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.