  Can we objectively distinguish chemical systems that are able to process
meaningful information from those that are not suitable for information
processing? Here, we present a formal method to assess the semantic capacity of
a chemical reaction network. The semantic capacity of a network can be measured
by analyzing the capability of the network to implement molecular codes. We
analyzed models of real chemical systems (Martian atmosphere chemistry and
various combustion chemistries), bio-chemical systems (gene expression, gene
translation, and phosphorylation signaling cascades), as well as an artificial
chemistry and random networks. Our study suggests that different chemical
systems posses different semantic capacities. Basically no semantic capacity
was found in the atmosphere chemistry of Mars and all studied combustion
chemistries, as well as in highly connected random networks, i.e., with these
chemistries molecular codes cannot be implemented. High semantic capacity was
found in the bio-chemical systems, as well as in random networks where the
number of second order reactions is at the number of species. Hypotheses
concern the origin and evolution of life. We conclude that our approach can be
applied to evaluate the information processing capabilities of a chemical
system and may thus be a useful tool to understand the origin and evolution of
meaningful information, e.g., at the origin of life.
