Chemical systems have the potential to direct the next generation of dynamic materials if they can be integrated with a material while acting as the material’s own regulatory network. Chemical networks that use DNA and RNA strand displacement coupled with RNA synthesis and degradation, such as genelets, are promising chemical systems for this role. Genelets can produce a range of dynamic behaviors that respond to unique sets of environmental inputs. While a number of networks that generate specific types of outputs which vary in both time and amplitude have been developed, there are fewer examples of networks that recognize specific types of inputs in time and amplitude. Advanced chemical circuits in biology are capable of reading a given substrate concentration with relatively high accuracy to direct downstream function, demonstrating that such a chemical circuit is possible. Taking inspiration from this, we designed a genelet circuit which responds to a range of inputs by delivering a binary output based on the input concentration, and tested the network’s performance using an in silico model of circuit behavior. By modifying the concentrations of two circuit elements, we demonstrated that such a network topography could yield various target input concentration profiles to which a given circuit is sensitive. The number of unique elements in the final network topography as well as the individual circuit element concentrations are commensurate with properties of circuits that have been demonstrated experimentally. These factors suggest that such a network could be built and characterized in the laboratory.
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Characterizing the relative onset time, strength, and duration of molecular signals is critical for understanding the operation of signal transduction and genetic regulatory networks. However, detecting multiple such molecules as they are produced and then quickly consumed is challenging. A MER can encode information about transient molecular events as stable DNA sequences and are amenable to downstream sequencing or other analysis. Here, we report the development of a de novo molecular event recorder that processes information using a strand displacement reaction network and encodes the information using the primer exchange reaction, which can be decoded and quantified by DNA sequencing. The event recorder was able to classify the order at which different molecular signals appeared in time with 88% accuracy, the concentrations with 100% accuracy, and the duration with 75% accuracy. This simultaneous and highly programmable multiparameter recording could enable the large-scale deciphering of molecular events such as within dynamic reaction environments, living cells, or tissues.
Free, publicly-accessible full text available April 5, 2025 -
In multi-cellular organisms, cells and tissues coordinate biochemical signal propagation across length scales spanning micrometres to metres. Designing synthetic materials with similar capacities for coordinated signal propagation could allow these systems to adaptively regulate themselves across space and over time. Here, we combine ideas from cell signalling and electronic circuitry to propose a biochemical waveguide that transmits information in the form of a concentration of a DNA species on a directed path. The waveguide could be seamlessly integrated into a soft material because there is virtually no difference between the chemical or physical properties of the waveguide and the material it is embedded within. We propose the design of DNA strand displacement reactions to construct the system and, using reaction–diffusion models, identify kinetic and diffusive parameters that enable super-diffusive transport of DNA species via autocatalysis. Finally, to support experimental waveguide implementation, we propose a sink reaction and spatially inhomogeneous DNA concentrations that could mitigate the spurious amplification of an autocatalyst within the waveguide, allowing for controlled waveguide triggering. Chemical waveguides could facilitate the design of synthetic biomaterials with distributed sensing machinery integrated throughout their structure and enable coordinated self-regulating programmes triggered by changing environmental conditions.