of multiscaled stochastic reaction networks. Multiscaled stochastic reaction networks are of relatively recent interest [Reference Cappelletti and Wiuf7, Reference Hoessly and Wiuf13, Reference Kang and Kurtz19, Reference Pfaffelhuber and Popovic25] and build further on the seminal work of Kurtz [Reference Kurtz20], which revealed a correspondence over finite time intervals between a sequence of stochastic reaction networks with (parameter) scaled kinetics and a (limiting) deterministic reaction network. In contrast, little work has been done to characterise the limiting stationary distribution in a multiscale setting [Reference Cappelletti and Wiuf8, Reference Hoessly and Wiuf13]. We address this problem here; in particular, we focus on complex balanced stochastic reaction networks with non-interacting species (to be defined later), assuming a sequence of one-parameter scaled stochastic reaction networks. We consider the limiting stationary distribution of this family of reaction networks and compare it with the stationary distribution of the reaction network obtained by elimination of non-interacting species (to be defined later). In general, it is known that the limiting stationary distribution and the stationary distribution obtained by elimination of non-interacting species may not be the same; see [Reference Cappelletti and Wiuf7, Example 5.4] and [Reference Hoessly and Wiuf13, Example 5.8].
To motivate our work, consider a reaction network with stochastic mass-action kinetics,
Here, A, B, and U represent distinct species, and
$x_A$
,
$x_B$
, and
$x_U$
are the molecule counts of the respective species. In addition, the edge labels are the intensity functions of the corresponding reactions. The intensity functions quantify the propensities of the reactions to occur.
The set
$\Gamma = \{x_A + x_B + x_U = T\}$
for
$T\in\mathbb{N}_0$
is an irreducible component of the CTMC. By a well-known result [Reference Anderson, Craciun and Kurtz2, Theorem 4.1], (1) has a unique Poisson-product-form stationary distribution on
$\Gamma$
given by
where M is a positive constant such that
$\pi$
is a probability distribution. By applying a scaling factor N to the reaction rates of
$U\ce{->}A$
and
$U\ce{->}B$
, we obtain a one-parameter sequence of stochastic reaction networks with stationary distribution
for all
$(x_A, x_B, x_U)\in\Gamma$
and
$ N\in\mathbb{N}$
, where
$M_N>0$
is a constant depending on N. This can be illustrated with a line graph, using R and ggplot2 [26, Reference Wickham28], plotting the marginal probabilities for species U on
$\Gamma$
with
$T=5$
; see Figure 1.

Figure 1. Marginal stationary distribution for the molecule count of species U on the irreducible component
$\Gamma = \{x_A + x_B + x_U = T\}$
with
$T=5$
, for different N.
Letting
$N\to \infty$
, one finds that
$\pi_N$
converges pointwise to a probability distribution
$\pi_0$
on
$\Gamma$
given by
\begin{align*}\pi_0 (x_A, x_B) \,:\!=\, \begin{cases}\frac{1}{\pi (\Gamma_0)} \,\pi (x_A,x_B), & \quad (x_A, x_B)\in \Gamma_0 \,:\!=\, \{x_A + x_B = T\},\\[3pt]0, & \quad\text{otherwise}.\end{cases}\end{align*}
This distribution
$\pi_0$
is in fact the unique stationary distribution of the reaction network on
$\Gamma_0$
with stochastic mass-action kinetics:
Moreover, the reaction network (2) can be seen as a reduced stochastic reaction network by elimination of the species U, in the sense of [Reference Hoessly and Wiuf13, Reference Hoessly, Wiuf and Xia14].
This motivating example raises the natural question of how general these observations are. We will address this question in the context of weakly reversible reaction networks with a complex balanced distribution and a set of non-interacting species. The species U in the example is a non-interacting species.
In this context, we will show that the conditional distribution is a stationary distribution of a reduced stochastic reaction network obtained by elimination of the non-interacting species [Reference Hoessly and Wiuf13, Reference Hoessly, Wiuf and Xia14]; cf. (2). Furthermore, this reduced reaction network could be considered the natural limit of a one-parameter sequence of scaled reaction networks, as in the example. Most proofs are given in a separate section at the end of the paper.
The concepts of non-interacting species and reduced reaction networks were introduced in [Reference Feliu and Wiuf11] within the framework of variable elimination for deterministic reaction networks, building on the notion of intermediate species [Reference Feliu and Wiuf12]. These concepts are widely applicable to realistic models of chemical reaction networks [Reference Marcondes de Freitas, Feliu and Wiuf21]. Non-interacting species are typically considered physically short-lived molecules. In the stochastic setting, convergence of CTMCs over finite time intervals has been studied for intermediate species as well as non-interacting species in a multiscale framework by means of the reduced reaction network [Reference Cappelletti and Wiuf7, Reference Hoessly and Wiuf13]. Here, we identify sufficient conditions for this correspondence to hold at
$t=\infty$
.
2. Preliminaries
2.1. Notation
Let
$\mathbb{R}$
,
$\mathbb{R}_{\geq 0}$
, and
$\mathbb{R}_{>0}$
be the sets of real, non-negative, and positive numbers, respectively. Let
$\mathbb{N}$
and
$\mathbb{N}_0$
be the sets of positive and non-negative integers, respectively, and let
$\mathbb{Z}$
denote the set of integers. For
$x=(x_1,\dots, x_n)\in \mathbb{R}^n$
and
$y=(y_1,\dots, y_n)\in \mathbb{R}^n$
, we say that
$x\geq y$
if
$x_i\geq y_i$
for all
$i=1,\dots, n$
and that
$x>y$
if
$x\geq y$
and
$x\neq y$
. In addition, we define
$x\vee y= (\!\max\{x_1,y_1\},\dots, \max\{x_n, y_n\})$
and
$\langle x,y\rangle=\sum_{i=1}^n x_iy_i$
for the inner product on
$\mathbb{R}^n$
. For a finite set
$\mathcal{I}$
,
$\:|\mathcal{I}|$
denotes the cardinality of
$\mathcal{I}$
.
2.2. Reaction networks
A reaction network is a triple
$\mathcal{N}=(\mathcal{S},\mathcal{C},\mathcal{R})$
of three finite sets: a set
$\mathcal{S}=\{S_1,\ldots,S_n\}$
of species, a set
$\mathcal{C}$
of complexes, which are non-negative linear combinations of the species (complexes are identified as elements of
$\mathbb{N}_0^n$
), and a set
$\mathcal{R}$
of reactions, which are elements of
$\mathcal{C}\times\mathcal{C}$
. One could regard
$(\mathcal{C},\mathcal{R})$
as a digraph. We assume that all species have a positive coefficient in some complex and that every complex is the source or target of some reaction. In this case,
$\mathcal{C}$
and
$\mathcal{S}$
can be determined from
$\mathcal{R}$
. In examples, we simply draw the reactions.
For simplicity, for a reaction
$r\in\mathcal{R}$
, we write
$r=(y,y')$
or
$r=y\ce{->}y'$
, where y is the source of r and y
′ the target. In the chemical literature, these are called the reactant
$\mathrm{reac} (r)=y$
and the product
$\mathrm{prod} (r)=y'$
of r, respectively. The reaction vector of r is
$\zeta(r) = y'-y\in\mathbb{Z}^n$
.
We introduce an associative binary operation [Reference Hoessly, Wiuf and Xia14]
for
$(y_1, y_1'), (y_2, y_2') \in \mathbb{N}_0^n \times \mathbb{N}_0^n$
. It can be interpreted as the sum of two reactions; for example,
corresponds to the sum (cf. [Reference Ingalls18, Subsection 3.1.2])
The left-hand side of the sum consists of the molecules required for the two reactions to proceed in succession, and the right-hand side consists of the molecules that are produced and not consumed in the two reactions.
In the following, we assume that a reaction network
$\mathcal{N}=(\mathcal{S},\mathcal{C},\mathcal{R})$
is given.
Definition 1. A state
$x_1\in \mathbb{N}_0^n$
leads to
$x_2\in \mathbb{N}_0^n$
via
$\mathcal{N}$
, denoted by
$x_1 \to_{\mathcal{N}} x_2$
, if there exists reactions
$r_1,\dots, r_k \in \mathcal{R}$
such that
$r_1\oplus \cdots \oplus r_k=(y,y')$
with
$x_1\geq y$
and
$x_2-x_1=y'-y$
.
Definition 2. ([Reference Cappelletti and Wiuf8, Definition 10].) A set
$\Gamma\subseteq \mathbb{N}_0^n$
is an irreducible component of
$\mathcal{N}$
if for all
$x\in \Gamma$
and all
$z\in \mathbb{N}_0^n$
,
$\:x\to_{\mathcal{N}} z$
if and only if
$z\in \Gamma$
.
Definition 3. ([Reference Feinberg10, Definitions 6.5.1 and 6.5.3].) A reaction network is:
-
(i) reversible if
$y\ce{->} y'\in \mathcal{R}$
implies
$y'\ce{->}y\in \mathcal{R}$
; -
(ii) weakly reversible if for any
$y\ce{->}y'\in \mathcal{R}$
, there exists a sequence
$r_1,\dots, r_m\in \mathcal{R}$
satisfying
$\mathrm{reac} (r_1)=y'$
,
$\mathrm{prod}(r_i)=\mathrm{reac}(r_{i+1})$
for all
$i=1,\dots, m-1$
, and
$\mathrm{prod}(r_m)=y$
.
In graph terminology, a reaction network is weakly reversible if the components of the digraph
$(\mathcal{C},\mathcal{R})$
are strongly connected.
A stochastic reaction network (SRN) models the stochastic dynamics of a reaction network. Specifically, the evolution of the molecule counts,
$(X(t)\,\colon t\in \mathbb{R}_{\ge0})$
, is modelled as an
$\mathbb{N}_0^n$
-valued CTMC satisfying the following stochastic equation:
\begin{align}X(t)=X(0)+\sum_{r\in \mathcal{R}}Y_r\Bigg(\int_0^t\lambda_{r}(X(s))\,{\mathrm{d}}s\Bigg)\zeta(r),\end{align}
where
$(Y_{r}\,\colon r\in \mathcal{R})$
is a collection of independent and identically distributed unit-rate Poisson processes and
$\lambda=(\lambda_{r}\,\colon r \in \mathcal{R})$
is a collection of non-negative intensity functions on
$\mathbb{N}_0^n$
, known as the kinetics. Stochastic mass-action kinetics is commonly assumed in the literature; however, it is not a prerequisite for the majority of this paper’s content. For a reaction
$r = y_r \to y_r'$
, the intensity function under mass-action kinetics takes the form
for some positive constant
$\kappa_r$
.
An SRN is denoted by
$(\mathcal{N},\lambda)$
. Throughout, we assume the following compatibility condition.
Condition 1. For any
$r=y\ce{->}y'\in \mathcal{R}$
,
$\lambda_{r}(x)>0$
if and only if
$x\geq y$
.
If Condition 1 is fulfilled, then
$x\to_{\mathcal{N}} z$
implies that there is positive probability of going from x to z and vice versa.
A probability measure
$\pi$
on an irreducible component
$\Gamma\subseteq \mathbb{N}_0^n$
of
$(\mathcal{N},\lambda)$
is (see [Reference Cappelletti and Joshi5, Definition 4.1]):
-
(i) a stationary distribution if for all
$x\in \Gamma$
, (5)
\begin{align}\pi(x)\sum_{r\in \mathcal{R}}\lambda_{r}(x)=\sum_{r\in \mathcal{R}}\pi \big(x - \zeta(r) \big)\lambda_{r} \big(x - \zeta(r) \big);\end{align}
-
(ii) a complex balanced distribution if for all complexes
$\eta\in \mathcal{C}$
and all
$x\in \Gamma$
, (6)
\begin{align}\pi(x)\sum_{y':\,\eta\to y'\in \mathcal{R}}\lambda_{\eta\to y'}(x)=\sum_{y:\,y\to \eta\in \mathcal{R}}\pi(x+y-\eta)\lambda_{y\to \eta}(x+y-\eta);\end{align}
-
(iii) a detailed balanced distribution if
$\mathcal{N}$
is reversible and for all
$y\ce{->}y'\in \mathcal{R}$
,
\begin{align*}\pi(x)\lambda_{y\to y'}(x)=\pi(x+y'-y)\lambda_{y'\to y}(x+y'-y).\end{align*}
We reserve the term ‘stationary distribution’ for a distribution on a single irreducible component. Hence, a stationary distribution
$\pi$
on an irreducible component
$\Gamma$
of
$(\mathcal{N},\lambda)$
satisfies
$\pi(x)>0$
for all
$x\in \Gamma$
. Equation (5) is known in the chemical literature as the master equation (also the Kolmogorov forward equation) for the SRN
$(\mathcal{N},\lambda)$
; see [Reference Anderson and Kurtz3]. A detailed balanced distribution is a complex balanced distribution, and a complex balanced distribution is a stationary distribution.
2.3. Non-interacting species and reduction
Let
$\mathcal{N}=(\mathcal{C},\mathcal{R},\mathcal{S})$
be a reaction network, and let
$\mathcal{U} = \{U_1,\dots, U_m\}$
be a proper subset of
$\mathcal{S}$
. Order the species such that
Let
$x=(z,u)\in\mathbb{N}_0^{n-m}\times \mathbb{N}_0^m=\mathbb{N}_0^n$
with
$z=(z_1, \ldots, z_{n-m})$
and
$u=(u_1,\ldots,u_m)$
. We define the projection
$\rho\,\colon\mathbb{N}_0^n \to \mathbb{N}_0^m$
onto the space of the species in
$\mathcal{U}$
by
Definition 4. ([Reference Feliu and Wiuf11, Definition 6.1].) The set
$\mathcal{U}$
is a set of non-interacting species if for any
$y\in \mathcal{C}$
, either
$\rho (y) = 0$
or
$\rho(y) = U_i$
for some
$U_i \in \mathcal{U}$
. The complement
$\mathcal{S} \setminus \mathcal{U}$
is the set of core species.
Example 1. Consider the reaction network (see [Reference Ingalls18, Subsection 3.1.2])
It models the conversion of two substrates A and B into two other substrates P and Q by means of an enzyme E and several short-lived intermediate molecules, EA and EAB. Therefore, it is natural to treat
$\mathcal{U} = \{EA, EAB\}$
as a set of non-interacting species, and thus
$\mathcal{S} \setminus \mathcal{U} = \{E, A, P, Q\}$
is a set of core species. The choice is not unique; for example,
$\mathcal{U}=\{E,EA,EAB\}$
is also a set of non-interacting species.
For a set
$\mathcal{U}=\{U_1,U_2,\dots, U_m\}$
of non-interacting species, let
$\{\mathcal{C}_0, \mathcal{C}_1, \dots, \mathcal{C}_m\}$
be the partition of
$\mathcal{C}$
by
\begin{align*} \mathcal{C}_i \,:\!=\, \begin{cases}\displaystyle \big\{y\in \mathcal{C}\,:\,\rho (y) = 0 \big\}, & i = 0,\\[3pt] \displaystyle \big\{y \in \mathcal{C}\,:\,\rho (y) = U_i \big\}, &1\leq i\leq m,\end{cases}\end{align*}
and let
$\{\mathcal{R}_{i,j}:\,i,j=0,\dots, m\}$
be the partition of
$\mathcal{R}$
where
For the reaction network in (8) with
$\mathcal{U} = \{EA, EAB\}$
,
\begin{gather*}\mathcal{C}_0 = \{E+A, E + P + Q\},\quad \mathcal{C}_1 = \{EA, EA + B\},\quad \mathcal{C}_2 = \{EAB\},\\\mathcal{R}_{0,0} = \mathcal{R}_{0,2} = \mathcal{R}_{1,1} = \mathcal{R}_{2,2} = \varnothing, \quad \mathcal{R}_{0,1} = \{E + A \ce{->} EA\},\\ \mathcal{R}_{1,0} = \{EA \ce{->} E + A\}, \quad \mathcal{R}_{1,2} = \{EA + B \ce{->} EAB \},\\\mathcal{R}_{2,0} = \{EAB \ce{->} E + P + Q\}, \quad \mathcal{R}_{2,1} = \{EAB \ce{->} EA + B\}.\end{gather*}
We introduce a multi-digraph
$(\mathcal{V},\mathcal{E})$
(see [Reference Sáez, Wiuf and Feliu27]) with
where
$U_0$
is an additional node corresponding to all complexes not containing species in
$\mathcal{U}$
. There is a one-to-one correspondence between the edges
$U_i\ce{->[{\textit{r}}]}U_j$
and the reactions
$r \in \mathcal{R}_{i,j}$
. As an example, the multi-digraph associated with (8) is
A q-step walk
$\theta$
in
$(\mathcal{V},\mathcal{E})$
is a sequence of
$q\in\mathbb{N}$
edges
where
$r_k\in \mathcal{R}_{i_{k-1},i_k}$
. A q-step walk is closed if
$U_{i_0} = U_{i_q}$
. If
$U_{i_0}=U_0$
, then
$U_{i_q}$
is said to be produced; and if
$U_{i_q}=U_0$
, then
$U_{i_0}$
is said to be degraded. Let
$\mathcal{U}_{\mathrm{pro}}$
and
$ \mathcal{U}_{\deg}$
be the sets of produced and degraded non-interacting species, respectively.
For
$q\in \mathbb{N}_0$
and
$i\in\{0,\dots, m\}$
, we let
$\Xi_i(q)$
be the collection of all
$(q + 1)$
-step closed walks that start and end at
$U_i$
without passing though
$U_0$
. Then every element in
$\Xi_i(q)$
is of the form
where
$i_1,\dots, i_q \in \{1,\dots, m\}$
,
$r_j \in \mathcal{R}_{i_j,i_{j+1}}$
for
$0\leq j\leq q$
, and by definition
$i_0 = i_{q+1}= i$
. In particular, if
$\gamma\in\Xi_i(0)$
, then
$\gamma$
consists of one edge
$U_i \ce{->[{\textit{r}}]} U_i$
only; and if
$i=0$
, then
$\gamma$
is a reaction in
$\mathcal{R}$
between core species only. Furthermore, define
\[\Xi_i \,:\!=\, \bigcup_{q=0}^{\infty}\Xi_i(q),\quad i=0,\ldots,m. \]
For the reaction network in (8) with
$\mathcal{U} = \{EA, EAB\}$
, we find that

Definition 5. ([Reference Feliu and Wiuf12, Section 2].) A set of non-interacting species
$\mathcal{U}$
is a set of intermediate species if
$y = \rho(y)$
for every
$y\in \bigcup_{i=1}^m\mathcal{C}_i$
.
In (8),
$\mathcal{U} = \{EA, EAB\}$
is a set of non-interacting species but not a set of intermediate species, because
$EA + B \in \mathcal{C}_1$
, yet
$\rho(EA + B) = EA \neq EA + B$
. On the other hand, if
$\mathcal{U} = \{EAB\}$
, then EAB is an intermediate species, since
$\rho(EAB) = EAB$
.
Definition 6. Let
$\mathcal{U}$
be a set of non-interacting species. If
$\mathcal{U}_{\mathrm{pro}} \subseteq \mathcal{U}_{\deg}$
, then
$\mathcal{U}$
is said to be eliminable.
In (8) we have
$\mathcal{U}_{\mathrm{pro}} = \mathcal{U}_{\deg} $
, and thus
$\mathcal{U}$
is eliminable. Definition 6 differs from Definition 4.1 of [Reference Hoessly, Wiuf and Xia14], with the latter encompassing more general situations. However, these two definitions align in the context of non-interacting species.
Lemma 1. Let
$\mathcal{N}$
be weakly reversible and
$\mathcal{U}$
a set of non-interacting species. Then,
$\mathcal{U}_{\mathrm{pro}} = \mathcal{U}_{\deg}$
and
$\mathcal{U}$
is eliminable.
Proof. To prove
$\mathcal{U}_{\mathrm{pro}} = \mathcal{U}_{\deg}$
, it suffices to prove the one-directional inclusion
$\mathcal{U}_{\mathrm{pro}} \subseteq \mathcal{U}_{\deg}$
. The reverse direction follows by the same reasoning. Let
$U \in \mathcal{U}_{\mathrm{pro}}$
. Then there exists
$q \in \mathbb{N}_{\geq 0}$
and a walk in
$(\mathcal{V}, \mathcal{E})$
,
where the edge labels are skipped for simplification. If
$q = 0$
, then
$U_0 \ce{->} U_i \in \mathcal{E}$
, and thus
$U_i \in \mathcal{U}_{\deg}$
by weak reversibility.
For
$q \geq 1$
, we prove the lemma by induction. First,
$U_{i_q} \ce{->} U_i$
implies the existence of a reaction
$y \ce{->} y' \in \mathcal{R}_{i_q, i}$
. Then, owing to weak reversibility, there exists a sequence of reactions in
$\mathcal{R}$
such that
If there exists
$j\in \{1,\dots, q'\}$
such that
$y_j \in \mathcal{C}_0$
, then we are done. Otherwise, by the induction hypothesis, we have
$U_{i_q} \in \mathcal{U}_{\deg}$
. This implies that there exists a walk of the form
Therefore
$U_i \in \mathcal{U}_{\deg}$
. The proof is complete.
We introduce a map
$\mathfrak{R}$
defined on the collection of all walks in
$(\mathcal{V},\mathcal{E})$
:

Then we have
In particular,
$\rho (y) = \rho (y') = 0$
for
$\gamma \in \Xi_0$
with
$ \mathfrak{R}(\gamma)=(y,y')$
.
Definition 7. Let
$\mathcal{U}$
be an eliminable set of species. The reduced reaction network
$\mathcal{N}_{\mathcal{U}}$
obtained by elimination of
$\mathcal{U}$
from
$\mathcal{N}$
is a triple
$(\mathcal{S}_{\mathcal{U}},\mathcal{C}_{\mathcal{U}},\mathcal{R}_{\mathcal{U}})$
where
Here,
$\textrm{supp} (y) = \{S_i\,\colon y_i>0\}$
is the support of
$y = \sum_{j=1}^n y_j S_j$
in
$\mathbb{N}_0^{\mathcal{S}}$
, and
$n_{\mathcal{U}} = |\mathcal{S}_{\mathcal{U}}|$
.
By Definition 7, we find that for (8),
\[\mathfrak{R}(\Xi_0) = \left\{\begin{array}{c} E + A \ce{->} E + A, \quad E + A + B \ce{->} E + A + B, \\[3pt] E + A + B \ce{->} E + P + Q \end{array} \right\}\!,\]
and thus the reduced reaction network is the triple
$(\mathcal{S}_{\mathcal{U}},\mathcal{C}_{\mathcal{U}}, \mathcal{R}_{\mathcal{U}})$
with
The set
$\mathcal{R}_{\mathcal{U}}$
may contain infinitely many reactions, and thus the reduced reaction network may not be a reaction network.
Example 2. ([Reference Hoessly, Wiuf and Xia14, Example 2].) Consider the following reaction network with
$\mathcal{U} = \{G'\}$
:
Then
$\mathcal{C}_0 = \{P, \varnothing\}$
,
$\mathcal{C}_1 = \{G, G', G' + P\}$
,
$\mathcal{R}_{0,0} = \{P \ce{->} \varnothing\}$
,
$\mathcal{R}_{0,1} = \{G \ce{->} G'\}$
,
$\mathcal{R}_{1,0} = \{G' \ce{->} G\}$
,
$\mathcal{R}_{1,1} = \{G' \ce{->} G' + P\}$
, and the associated multi-digraph is
Therefore,
$\Xi_0(0) = \{U_0 \ce{->[{ \rm{P}}]} U_0\}$
and
for all
$k \geq 1$
. As a result,
is an infinite set.
The example is further discussed in the conclusion section. Furthermore, as observed in [Reference Hoessly, Wiuf and Xia14], if
$\mathcal{N}$
is weakly reversible and
$\mathcal{U}$
is a set of non-interacting species,
$\mathcal{N}_{\mathcal{U}}$
is not necessarily weakly reversible.
Example 3. ([Reference Hoessly, Wiuf and Xia14, Example 8].) Consider the following weakly reversible reaction network with the set of non-interacting species
$\mathcal{U}=\{U_1,U_2\}$
:
The reduced reaction network with
$\mathcal{R}_\mathcal{U}=\{S_1\ce{<=>} S_2, S_2+S_3\ce{->} S_4\ce{->} S_3+S_1\}$
is not weakly reversible.
Condition 2 below is equivalent to
$\mathcal{R}_{\mathcal{U}}$
being finite [Reference Hoessly, Wiuf and Xia14], and
$\mathcal{N}_\mathcal{U}$
is thus a reaction network if Condition 2 is fulfilled. The proof is given in Subsection 6.1.
Condition 2. If
$\gamma\in \Xi_i$
for some
$i=1,\ldots,m$
, then
$\mathfrak{R}(\gamma) = (y,y)$
for some
$y\in \mathbb{N}_0^n$
.
Lemma 2. The set
$\mathcal{R}_{\mathcal{U}}$
is finite if and only if Condition 2 holds.
Let
$(\mathcal{N},\lambda)$
be an SRN. We introduce a kinetics
$\lambda_{\mathcal{U}}$
on the reduced reaction network
$\mathcal{N}_{\mathcal{U}}$
(see [Reference Hoessly and Wiuf13]). For
$r \in \mathcal{R}_{i,j}\subseteq \mathcal{R}$
where
$i,j\in \{0,\dots, m\}$
, define the function
$\beta_r \,\colon \mathbb{N}_0^{n}\to [0,1]$
by
\begin{align}\beta_r (x) \,:\!=\, \lambda_r(x) \bigg/ \sum_{k = 0}^m\sum_{r'\in \mathcal{R}_{i,k}}\lambda_{r'}(x),\end{align}
with the convention
$0/0 \,:\!=\, 0$
. Then
\begin{align}\sum_{k=0}^m \sum_{r\in \mathcal{R}_{i,k}} \beta_r(x) = \mathbf{1}_{\mathcal{D}_i}(x),\quad i=0,\ldots,m,\end{align}
where
\[\mathcal{D}_i \,:\!=\, \bigcup_{k=0}^m \bigcup_{r\in \mathcal{R}_{i,k}} \big\{x\in \mathbb{N}_0^n\,\colon x\geq \mathrm{reac} (r)\big\}.\]
For
$\gamma\in \Xi_0$
, define
$\lambda^*_{\mathcal{U},\gamma}\,\colon\mathbb{N}_0^{n}\to \mathbb{R}_{\geq 0}$
by
\begin{align}\lambda^*_{\mathcal{U},\gamma}(x) \,:\!=\, \lambda_{r_0}(x) \prod_{j=1}^{q}\beta_{r_j} \bigg(x+\sum_{i=1}^{j}\zeta_{i - 1}\bigg),\end{align}
where
$\zeta_{i- 1} \,:\!=\, \zeta (r_{i- 1})$
is the reaction vector of
$r_{i - 1}$
and
$i_0 = i_{q+1} \,:\!=\, 0$
. Define the kinetics
$\lambda_{\mathcal{U}} = (\lambda_{\mathcal{U},r} \,\colon r\in \mathcal{R}_{\mathcal{U}})$
by
Then
$\lambda_{\mathcal{U},r}(x)> 0$
if and only if
$x\geq \mathrm{reac}(r)$
; see [Reference Hoessly and Wiuf13, Corollary 3.11]. As a consequence,
$(\mathcal{N}_{\mathcal{U}},\lambda_{\mathcal{U}})$
satisfies Condition 1. Furthermore, using Definition 7, we have
(see [Reference Hoessly and Wiuf13, Lemma 3.10]). This implies that the evolution of the molecule counts of the core species,
$(Z(t)\,\colon t\in \mathbb{R}_{\ge0})$
, satisfies the following stochastic equation, similar to (4):
where
$(Y_r$
,
$r\in\mathcal{R}_\mathcal{U})$
, is a (possibly infinite) collection of unit-rate independent and identically distributed Poisson processes.
Definition 8. The pair
$(\mathcal{N}_{\mathcal{U}},\lambda_{\mathcal{U}})$
defined in Definition 7 and (14) is the reduced SRN of
$(\mathcal{N},\lambda)$
(obtained by elimination the non-interacting species in
$\mathcal{U}$
). In particular, if
$\mathcal{R}_\mathcal{U}$
is finite, then
$(\mathcal{N}_{\mathcal{U}},\lambda_{\mathcal{U}})$
is an SRN.
For example, consider the reduced reaction network in (8) and (10). We have
where
Therefore, by (11), (13), and (14), the kinetics for the reduced reaction network is
where
\begin{align*} \beta_1 (x) \,:\!& =\beta_{EA + B \to EAB} (x + EA - E - A) \\ & =\frac{\lambda_{EA + B \to EAB}}{\lambda_{EA + B \to EAB} + \lambda_{EA \to E + A}}\,(x + EA - E - A),\\ \beta_2 (x) \,:\!& =\beta_{EAB \to E + P + Q} (x - E - A - B + EAB)\\ &= \frac{\lambda_{EAB \to E + P + Q}}{\lambda_{EAB \to EA + B} + \lambda_{EAB \to E + P + Q}}\,(x - E - A - B + EAB).\end{align*}
3. The conditional distribution as a stationary distribution
Condition 3. If
$\mathfrak{R}(\gamma)=(y+U_i,y'+U_i)$
with
$\gamma\in \Xi_i$
for some
$i=1,\ldots,m$
, then (i)
$y\not< y'$
and (ii)
$y\not> y'$
.
Condition 3 is a weaker version of Condition 2. If the condition is violated and
$y< y'$
, then the system is open with an influx of molecules or able to self-replicate [Reference Deshpande and Gopalkrishnan9]. Likewise, if
$y'<y$
, then the system is self-drainable [Reference Deshpande and Gopalkrishnan9]. Example 2 does not satisfy Condition 3.
Lemma 3. Let
$\mathcal{N}$
be weakly reversible, and let
$\mathcal{U}$
be a set of eliminable non-interacting species. Then Condition 2 implies Condition 3.
Below we present our main result, Theorem 1, which shows that under mild conditions, the limit stationary distribution of a complex balanced reaction network with multiscale kinetics equals the stationary distribution of its reduction by eliminating non-interacting species. Previous work [Reference Cappelletti and Wiuf7, Reference Hoessly and Wiuf13] explored related questions under stronger assumptions. The key challenge is the potentially infinite state space, preventing direct application of classic CTMC limit theorems. However, leveraging the special structure of SRNs, we prove the result using a graphical method, extending techniques from [Reference Hoessly, Wiuf and Xia15].
Theorem 1. Let
$(\mathcal{N},\lambda)$
be a weakly reversible SRN with a set
$\mathcal{U}$
of non-interacting species satisfying Condition 3. Let
$(\mathcal{N}_{\mathcal{U}},\lambda_{\mathcal{U}})$
be the reduced SRN of
$(\mathcal{N},\lambda)$
by eliminating the species in
$\mathcal{U}$
. Let
$\Gamma$
be an irreducible component of
$\mathcal{N}$
such that
Suppose that
$\pi$
is a complex balanced distribution for
$(\mathcal{N},\lambda)$
on
$\Gamma$
. Then the following hold:
-
(i)
$\Gamma_0$
is either an irreducible component of
$\mathcal{N}_{\mathcal{U}}$
or the union of at most countably many disjoint irreducible components of
$\mathcal{N}_{\mathcal{U}}$
; -
(ii) the conditional distribution
$\pi_0$
of
$\pi$
restricted to
$\Gamma_0$
is a stationary distribution for
$(\mathcal{N}_{\mathcal{U}},\lambda_{\mathcal{U}})$
on
$\Gamma_0$
if
$\Gamma_0$
consists of a single irreducible component; otherwise, it is a linear combination of stationary distributions.
Example 4. Consider the reaction network
$A\ce{<=>}U,\; A+U\ce{<=>}U.$
Here,
$\Gamma = \mathbb{N}_0^2 \setminus \{0\}$
is the only irreducible component. Let
$\mathcal{U} = \{U\}$
be a set of non-interacting species. Then
For a finite state space
$\Gamma$
, the theorem follows from [Reference Hoessly and Wiuf13]. The proof in the general situation is lengthy and technical, and is deferred to Subsection 6.4.
Upon combining Lemmas 2 and 3 and Theorem 1, the next corollary follows.
Corollary 1. If
$\mathcal{N}_{\mathcal{U}}$
has finitely many reactions, then both conclusions in Theorem 1 are valid.
3.1. A remark on Theorem 1
The proof of Theorem 1(ii) presented in Subsection 6.4 is based on the recurrence of two irreducible discrete-time Markov chains (DTMCs). Recurrence is deduced from the finiteness of the state spaces (of the DTMCs), as a result of Condition 3. The state space being finite is not a necessary condition for recurrence, and nor is recurrence necessary for the validity of Theorem 1. However, we have not been able to identify any other simple conditions that imply recurrence of these DTMCs. Condition 3 is not necessary, as demonstrated by the following (artificial) example, although Theorem 1(ii) remains valid in this particular case.
Example 5. Consider the reaction network in Example (4),
where for all
$x=(x_A,x_U)\in \mathbb{N}_0^2$
,
Note the unusual squared factorial rates that are instrumental for showing that Condition 3 is not necessary. The compatibility condition (Condition 1) is fulfilled, even though all rates depend on
$x_A$
. Then
$\Gamma=\mathbb{N}_0^2\setminus \{(0,0)\}$
is an irreducible component of (16). One can show that
is a detailed balanced distribution of (16) on
$\Gamma$
. Therefore, Proposition 2 ensures that the reduced SRN
where
$\lambda_{n,m}$
is defined in Subsection 2.3, has detailed balanced stationary distribution
$\pi_0(x_A) = C \pi (x_A,0)$
,
$x_A \in \mathbb{N}$
, for some constant
$C > 0$
. In other words, Condition 3 is not necessary in Theorem 1.
Following the strategy in Step 1 of the proof of Theorem 1 (see Subsection 6.4), we construct a DTMC on
$\mathcal{X} = \{x\,\colon x \geq 0\}\cup \{\partial\}$
(where
$\partial$
is an ceremony state) with transition probabilities
\begin{align*}P_{\partial,\partial} = 0,\quad P_{\partial, 0} = 1,\quad P_{x,\partial} = \frac{\lambda_2(x,1)}{(\lambda_2+\lambda_3+\lambda_4)(x,1)},\\[3pt] P_{x,x + 1} = \frac{\lambda_4(x,1)}{(\lambda_2+\lambda_3+\lambda_4)(x,1)},\quad P_{x,x - 1} = \frac{\lambda_3(x,1)}{(\lambda_2+\lambda_3+\lambda_4)(x,1)},\end{align*}
and
$P_{x,x'} = 0$
otherwise. Then Theorem 1(ii) is valid if
$\mathbb{P}_{x} (\tau < \infty) = 1$
, where
$\tau \,:\!=\, \min\{ n\geq 1\,\colon X_n = \partial\}$
. However, this is unfortunately not true. In fact, for any
$n\geq 2$
,
\begin{align*}\mathbb{P}_{x}(\tau > n) &\geq\mathbb{P}_{x}\big(X_k=x + k\ \forall k=1,\dots,n\big)\\& =\prod_{k=1}^{n} P_{x + k -1,x + k } = \prod_{k = 1}^{n} \frac{\lambda_4(x + k-1,1)}{(\lambda_2+\lambda_3+\lambda_4)(x + k - 1,1)}\\&=\prod_{k=1}^{n}\frac{\prod_{j=1}^{x + k+1}j^2}{ \prod_{j=1}^{x + k }j^2 + 2\prod_{j=1}^{x + k }j^2 + \prod_{j=1}^{x + k+1} j^2 }\\ &=\prod_{k=1}^{n}\big(3 (x + k+1)^{-2}+1\big)^{-1}\ge \exp\!\left(-3\sum_{k=1}^\infty x^{-2} \right)>0,\end{align*}
using that
$-\log (3 x^{-2} + 1) \geq - 3x^{-2}.$
Therefore,
$\mathbb{P}_{x}(\tau = \infty)>0$
. This implies that replicating the arguments in the proof of Theorem 1 does not suffice to show that
$\pi_0$
is a stationary distribution for the reduced SRN. This is due to the failure of Condition 3 in (16).
3.2. Two special cases
Assume that
$\mathcal{U}$
is a set of intermediate species in a weakly reversible SRN. Then it can be demonstrated that Conditions 2 and 3 are both satisfied. Therefore Theorem 1 applies. For a set of intermediate species
$\mathcal{U}$
, we have that for
$i,j \in\{1,\dots, m\}$
:
-
(i)
$\mathcal{C}_i = \{U_i\}$
; -
(ii)
$\mathcal{R}_{i,i} = \varnothing$
and
$\mathcal{R}_{i,j} \subseteq \{U_i\ce{->}U_j\}$
for
$i\neq j$
.
Proposition 1. Suppose that
$\pi$
is a complex balanced distribution for
$(\mathcal{N},\lambda)$
on an irreducible component
$\Gamma$
and that
$\mathcal{U}$
consists of intermediate species. Then
$\Gamma_0 \,:\!=\, \{x\in \Gamma\,\colon \rho (x) = 0\}$
is an irreducible component of
$(\mathcal{N}_{\mathcal{U}},\lambda_{\mathcal{U}})$
, and
$\pi_{0}(\!\cdot\!) \,:\!=\, \pi(\!\cdot\!)/\pi(\Gamma_{0})$
is a complex balanced distribution for
$(\mathcal{N}_{\mathcal{U}},\lambda_{\mathcal{U}})$
on
$\Gamma_0$
.
Proposition 2. Suppose that
$\pi$
is a detailed balanced distribution for
$(\mathcal{N},\lambda)$
on an irreducible component
$\Gamma$
. Then
$\pi_{0}(\!\cdot\!) \,:\!=\, \pi(\!\cdot\!)/\pi(\Gamma_{0})$
is either a detailed balanced distribution for
$(\mathcal{N}_{\mathcal{U}},\lambda_{\mathcal{U}})$
on
$\Gamma_0$
, as in (15), or a linear combination of detailed balanced distributions on disjoint irreducible components.
As noted in Subsection 2.2, a detailed balanced distribution is a complex balanced distribution, and a complex balanced distribution is a stationary distribution. Therefore, the conclusions of Propositions 1 and 2 are much stronger than those in the general case presented in Theorem 1. This is because, in the case of intermediate species or detailed balance, the reduced SRN takes a simpler form while preserving essential properties of the original SRN. The proofs of Propositions 1 and 2 are given in Subsections 6.2 and 6.3, respectively.
3.3. Positive recurrence of the reduced SRN
Theorem 2. Let
$(\mathcal{N},\lambda)$
be an SRN with stochastic mass-action kinetics,
$\mathcal{U}$
a set of non-interacting species,
$(\mathcal{N}_{\mathcal{U}},\lambda_{\mathcal{U}})$
the reduced SRN obtained by elimination of the species in
$\mathcal{U}$
, and
$\Gamma$
an irreducible component of
$(\mathcal{N},\lambda)$
. Let
$\pi$
be a complex balanced stationary distribution of
$(\mathcal{N},\lambda)$
on
$\Gamma$
, and assume that
$\pi_{0}(\!\cdot\!) \,:\!=\, \pi(\!\cdot\!)/\pi(\Gamma_{0})$
is a stationary distribution (linear combination of stationary distributions on disjoint irreducible components) of
$(\mathcal{N}_{\mathcal{U}},\lambda_{\mathcal{U}})$
supported on
$\Gamma_0\not=\varnothing$
, as in (15). Then
$(\mathcal{N}_{\mathcal{U}},\lambda_{\mathcal{U}})$
is positive recurrent on each irreducible component of
$\Gamma_0$
.
Proof. By assumption,
$\pi_{0}$
is a stationary distribution on an irreducible component
$\tilde{\Gamma}\subseteq\Gamma_0$
. To show positive recurrence on
$\tilde{\Gamma}$
, we apply the sufficient condition for positive recurrence from [Reference Asmussen4, Corollary 4.4]. The condition holds if the following is true for
$\tilde{\Gamma}$
:
Using that complex balanced distributions for mass-action SRNs take the form
$M\frac{c^x}{x!}$
(see [Reference Anderson, Craciun and Kurtz2]), we can replace
$\pi_0(z)=\pi(z)/\pi(\Gamma_{0})$
by
$M_0\frac{c^x}{x!}$
with
$M_0=M/\pi(\Gamma_0)$
and
$c^x \,:\!=\, \prod_{i=1}^n c_i^{x_i}$
, for any
$c \in \mathbb{R}_+^n$
and
$x\in \mathbb{R}^n$
. Furthermore, by Lemma 3.10 in [Reference Hoessly and Wiuf13],
Since
$\tilde{\Gamma}\subseteq\Gamma_0\subseteq \mathbb{N}^{n_\mathcal{U}}_0$
and
$\mathcal{R}$
is finite, it is enough to show that
$$M_0\sum_{z\in\mathbb{Z}^{n_\mathcal{U}}_{\geq 0}}\lambda_{r}(z,0)\frac{c^{(z,0)}}{z!}<\infty. $$
This final inequality follows as
$\lambda_{r}(z,0)$
is a polynomial of fixed degree (in z) for stochastic mass-action kinetics.
As a corollary of Theorem 1, Propositions 1 and 2, and Theorem 2, we get the following result for reductions of complex balanced SRNs with stochastic mass-action kinetics.
Corollary 2. Let
$(\mathcal{N},\lambda)$
be an SRN with stochastic mass-action kinetics,
$\mathcal{U}$
a set of non-interacting species,
$(\mathcal{N}_{\mathcal{U}},\lambda_{\mathcal{U}})$
the reduced SRN obtained by elimination of the species in
$\mathcal{U}$
, and
$\Gamma$
an irreducible component of
$(\mathcal{N},\lambda)$
. Let
$\pi$
be a complex balanced stationary distribution of
$(\mathcal{N},\lambda)$
on
$\Gamma$
. Assume that at least one of the following conditions holds:
-
• Condition 3;
-
•
$\mathcal{U}$
consists of intermediate species; -
•
$\mathcal{N}$
is reversible.
Then
$(\mathcal{N}_{\mathcal{U}},\lambda_{\mathcal{U}})$
is positive recurrent on each irreducible component of
$\Gamma_0$
.
4. Convergence of complex balanced distributions
Consider an SRN
$(\mathcal{N},\lambda)$
with
$\mathcal{N}=(\mathcal{S},\mathcal{C},\mathcal{R})$
, and suppose that
$\mathcal{U}$
is a proper subset of
$\mathcal{S}$
with
$|\mathcal{U}|=m$
. In particular,
$\mathcal{U}$
does not need to be a set of non-interacting species.
Let
$\alpha = (\alpha_1,\dots, \alpha_m)\in\mathbb{R}^m_{>0}$
. For
$N\in\mathbb{N}$
, let
$\lambda_{N}^{\alpha}=(\lambda_{N,y\to y'}^{\alpha}\,\colon y\ce{->}y'\in \mathcal{R})$
be a kinetics on
$\mathcal{N}$
defined by
where
$\rho$
is the projection from
$\mathbb{N}_0^n$
to
$\mathbb{N}_0^m$
defined in (7). It is evident that the scaling affects only reactions that include species in
$\mathcal{U}$
among their reactants.
Proposition 3. Suppose that
$\pi$
is a complex balanced distribution for
$(\mathcal{N},\lambda)$
on an irreducible component
$\Gamma$
. Define
$g_{N}^{\alpha}(x)=N^{-\langle\alpha, \rho (x) \rangle}\pi(x)$
for
$N\in \mathbb{N}$
. Then
$M_{N}^{\alpha}=\sum_{x\in \Gamma}g_{N}^{\alpha}(x)\in (0,1)$
, and
$\pi_{N}^{\alpha}(x)=\frac{1}{M_{N}^{\alpha}}g_{N}^{\alpha}(x)$
is a complex balanced distribution for
$(\mathcal{N},\lambda_N^\alpha)$
on
$\Gamma$
.
Proof. Since
$\pi$
is a complex balanced distribution for
$(\mathcal{N},\lambda)$
on
$\Gamma$
, (6) states that for any
$\eta\in \mathcal{C}$
and
$x\in\Gamma$
,
By the definitions of
$\lambda_N^{\alpha}$
and
$g_N^{\alpha}$
and by linearity of
$\rho $
, we have
\begin{align*}g_N^{\alpha}(x)\sum_{y':\,\eta\to y'\in \mathcal{R}}&\lambda_{N,\eta\to y'}^{\alpha}(x)=N^{-\langle \alpha,\rho (x-\eta)\rangle}\pi(x)\sum_{y'\,\colon \eta\to y'\in \mathcal{R}}\lambda_{\eta\to y'}(x)\\& =N^{-\langle \alpha,\rho (x-\eta)\rangle} \sum_{y\,\colon y\to \eta\in \mathcal{R}}\pi(x+y-\eta)\lambda_{y\to \eta}(x+y-\eta)\\& =\sum_{y:\,y\to \eta\in \mathcal{R}} N^{-\langle \alpha, \rho (x+y-\eta) \rangle} \pi(x+y-\eta)\ N^{\langle \alpha, \rho (y) \rangle} \lambda_{y\to \eta}(x+y-\eta) \\ & =\sum_{y:\,y\to \eta\in \mathcal{R}}g_N^{\alpha}(x+y-\eta)\lambda_{N,y\to \eta}(x+y-\eta),\end{align*}
provided that
$x\geq \eta$
. Furthermore, for
$x\not\geq \eta$
,
Note that
$g_N^{\alpha} (x) \leq \pi(x)$
for all
$x\in \Gamma$
, and
$g_N^{\alpha}(x)>0$
if and only if
$\pi(x)>0$
. This implies that
$M_{N}^{\alpha}=\sum_{x\in \Gamma}g_{N}^{\alpha}(x)\in (0,1)$
and
$\pi_N$
is a complex balanced distribution for
$(\mathcal{N},\lambda_N^{\alpha})$
on
$\Gamma$
. The proof is complete.
We have the following theorem on the asymptotic behaviour of
$\pi_{N}^{\beta}$
. It states that
$\pi_{N}^{\beta}$
converges to a probability distribution with support on a subset of
$\Gamma$
, which further satisfies a conservation relation.
Theorem 3. Assume the conditions in Proposition 3. Let
Then for every
$x\in\Gamma$
,
\begin{align}\pi_0^{\alpha}(x) = \lim_{N\to\infty} \pi_{N}(x) &= \begin{cases}\pi(x)/\pi(\Gamma_0^{\alpha}) &\textrm{if}\ x\in \Gamma_0^{\alpha}, \\0 & \textrm{if}\ x\in \Gamma\setminus\Gamma_0^{\alpha}.\end{cases}\end{align}
Intuitively, when taking
$N \to \infty$
, the reactions involving non-interacting species are more likely to occur prior to other reactions. Therefore, it is expected that the non-interacting species will be depleted and would reach a minimal number in the limit. This leads to the conservation relation
$\gamma_0^{\alpha}$
and the state space
$\Gamma_0^{\alpha}$
as defined in (18). Consider the reaction network (4) with
$\mathcal{U} = \{U\}$
. It follows that
$\Gamma = \mathbb{N}_0^2 \setminus \{0\}$
is an irreducible component. For any
$\alpha \in \mathbb{R}_{> 0}$
we have
$\gamma_0^{\alpha} = \min \{\langle \alpha, \rho(x) \rangle \,\colon x \in \Gamma\} = 0$
, and therefore
$\Gamma_0^{\alpha} = \{(x_A, x_U) \in \Gamma \,\colon x_U = 0\}$
.
Proof. By Proposition 3, we have
Decompose
$\Gamma=\Gamma_0^{\alpha}\cup (\Gamma\setminus \Gamma_0^{\alpha})$
. Then it follows that
and thus
\begin{align*}\pi_{N}(x)&=\frac{N^{-\langle\alpha, \rho (x) \rangle} \pi(x)}{\sum_{x'\in \Gamma_0^{\alpha}}N^{-\gamma_0^{\alpha}} \pi(x')+\sum_{x'\in \Gamma\setminus \Gamma_0^{\alpha}}N^{-\langle\alpha, \rho (x')\rangle} \pi(x')}\\&=\frac{N^{\gamma_0^{\alpha}-\langle \alpha, \rho (x)\rangle } \pi(x)}{\sum_{x'\in \Gamma_0^{\alpha}} \pi(x')+\sum_{x'\in \Gamma\setminus \Gamma_0^{\alpha}}N^{\gamma_0^{\alpha}-\langle\alpha, \rho (x')\rangle} \pi(x')}.\end{align*}
Note that
$\Gamma$
is an irreducible component of
$\mathcal{N}$
, and
$\pi$
is a complex balanced distribution for
$(\mathcal{N},\lambda)$
. Thus,
$\pi(x)>0$
for all
$x\in \Gamma$
. Therefore, for
$N\to\infty$
, (19) follows from the fact that on the set
$\Gamma$
,
$\gamma_0^{\alpha}-\langle\alpha, \rho (x')\rangle>0$
if and only if
$x'\in \Gamma\setminus \Gamma_0^{\alpha}$
. The proof is complete.
The distribution
$\pi_0^{\alpha}$
in Theorem 3 is the conditional probability of
$\pi$
on
$\Gamma_0^{\alpha}$
. By replacing
$\pi$
with
$\pi_N$
(defined in Proposition 3) for any N, we can conclude that
$\pi_0^{\alpha}$
is the conditional probability of
$\pi_N^{\alpha}$
on
$\Gamma_0$
. This follows from
$\pi_N^{\alpha} (x) = N^{-\gamma_0^{\alpha}} \pi(x)$
for all
$x\in\Gamma_0^{\alpha}$
. In comparison with (1), Proposition 3 and Theorem 3 show that the mass-action assumption is not necessary and can be replaced by complex balancing.
As a result of Proposition 3, of Theorem 1, Proposition 1, or Proposition 2, and of Theorem 3, we have the next theorem.
Theorem 4. Let
$(\mathcal{N},\lambda)$
be a weakly reversible SRN with a set of non-interacting species
$\mathcal{U}$
, and let
$(\mathcal{N}, \lambda_N^{\alpha})$
be the SRN with
$\lambda_N^{\alpha}$
, for
$N\in \mathbb{N}$
and
$\alpha \in \mathbb{R}_{> 0 }^{m}$
, as defined in (17). Suppose that
$\pi$
is a complex balanced distribution for
$(\mathcal{N},\lambda)$
on an irreducible component
$\Gamma$
and that
$\Gamma_0$
defined as in (15) is non-empty. Assume that at least one of the following conditions holds:
-
• Condition 3;
-
•
$\mathcal{U}$
are intermediates; -
•
$\mathcal{N}$
is reversible.
Also, let
$(\mathcal{N}_{\mathcal{U}},\lambda_{\mathcal{U}})$
be the reduced SRN of
$(\mathcal{N},\lambda)$
by elimination of the species in
$\mathcal{U}$
. Then
$\pi_0^{\alpha} = \lim_{N\to\infty}\pi_N^{\alpha}$
, where
$\pi_0$
is defined in Proposition 3, is a stationary distribution (linear combination of stationary distributions on disjoint irreducible components of
$\mathcal{N}_{\mathcal{U}}$
) of
$(\mathcal{N}_{\mathcal{U}},\lambda_{\mathcal{U}})$
supported on
$\Gamma_0$
.
5. Conclusion
This paper investigates the asymptotic behaviour of complex balanced stationary distributions in a multiscale setting. It addresses a fundamental question in stochastic modelling: how the stationary distribution of a reaction network evolves when certain reaction rates are scaled by a large parameter N, leading to the rapid degradation of certain species.
A key result, presented in Theorems 1 and 3, establishes that under specific conditions, the limiting distribution of complex balanced SRNs corresponds to the stationary distribution of the reduced network obtained by eliminating non-interacting species. In particular, when the non-interacting species are intermediate species (Proposition 1), or when the original SRN is detailed balanced (Proposition 2), the limiting distributions retain their complex balanced or detailed balanced nature in the reduced reaction network. Furthermore, the results imply that under appropriate conditions, the form of the stationary distribution (Theorem 4) and positive recurrence (Corollary 2) are preserved for the reduced reaction network.
This work extends prior research [Reference Cappelletti and Wiuf7, Reference Hoessly and Wiuf13] by addressing scenarios where the reduced reaction network may lack weak reversibility or display unconventional kinetics. It would be interesting to know if there are other classes of SRNs for which a correspondence between the limiting stationary distribution and the stationary distribution of the reduced network exists. Even in simple cases it is difficult to determine the stationary distribution. Example 2 with mass-action kinetics is one such case; it is not weakly reversible, and the presence of P does not influence the stationary behaviour of G and G
′ (which is binomial). The stationary distribution of the full network is a Poisson mixture distribution, but the mixing measure is in general intractable [Reference Cappelletti, Majumder and Wiuf6]. If
$x_G+x_{G'}=1$
, then the generating function of
$x_P$
can be expressed through Kummer’s function, from which the descending factorial moments of
$x_P$
can be found [Reference Peccoud and Ycart24, p. 281]. This provides a means to determine the stationary distribution of the reduced network with species G and P, but
$x_G=1$
always. By scaling the reaction rates of
$G'\ce{->}G$
and
$G' \ce{->} G' + P$
in N, the descending factorial moments of
$x_P$
in the reduced reaction network can be found. However, these do not take a familiar form [Reference Peccoud and Ycart24, p. 281].
6. Proofs
In this section, we give proofs of Lemma 2, Theorem 1, and Propositions 1 and 2. In particular, the proofs of Theorem 1 and Proposition 1 extend ideas from Theorem 17 in [Reference Hoessly, Wiuf and Xia15]. A key step in the proofs involves verifying an identity of the form
where
$x > 0 $
and the
$y_i$
are non-negative numbers using DTMCs. Dividing both sides of (20) by x gives an equivalent identity,
Since
$x > 0$
and the
$y_i$
are non-negative, this suggests interpreting the
$\frac{y_i}{x}$
as probabilities of events that form a partition of some probability space. We associate
$\frac{y_i}{x}$
with the probability of the first hitting time
$\tau$
of a DTMC to a particular state, so that
As a result, (21), and hence the original identity (20), holds if and only if the associated DTMC is recurrent.
The insight that allows us to make this association here is that
$\beta_r$
defined in (11) has a similar form to the transition probabilities of a DTMC, namely
$\beta_r \geq 0$
and
\[ \sum_{k = 0}^m \:\sum_{r \in \mathcal{R}_{i,k}} \beta_r = 1.\]
We introduce some notation. Let
$ \mathcal{N} $
be a (reduced) reaction network. A reaction
$r=y\ce{->}y$
is an incoming reaction of y
′ and an outgoing reaction of y. For a complex
$y\in \mathcal{C}$
,
$\mathrm{inc} (y)$
and
$\mathrm{out} (y)$
denote the sets of all incoming and outgoing reactions of y, respectively. In a reduced reaction network, we often add the subscript
$\mathcal{U}$
, for example
$\mathrm{out}_\mathcal{U}$
.
For
$\gamma\in\Xi_i(q)$
, let
be the initial and terminal edges of
$\gamma$
, respectively. Finally, we define
such that
$\mathcal{U}\setminus \mathcal{U}_{\mathrm{pro}} =\{U_{m_{\mathrm{pro}}+1},\ldots,U_m\}$
.
6.1. Proof of Lemma 2
(
$\Rightarrow$
) Suppose that Condition 2 holds. Given a walk
$\theta\subseteq (\mathcal{V}, \mathcal{E})$
, define
$\mathcal{U}_{\theta} \,:\!=\, \{U_i\in \mathcal{U} \,\colon U_i \in \theta\}$
. Consider a closed walk
$\gamma \in \Xi_{i_0}$
for any
$i_0 \in \{1,\dots, m_{\mathrm{pro}}\}$
. Then
$U_{i_0} \in \mathcal{U}_{\gamma}$
. In addition,
$\mathcal{R}_{i_0,i_0}=\varnothing$
, because otherwise
$r = y + U_{i_0} \ce{->} y' + U_{i_0} \in \mathcal{R}_{i_0,i_0}$
with
$y \neq y'$
contradicts Condition 2. This implies
$|\mathcal{U}_{\gamma}|\geq 2$
, and
\[\mathfrak{R}(\Xi_{i_0})=\bigcup_{k=2}^{m_{\mathrm{pro}}}A_{i_0,k}, \quad A_{i_0, k} \,:\!=\, \{\mathfrak{R}(\gamma)\,\colon |\mathcal{U}_{\gamma}|=k\}.\]
We will show that
$|\mathfrak{R}(\Xi_0)| < \infty$
, which is the key to proving
$|\mathcal{R}_{\mathcal{U}}| < \infty$
.
Suppose that
$k=2$
. Then
$\gamma$
has the form
for some
$j \in \{1,\dots, i_0 - 1, i_0 + 1,\dots, m_{\mathrm{pro}}\}$
and
$q\in\mathbb{N}_0$
. As a result of Proposition 1 from [Reference Hoessly, Wiuf and Xia14] and Condition 2,
for some
$y_i \in \mathbb{N}_0^{\mathcal{S}}$
with
$i=0,\dots, q$
. Note that
$\{r\oplus r' \,\colon r \in \mathcal{R}_{i_0,j},\, r' \in \mathcal{R}_{j,i_0}\}$
is a finite set. This implies that
is a finite set. Combining this observation with Proposition 2(iii) from [Reference Hoessly, Wiuf and Xia14], we conclude that
$A_{i_0,2}$
is finite.
We prove finiteness of
$A_{i_0,k}$
for general
$k\geq 3$
by induction. Let
$\gamma \in A_{i_0,k}$
be of the form (9) with
$i=i_0$
. It suffices to prove the claim that
Otherwise, we can decompose
$\gamma$
into a ‘summation’ of closed walks, where
$U_{i_0}$
only appears as the initial and terminal nodes in each closed walk. Here, we say that a walk
$\theta$
is the summation of two walks
$\theta_1$
and
$\theta_2$
, written as
$\theta = \theta_1 + \theta_2$
, if
Therefore,
$\mathfrak{R}(\gamma)$
can be written in the form of (23) and finiteness of
$A_{i_0,k}$
follows from [Reference Hoessly, Wiuf and Xia14, Proposition 2(iii)], and the finiteness of the collection of all possible
$y_i$
is finite, where the latter is a consequence of claim (*).
Suppose that
$\gamma$
of the form (9) with
$i=i_0$
and
$i_0 \notin \{i_1,\dots, i_q\}$
. Then the collection of all possible
$\gamma$
can be divided into two sets,
It is trivial that
$B_1$
is a finite set. It suffices to show that
$B_2$
is finite, too. Let
$\gamma \in B_2$
. We decompose
$\gamma$
into the summation of closed walks connected by paths
where for all i,
$\gamma_{i}\in \Xi_{j_i'}$
and
$\theta_{i}$
is a path in the sense that no repeated nodes are allowed.
Without loss of generality, we assume that
$j_i'\neq j_{i'}'$
for all
$1\leq i< i'\leq q'$
, where
$j_i'$
is such that
$\gamma_i \in \Xi_{j_i'}$
. Thus,
$q\leq m_{\mathrm{pro}}$
. This is because, otherwise, if we let
$\gamma'_{i} = \gamma_{i} + \theta_{i+1} + \cdots + \theta_{i'} + \gamma_{i'}\in \Xi_{j_i'}$
and write
this process can be iterated until no repeated
$j_i'$
occurs. Note that the collection of all paths in
$(\mathcal{V},\mathcal{E})$
is finite. This implies that the collection of all possible
$\mathfrak{R}(\theta_{i})$
for
$1\leq i\leq q'+1$
is finite. In addition, for any
$i\in\{1,\dots, q\}$
,
$U_{i_0}$
is not included in
$\gamma_{i}$
, and thus
$\mathfrak{R}(\gamma_{i})\in A_{j_i', k'}$
with some
$k'<k$
. Thanks to the induction hypothesis
$|A_{j_i', k'}| < \infty$
, the collection of all possible
$\mathfrak{R}(\gamma_i)$
is finite for all
$i = 1,\dots, q$
. Hence,
$B_2$
(the collection of all possible
$\mathfrak{R}(\gamma) = \mathfrak{R} (\theta_{1})\oplus \mathfrak{R}(\gamma_1) \oplus \cdots \oplus \mathfrak{R}(\theta_{q+1})$
) is finite owing to [Reference Hoessly, Wiuf and Xia14, Proposition 2(iii)] and the fact that
$q\leq m_{\mathrm{pro}}$
. This completes the proof of claim (*) for
$i_0$
and thus for all
$i = 1,\dots, m_{\mathrm{pro}}$
and
$k = 2, \dots, m_{\mathrm{pro}}$
.
The rest of the proof is straightforward. For any
$\gamma \in \Xi_0$
, we can decompose it into a summation of at most
$m_{\mathrm{pro}}$
closed walks connected by paths. Since the ranges of the function
$\mathfrak{R}$
on paths and closed walks are all finite, the collection of all possible
$\mathfrak{R}(\gamma)$
is also finite. As a result,
$|\mathfrak{R}(\Xi_0)| < \infty$
.
(
$\Leftarrow$
) Suppose that
$\mathcal{R}_{\mathcal{U}}$
consists of finitely many reactions but Condition 2 fails. Then there exists a closed walk
$\gamma_0 \in \Xi_{i_0}$
for some
$i_0\in \{1,\dots, m_{\mathrm{pro}}\}$
such that
$\mathfrak{R}(\gamma_0) = y_0 + U_{i_0} \ce{->} y_0' + U_{i_0}$
with
$y_0\neq y_0'$
. Since
$\mathcal{U}_{\mathrm{pro}}\subseteq\mathcal{U}_{\deg}$
(the set
$\mathcal{U}$
can be eliminated), there are paths
$\theta_1$
and
$\theta_2$
directed from
$U_0$
to
$U_{i_0}$
and from
$U_{i_0}$
to
$U_{0}$
, respectively. It follows that for any
$q\in \mathbb{N}$
,
Let
$(x_q,x_q') \,:\!=\, \mathfrak{R}(\gamma_{q})$
. We will show that
$\{ (x_q,x_q') \,\colon q\in \mathbb{N}\}\cap \mathcal{R}_\mathcal{U}$
is an infinite set. Recall that
$y_0\neq y_0'$
. Without loss of generality, assume that
$y_{0,1} \neq y_{0,1}'$
. Here, we only prove the case of
$y_{0,1}>y_{0,1}'$
; the other case,
$y_{0,1} < y_{0,1}'$
, can be proved similarly. Let
Then, by Definition 3 and
$y_{0,1}>y_{0,1}'$
, we have
This yields that
$\{z_{q,1}\,\colon q \in \mathbb{N}\}$
is a strictly increasing sequence. Let
$\mathfrak{R}(\theta_1)=(y_1,y_1'+U_{1})$
and
$\mathfrak{R}(\theta_2)=(y_2+U_{1},y_2')$
. Then, for all
$q > M$
with M large enough,
$z_{q,1}\geq y_{1,1}'$
holds. As a result,
\begin{align*}x_{q,1} &=y_{1,1}+z_{q,1}-y_{0,1}'+(y_{2,1}-z_{q,1}')\vee 0\\[3pt] \nonumber &=y_{1,1}+y_{0,1}+q(y_{0,1}-y_{0,1}')-y_{1,1}'+(y_{2,1}-y_{0,1}')\vee 0.\end{align*}
Thus,
$\{x_{q,1}, q > M\}$
is a strictly increasing sequence. This contradicts
$\{\mathfrak{R}(\gamma_{q}) = (x_{q},x_{q}') \,\colon q>M\} \subseteq \mathcal{R}_\mathcal{U}$
being a finite set. The proof is complete.
6.2. Proof of Proposition 1
If
$\mathcal{U}$
consists of intermediate species, then
$\Gamma_0$
is an irreducible component of
$\mathcal{N}_{\mathcal{U}}$
; see [Reference Hoessly, Wiuf and Xia14, Theorem 5.6]. In addition, if
$\gamma\in \Xi_0$
, then
$\mathfrak{R}(\gamma)=y\ce{->}y'$
, where
$y=\mathrm{reac} (r_{0})$
and
$y'=\mathrm{prod} (r_{q})$
. Thus,
$\mathcal{C}_{\mathcal{U}}\subseteq \mathcal{C}_0=\{y\in \mathcal{C}\,\colon \rho (y) = 0\}$
. To prove the proposition, it suffices to show that the following equation holds for all
$\eta\in \mathcal{C}_{\mathcal{U}}$
and
$x\in\Gamma_0$
:
where
$\mathrm{out}_\mathcal{U} (\eta)$
and
$\mathrm{inc}_\mathcal{U}(\eta)$
denote the sets of outgoing and incoming reactions of
$\eta$
in
$\mathcal{R}_{\mathcal{U}}$
, respectively. If
$r\in \mathfrak{R}(\Xi_0)\setminus\mathcal{R}_\mathcal{U}$
, then
$r=(\eta,\eta)$
for some
$\eta$
, and r is both an outgoing ‘reaction’ of
$\eta$
and an incoming ‘reaction’ of
$\eta$
, with
$\zeta(r) = \eta - \eta = 0$
. It follows that (24) is equivalent to
where
$\mathrm{out}_\mathcal{U}^* (\eta)$
and
$\mathrm{inc}_\mathcal{U}^*(\eta)$
denote the sets of outgoing and incoming ‘reactions’ of
$\eta$
in
$\mathfrak{R}(\Xi_0)$
and
$\lambda_{\mathcal{U}}$
is extended to
$\mathfrak{R}(\Xi_0)$
by (14).
Recall that
$\pi$
is a complex balanced distribution for
$(\mathcal{N},\lambda)$
and that
$\eta$
as in (25) is in
$\mathcal{C}_{\mathcal{U}}\subseteq \mathcal{C}_0\subseteq \mathcal{C}$
. It follows that
where
$\mathrm{out} (\eta)$
and
$\mathrm{inc} (\eta)$
denote the sets of outgoing and incoming reactions of
$\eta$
in
$\mathcal{R}$
.
Denote by
$L_0 (\eta)$
and
$R_0 (\eta)$
the left- and right-hand sides of (25), respectively, and by
$L (\eta)$
and
$R (\eta)$
the left- and right-hand sides of (24), respectively. If
$x\not\geq \eta$
, then
$L_0(\eta)=R_0(\eta)=0$
. Thus, it suffices to show (25) assuming
$x\geq \eta$
.
Step 1:
$L(\eta)=L_0(\eta)$
. We have
\[L(\eta)=\pi(x)\left(\sum_{r\in \mathrm{out}(\eta)\cap \mathcal{R}_{0,0}}\lambda_r(x)+\sum_{i = 1}^{m_{\mathrm{pro}}} \sum_{r\in \mathrm{out}(\eta)\cap \mathcal{R}_{0,i}}\lambda_r(x)\right)\]
and
\begin{align*}L_0(\eta) = & \pi(x)\sum_{\gamma\in \Xi_0 :\,g(\gamma)=\eta}\lambda^*_{\gamma}(x) \\ = & \pi(x) \left(\sum_{r\in \mathrm{out}(\eta)\cap \mathcal{R}_{0,0}}\lambda_r(x) + \sum_{k = 1}^{\infty} \;\sum_{\gamma\in \Xi_0 (k) :\,g(\gamma)=\eta}\lambda^*_{\gamma}(x)\right)\!,\end{align*}
where
$m_{\mathrm{pro}}$
is given in (22) and
$g(\gamma) \,:\!=\, \mathrm{reac}(\mathrm{init}(\gamma))$
is the reactant of the reaction of the initial edge of
$\gamma$
. We claim that for any reaction
$r_* = \eta$
$\overset{r_*}{\longrightarrow} U_{\iota}$
with
$U_\iota\in \mathcal{U}_{\mathrm{pro}}$
,
\begin{align}\lambda_{r_*} (x) = \sum_{k = 1}^{\infty} \sum_{\gamma\in \Xi_0(k):\, \mathrm{init}(\gamma)=U_0\overset{r_*}{\longrightarrow} U_{\iota}}\lambda^*_{\gamma}(x).\end{align}
Then, by summing both sides of (26) over
$r_* \in \mathrm{out} (\eta)\setminus \mathcal{R}_{0,0}$
, we get
and thus
$L(\eta)=L_{0}(\eta)$
.
To prove (26), we construct an auxiliary DTMC X on
$\mathcal{X}=\{U_0\} \cup \mathcal{U}_{\mathrm{pro}}$
. By (12), the transition probabilities can be defined as
Thus, for any
$i,j\in\{0,\dots, m_{\mathrm{pro}}\}$
,
$P_{i,j}>0$
if and only if
$U_i \ce{->} U_j \in \mathcal{R}$
. In addition, since
$\mathcal{U}$
is eliminable, it follows that X is irreducible. Let
$\tau=\min\{k\geq 1\,\colon X_k=U_0\}$
. Note that
\begin{align*} \mathbb{P}_{U_{\iota}} (\tau = 1) = P_{\iota, 0} &= \sum_{r \in \mathcal{R}_{\iota, 0}} \beta_r (x - \eta + U_{\iota}) = \lambda_{r_*}(x)^{-1} \sum_{r \in \mathcal{R}_{\iota, 0}} \lambda_{r_*}(x) \beta_r (x - \eta + U_{\iota})\\ &= \lambda_{r_*}(x)^{-1} \sum_{\gamma \in \Xi_0(1):\, \mathrm{init} (\gamma) = U_0\overset{r_*}{\longrightarrow} U_{\iota} } \lambda_{\gamma}^*(x).\end{align*}
By iteration, one can show that for every
$k \in \mathbb{N}$
,
Taking (13) into account and using that the Markov chain is irreducible (cf. [Reference Norris23, Theorems 1.5.6 and 1.5.7]), we have
\begin{align*}1 = \mathbb{P}_{U_{\iota}}(\tau<\infty) = \sum_{k=1}^{\infty}\mathbb{P}_{U_{\iota}}(\tau = k) = \lambda_{r_*} (x)^{-1} \sum_{k=1}^{\infty} \sum_{\gamma\in \Xi_0 (k):\, \mathrm{init}(\gamma) = U_0\overset{r_*}{\longrightarrow} U_{\iota}}\lambda_{\gamma}^*(x).\end{align*}
This proves (26) and thus
$L(\eta)=L_0(\eta)$
.
Step 2:
$R(\eta)=R_0(\eta)$
. Similarly, in order to show that
$R(\eta)=R_0(\eta)$
, it suffices to verify that for any
$r^{*} = U_{\iota} $
$\overset{r^*}{\longrightarrow} \eta$
with some
$U_{\iota} \in \mathcal{U}_{\deg} = \mathcal{U}_{\mathrm{pro}}$
(see Lemma 1), the following equation holds:
\begin{align}&\pi(x-\eta+U_{\iota}) \lambda_{r^*} (x-\eta+U_{\iota}) \nonumber \\[4pt]& \quad = \sum_{k=1}^{\infty} \sum_{\gamma\in \Xi_0(k) :\, \mathrm{ter}(\gamma) = U_{\iota} \overset{r^*}{\longrightarrow} U_0}\pi \big(x - \zeta (\mathfrak{R}(\gamma)) \big) \lambda_{\gamma}^* \big(x - \zeta (\mathfrak{R}(\gamma)) \big).\end{align}
For any
$i,j \in \{0,\dots, m_{\mathrm{pro}}\}$
, let
\[P_{i,j}=\begin{cases}\displaystyle \frac{\sum_{y :\, y \to \eta \in \mathcal{R}_{j,0}}\pi(x - \eta + y) \lambda_{y \to \eta}(x - \eta + y) }{\pi (x) \sum_{y' :\, \eta \to y'\in \mathcal{R} } \lambda_{\eta \to y'}(x) }, & i = 0,\\[12pt]\displaystyle \frac{ \sum_{r\in \mathcal{R}_{j,i}} \pi(x - \eta + \mathrm{reac}(r)) \lambda_{r}(x - \eta + \mathrm{reac}(r)) }{\pi (x - \eta + U_i)\sum_{y':\, U_i \to y' \in \mathcal{R}} \lambda_{U_i \to y' }(x - \eta + U_i) }, & i \neq 0. \\[12pt]\end{cases}\]
Recall that
$\pi$
is a complex balanced distribution for
$(\mathcal{N},\lambda)$
on
$\mathcal{N}$
; then, from (6) and Condition 1, we see that
$\mathbb{P}_{U_i} (U_j) = P_{i,j}$
for
$i,j \in \{0,\dots, m_{\mathrm{pro}}\}$
forms a transition probability for a DTMC, say Y, on
$\mathcal{X}$
. Let
$\tau \,:\!=\, \min\{q\geq 1:\, Y_q = U_0\}$
, and for any
$k\in \mathbb{N}$
let
We aim to show that
for all
$k \geq 1$
, where
$L_1(\eta)$
is the left-hand side of (27). Suppose that
$k = 1$
. Then
On the other hand, for any
$r' = y\ce{->} y'\in \mathcal{R}_{i,j}$
with some
$i,j\in \{0,\dots, m_{\mathrm{pro}}\}$
and
$x'\geq y$
, it follows from (11) and (12) that
\begin{align*}\lambda_{r'}(x') = \lambda_{r'}(x')\sum_{j'=0}^{m}\sum_{r''\in \mathcal{R}_{i,j'}}\beta_{r''}(x') = \beta_{r'}(x') \sum_{j'=0}^{m}\sum_{r'' \in \mathcal{R}_{i,j'}}\lambda_{r''}(x') > 0\end{align*}
and therefore
\begin{align}\beta_{r'}(x') = \lambda_{r'}(x') \bigg/ \sum_{j'=0}^{m}\sum_{r'' \in \mathcal{R}_{i,j'}}\lambda_{r''}(x').\end{align}
As a consequence, we have
\begin{align}R_{1,1}(\eta) = & \sum_{y :\, y\to U_{\iota} \in \mathcal{R}_{0,\iota}} \pi(x - \eta + y) \lambda_{y \to U_{\iota}} (x-\eta + y) \beta_{r^{\,*}} (x-\eta + y - y + U_{\iota}) \nonumber\\[3pt]= & \frac{ \lambda_{r^*} (x-\eta + U_{\iota})\sum_{y :\, y\to U_{\iota} \in \mathcal{R}_{0,\iota}} \pi(x-\eta + y) \lambda_{y \to U_{\iota}} (x-\eta + y) }{ \sum_{y' :\, U_{\iota} \to y' \in \mathcal{R}}\lambda_{U_{\iota} \to y'}(x-\eta + U_{\iota})}.\end{align}
By comparing (27), (29), and (31), one obtains equation (28) with
$k = 1$
.
Similarly, assume
$k = 2$
. Then we can write
\begin{align*}\mathbb{P}_{U_{\iota}} (\tau = 2) = \sum_{i=1}^{m_{\mathrm{pro}}} \sum_{y :\, y \to U_{i}\in \mathcal{R}_{0,i} } \bigg[ &\frac{\pi(x+\eta-y) \lambda_{y\to U_{i}}(x - \eta + y) }{\sum_{y' :\, U_i \to y'\in\mathcal{R}} \lambda_{U_i \to y'}(x) }\\&\times \frac{ \lambda_{U_{i} \to U_{\iota}}(x- \eta + U_i) }{\pi(x-\eta+U_{\iota})\sum_{y' :\, U_{\iota} \to y' \in \mathcal{R}}\lambda_{U_{\iota} \to y'}(x-\eta + U_{\iota})}\bigg],\end{align*}
and using (30) we have
\begin{align*}R_{1,2}(\eta)& =\sum_{i=1}^{m_{\mathrm{pro}}} \sum_{y :\, y \to U_{i}\in \mathcal{R}_{0,i} } \bigg[ \pi(x-\eta + y) \lambda_{y \to U_{i}} (x-\eta + y) \\ & \hspace{7.5em}\times \,\beta_{U_i\to U_{\iota}} (x-\eta + U_i) \beta_{r^{\,*}} (x-\eta + U_{\iota}) \bigg]\\ &= \bigg[\sum_{i=1}^{m_{\mathrm{pro}}} \sum_{y :\, y \to U_{i}\in \mathcal{R}_{0,i} } \frac{ \lambda_{y \to U_{i}} (x-\eta + y) \lambda_{U_i\to U_{\iota}} (x-\eta + U_i) }{ \sum_{y' :\, U_{i} \to y' \in \mathcal{R}}\lambda_{U_i \to y'}(x-\eta + U_i)}\bigg]\\[3pt] \nonumber & \quad \times \frac{ \lambda_{U_{\iota}\to \eta}(x-\eta + U_{\iota}) }{ \sum_{y' :\, U_{\iota} \to y' \in \mathcal{R}}\lambda_{U_{\iota} \to y'}(x-\eta + U_{\iota}) }.\end{align*}
This implies that equation (28) holds with
$k = 2$
as well. By using an iteration argument, one should be convinced that (28) is true for all
$k \in \mathbb{N}$
. This leads to (27) and hence
$R_0(\eta) = R(\eta)$
. The proof of this proposition is complete.
6.3. Proof of Proposition 2
The reduced reaction network
$\mathcal{N}_{\mathcal{U}}$
is reversible; see [Reference Hoessly, Wiuf and Xia14, Theorem 5.1]. For
$\gamma\in \Xi_0$
, by reversibility, there exists the walk in the opposite direction,
where
$r_j' =y'\ce{->}y$
if
$r_j = y\ce{->}y'$
for
$0\leq j\leq q$
with
$i_0=i_{q+1}=0$
. This implies that
$\zeta (r_j) = -\zeta (r_j')$
for all
$0\leq j\leq q$
, and thus
$\zeta (\gamma) = - \zeta(\gamma')$
with
$\zeta(\gamma) \,:\!=\, \sum_{j=0}^q \zeta (r_j)$
. For any
$x\in \Gamma_0$
, define
\begin{align*}x' \,:\!=\, x+\zeta(\gamma) = x + \sum_{j=0}^q \zeta(r_j) = x - \sum_{j=0}^q \zeta(r_j') = x - \zeta(\gamma').\end{align*}
By (14), it is enough to verify the following equation:
provided that
$x\geq \mathrm{reac}(\mathfrak{R}(\gamma))$
. Recall that
$\pi$
is a detailed balanced distribution for
$(\mathcal{N},\lambda)$
. By definition,
Therefore,
\begin{align*}\pi(x)\lambda^*_{\gamma}(x)&=\pi(x)\lambda_{r_0}(x)\prod_{j=1}^{q}\frac{\lambda_{r_j}\big(x + \zeta(r_0) + \dots + \zeta (r_{j-1}) \big)}{\sum_{j'=0}^m\sum_{r \in \mathcal{R}_{i_j,j'}} \lambda_{r} \big(x + \zeta(r_0) + \dots + \zeta(r_{j-1})\big)}\\[3pt]&=\lambda_{r_0'} \big(x+\zeta(r_0)\big) \pi\big(x + \zeta(r_0)\big) \frac{\lambda_{r_1}\big(x+\zeta(r_0) \big)}{\sum_{j'=0}^m\sum_{r'\in \mathcal{R}_{i_1,j'}}\lambda_{r'}\big(x+\zeta(r_0)\big)}\\[3pt]&\quad\times\prod_{j=2}^{q}\frac{\lambda_{r_j} \big(x + \zeta(r_0) + \dots + \zeta(r_{j-1}) \big)}{\sum_{j'=0}^m \sum_{r''\in \mathcal{R}_{i_j,j'}} \lambda_{r''}\big(x + \zeta (r_0) + \dots + \zeta(r_{j-1}) \big)}\\[3pt]&=\beta_{r_0'}\big(x'+\zeta(r_q') + \dots + \zeta(r_1') \big) \pi\big(x+\zeta(r_0)\big) \lambda_{r_1} \big(x + \zeta(r_0)\big)\\[3pt]&\quad \times\prod_{j=2}^{q}\frac{\lambda_{r_j}\big(x+\zeta(r_0) + \dots + \zeta(r_{j-1}) \big)}{\sum_{j'=0}^m\sum_{r''\in\mathcal{R}_{i_j,j'}}\lambda_{r''}\big(x+\zeta(r_0)+\dots+\zeta(r_{j-1})\big)}.\end{align*}
Similarly, we deduce that
\begin{align*}& \pi \big(x+\zeta(r_0)\big)\lambda_{r_1} \big(x+\zeta(r_0)\big) \prod_{j=2}^{q}\frac{\lambda_{r_j}\big(x+\zeta(r_0)+\dots+\zeta(r_{j-1})\big)}{\sum_{j'=0}^m\sum_{r \in \mathcal{R}_{i_j,j'}}\lambda_{r}\big(x+\zeta(r_0)+\dots+\zeta(r_{j-1})\big)}\\[3pt]&\quad = \beta_{r_1'}\big(x' + \zeta(r_q')+\dots+\zeta(r_2')\big) \pi \big(x+\zeta(r_0)+\zeta(r_1)\big) \lambda_{r_2} \big(x+\zeta(r_0)+\zeta(r_1)\big)\\[3pt]&\qquad\times\prod_{j=3}^{q}\frac{\lambda_{r_j}\big(x+\zeta(r_0)+\dots+\zeta(r_{j-1})\big)}{\sum_{j'=0}^m\sum_{r'\in \mathcal{R}_{i_j,j'}}\lambda_{r'}\big(x+\zeta(r_0)+\dots+\zeta(r_{j-1})\big)}.\end{align*}
Upon repeating the above arguments, one ends up with the equation
\begin{align*}\pi(x)\lambda^*_{\gamma}(x) = & \pi(x')\lambda_{\gamma'}^*(x')\prod_{j=1}^q \beta_{r_{j-1}'} \left(x' + \sum_{j'=j}^{q} \zeta(r_{j'}') \right) = \pi(x')\lambda_{\gamma'}^* (x').\end{align*}
This proves (32), and thus
$\pi_0$
is a detailed balanced distribution (or linear combination of detailed balanced distributions) for
$(\mathcal{N}_{\mathcal{U}},\lambda_{\mathcal{U}})$
on
$\Gamma_0$
. The proof is complete.
6.4. Proof of Theorem 1
Proof of (i). It suffices to show that for any
$x\in \Gamma_0$
and
$x'\in\mathbb{N}_0^{n_{\mathcal{U}}}$
,
$x\to_{\mathcal{N}_{\mathcal{U}}} x'$
implies (a)
$x'\in\Gamma_0$
and (b)
$x'\to_{\mathcal{N}_{\mathcal{U}}} x$
.
By definition,
$x\to_{\mathcal{N}_{\mathcal{U}}} x'$
if and only if there exist reactions
$r_1,\dots, r_k \in \mathcal{R}_{\mathcal{U}}$
such that
$r_1\oplus \cdots \oplus r_k = (y,y')$
with
$x\geq y$
and
$x' - x = y' - y$
. Using an iteration argument, we can assume that
$k = 1$
. By the definition of the reduction of reaction networks, either
$r_1 \in \mathcal{R}$
or there exists a
$\gamma \in \Xi_0$
such that
$r_1 = \mathfrak{R} (\gamma)$
. In either case, we have
$x\to_{\mathcal{N}} x'$
with
$\rho(x') = 0$
and thus
$x' \in \Gamma_0$
.
By the weak reversibility of
$\mathcal{N}$
, if
$r_1 = y_1\ce{->}y' \in \mathcal{R}$
, then there exists a sequence of reactions such that
$y'\ce{->}y_1\ce{->}y_2\ce{->}\cdots \ce{->}y_{k'}\ce{->} y \subseteq \mathcal{R}$
. We further assume that
$y_i \neq y_j$
for all
$1 \leq i < j \leq k'$
. Otherwise, if
$y_i = y_j$
, one can work with the sequence
$y'\ce{->}y_1\ce{->} \cdots \ce{->}y_{i} \ce{->}y_{j + 1} \ce{->} \cdots \ce{->} y$
, which is also included in
$\mathcal{R}$
. If
$y_i \in \mathcal{C}_{0}$
for all
$i=1,\dots, k'$
, then each reaction is also an element of
$\mathcal{R}_{\mathcal{U}}$
. This proves
$x'\to_{\mathcal{N}_{\mathcal{U}}} x$
. Otherwise, there are
$1 \leq i_1 < i_2 < \dots < i_{k''} \leq k'$
for some
$k''<k'$
such that
$y_{i}\in \mathcal{C}_0$
if and only if
$i \in \{i_1,\dots, i_{k''}\}$
. For each
$q \in \{0,\dots, k''\}$
, let
$\gamma_q$
be the closed walk in
$(\mathcal{V},\mathcal{E})$
given by the reactions
$y_{i_q} \ce{->} y_{i_q+1} \ce{->}\cdots \ce{->} y_{i_{q+1}}$
, where by convention
$y_{i_0} \,:\!=\, y'$
and
$y_{i_{k'+1}} \,:\!=\, y$
. Then
$\gamma_q \in \Xi_0$
for all q. By removing all
$\gamma_q$
such that
$\mathfrak{R}(\gamma_q) = (y,y)$
for some
$y\in\mathbb{N}_0^n$
and recalling the assumption that
$y_i \neq y_j$
for all
$1 \leq i < j \leq k'$
, we know that
$r_{(q)} \,:\!=\, \mathfrak{R}(\gamma_q) = y_{i_q} \ce{->} y_{i_{q + 1}} \in \mathcal{R}_{\mathcal{U}}$
. Therefore
$\bigoplus_{q=0}^{k''} r_{(q)} = (y',y)$
. This proves
$x'\to_{\mathcal{N}_{\mathcal{U}}} x$
. The proof of Theorem 1(i) is thus complete.
Proof of (ii). For any
$\eta \in \mathcal{C}_0$
, let
\begin{align*}L_0 (\eta) = &\sum_{r \in \mathrm{out}_{\mathcal{U}}^* (\eta)} \pi(x) \lambda_{\mathcal{U},r}(x) \\= & \sum_{r\in \mathrm{out} (\eta)\cap \mathcal{R}_{0,0}} \pi(x)\lambda_r(x)+\sum_{\gamma\in \Xi_0:\, \mathrm{init}(\gamma)\in \mathrm{out}(\eta)} \pi(x)\lambda^*_{\gamma}(x),\end{align*}
\begin{align*}R_0 (\eta) = &\sum_{r\in \mathrm{inc} (\eta)\cap \mathcal{R}_{0,0}} \pi \big(x - \zeta(r) \big) \lambda_r \big(x - \zeta (r) \big)\\&+ \sum_{\gamma\in \Xi_0 :\, \mathrm{ter}(\gamma)\in \mathrm{inc}(\eta)} \pi \big(x - \zeta (\mathfrak{R}(\gamma))\big)\lambda^*_{\gamma} \big(x - \zeta (\mathfrak{R}(\gamma)\big),\end{align*}
where
$x\in \Gamma_0$
satisfies
$x\geq \eta$
. We use
$L (\eta)$
and
$R (\eta)$
to denote the left- and right-hand sides, respectively, of the following complex balanced equation:
Then we prove
$L_0 (\eta) = R_0 (\eta)$
by verifying that
$L (\eta) = L_0(\eta)$
and
$R (\eta)=R_0 (\eta)$
. Once this has been done, because
$L_0(\eta) = R_0(\eta) = 0$
if
$x\not\geq \eta$
, we have
This implies that
and thus
$\pi$
is a stationary distribution for
$(\mathcal{N}_{\mathcal{U}},\lambda_{\mathcal{U}})$
.
To achieve this goal, we introduce two DTMCs, which are used in the proofs of
$L (\eta) = L_0(\eta)$
and
$R (\eta)=R_0 (\eta)$
. Denote by
$\Theta_0$
the set of all open (not closed) walks in
$(\mathcal{V},\mathcal{E})$
of the form
Define
$\mathcal{X} \,:\!=\, \mathcal{X}_0\cup \{\partial\}$
, where
$\partial$
is a ceremony state, and
Analogously, define
$\mathcal{Y} \,:\!=\, \mathcal{Y}_0 \cup \{\partial\}$
, where
with
$\Theta_0'$
consisting of all open walks in
$(\mathcal{V},\mathcal{E})$
of the form
Lemma 4.
The proof of Lemma 4 is in Subsection 6.5.
Step 1:
$L(\eta) = L_0(\eta)$
. As in Step 1 of Subsection 6.2, to prove
$L(\eta) = L_0(\eta)$
it suffices to show that for every reaction
$r_* = \eta\ce{->}y'+U_{\iota} \in \mathcal{R}_{0,\iota}$
,
\begin{align}\sum_{k=1}^{\infty}\:\sum_{\gamma\in \Xi_0(k) :\, \mathrm{init}(\gamma) = r_*} \lambda^*_{\gamma}(x)= \lambda_{r_*}(x).\end{align}
To prove (35), we introduce a DTMC X and relate (35) to the recurrence of X. For a clearer understanding, we refer readers to Subsection 6.2, where a simpler instance is detailed to illustrate how the recurrence of X leads to the validity of (35). Because of the non-interacting structure, for any
$z\in \mathcal{X}_0$
we have
$\rho(z) = U_i$
for some
$U_i\in \mathcal{U}_{\mathrm{pro}}$
. This allows us to define the transition probabilities of the Markov chain X as follows:
\begin{align*}\mathbb{P}_{z} (z') = P_{z,z'} \,:\!=\, \begin{cases}\displaystyle \sum_{r\in \mathcal{R}_{0,0}}\beta_{r}(x), & \quad z = z' = \partial,\\[12pt]\displaystyle \sum_{r \in \mathcal{R}_{i,0}} \beta_{r}(z), & \quad \rho (z) = U_i, \,z' = \partial,\\[12pt]\displaystyle \sum_{r \in \mathcal{R}_{0,j}:\, \zeta (r) = z' - x }\beta_{r}(x), & \quad z = \partial, \,\rho (z') = U_j, \\[12pt]\displaystyle \sum_{r\in \mathcal{R}_{i,j}:\, \zeta (r) = z' - z} \beta_{r} (z), & \quad \rho (z) = U_i, \,\rho (z') = U_j,\end{cases}\end{align*}
where
$i,j\in \{1,\dots,m_{\mathrm{pro}}\}$
. Moreover, (35)
By weak reversibility of
$\mathcal{N}$
, X is irreducible, and thus (36) is true because
$\mathcal{X}_0$
is a finite set; see Lemma 4(i).
Step 2:
$R(\eta) = R_0(\eta)$
. Using the same idea as in Step 2 of Subsection 6.2, we can reduce this task to showing that for every reaction
$r^* = y' + U_{\iota} \ce{->} \eta \in \mathcal{R}_{\iota,0}$
,
for all
$x\in \Gamma_0$
such that
$x - \zeta (r^*) \geq \mathrm{reac} (r^*)$
.
Let Y be a DTMC on
$\mathcal{Y}$
with transition probabilities
\[P_{z,z'}=\begin{cases}\displaystyle \sum_{r\in \mathcal{R}_{0,0}}\frac{ \pi \big(x - \zeta (r) \big) \lambda_{y\to z} \big(x - \zeta(r) \big) }{\pi(x)\sum_{j = 0}^m \sum_{r'\in \mathcal{R}_{0,j}} \lambda_{r'}(x) }, & \quad z = z' = \partial,\\[12pt]\displaystyle \sum_{r\in \mathcal{R}_{i,0}:\, \zeta(r) = z' - x}\frac{\pi(z') \lambda_{r}(z') }{\pi(x)\sum_{j = 0}^m \sum_{r'\in \mathcal{R}_{0,j}} \lambda_{r'}(x) }, &\quad z = \partial, \,\rho (z') = U_i, \\[12pt]\displaystyle \sum_{r\in \mathcal{R}_{0, j}}\frac{\pi \big(z - \zeta(r) \big) \lambda_{r} \big(z - \zeta(r) \big) }{\pi (z)\sum_{j'=0}^{m}\sum_{r'\in \mathcal{R}_{j,j'}} \lambda_{r'}(z) }, &\quad \rho (z) = U_j , \,z' = \partial,\\[12pt]\displaystyle \sum_{r\in \mathcal{R}_{i, j}:\, \zeta (r) = z' - z}\frac{\pi(z') \lambda_{r}(z' ) }{\pi (z)\sum_{j'=0}^{m}\sum_{r'\in \mathcal{R}_{j,j'}} \lambda_{r'}(z) }, &\quad \rho (z) = U_j , \, z' = U_i,\end{cases}\]
where
$i,j\in \{1,\dots, m_{\mathrm{pro}}\}$
. Following the same line of argument as in Step 2 of Subsection 6.2, we can show that (37) is equivalent to
$\mathbb{P}_{y' + U_i} (\tau < \infty) = 1$
, where
$\tau \,:\!=\, \min \{k \geq 1 :\, Y_k = \partial\}$
. This is due to the recurrence of an irreducible DTMC in a finite state space. The proof of Theorem 1(ii) is thus complete.
6.5. Proof of Lemma 4
It suffices to show Lemma 4(i) by proving the following statements.
-
(A) If Condition 3(i) fails, then
$\mathcal{X}_0$
in (33) is infinite for some
$x\in \mathbb{N}_0^n$
. -
(B) If Condition 3(i) holds, then
$\mathcal{X}_0$
in (33) is finite for all
$x\in \mathbb{N}_0^n$
.
Proof of statement (A). Suppose that Condition 3(i) fails; then there exists
$\gamma \in \Xi_i$
such that
$\mathfrak{R} (\gamma) = (y + U_i, y' + U_i) \in \Xi_i$
for some
$U_i\in \mathcal{U}_{\mathrm{pro}}$
and
$y < y'$
. Since
$U_i\in \mathcal{U}_{\mathrm{pro}}$
, there exists a walk
$\theta$
in
$(\mathcal{V},\mathcal{E})$
with
$\mathrm{init} (\theta) = U_0$
and
$\mathrm{ter} (\theta) = U_i$
. Consider the sequence of paths
Let
$\mathfrak{R}(\theta) = (y_0, y_0')$
. Then
Using Proposition 2(4) from [Reference Hoessly, Wiuf and Xia14], we have
$z_q\leq y_0+y_1$
and
$z_q' \geq y+q(y'-y)$
. It follows that the set
is an infinite subset of
$\mathcal{X}_0$
. This completes the proof of statement (A).
The proof of statement (B) is based on the next two lemmas.
Lemma 5. Let n be a positive integer and let
$\{x_n\}_{n\geq 1} \subseteq \mathbb{R}^n$
be a sequence of vectors that is bounded from below. Then there exists a non-decreasing subsequence
$\{x_{n_k}\}_{k\geq 1}$
of
$\{x_n\}_{n \geq 1}$
. Moreover, if
$x_n \neq x_m$
for all
$n\neq m$
,
$\{x_{n_k}\}_{k\geq 1}$
is strictly increasing.
Lemma 6. Let
$x\in \mathbb{N}_0^n$
, let q be a positive integer, and let
$\mathcal{Z} \,:\!=\, \{\zeta_1,\dots, \zeta_q\}\subseteq \mathbb{Z}^n\setminus \mathbb{N}_0^n$
. Let
$\mathcal{H}$
consist of all linear combinations of elements in
$\mathcal{Z}$
with non-negative integer coefficients,
Then (i)
$\mathcal{H}_x \,:\!=\, \{\eta\in \mathcal{H}\,:\, x+\eta\in \mathbb{N}_0^n\}$
is a finite set if and only if (ii)
$\mathcal{H}\cap \mathbb{N}_0^n\setminus\{0\}=\varnothing$
.
Remark 1. Although we believe that this lemma exists in the literature, we have not been able to find it; hence we provide a proof.
Proof of statement (B). Suppose that Condition 3(i) holds. For any
$\theta\in \Theta_0$
, there exists a collection of closed walks
$\{\gamma_i\}_{i=1}^q$
connected by paths
$\{\theta_i\}_{i=1}^{q+1}$
such that
where
$\gamma_i\in \Xi_{j_i}$
with
$j_i\in \{1,\dots, m_{\mathrm{pro}}\}$
for
$i=1,\dots, q$
such that
$j_i\neq j_{i'}$
for all
$i\neq i'$
. Then
$q\leq m_{\mathrm{pro}}$
. As the number of paths in
$(\mathcal{V},\mathcal{E})$
is finite, it suffices to show that the set
is finite for every
$x\in \mathbb{N}_0^n$
and
$i\in \{1,\dots, m_{\mathrm{pro}}\}$
. For any
$i\in\mathcal{C}$
, let
with
$k\in\{1,\dots, m_{\mathrm{pro}}\}$
and
$\mathcal{C}_{\gamma} = \{U_i\in \mathcal{U} \,:\,U_i\in\gamma\}$
. Then
$A_i(x) = \bigcup_{k=1}^{m_{\mathrm{pro}}} A_{i,k} (x)$
for
$i\in \{1,\dots, m_{\mathrm{pro}}\}$
. We aim to show that
$A_{i,k} (x)$
is a finite set for all
$i\in \{1,\dots, m_{\mathrm{pro}}\}$
,
$1\leq k\leq m_{\mathrm{pro}}$
, and
$x\in \mathbb{N}_0^n$
by induction on k.
Fix any
$i_0\in \{1,\dots, m_{\mathrm{pro}}\}$
. Note that
$\{\Gamma\in \Xi_{i_0}:\, |\mathcal{C}_{\gamma}| = 1\} = \mathcal{R}_{i_0,i_0}$
is a finite set. Thus, by Condition 3(i) and Lemma 6,
$A_{i_0,1}(x)$
is finite. For
$k\geq 2$
, we show that
$A_{i_0,k}(x)$
is finite by contradiction. Suppose that
$A_{i_0,k}(x)$
is not a finite set. Then there exists a sequence of closed walks
$\{\gamma_j\}_{j\geq 1} \subseteq \Xi_{i}$
such that the following hold:
-
(a)
$|\mathcal{C}_{\gamma_j}| = k$
and
$x\geq \mathrm{reac} (\mathfrak{R} (\gamma_j))$
for all
$i\in \mathbb{N}$
; -
(b)
$\{x + \zeta(\mathfrak{R}(\gamma_j))\}_{j\geq 1} \subseteq A_{i,k}$
is an infinite subset of
$\mathbb{N}_0^n$
; -
(c)
$ \zeta(\mathfrak{R} (\gamma_j)) \neq \zeta(\mathfrak{R} (\gamma_{j'}))$
for all indexes
$j\neq j'$
.
Using Lemma 5, we can assume that
$\{\zeta(\mathfrak{R}(\gamma_j))\}_{j\geq 1}$
is a strictly increasing sequence. Next, for every
$j\in \mathbb{N}$
,
$\gamma_j$
can be decomposed as
$\gamma_j = \gamma_{j,1} + \dots + \gamma_{j,q_j}$
such that for
$j'=1,\dots, q_j$
,
$\gamma_{j,j'}\in \Xi_{i_0}$
, and
$U_{i_0}$
only appears as the initial and terminal nodes of
$\gamma_{j,j'}$
. Assume that
\begin{align}\sum_{j'=k_1}^{k_2} \zeta \big(\mathfrak{R}(\gamma_{j,j'})\big) \neq 0\quad \text{for all }1\leq k_1\leq k_2\leq q_i.\end{align}
Otherwise, we can replace
$\gamma_j$
by
$\gamma_j' = \gamma_{j,1} + \dots + \gamma_{j,k_1 - 1} + \gamma_{j,k_2 + 1}$
. Then
$\mathrm{reac} (\gamma_j') \leq \mathrm{reac}(\gamma_j) \leq x$
and
$\zeta(\mathfrak{R}(\gamma_j')) = \zeta(\mathfrak{R}(\gamma_j))$
. Thus, with this substitution, properties (a)–(c) still hold.
For any
$i\in \mathbb{N}$
,
$\gamma_{i,1}$
can be decomposed into at most
$k-1$
closed walks connected by paths. By the induction hypothesis, for any
$x\in\mathbb{N}_0^n$
, the set
$\{x + \zeta(\mathfrak{R}(\gamma_{i,1}))\}_{i\geq 1}$
is finite. Thus, for any positive integer q and any index j,
$\{x + \zeta(\mathfrak{R}(\gamma_{j,1})) + \dots + \zeta(\mathfrak{R}(\gamma_{j,j'}))\}_{j'=1}^{q_j\wedge q}$
is a finite set. Therefore, if
$\{q_j\}_{j\geq 1}$
is finite, then so is
$\{x + \zeta(\mathfrak{R}(\gamma_j))\}_{j\geq 1}$
. This contradicts (b). Thus, by taking subsequences if necessary, we can assume that
$\{q_j\}_{j\geq 1}$
is a strictly increasing sequence of positive integers. Let
for all
$j\in \mathbb{N}$
and
$j' \in \{1,\dots, q_j\}$
. It follows from (a) that
$\eta_{j,j'} \in \mathbb{N}_0^n$
. Recall that
$\{\eta_{j,1} \}_{j\geq 1} = \{x + \zeta(\mathfrak{R}(\gamma_{j,1}))\}_{j\geq 1}$
is a finite set. There exists a subsequence
$\{\eta_{n_{j}^1,1}\}_{j\geq 1}$
of
$\{\eta_{j,1}\}_{j\geq 1}$
such that
$\eta_{n_j^1,1} = y_1\in\mathbb{N}_0^n$
for all indexes j
′. Iteratively, for any
$k\geq 2$
, there exists a subsequence
$\{\eta_{n_j^k,k} \}_{j\geq 1}$
of
$\{\eta_{n_j^{k-1},k} \}_{j\geq 1}$
such that
$\eta_{n_j^k,k} = y_{k}\in\mathbb{N}_0^n$
for all
$j\in \mathbb{N}$
. Concerning Condition 3(i), the sequence
$\{y_k\}_{k\geq 1}\subseteq \mathbb{N}_0^n$
satisfies the property that
\begin{align}y_{k} - y_{k'} = \eta_{n_{k}^{k}, k} - \eta_{n_{k}^{k}, k'} = \sum_{j=k'+1}^{k} \zeta\big(\mathfrak{R}(\gamma_{n_{k}^k, j})\big) \notin \mathbb{N}_0^n\setminus\{0\}\quad \text{for all } k > k' \geq 1.\end{align}
On the other hand, as a result of assumption (38), we have
$y_{k} \neq y_{k'}$
for all
$k\neq k'$
. Thus, according to Lemma 5, there exists a strictly increasing subsequence of
$\{y_k\}_{k\geq 1}$
, which contradicts (39). This proves that
$A_{i,k}(x)$
is a finite set for all
$i\in \{1,\dots, m_{\mathrm{pro}}\}$
and
$1\leq k\leq m_{\mathrm{pro}}$
. Therefore,
$A_i(x) = \bigcup_{k=1}^{m_{\mathrm{pro}}} A_{i,k}(x)$
is also finite, and so is
$\mathcal{X}_0$
. The proof of statement (B) is complete.
Now it suffices to prove Lemmas 5 and 6. Note that Lemma 5 is elementary, so we only provide the proof of Lemma 6.
Proof of Lemma 6. (i)
$\implies$
(ii) is straightforward: if (ii) fails, then there exists
$\eta\in \mathcal{H}\cap \mathbb{N}_0^n\setminus\{0\}$
; this implies that
$\mathcal{G} \,:\!=\, \{j\eta\,:\, j\in \mathbb{N}\}$
is an infinite subset of
$\mathcal{H}_x$
, which contradicts (i).
Conversely, suppose that (ii) holds. Let
$V\subseteq \mathbb{N}_0^q$
be a set such that
$\mathcal{H}_{x,V} \,:\!=\, \{x+v(\mathcal{Z}) \,:\, v\in V\}=\mathcal{H}_x$
. By Zorn’s lemma, we can assume that V is minimal, namely for any
$v\in V$
, with
$V'=V\setminus \{v\}$
one has that
$\mathcal{H}_{x,V'}\neq \mathcal{H}_x$
. To prove that
$H_x$
is finite, it suffices to show that V is a finite set. Suppose that V is an infinite set. Then there is a sequence
$\{v_i\}_{i \geq 1} \subseteq V$
such that
$v_i\neq v_j$
for any
$i\neq j$
. Referring to Lemma 5, by taking subsequences, we can assume that
$\{v_i\}_{i\geq 1}$
is strictly increasing.
For any
$\eta\in \mathcal{H}_x$
, we have
$\eta \geq -x$
. Thus,
$\{v_i(\mathcal{Z})\}_{i\geq 1}\subseteq \mathcal{H}_x$
is bounded from below. Owing to the minimality of V, we have
$v_i(\mathcal{Z})\neq v_j(\mathcal{Z})$
whenever
$i\neq j$
, and thus by Lemma 5 we assume that
$\{v_i(\mathcal{Z})\}_{i\geq 1}$
is strictly increasing. Because
$\{v_i\}_{i\geq 1}$
is strictly increasing, it follows that
$v_0 \,:\!=\, v_{2}-v_{1} \in \mathbb{N}_0^{q,*}$
and
$ v_0(\mathcal{Z})=v_{2}(\mathcal{Z})-v_{1}(\mathcal{Z}) \in \mathcal{H}\cap \mathbb{N}_0^n\setminus\{0\}$
. This contradicts (ii). The proof is complete.
Funding Information
L.H. was partially supported by the Swiss National Science Foundation’s Early Postdoc Mobility grant P2FRP2 188023. P.X. was partially supported by the U.S. National Science Foundation grant DMS-2246850. The work presented in this article was supported by the Novo Nordisk Foundation (Denmark) grant NNF19OC0058354.
Competing Interests
There were no competing interests to declare which arose during the preparation or publication process of this article.




