In this article, we implement a localized autocatalytic molecular system inside a closed microfluidic chamber. Due to the enzymatic nature of the catalytic process, a variety of parasitic species eventually emerge and compete with the legitimate molecular process for fuel. The behaviors observed range from the creation of large stable structures to that of small diffusing particles. Those results, along with the modularity of the molecular system, show that the proposed experimental setup can be used safely for further study of the evolution of parasitic behaviors at the molecular scale.

We design and implement a temperature-based input mechanism for molecular reservoir computing. Using temperature allows us to interact with the system while keeping it chemically closed, a crucial step to use the reservoir computing approach with standard laboratory equipment. We implement the reservoir with a robust molecular oscillator, subjecting it to sudden temperature variations and monitoring its response with fluorescent reporters. We then train in-silico neural networks on the fluorescence traces to predict the inputted temperature profiles. We reach an average of 87% accuracy for a single layer and 91% for two layers, showing the potential of such reservoir.