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Research article Special Issues

Complex-balanced equilibria of generalized mass-action systems: necessary conditions for linear stability

  • It is well known that, for mass-action systems, complex-balanced equilibria are asymptotically stable. For generalized mass-action systems, even if there exists a unique complex-balanced equilibrium (in every stoichiometric class and for all rate constants), it need not be stable. We first discuss several notions of matrix stability (on a linear subspace) such as D-stability and diagonal stability, and then we apply abstract results on matrix stability to complex-balanced equilibria of generalized mass-action systems. In particular, we show that linear stability (on the stoichiometric subspace and for all rate constants) implies uniqueness. For cyclic networks, we characterize linear stability (in terms of D-stability of the Jacobian matrix); and for weakly reversible networks, we give necessary conditions for linear stability (in terms of D-semistability of the Jacobian matrices of all cycles in the network). Moreover, we show that, for classical mass-action systems, complex-balanced equilibria are not just asymptotically stable, but even diagonally stable (and hence linearly stable). Finally, we recall and extend characterizations of D-stability and diagonal stability for matrices of dimension up to three, and we illustrate our results by examples of irreversible cycles (of dimension up to three) and of reversible chains and S-systems (of arbitrary dimension).

    Citation: Balázs Boros, Stefan Müller, Georg Regensburger. Complex-balanced equilibria of generalized mass-action systems: necessary conditions for linear stability[J]. Mathematical Biosciences and Engineering, 2020, 17(1): 442-459. doi: 10.3934/mbe.2020024

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  • It is well known that, for mass-action systems, complex-balanced equilibria are asymptotically stable. For generalized mass-action systems, even if there exists a unique complex-balanced equilibrium (in every stoichiometric class and for all rate constants), it need not be stable. We first discuss several notions of matrix stability (on a linear subspace) such as D-stability and diagonal stability, and then we apply abstract results on matrix stability to complex-balanced equilibria of generalized mass-action systems. In particular, we show that linear stability (on the stoichiometric subspace and for all rate constants) implies uniqueness. For cyclic networks, we characterize linear stability (in terms of D-stability of the Jacobian matrix); and for weakly reversible networks, we give necessary conditions for linear stability (in terms of D-semistability of the Jacobian matrices of all cycles in the network). Moreover, we show that, for classical mass-action systems, complex-balanced equilibria are not just asymptotically stable, but even diagonally stable (and hence linearly stable). Finally, we recall and extend characterizations of D-stability and diagonal stability for matrices of dimension up to three, and we illustrate our results by examples of irreversible cycles (of dimension up to three) and of reversible chains and S-systems (of arbitrary dimension).


    In their foundational paper from 1972, Horn and Jackson considered chemical reaction networks (CRNs) with mass-action kinetics [1]. In particular, they proved that complex-balanced equilibria are asymptotically stable (for all rate constants), by using an entropy-like Lyapunov function. However, mass-action kinetics is an assumption that holds for elementary reactions in homogeneous and dilute solutions. In intracellular environments, which are highly structured and crowded, more general kinetics such as power laws are needed [2,3]. Already Horn and Jackson observed that every CRN with power-law kinetics can be written as another CRN with mass-action kinetics (possibly with non-integer stoichiometric coefficients). Typically, the resulting network does not have desired properties such as weak reversibility and zero deficiency. The more recent definition of CRNs with generalized mass-action kinetics (involving both stoichiometric coefficients and kinetic orders) allows to study power-law kinetics without having to rewrite the network [4,5]. Moreover, a CRN with classical mass-action kinetics may not have zero deficiency and may not be weakly reversible, but there may be a dynamically equivalent CRN with generalized mass-action kinetics that has the desired properties [6].

    For the resulting generalized mass-action systems, existence and uniqueness of complex-balanced equilibria (in every stoichiometric class and for all rate constants) is well understood [4,7], but not much is known about their stability. Even planar S-systems with a unique complex-balanced equilibrium display rich dynamical behavior, including super/sub-critical or degenerate Hopf bifurcations, centers, and up to three limit cycles; see [8,9,10,11].

    For chemical reaction networks with classical mass-action kinetics, linear Lyapunov functions have been used in approaches to the global attractor conjecture [12,13]. For certain classes of networks, in particular, non-autocatalytic networks with monotonic kinetics, piecewise linear/polyhedral Lyapunov functions have been constructed [14,15]. However, no Lyapunov functions are known for complex-balanced equilibria of arbitrary generalized mass-action systems, and hence we first approach the problem by linearization. In other words, instead of asymptotic stability, we investigate linear stability (on the stoichiometric subspace and for all rate constants). To this end, we first discuss several notions of matrix stability (on a linear subspace) such as D-stability and diagonal stability; see [16,Ch. 2], [17,Ch. 26] and the references therein and [18] for the first application to chemical reaction networks. In particular, diagonal stability of the Jacobian matrix allows to construct Lyapunov functions with separated variables. Our main results characterize linear stability of complex-balanced equilibria (on the stoichiometric subspace and for all rate constants) for cyclic networks and give necessary conditions for weakly reversible networks.

    In the setting of classical mass-action systems, we prove that complex-balanced equilibria are not just asymptotically stable, but even diagonally stable (and hence linearly stable). For an alternative proof, see [19,Thm. 15.2.2.]. As opposed to asymptotic stability, linear stability is robust with respect to small perturbations of the system. In particular, this allows to show the robustness of the classical deficiency zero theorem with respect to small perturbations of the kinetic orders from the stoichiometric coefficients; see [7,Cor. 47].

    Organization of the work and main results. In Section 2, we introduce generalized mass-action systems, and in Section 3, we discuss several notions of matrix stability (on a linear subspace). In Section 4, we present our main results: For classical mass-action systems, complex-balanced equilibria are diagonally stable (and hence linearly stable); see Theorem 4.1. For generalized mass-action systems, linear stability of complex balanced equilibria implies uniqueness; see Theorem 4.3. For cyclic networks, linear stability is equivalent to D-stability of the Jacobian matrix; see Theorem 11. For weakly reversible networks, linear stability implies D-semistability of the Jacobian matrices of all cycles in the network; see Theorem 13. In Section 5, we recall and extend characterizations of notions of matrix stability, and finally, in Section 6, we illustrate our results by a series of examples.

    A generalized chemical reaction network (G,y,˜y) is based on a directed graph G=(V,E) without self-loops; every vertex iV={1,,m} is labeled with a (stoichiometric) complex y(i)Rn0, and every source vertex iVsV is labeled with a kinetic-order complex ˜y(i)Rn. If every component of G is strongly connected, we call G and (G,y,˜y) weakly reversible.

    A generalized mass-action system (Gk,y,˜y) is a generalized chemical reaction network (G,y,˜y) together with edge labels kRE+, resulting in the labeled digraph Gk. Every edge (ii)E, representing the chemical reaction y(i)y(i), is labeled with a rate constant kii>0.

    The ODE system for the species concentrations xRn+, associated with the generalized mass-action system (Gk,y,˜y), is given by

    dxdt=(ii)Ekiix˜y(i)(y(i)y(i)). (1)

    The sum ranges over all reactions, and every summand is a product of the reaction rate and the difference of product and educt complexes. Thereby, for xRn+ and yRn, we define xy=xy11xynnR+. Moreover, for Y=(y1,,ym)Rn×m, we define xYRm+ via (xY)j=xyj for j=1,,m.

    Example 1. Consider the Lotka reactions 0X, XY, and Y0 with generalized mass-action kinetics

    Thereby, the first vertex is labeled with the stoichiometric complex 0 and the kinetic-order complex α1X+β1Y in brackets. (Analogously, for the second and third vertex.) Hence, the reaction rate of 0X is given by k12xα1yβ1.

    The associated ODE system is given by

    dxdt=k12xα1yβ1k23xα2yβ2,dydt=k23xα2yβ2k31xα3yβ3.

    The right-hand-side of the ODE system (1) can be decomposed into stoichiometric, graphical, and kinetic-order contributions,

    dxdt=YIEdiag(k)(IsE)Tx˜Y=YAkx˜Y, (2)

    where Y,˜YRn×V are the matrices of stoichiometric and kinetic-order complexes, IE,IsERV×E are the incidence and source matrices* of the digraph G, and Ak=IEdiag(k)(IsE)TRV×V is the Laplacian matrix of the labeled digraph Gk. (We note that columns of ˜Y corresponding to non-source vertices can be chosen arbitrarily.)

    *Explicitly, (IE)i,jj=1 if i=j, (IE)i,jj=1 if i=j, and (IE)i,jj=0 otherwise. Further, (IsE)i,jj=1 if i=j and (IsE)i,jj=0 otherwise. Finally, (Ak)i,i=kii if (ii)E, (Ak)i,i=iikii, and (Ak)i,i=0 otherwise.

    Clearly, the change over time lies in the stoichiometric subspace

    S=im(YIE),

    that is, dxdtS. Equivalently, trajectories are confined to cosets of S, that is, x(t)x(0)+S. For xRn+, the set (x+S)Rn+ is called a stoichiometric class.

    Analogously, we introduce the kinetic-order subspace

    ˜S=im(˜YIE).

    A positive equilibrium xRn+ of the ODE system (2) that fulfills Akx˜Y=0 is called a complex-balanced equilibrium. The set of all complex-balanced equilibria is given by

    Zk={xRn+Akx˜Y=0}.

    The Jacobian matrix of the right-hand-side of (2) is given by

    J(x)=YAkdiag(x˜Y)˜YTdiag(x1). (3)

    For given generalized chemical reaction network (G,y,˜y), we study whether, for all rate constants kRE+ (such that Zk), all complex-balanced equilibria xZk are linearly stable on S, that is, the corresponding Jacobian matrices J(x) are stable on S.

    Before we turn to stability, we recall the well-known fact that every positive vector is a complex-balanced equilibrium for some rate constant (see e.g. the proof of Lemma 1 in [5]). Further, we state a characterization of the uniqueness of complex-balanced equilibria in terms of sign vectors of the stoichiometric and kinetic-order subspaces (see Proposition 3.1 in [4]).

    Proposition 2. Consider a weakly reversible generalized chemical reaction network, and let xRn+. Then, there exist rate constants kRE+ such that xZk.

    Proposition 3. Consider a weakly reversible generalized chemical reaction network. There exists at most one complex-balanced equilibrium in every stoichiometric class and for all rate constants if and only if sign(S)sign(˜S){0}.

    For surveys on the uniqueness of equilibria and related injectivity results, see [20,21,22].

    Let S be a linear subspace of Rn. For an ODE system ˙x=f(x) with f:RnRn and imfS, cosets of S are forward invariant. Hence, given an equilibrium xRn, one is interested in its asymptotic stability on the coset x+S. To approach the problem via linearization, one considers the Jacobian matrix J(x)=fxRn×n, more precisely, the linear map J(x)|S:SS. By the Hartman-Grobman Theorem, if x is hyperbolic (that is, if all eigenvalues of J(x)|S have non-zero real part), then the original ODE system is dynamically equivalent (technically: topologically conjugate) to the linear ODE system ˙y=J(x)y on S; see e.g. [23,Thm. 9.9]. In the following, we recall notions of stability of a square matrix and extend them to stability on a linear subspace.

    A square matrix is stable (respectively, semistable) if all its eigenvalues have negative (respectively, non-positive) real part. We start with a matrix formulation of Lyapunov's Theorem; see [24,Ch. XV,Thm. 3'] or [16,Thm. 2.2.1].

    Proposition 4. Let ARn×n. The following implications hold:

    Proof. Obviously, the implications from left to right hold.

    The equivalence on the left is Lyapunov's Theorem.

    Finally, if there exists P=PT>0 with PA+ATP0, then the origin is Lyapunov stable for the linear ODE ˙x=Ax (by the Lyapunov function xxTPx), and thus A cannot have an eigenvalue with positive real part.

    Let S be a linear subspace. We say that a square matrix A with imAS is stable on S (respectively, semistable on S) if all eigenvalues of the linear map A|S:SS have negative (respectively, non-positive) real part. We extend Lyapunov's Theorem to stability on a linear subspace.

    Proposition 5. Let ARn×n and SRn be a linear subspace with imAS. The following implications hold:

    Proof. Obviously, the implications from left to right hold.

    To prove the equivalence on the left, let s=dimS and S=imB with BRn×s and BTB=IRs×s. Then, for every xS, there exists a unique yRs such that x=By. Further, y=BTx and, using ˙x=Ax (on S), we have ˙y=BTABy (on Rs). Thus, A is stable on S if and only if BTABRs×s is stable on Rs. By Proposition 3.1, the latter is equivalent to the existence of QRs×s with Q=QT>0 such that yT(QBTAB+BTATBQ)y<0 for all 0yRs. This is further equivalent to the existence of P=BQBTRn×n with P=PT>0 on S such that xT(PA+ATP)x<0 for all 0xS.

    Finally, let P=PT>0 on S and PA+ATP0 on S. With B as above, let Q=BTPBRs×s and note that A=BBTA (because x=BBTx for all xS). Then Q=QT>0 and QBTAB+BTATBQ0. By Proposition 3.1, BTAB is semistable, and, as in the previous paragraph, the latter is equivalent to A being semistable on S.

    We recall more notions of stability of square matrices. For convenience, let D+Rn×n denote the set of diagonal matrices with positive diagonal. A matrix ARn×n is diagonally stable (respectively, diagonally semistable) if there exists PD+ such that PA+ATP<0 (respectively, PA+ATP0). We note that diagonal stability is also known as Lyapunov diagonal stability or Volterra-Lyapunov stability. A matrix ARn×n is D-stable (respectively, D-semistable) if AD is stable (respectively, semistable) for all DD+. The following statement summarizes the relations between these notions.

    Proposition 6. Let ARn×n. The following implications hold:

    Proof. Obviously, the implications from left to right hold.

    To prove the implications from the first to the second row, let PD+ be such that PA+ATP<0 (respectively, PA+ATP0). Then, for any DD+, we have (DP)(AD)+(AD)T(DP)=D(PA+ATP)D<0 (respectively, 0), and by Proposition 3.1, AD is stable (respectively, semistable).

    Finally, the implications from the second row to the third row are trivial. (If AD is (semi)stable for any DD+, then this holds for D=I.)

    Let S be a linear subspace. We say that ARn×n with imAS is diagonally stable on S (respectively, diagonally semistable on S) if there exists PD+ such that PA+ATP<0 on S (respectively, PA+ATP0 on S). We say that ARn×n with imAS is D-stable on S (respectively, D-semistable on S) if AD is stable on S (respectively, semistable on S) for all DD+. Finally, we introduce an even stronger notion. We say that ARn×n with imAS is diagonally D-stable on S (respectively, diagonally D-semistable on S) if, for all DD+, there exists PD+ such that PAD+DATP<0 on S (respectively, PAD+DATP0 on S). We note that diagonal D-stability on S trivially implies diagonal stability on S, and the two notions agree for S=Rn, see also [25,p. 257]. Moreover, we have the following relations.

    Proposition 7. Let ARn×n and SRn be a linear subspace with imAS. The following implications hold:

    Proof. Obviously, the implications from left to right hold.

    The implications from the first to the second row follow immediately from Proposition 3.2.

    Finally, the implications from the second row to the third row are trivial.

    In the following, we say that an equilibrium x is linearly stable (in its stoichiometric class x+S) if the Jacobian matrix J(x) is stable on S. In short:

    x is linearly stable (in x+S), if J(x) is stable on S.

    Analogously, we say that x is diagonally D-stable/diagonally stable/D-stable (in x+S) if J(x) is diagonally D-stable/diagonally stable/D-stable on S.

    We start by showing that, for classical mass-action systems (where ˜y=y), complex-balanced equilibria are diagonally D-stable (and hence diagonally stable/D-stable/linearly stable) in their stoichiometric classes, and not just locally asymptotically stable (as shown via the classical entropy-like Lyapunov function).

    Linear stability of complex-balanced equilibria was shown in [26,Theorem 4.3.2] and [19,Theorem 15.2.2.]. In the latter work, the author also proved diagonal stability, without noting it and using an ad-hoc inequality. Here, we even show diagonal D-stability, thereby using the negative semi-definiteness of the Laplacian matrix of an undirected graph.

    Theorem 8. Consider a weakly reversible chemical reaction network. Then, for all rate constants, complex-balanced equilibria are linearly stable (in their stoichiometric classes). In fact, they are diagonally D-stable (and hence also diagonally stable, D-stable, and linearly stable).

    Proof. Let kRE+ and xZk. We show that the corresponding Jacobian matrix J=J(x) is diagonally D-stable on S. By Proposition 3.4, all other conclusions follow.

    Let DD+. We show that there exists P=diag((x)1)DD+ such that H=PJD+DJTP<0 on S. Let kRE+ be defined by kii=kii(x)y(i) for (ii)E. Then, Akdiag((x)Y)=Ak. Using (3) for the Jacobian matrix J, we have

    H=PY(Ak+ATk)YTP.

    Now, let Aˉk=Ak+ATk and hence H=PYAˉkYTP. The symmetric matrix Aˉk is the Laplacian matrix of a labeled undirected graph ˉGˉk with ˉG=(V,ˉE) and ˉkRˉE+. In particular, Aˉk=IEdiag(ˉk)ITE for some directed version E of the undirected edges ˉE and the corresponding incidence matrix IE (with imIE=imIE); see e.g. [27]. Hence, Aˉk0 and H=PYIEdiag(ˉk)ITEYTP0 on S.

    Suppose vTHv=0 for some vS. Then, ITEYTPv=0. Now, S=im(YIE)=im(YIE), S=ker(ITEYT), and hence PvS. Clearly, vTPv=0 which finally implies v=0, since P>0.

    Hence, H<0 on S, and J is diagonally D-stable on S.

    In the result above, we prove diagonal stability of J on S, by providing a diagonal, positive definite matrix P such that PJ+JTP<0 on S. This property implies the existence of a function with separated variables L(x)=ni=1Li(xi) that serves as a Lyapunov function for showing the asymptotic stability of the complex-balanced equilibrium xRn+ (in its stoichiometric class).

    As an example, consider a scaled version of the classical entropy-like Lyapunov function L(x)=ni=1pixi[xi(log(xi/xi)1)+xi], where P=diag(p1,,pn), and the function g(x)=L(x)f(x), where dxdt=f(x). Then, the gradient of g at x vanishes, and the Hessian matrix of g at x is H=PJ+JTP with H<0 on S. Hence, in x+S, the function g has a local maximum at x with g(x)=0. That is, L is a Lyapunov function at x, and x is asymptotically stable (in x+S).

    Now, we turn to generalized mass-action systems. We show that linear stability of complex-balanced equilibria implies uniqueness (in their stoichiometric classes). We start with a useful technical result.

    Lemma 9. Consider a weakly reversible generalized mass-action system, and let xZk. Then, ker(Akdiag((x)˜Y))=kerITE.

    Proof. The dimension of kerITE equals the number of connected components of the graph G=(V,E), and the characteristic vectors of the vertex sets of the connected components form a basis.

    Let kRE+ be defined by kii=kii(x)˜y(i) for (ii)E. Then, Akdiag((x)˜Y)=Ak. Clearly, Ak is the Laplacian matrix of the labeled directed graph Gk, and the dimension of its kernel also equals the number of connected components of G; see e.g. [5,Section 4] and the references therein.

    By definition, xZk if Ak(x)˜Y=0. Due to the block structure of the Laplacian matrix, the characteristic vectors of the vertex sets of the connected components are in the kernel of Akdiag((x)˜Y). (As an example, consider a strongly connected graph G, and let 1V be the characteristic vector of V. Then, Akdiag((x)˜Y)1V=Ak(x)˜Y=0.) Altogether, ker(Akdiag((x)˜Y))=kerITE.

    Theorem 10. Consider a weakly reversible generalized chemical reaction network. If, for all rate constants, complex-balanced equilibria are linearly stable (in their stoichiometric classes), then they are unique (in their stoichiometric classes).

    Proof. Assume that, for some kRE+, there exists a stoichiometric class with at least two complex-balanced equilibria. Then, by Proposition 2.3, there exist vectors 0u˜S and 0vS with sign(u)=sign(v). Now, ˜S=im(˜YIE), ˜S=ker(ITE˜YT), and hence ITE˜YTu=0. Let xRn+ be such that u=diag((x)1)v, and let kRE+ be such that xZk, see Proposition 2.2. Then,

    ITE˜YTdiag((x)1)v=0,

    that is, ˜YTdiag((x)1)vkerITE=ker(Akdiag((x)˜Y)), the latter by Lemma 4.2. Using (3) for the Jacobian matrix J, we have

    Jv=YAkdiag((x)˜Y)˜YTdiag((x)1)v=0.

    Hence, for all P=PT>0, we have vT(PJ+JTP)v=0, and by Proposition 3.2, J is not stable on S.

    As can be demonstrated by the generalized reaction network X(X)Y(0), linear stability implies uniqueness, but not existence in every stoichiometric class.

    In the following result, we characterize diagonal stability and linear stability of complex-balanced equilibria, provided that the network is a cycle.

    Theorem 11. Consider a cyclic generalized chemical reaction network, and let A=YAk=1˜YT. The following implications hold:

    Proof. The implication in the first (respectively, second) row follows immediately from Proposition 3.2 (respectively, Proposition 3.4).

    To prove the two equivalences, let kRE+ and xZk. Since G=(V,E) is a cycle, the quantity c=kii(x)˜y(i) is the same for all (ii)E. In particular, Akdiag((x)˜Y)=cAk=1. Using (3) for the Jacobian matrix J, we have

    J=cYAk=1˜YTD

    with D=diag((x)1). Finally, recall that every xRn+ is a complex-balanced equilibrium for some kRE+, see Proposition 2.2. Hence, every DD+ is of the form D=diag((x)1) for some k, and the equivalences follow.

    In the previous subsection, we characterized diagonal stability and linear stability of complex-balanced equilibria, provided that the network is a cycle. In this subsection, we extend those results to weakly reversible networks, but instead of equivalent we only give necessary conditions for diagonal and linear stability. We start with a useful technical result.

    Lemma 12. Consider a weakly reversible generalized chemical reaction network, let C be a cycle of the network, and let xRn+. Then, there exists a family of rate constants kεRE+ (with ε>0) such that xZkε for every ε>0 and JεYACk=1˜YTdiag((x)1) as ε0, where ACk=1 is the Laplacian matrix of the cycle C with all rate constants set to 1.

    Proof. For a cycle C, let kCRE0 be defined by

    kCii=1(x)˜y(i){1, if (ii)C,0, if (ii)C.

    For ε>0, let kε=kC+εCCkC, where the summation is over all cycles C of G, except for the given cycle C. Then, xZkε and

    Akεdiag((x)˜Y)=ACk=1+εCCACk=1.

    Using (3) for the Jacobian matrix Jε, we obtain the desired limit as ε0.

    Theorem 13. Consider a weakly reversible generalized chemical reaction network, and, for a cycle C of the network, let AC=YACk=1˜YT and SC be the corresponding stoichiometric subspace. The following implications hold:

    Proof. The implications in the first (respectively, second) row follows immediately from Proposition 3.2 (respectively, Proposition 7).

    Next, we prove the implication in the right column. Assume there exists a cycle C in G and a matrix DD+ such that ACD has an eigenvalue with positive real part. Let xRn+ be such that D=diag((x)1). Further, let kεRE+ (with ε>0) be a family of rate constants as in Lemma 4.5, and let Jε denote the corresponding Jacobian matrix. Since JεACD as ε0 and, in general, the spectrum of a matrix depends continuously on its entries, the matrix Jε has an eigenvalue with positive real part for ε>0 small enough. That is, the complex-balanced equilibrium x is not linearly stable.

    Finally, we prove the implication in the left column (by contradiction). Assume there exists a cycle C in G and a matrix DD+ such that for all PD+ there exists a wSC with

    wT(PACD+D(AC)TP)w>0. (4)

    Clearly, the term vT(PB+BTP)v is continuous (in all arguments vS, PD+, BRn×n), and hence the map g, defined by

    g(B)=minPD+:P=1maxvS:v=1vT(PB+BTP)v

    is also continuous, since maximum and minimum are taken over compact sets. Since SC is a subspace of S (and hence wS), the inequality (4) implies g(ACD)>0.

    As above, let xRn+ be such that D=diag((x)1). Further, let kεRE+ (with ε>0) be a family of rate constants as in Lemma 4.5, and let Jε denote the corresponding Jacobian matrix. Since, by assumption, all complex-balanced equilibria are diagonally stable, there exists PεD+ such that PεJε+(Jε)TPε<0 on S. As a consequence, g(Jε)<0 for all ε>0, and

    0<g(ACD)=g(limε0Jε)=limε0g(Jε)0,

    a contradiction.

    For both D-stability and diagonal stability, an explicit characterization is available only up to dimension three. For arbitrary dimension, we recall the following necessary conditions (see e.g. [25,Section 2]).

    Proposition 14. Let ARn×n. The following statements hold.

    (i) If A is D-stable, then A is a P+0-matrix (that is, all its signed principal minors are non-negative and at least one of each order is positive).

    (ii) If A is diagonally stable, then A is a P-matrix (that is, all its signed principal minors are positive).

    It is relatively easy to check that, for dimension two, equivalence holds in the previous result.

    Proposition 15. Let AR2×2. The following statements hold.

    (i) The matrix A is D-stable if and only if it is a P+0-matrix.

    (ii) The matrix A is diagonally stable if and only if it is a P-matrix.

    For dimension three, we have the following characterizations (see [28] and [25,Theorem 4(c)]). Thereby, for ARn×n, let Mij denote the principal minor (of order two) aiiajjaijaji.

    Proposition 16. Let AR3×3. The following statements hold.

    (i) The matrix A is D-stable if and only if

    (a) A is a P+0-matrix and

    (b) the three pairs (a11,M23), (a22,M13), (a33,M12) dominate detA, that is,

    (a11M23+a22M13+a33M12)2detA

    with equality implying that at least one of the three pairs has exactly one member equal to zero.

    (ii) The matrix A is diagonally stable if and only if

    (a) A is a P-matrix and

    (b) there exists yR such that

    (a13y+a31)24a11a33y<0  and(b1y+b2)24M12M23y<0,

    where b1=a12a23a22a13 and b2=a21a32a22a31.

    Finally, as new results, we characterize D-(semi)stability on a linear subspace with dimension one or two.

    Proposition 17. Let ARn×n and SRn be a linear subspace with imAS and dimS=1. Then, A is D-semistable on S if and only if aii0 for all i.

    Proof. For DD+, the characteristic polynomial of AD is

    det(λIAD)=λn1(λ+b) with b=1in(aii)di.

    Thus, A is D-semistable on S if and only if b0 for all d1,,dn>0 which is equivalent to aii0 for all i.

    Proposition 18. Let ARn×n and SRn be a linear subspace with imAS and dimS=2. Then, A is D-stable on S if and only if

    (a) aii0 for all i and aii<0 for some i and

    (b) Mij0 for all ij and Mij>0 for some ij.

    Proof. For DD+, the characteristic polynomial of AD is

    det(λIAD)=λn2(λ2+bλ+c)

    with

    b=1in(aii)diandc=1i,jnijMijdidj.

    Thus, A is D-stable on S if and only if b>0 and c>0 for all d1,,dn>0 which is equivalent to (a) and (b).

    To illustrate our main results, Theorems 4.4 and 4.6, we present a series of examples. In particular, we consider the following networks, leading to special ODE systems:

    ● irreversible three-cycle (dimension two) trinomial ODE system

    ● irreversible three-cycle (dimension three, stoichiometric subspace of dimension two) binomial ODE system

    ● irreversible four-cycle (dimension three) binomial ODE system

    ● reversible chain (arbitrary dimension)

    ● S-system (arbitrary dimension) binomial ODE system

    For the two- and three-dimensional examples, we use the characterizations of D-stability and diagonal stability given in Propositions 5.2, 5.3, and 5.5. For the examples with arbitrary dimension, we use Proposition 17.

    Example 19. Consider the generalized mass-action system

    and the resulting ODE system

    dxdt=k12(a2a1)xα1yβ1+k23(a3a2)xα2yβ2+k31(a1a3)xα3yβ3,dydt=k12(b2b1)xα1yβ1+k23(b3b2)xα2yβ2+k31(b1b3)xα3yβ3.

    The matrix YAk=1˜YT is given by

    (YAk=1˜YT)11=α1(a2a1)+α2(a3a2)+α3(a1a3),(YAk=1˜YT)12=β1(a2a1)+β2(a3a2)+β3(a1a3),(YAk=1˜YT)21=α1(b2b1)+α2(b3b2)+α3(b1b3),(YAk=1˜YT)22=β1(b2b1)+β2(b3b2)+β3(b1b3),

    and

    det(YAk=1˜YT)=det[a2a1b2b1a3a2b3b2]dabdet[α2α1β2β1α3α2β3β2]dαβ.

    Now, assume dab0, that is, dimS=2. By Theorem 11 and Proposition 5.2(i), the unique complex-balanced equilibrium is linearly stable for all k if and only if

    dabdαβ>0,α1(a2a1)+α2(a3a2)+α3(a1a3)0,β1(b2b1)+β2(b3b2)+β3(b1b3)0,

    and the latter two are not both zero.

    Example 20. Consider the generalized mass-action system

    and the resulting ODE system

    dxdt=k31xα3yβ3zγ3k12xα1yβ1zγ1,dydt=k12xα1yβ1zγ1k23xα2yβ2zγ2,dzdt=k23xα2yβ2zγ2k31xα3yβ3zγ3.

    Clearly, dimS=2. The matrix YAk=1˜YT is given by

    [α3α1β3β1γ3γ1α1α2β1β2γ1γ2α2α3β2β3γ2γ3].

    By Theorem 11 and Proposition 18, all complex-balanced equilibria are linearly stable if and only if

    α3α1,β1β2,γ2γ3

    with at least one of the three inequalities satisfied strictly and

    det[α3α1β3β1α1α2β1β2]0,det[α3α1γ3γ1α2α3γ2γ3]0,det[β1β2γ1γ2β2β3γ2γ3]0

    with at least one of the three inequalities satisfied strictly.

    Example 21. Consider the generalized mass-action system

    and the resulting ODE system

    dxdt=k12xα1yβ1zγ1k23xα2yβ2zγ2,dydt=k23xα2yβ2zγ2k34xα3yβ3zγ3,dzdt=k34xα3yβ3zγ3k41xα4yβ4zγ4.

    Clearly, dimS=3. The matrix YAk=1˜YT is given by

    [α1α2β1β2γ1γ2α2α3β2β3γ2γ3α3α4β3β4γ3γ4].

    For simplicity, we consider particular kinetic orders

    and the resulting matrix

    YAk=1˜YT=10γ1α10α1β1.

    In the following table, we choose particular values for the kinetic orders α, β, and γ, leading to different situations regarding stability, D-stability, diagonal stability, and being a P+0-matrix.

    In the first and second case, for all rate constants, there exists a complex-balanced equilibrium which is linearly stable by Theorem 11 and Proposition 16(i) and unique by Theorem 10. In the third and fourth case, the unique complex-balanced equilibrium is linearly stable for some rate constants, but not for all.

    Example 22. Consider the generalized chemical reaction network

    \begin{matrix} y(1) \\ (\tilde{y}(1)) \\ \end{matrix}\rightleftarrows \begin{matrix} y(2) \\ (\tilde{y}(2)) \\ \end{matrix}\rightleftarrows \cdots \rightleftarrows \begin{matrix} y(m) \\ (\tilde{y}(m)) \\ \end{matrix}

    where the graph G is a reversible chain. In particular, all cycles of G correspond to reversible reactions. For every cycle i \rightleftarrows i+1 (denoted by C ), we have

    Y A_{k = 1}^C \widetilde Y^ \mathsf{T} = -(y(i+1)-y(i))(\tilde{y}(i+1)-\tilde{y}(i))^ \mathsf{T} ,

    a dyadic product. By Proposition 17, this (rank-one) matrix is D-semistable on S^C = {\rm{im}} (y(i+1)-y(i)) if and only if all diagonal entries are non-positive. By Theorem 13, if there is a cycle i \rightleftarrows i+1 and an index (species) s \in \{1, \ldots, n\} with

    -(y(i+1)-y(i))_s(\tilde{y}(i+1)-\tilde{y}(i))_s \gt 0 ,

    then there is a complex-balanced equilibrium (for some rate constants) that is not linearly stable.

    Example 23. For the real matrices G, H \in {\mathbb R}^{n \times n} and the positive vectors \alpha, \beta \in {\mathbb R}^n_+ , the ODE system

    \begin{align*} \dot{x}_1 & = \alpha_1 \, x_1^{g_{11}}\cdots x_n^{g_{1n}} - \beta_1 \, x_1^{h_{11}}\cdots x_n^{h_{1n}} \\ & \;\, \vdots \\ \dot{x}_n & = \alpha_n \, x_1^{g_{n1}}\cdots x_n^{g_{nn}} - \beta_n \, x_1^{h_{n1}}\cdots x_n^{h_{nn}} \\ \end{align*}

    on {\mathbb R}^n_+ is called an S-system [2,3].

    This ODE is associated to the generalized mass-action system

    \begin{matrix} \begin{align} & \mathsf{0} \\ & ({{g}_{11}}{\mathsf{X}_{1}}+\cdots +{{g}_{1n}}{\mathsf{X}_{n}}) \\ \end{align} & \underset{{{\beta }_{1}}}{\overset{{{\alpha }_{1}}}{\longleftrightarrow}} & \begin{matrix} {\mathsf{X}_{1}} \\ ({{h}_{11}}{\mathsf{X}_{1}}+\cdots +{{h}_{1n}}{\mathsf{X}_{n}}) \\ \end{matrix} \\ {} & \vdots & {} \\ \begin{matrix} \mathsf{0} \\ ({{g}_{n1}}{\mathsf{X}_{1}}+\cdots +{{g}_{nn}}{\mathsf{X}_{n}}) \\ \end{matrix} & {} & \begin{matrix} {\mathsf{X}_{n}} \\ ({{h}_{n1}}{\mathsf{X}_{1}}+\cdots +{{h}_{nn}}{\mathsf{X}_{n}}) \\ \end{matrix} \\ \end{matrix}

    For every cycle C (corresponding to a reversible reaction \mathsf{0} \rightleftarrows \mathsf{X}_i ), the matrix YA_{k = 1}^C\widetilde{Y}^ \mathsf{T} has (g_{\cdot i}-h_{\cdot i})^ \mathsf{T} as its i -th row, and all other rows are zero. By Proposition 17, this (rank-one) matrix is D-semistable on S^C = {\rm{im}} (e_i) if and only if the diagonal entry g_{ii}-h_{ii} is non-positive. By Theorem 13, if there is a reversible reaction \mathsf{0} \rightleftarrows \mathsf{X}_i such that g_{ii} > h_{ii} , then there is a complex-balanced equilibrium (for some rate constants) that is not linearly stable.

    We thank three anonymous referees for helpful comments. BB and SM were supported by the Austrian Science Fund (FWF), project 28406. GR was supported by the FWF, project 27229.

    The authors declare there is no conflicts of interest.



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