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At the mesoscale, reaction networks are described in terms of stochastic processes. In well-stirred solutions, the time evolution is ruled by the chemical master equation for the probability distribution of the random numbers of molecules. The entropy production is obtained for these reactive processes in the framework of stochastic thermodynamics. The entropy production can be decomposed using the Hill–Schnakenberg cycle decomposition in terms of the affinities and the reaction rates of the stoichiometric cycles of the reaction network. The multivariate fluctuation relation is established for the reactive currents. The results are applied to several examples of reaction networks, in particular, describing autocatalytic bistability, noisy chemical clocks, enzymatic kinetics, and copolymerization processes.
It is standard in chemistry to represent a sequence of reactions by a single overall reaction, often called a complex reaction in contrast to an elementary reaction. Photosynthesis
$6 \text{CO}_2+6 \text{H}_2\text{O} \longrightarrow \text{C}_6\text{H}_{12}\text{O}_6 + 6 \text{O}_2$
is an example of such complex reaction. We introduce a mathematical operation that corresponds to summing two chemical reactions. Specifically, we define an associative and non-communicative operation on the product space
${\mathbb{N}}_0^n\times {\mathbb{N}}_0^n$
(representing the reactant and the product of a chemical reaction, respectively). The operation models the overall effect of two reactions happening in succession, one after the other. We study the algebraic properties of the operation and apply the results to stochastic reaction networks (RNs), in particular to reachability of states, and to reduction of RNs.
Reaction networks are commonly used within the mathematical biology and mathematical chemistry communities to model the dynamics of interacting species. These models differ from the typical graphs found in random graph theory since their vertices are constructed from elementary building blocks, i.e. the species. We consider these networks in an Erdös–Rényi framework and, under suitable assumptions, derive a threshold function for the network to have a deficiency of zero, which is a property of great interest in the reaction network community. Specifically, if the number of species is denoted by n and the edge probability by
$p_n$
, then we prove that the probability of a random binary network being deficiency zero converges to 1 if
$p_n\ll r(n)$
as
$n \to \infty$
, and converges to 0 if
$p_n \gg r(n)$
as
$n \to \infty$
, where
$r(n)=\frac{1}{n^3}$
.
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