Search for: All records

Natural biological branching processes can form tree-like structures at all scales and, moreover, can perform various functions to achieve specific goals; these include receiving stimuli, performing two-way communication along their branches, and dynamically reforming (extending or retracting branches). They underlie many biological systems with considerable diversity, frequency, and geometric complexity; these include networks of neurons, organ tissue, mycorrhizal fungal networks, plant growth, foraging networks, etc. This paper presents a biomimetic DNA tile assembly model (Y-STAM) to implement dynamic branching processes. The Y-STAM is a relatively compact mathematical model providing a design space where complex, biomimetic branch-like growth and behaviour can emerge from the appropriate parametrization of the model. We also introduce a class of augmented models (Y-STAM⁺) that provide time- and space-dependent modulations of tile glue strengths, which enable further diverse behaviours that are not possible in the Y-STAM; these additional behaviours include refinement of network assemblies, obstacle avoidance, and programmable growth patterns. We perform and discuss extensive simulations of the Y-STAM and the Y-STAM⁺. We envision that these models could be applied at the mesoscale and the molecular scale to dynamically assemble branching DNA nanostructures and offer insights into complex biological self-assembly processes. 

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’. 

Heuristic algorithms can generalize the design process of stiff and round capsule-like nanostructures made from DNA. 

Abstract Access to fast and reliable nucleic acid testing continues to play a key role in controlling the COVID-19 pandemic, especially in the context of increased vaccine break-through risks due to new variants. We report a rapid, low-cost (~ 2 USD), simple-to-use nucleic acid test kit for self-administered at-home testing without lab instrumentation. The entire sample-to-answer workflow takes < 60 min, including noninvasive sample collection, one-step RNA preparation, reverse-transcription loop-mediated isothermal amplification (RT-LAMP) in a thermos, and direct visual inspection of a colorimetric test result. To facilitate long-term storage without cold-chain, a fast one-pot lyophilization protocol was developed to preserve all required biochemical reagents of the colorimetric RT-LAMP test in a single microtube. Notably, the lyophilized RT-LAMP assay demonstrated reduced false positives as well as enhanced tolerance to a wider range of incubation temperatures compared to solution-based RT-LAMP reactions. We validated our RT-LAMP assay using simulated infected samples, and detected a panel of SARS-CoV-2 variants with successful detection of all variants that were available to us at the time. With a simple change of the primer set, our lyophilized RT-LAMP home test can be easily adapted as a low-cost surveillance platform for other pathogens and infectious diseases of global public health importance. 

Structural DNA nanotechnology is a pioneering biotechnology that presents the opportunity to engineer DNA-based hardware that will mediate a profound interface to the nanoscale. To date, an enormous library of shaped 3D DNA nanostructures have been designed and assembled. Moreover, recent research has demonstrated DNA nanostructures that are not only static but can exhibit specific dynamic motion. DNA nanostructures have thus garnered significant research interest as a template for pursuing shape and motion-dependent nanoscale phenomena. Potential applications have been explored in many interdisciplinary areas spanning medicine, biosensing, nanofabrication, plasmonics, single-molecule chemistry, and facilitating biophysical studies. In this review, we begin with a brief overview of general and versatile design techniques for 3D DNA nanostructures as well as some techniques and studies that have focused on improving the stability of DNA nanostructures in diverse environments, which is pivotal for its reliable utilization in downstream applications. Our main focus will be to compile a wide body of existing research on applications of 3D DNA nanostructures that demonstrably rely on the versatility of their mechanical design. Furthermore, we frame reviewed applications into three primary categories, namely encapsulation, surface templating, and nanomechanics, that we propose to be archetypal shape- or motion-related functions of DNA nanostructures found in nanoscience applications. Our intent is to identify core concepts that may define and motivate specific directions of progress in this field as we conclude the review with some perspectives on the future.