Exploring the Programmability of Autocatalytic Chemical Reaction Networks

Abstract Chemical reaction networks (CRNs) could offer chemical complexity1 to contribute to future intelligent material design2 or neuromorphic computing technologies3,4. Recent advances in systems chemistry use the complex Belousov–Zhabotinsky5-7 and Formose reactions8,9, or simpler chemical systems with fewer feedback loops10-12, to demonstrate that (spatio)temporal patterns can be harnessed to emulate properties important for intelligent behavior (i.e., the ability to perceive information and retain it as knowledge to execute complex tasks13). In all examples, autocatalysis appears an essential element for facilitating a nonlinear response. How this chemical analogue of a positive feedback mechanism14,15 can be reconfigured in a programmable manner is, however, unknown. Here, we developed a strategy that uses metal ions (Ca2+, La3+, and Nd3+) to control the rate of a trypsin-catalysed autocatalytic reaction network. A flow setup is employed to sustain the reaction network under out-of-equilibrium conditions and demonstrate that various kinetically controllable responses can be mapped onto polynomial and Boolean functions. Remarkably, these functions cannot only be programmed but their temporal and history-dependent nature bestows them with neuromorphic properties, promising novel strategies in designing intelligent chemical systems. Beyond, the easy-to-use method to control autocatalysis will impact future development focusing on biocatalysis, nanobiotechnology, and the chemical origin of life.