Exploring the programmability of autocatalytic chemical reaction networks

Abstract

Abstract Networks of chemical reactions exhibit emergent properties under out-of-equilibrium conditions. Recent advances in systems chemistry demonstrate that networks with sufficient chemical complexity can be harnessed to emulate properties important for neuromorphic computing. In all examples, autocatalysis appears an essential element for facilitating the nonlinear integration of the input and self-regulatory abilities in the output. How this chemical analogue of a positive feedback mechanism can be controlled in a programmable manner is, however, unexplored. Here, we develop a strategy that uses metal ions (Ca2+, La3+, and Nd3+) to control the rate of a trypsin-catalysed autocatalytic reaction network. We demonstrate that this type of control allows for tuning the kinetics in the network, thereby changing the nature of the positive feedback. The simulations and experiments reveal that an input with one or more metal ions allow for temporal and history-dependent outputs that can be mapped onto a variety of mathematical functions.