Autocatalytic sets in polymer networks with variable catalysis distributions

TL;DR

This paper studies the emergence of autocatalytic sets in polymer networks with variable catalysis distributions, providing bounds, simulations, and insights into how catalysis and inhibition affect self-sustaining biochemical systems.

Contribution

It introduces a universal bound for catalysis rates in autocatalytic sets and explores how different distributions influence their emergence and size.

Findings

Universal linear bounds for catalysis rate $f$ in autocatalytic sets.

Distribution-dependent probability and size of autocatalytic sets.

Impact of inhibition on the formation of autocatalytic networks.

Abstract

All living systems -- from the origin of life to modern cells -- rely on a set of biochemical reactions that are simultaneously self-sustaining and autocatalytic. This notion of an autocatalytic set has been formalized graph-theoretically (as `RAF'), leading to mathematical results and polynomial-time algorithms that have been applied to simulated and real chemical reaction systems. In this paper, we investigate the emergence of autocatalytic sets in polymer models when the catalysis rate of each molecule type is drawn from some probability distribution. We show that although the average catalysis rate $f$ for RAFs to arise depends on this distribution, a universal linear upper and lower bound for $f$ (with increasing system size) still applies. However, the probability of the appearance (and size) of autocatalytic sets can vary widely, depending on the particular catalysis distribution. We use simulations to explore how tight the mathematical bounds are, and the reasons for the observed variations. We also investigate the impact of inhibition (where molecules can also inhibit reactions) on the emergence of autocatalytic sets, deriving new mathematical and algorithmic results.