DNA nanotechnology has introduced the ability to create structures at the molecular scale, which is a promising approach for the implementation of very large swarms. However, the movement of such structures is heavily influenced by their size, prompting shape design optimization. Here, we use a quality-diversity approach to optimize the size of structures assembled from sets of DNA strands. We introduced a surrogate model to accelerate evaluations, with the ground truth provided by oxDNA, a physics-based simulator. We then iterate between optimization rounds using the QD algorithm, direct evaluation of promising and potentially mispredicted sets with oxDNA, and training of the surrogate model. We show that this approach efficiently generates diverse candidate sets at a fraction of simulation costs. Additionally, the surrogate model is reusable, enhancing the overall performance of future optimization tasks.

Recent work has demonstrated the viability of DNA robotics and artificial molecular machines for molecular transportation and cargo sorting with potential applications in manufacturing responsive molecular devices, programmable therapeutics, and autonomous chemical synthesis. We extend previous work on cooperative molecular transportation using artificial molecular machines, where we similarly functionalize DNA-conjugated microtubules driven by kinesin motor proteins. DNA-functionalized microtubules propelled by surface-adhered kinesin motors enable the self-organization of molecular swarms, where such swarms load and transport cargo (microbead) in a simulated chemical environment. We demonstrate programmable molecular swarms for cargo sorting and cooperative transport. Cargo loading occurs when sufficient microtubules are at the same location as the cargo, and cargo unloading occurs at specific points in the environment through interaction with localized DNA species. Our contribution is the design of a chemotaxis molecular controller, forcing the swarm to tumble (random change direction) when the system is not following a molecular gradient corresponding to the cargo type, thus directing it to specific points for cargo unloading. This work thus contributes to the open problem of how to best design programmable molecular machines for various tasks in microscopic environments.

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.

In this paper, we apply the Polymerase-Exonuclease-Nickase Dynamic Network Assembly (PEN DNA) toolbox, a modular framework for molecular computing, to reservoir computing. While reservoir computing is traditionally implemented with recurrent neural networks, any system with similar recurrent properties, here chemical reaction networks (CRNs), can be used as a reservoir. We compared our approach to a previous CRN implementation of reservoir computing by Goudarzi et al. Our implementation yielded similar performance with respect to their benchmark tasks. We then took advantage of the modularity of the PEN DNA toolbox to investigate the impact of the CRN size on performance, both by hand and with an automated optimization process. In both cases, we were able to find systems with excellent performance while also being realistic with respect to in vitro implementation. Finally, we investigated the impact of constraining the weights of the output layer to be positive. This constraint guarantees that the system will remain relatively small, and thus makes it easier to implement in vitro . While this constraint led to an expected degradation in performance, we were still able to find good implementations of the reservoir.