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DNA strand displacement (DSD) emerged as a prominent reaction motif for engineering nucleic acid-based computational devices with programmable behaviours. However, strand displacement circuits are susceptible to background noise, known as leaks, which disrupt their intended function. The ill effects of leaks are particularly severe in circuits with complex dynamics, as leaks in them amplify nonlinearly, resulting in rapid circuit degradation. Shadow cancellation is a dynamic leak-elimination strategy originally proposed to control the leak growth in such circuits. However, the kinetic restrictions of the method incur a significant design overhead, making it less accessible. In this work, we use domain-level DSD simulations to examine the method’s capabilities, the inner workings of its components and, most importantly, its robustness to the practical deviations in its design requirements. First, we show that the method could stabilize the dynamics of several catalytic and autocatalytic dynamical systems heavily affected by leaks. Then, through several probing experiments, we show that its design restrictions could be significantly relaxed without impacting the circuit function by simply adjusting the circuit parameters. Finally, we discuss several ideas to tackle the practical challenges in applying the method to arbitrary DSD circuits, paving the way for future experimental work. 

DNA computing has emerged as a promising alternative to achieve programmable behaviors in chemistry by repurposing the nucleic acid molecules into chemical hardware upon which synthetic chemical programs can be executed. These chemical programs are capable of simulating diverse behaviors, including boolean logic computation, oscillations, and nanorobotics. Chemical environments such as the cell are marked by uncertainty and are prone to random fluctuations. For this reason, potential DNA-based molecular devices that aim to be deployed into such environments should be capable of adapting to the stochasticity inherent in them. In keeping with this goal, a new subfield has emerged within DNA computing, focusing on developing approaches that embed learning and inference into chemical reaction systems. If realized in biochemical contexts, such molecular machines can engender novel applications in fields such as biotechnology, synthetic biology, and medicine. Therefore, it would be beneficial to review how different ideas were conceived, how the progress has been so far, and what the emerging ideas are in this nascent field of ‘molecular-scale learning’.