Molecular Codes in Biological and Non-Biological Reaction Networks

TL;DR

This paper introduces a formal method to evaluate the semantic capacity of chemical reaction networks, distinguishing systems capable of meaningful information processing from those that are not, with implications for understanding the origin of life.

Contribution

The study presents a novel formal approach to measure semantic capacity in chemical networks, applying it to various real, biological, and artificial systems to identify their information processing potential.

Findings

Bio-chemical systems exhibit high semantic capacity.

Atmospheric and combustion chemistries show no semantic capacity.

Random networks with specific reaction counts can have high semantic capacity.

Abstract

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.