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Abstract The Hilbert numberH(n) is defined as the maximum number of limit cycles of a planar autonomous system of ordinary differential equations (ODEs) with right-hand sides containing polynomials of degree at most$$n \in {{\mathbb {N}}}$$ $n\in N$. The dynamics of chemical reaction systems with two chemical species can be (under mass-action kinetics) described by such planar autonomous ODEs, wherenis equal to the maximum order of the chemical reactions in the system. Analogues of the Hilbert number H(n) for three different classes of chemical reaction systems are investigated: (i) chemical systems with reactions up to then-th order; (ii) systems with up ton-molecular chemical reactions; and (iii) weakly reversible chemical reaction networks. In each case (i), (ii) and (iii), the question on the number of limit cycles is considered. Lower bounds on the modified Hilbert numbers are provided for both algebraic and non-algebraic limit cycles. Furthermore, given a general algebraic curve$$h(x,y)=0$$ $h(x,y)=0$of degree$$n_h \in {{\mathbb {N}}}$$ $n_h\in N$and containing one or more ovals in the positive quadrant, a chemical system is constructed which has the oval(s) as its stable algebraic limit cycle(s). The ODEs describing the dynamics of the constructed chemical system contain polynomials of degree at most$$n=2\,n_h+1.$$ $n=2n_h+1.$Considering$$n_h \ge 4,$$ $n_h\ge 4,$the algebraic curve$$h(x,y)=0$$ $h(x,y)=0$can contain multiple closed components with the maximum number of ovals given by Harnack’s curve theorem as$$1+(n_h-1)(n_h-2)/2$$ $1+(n_h-1)(n_h-2)/2$, which is equal to 4 for$$n_h=4.$$ $n_h=4.$Algebraic curve$$h(x,y)=0$$ $h(x,y)=0$with$$n_h=4$$ $n_h=4$and the maximum number of four ovals is used to construct a chemical system which has four stable algebraic limit cycles. 

Chemical reaction networks describe interactions between biochemical species. Once an underlying reaction network is given for a biochemical system, the system dynamics can be modelled with various mathematical frameworks such as continuous-time Markov processes. In this manuscript, the identifiability of the underlying network structure with a given stochastic system dynamics is studied. It is shown that some data types related to the associated stochastic dynamics can uniquely identify the underlying network structure as well as the system parameters. The accuracy of the presented network inference is investigated when given dynamical data are obtained via stochastic simulations.