Multimode adaptive logic gates based on temperature-responsive DNA strand displacement.

Living organisms switch their intrinsic biological states to survive environmental turbulence, in which temperature changes are prevalent in nature. Most artificial temperature-responsive DNA nanosystems work as switch modules that transit between "ON-OFF" states, making it difficult to construct nanosystems with diverse functions. In this study, we present a general strategy to build multimode nanosystems based on a temperature-responsive DNA strand displacement reaction. The temperature-responsive DNA strand displacement was controlled by tuning the sequence of the substrate hairpin strands and the invading strands. The nanosystems were demonstrated as logic gates that performed a set of Boolean logical functions at specific temperatures. In addition, an adaptive logic gate was fabricated that could exhibit different logic functions when placed in different temperatures. Specifically, upon the same input strands, the logic gate worked as an XOR gate at 10 °C, an OR gate at 35 °C, an AND gate at 46 °C, and was reset at 55 °C. The design and fabrication of the multifunctional nanosystems would help construct advanced temperature-responsive systems that may be used for temperature-controlled multi-stage drug delivery and thermally-controlled multi-step assembly of nanostructures.

Presketched DNA Origami Canvas for Polymerase-Driven DNA Kirigami.

Reconfigurable DNA origami provides a versatile tool to manipulate the conformation of matter on the nanometer scale. Typically, the DNA kirigami method enables the transformation of an origami structure from an initial shape to another predesigned shape by reconfiguring the staple strands. In a regular origami structure, since the perfectly matched and densely packed DNA duplexes block the removal of staple strands, the construction of finely trimmed "sub-origami" structures by the DNA kirigami method has remained challenging. Herein, we proposed a strategy to construct the presketched DNA origami canvas, where the offcut area in the canvas was sketched by loosely fixed staple strands with single-base insertion, to enhance the fineness of polymerase-driven DNA kirigami. We successfully trimmed presketched two-dimensional rectangular canvas, three-dimensional Möbius strip, and genie bottle canvases into complex letter patterns, supercoiled rings, and nanorods, respectively. Finally, we demonstrated a size-controlled DNA kirigami system: a presketched 6HB origami was trimmed into a set of shorter nanowires with predefined lengths, which quantitatively characterized the fineness of the improved DNA kirigami. The presketched origami design was a general method that applied to both 2D and 3D DNA origami structures in square and honeycomb lattices. Loosening DNA origami structures by introducing single-base insertions provides a practical approach to constructing dynamic components when designing DNA nanomachines. Furthermore, the delicate trimming of the DNA origami canvas driven by polymerase may inspire strategies for graphical information encryption and storage.

Regulating the Polymerization of DNA Structures via Allosteric Control of Monomers.

Regulation of self-assembly is crucial in constructing structural biomaterials, such as tunable DNA nanostructures. Traditional tuning of self-assembled DNA nanostructures was mainly conducted by introducing external stimuli after the assembly process. Here, we explored the allosteric assembly of DNA structures via introducing external stimuli during the assembly process to produce structurally heterogeneous polymerization products. We demonstrated that ethidium bromide (EB), a DNA intercalator, could increase the left-handed out-of-plane chirality of curved DNA structures. Then, EB and double strands were introduced as competing stimuli to transform monomers into allosteric conformations, leading to three different polymerization products. The steric trap between different polymerization products promoted the polymerized structures to keep their geometric properties, like chirality, under varying intensity of external stimuli. Our strategy harnesses allosteric effects for assembly of DNA-based materials and is expected to expand the design space for advanced control in synthetic materials.

Robustness of Interdependent Networks with Weak Dependency Based on Bond Percolation.

Real-world systems interact with one another via dependency connectivities. Dependency connectivities make systems less robust because failures may spread iteratively among systems via dependency links. Most previous studies have assumed that two nodes connected by a dependency link are strongly dependent on each other; that is, if one node fails, its dependent partner would also immediately fail. However, in many real scenarios, nodes from different networks may be weakly dependent, and links may fail instead of nodes. How interdependent networks with weak dependency react to link failures remains unknown. In this paper, we build a model of fully interdependent networks with weak dependency and define a parameter α in order to describe the node-coupling strength. If a node fails, its dependent partner has a probability of failing of 1−α. Then, we develop an analytical tool for analyzing the robustness of interdependent networks with weak dependency under link failures, with which we can accurately predict the system robustness when 1−p fractions of links are randomly removed. We find that as the node coupling strength increases, interdependent networks show a discontinuous phase transition when α<αc and a continuous phase transition when α>αc. Compared to site percolation with nodes being attacked, the crossover points αc are larger in the bond percolation with links being attacked. This finding can give us some suggestions for designing and protecting systems in which link failures can happen.

Disassembly of DNA origami dimers controlled by programmable polymerase primers.

Dynamic regulation of DNA origami nanostructures is important for the fabrication of intelligent DNA nanodevices. Toehold-mediated strand displacement is a common regulation strategy, which utilizes trigger strands to assemble and disassemble nanostructures. Such trigger strands are required to be completely complementary to the corresponding substrate strands, which strictly demands orthogonality and accuracy of the sequence design. Herein, we present a disassembly strategy of DNA origami dimers based on polymerase-triggered strand displacement, where the polymerase primers, as the trigger strands, were only partially complementary to the toehold region of the substrate strands. To demonstrate the programmability of trigger strands, we utilized primers with different sequence combination patterns to disassemble DNA origami dimers. The statistical summary of AFM images and fluorescence curves proved the feasibility of the new strategy. The utilization of polymerase-triggered strand displacement on the disassembly of DNA origami structures enriches the toolbox for the dynamic regulation of DNA nanostructures.

Cyclic transitions of DNA origami dimers driven by thermal cycling.

It is widely observed that life activities are regulated through conformational transitions of biological macromolecules, which inspires the construction of environmental responsive nanomachines in recent years. Here we present a thermal responsive DNA origami dimers system, whose conformations can be cyclically switched by thermal cycling. In our strategy, origami dimers are assembled at high temperatures and disassembled at low temperatures, which is different from the conventional strategy of breaking nanostructures using high temperatures. The advantage of this strategy is that the dimers system can be repeatedly operated without significant performance degradation, compared to traditional strategies such as conformational transitions via i-motif and G-quadruplexes, whose performance degrades with sample dilution due to repeated addition of trigger solutions. The cyclic conformational transitions of the dimers system are verified by fluorescence curves and AFM images. This research offered a new way to construct cyclic transformational nanodevices, such as reusable nanomedicine delivery systems or nanorobots with long service lifetimes.

On the Tuning of the Computation Capability of Spiking Neural Membrane Systems with Communication on Request.

Spiking neural P systems (abbreviated as SNP systems) are models of computation that mimic the behavior of biological neurons. The spiking neural P systems with communication on request (abbreviated as SNQP systems) are a recently developed class of SNP system, where a neuron actively requests spikes from the neighboring neurons instead of passively receiving spikes. It is already known that small SNQP systems, with four unbounded neurons, can achieve Turing universality. In this context, 'unbounded' means that the number of spikes in a neuron is not capped. This work investigates the dependency of the number of unbounded neurons on the computation capability of SNQP systems. Specifically, we prove that (1) SNQP systems composed entirely of bounded neurons can characterize the family of finite sets of numbers; (2) SNQP systems containing two unbounded neurons are capable of generating the family of semilinear sets of numbers; (3) SNQP systems containing three unbounded neurons are capable of generating nonsemilinear sets of numbers. Moreover, it is obtained in a constructive way that SNQP systems with two unbounded neurons compute the operations of Boolean logic gates, i.e., OR, AND, NOT, and XOR gates. These theoretical findings demonstrate that the number of unbounded neurons is a key parameter that influences the computation capability of SNQP systems.

Tuning curved DNA origami structures through mechanical design and chemical adducts.

The bending and twisting of DNA origami structures are important features for controlling the physical properties of DNA nanodevices. It has not been fully explored yet how to finely tune the bending and twisting of curved DNA structures. Traditional tuning of the curved DNA structures was limited to controlling the in-plane-bending angle through varying the numbers of base pairs of deletions and insertions. Here, we developed two tuning strategies of curved DNA origami structures fromin silicoandin vitroaspects.In silico, the out-of-plane bending and twisting angles of curved structures were introduced, and were tuned through varying the patterns of base pair deletions and insertions.In vitro, a chemical adduct (ethidium bromide) was applied to dynamically tune a curved spiral. The 3D structural conformations, like chirality, of the curved DNA structures were finely tuned through these two strategies. The simulation and TEM results demonstrated that the patterns of base pair insertions and deletions and chemical adducts could effectively tune the bending and twisting of curved DNA origami structures. These strategies expand the programmable accuracy of curved DNA origami structures and have potential in building efficient dynamic functional nanodevices.

DNA Kirigami Driven by Polymerase-Triggered Strand Displacement.

The precursors of functional biomolecules in living cells are synthesized in a bottom-up manner and subsequently activated by modification into a delicate structure with near-atomic precision. DNA origami technology provides a promising way to mimic the synthesis of precursors, although mimicking the modification process is a challenge. Herein, a DNA paper-cutting (DNA kirigami) method to trim origami into designer nanostructures is proposed, where the modification is implemented by a polymerase-triggered DNA strand displacement reaction. Six geometric shapes are created by cutting rectangular DNA origami. Gel electrophoresis and atomic force microscopy results demonstrate the feasibility and capability of the DNA paper-cutting method. The proposed DNA paper-cutting strategy can enrich the toolbox for dynamically transforming DNA origami and has potential applications in biomimetics. .

Synaptic Learning With Augmented Spikes.

Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones. With a more brain-like processing paradigm, spiking neurons are more promising for improvements in efficiency and computational capability. They extend the computation of traditional neurons with an additional dimension of time carried by all-or-nothing spikes. Could one benefit from both the accuracy of analog values and the time-processing capability of spikes? In this article, we introduce a concept of augmented spikes to carry complementary information with spike coefficients in addition to spike latencies. New augmented spiking neuron model and synaptic learning rules are proposed to process and learn patterns of augmented spikes. We provide systematic insights into the properties and characteristics of our methods, including classification of augmented spike patterns, learning capacity, construction of causality, feature detection, robustness, and applicability to practical tasks, such as acoustic and visual pattern recognition. Our augmented approaches show several advanced learning properties and reliably outperform the baseline ones that use typical all-or-nothing spikes. Our approaches significantly improve the accuracies of a temporal-based approach on sound and MNIST recognition tasks to 99.38% and 97.90%, respectively, highlighting the effectiveness and potential merits of our methods. More importantly, our augmented approaches are versatile and can be easily generalized to other spike-based systems, contributing to a potential development for them, including neuromorphic computing.

A strategy for programming the regulation of in vitro transcription with application in molecular circuits.

In vitro transcription is a convenient platform for fabricating nanodevices and has been used for assembling synthetic networks. However, it remains challenging to regulate synthetic cell-free in vitro transcription by multiple stimuli in a simple and programmable way. We proposed a strategy to regulate in vitro transcription by controlling the transcription templates' promoter domain via variable DNA inputs. To demonstrate the utility of this strategy, various logic circuits and cascading circuits were implemented. With the advantage of simplicity, modularity, programmability, and extensibility, the proposed strategy has potential in biocomputing, bioanalytical, and therapeutic applications.

Manifold Learning-Inspired Mating Restriction for Evolutionary Multiobjective Optimization With Complicated Pareto Sets.

Under certain smoothness assumptions, the Pareto set of a continuous multiobjective optimization problem is a piecewise continuous manifold in the decision space, which can be derived from the Karush-Kuhn-Tucker condition. Despite that a number of multiobjective evolutionary algorithms (MOEAs) have been proposed, their performance on multiobjective optimization problems with complicated Pareto sets (MOP-cPS) is still unsatisfying. In this article, we adopt the concept of manifold and propose a manifold learning-inspired mating strategy to enhance the diversity maintenance in MOEAs for solving MOP-cPS efficiently. In the proposed strategy, all of the individuals are first clustered into different manifolds according to their distribution in the objective space, and then the mating reproduction is restricted among individuals in the same manifold. Moreover, we embed the proposed mating strategy in three representative MOEAs and compare the embedded MOEAs with their original versions using the assortative genetic operators on a variety of MOP-cPS. The experimental results demonstrate the significant performance improvements benefitting from the proposed mating restriction strategy.

Numerical Spiking Neural P Systems.

Spiking neural P (SN P) systems are a class of discrete neuron-inspired computation models, where information is encoded by the numbers of spikes in neurons and the timing of spikes. However, due to the discontinuous nature of the integrate-and-fire behavior of neurons and the symbolic representation of information, SN P systems are incompatible with the gradient descent-based training algorithms, such as the backpropagation algorithm, and lack the capability of processing the numerical representation of information. In this work, motivated by the numerical nature of numerical P (NP) systems in the area of membrane computing, a novel class of SN P systems is proposed, called numerical SN P (NSN P) systems. More precisely, information is encoded by the values of variables, and the integrate-and-fire way of neurons and the distribution of produced values are described by continuous production functions. The computation power of NSN P systems is investigated. We prove that NSN P is Turing universal as number generating devices, where the production functions in each neuron are linear functions, each involving at most one variable; as number accepting devices, NSN P systems are proved to be universal as well, even if each neuron contains only one production function. These results show that even if a single neuron is simple in the sense that it contains one or two production functions and the production functions in each neuron are linear functions with one variable, a network of simple neurons are still computationally powerful. With the powerful computation power and the characteristic of continuous production functions, developing learning algorithms for NSN P systems is potentially exploitable.

Evolution-Communication Spiking Neural P Systems.

Spiking neural P systems (SNP systems) are a class of distributed and parallel computation models, which are inspired by the way in which neurons process information through spikes, where the integrate-and-fire behavior of neurons and the distribution of produced spikes are achieved by spiking rules. In this work, a novel mechanism for separately describing the integrate-and-fire behavior of neurons and the distribution of produced spikes, and a novel variant of the SNP systems, named evolution-communication SNP (ECSNP) systems, is proposed. More precisely, the integrate-and-fire behavior of neurons is achieved by spike-evolution rules, and the distribution of produced spikes is achieved by spike-communication rules. Then, the computational power of ECSNP systems is examined. It is demonstrated that ECSNP systems are Turing universal as number-generating devices. Furthermore, the computational power of ECSNP systems with a restricted form, i.e. the quantity of spikes in each neuron throughout a computation does not exceed some constant, is also investigated, and it is shown that such restricted ECSNP systems can only characterize the family of semilinear number sets. These results manifest that the capacity of neurons for information storage (i.e. the quantity of spikes) has a critical impact on the ECSNP systems to achieve a desired computational power.

Switching the activity of Taq polymerase using clamp-like triplex aptamer structure.

In nature, allostery is the principal approach for regulating cellular processes and pathways. Inspired by nature, structure-switching aptamer-based nanodevices are widely used in artificial biotechnologies. However, the canonical aptamer structures in the nanodevices usually adopt a duplex form, which limits the flexibility and controllability. Here, a new regulating strategy based on a clamp-like triplex aptamer structure (CLTAS) was proposed for switching DNA polymerase activity via conformational changes. It was demonstrated that the polymerase activity could be regulated by either adjusting structure parameters or dynamic reactions including strand displacement or enzymatic digestion. Compared with the duplex aptamer structure, the CLTAS possesses programmability, excellent affinity and high discrimination efficiency. The CLTAS was successfully applied to distinguish single-base mismatches. The strategy expands the application scope of triplex structures and shows potential in biosensing and programmable nanomachines.

Fuzzy DNA Strand Displacement: A Strategy to Decrease the Complexity of DNA Network Design.

Toehold-mediated DNA strand displacement endows DNA nanostructures with dynamic response capability. However, the complexity of sequence design dramatically increases as the size of the DNA network increases. We attribute this problem to the mechanism of toehold-mediated strand displacement, termed exact strand displacement (ESD), in which one input strand corresponds to one specific substrate. In this work, we propose an alternative to toehold-mediated DNA strand displacement, termed fuzzy strand displacement (FSD), in which one-to-many and many-to-one relationships are established between the input strand and the substrate, to reduce the complexity. We have constructed four modules, termed converter, reporter, fuzzy detector, and fuzzy trigger, and demonstrated that a sequence pattern recognition network composed of these modules requires less complex sequence design than an equivalent network based on toehold-mediated DNA strand displacement.

Local Synchronization on Asynchronous Tissue P Systems With Symport/Antiport Rules.

Asynchronous tissue P systems with symport/antiport rules are a class of parallel computing models inspired by cell tissue working in a non-synchronized way, where the use of rules is not obligatory, that is, at a computation step, an enabled rule may or may not be applied. In this work, the notion of local synchronization is introduced at three levels: rules, channels, and cells. If a rule in a locally synchronous set of rules (resp., cells or channels) is used, then all enabled rules in the same locally synchronous set of rules (resp., whose involved channels or cells) should be applied in a maximally parallel manner and the implementation of these rules is finished in one computation step. The computational power of local synchronization on asynchronous tissue P systems with symport/antiport rules at the three levels is investigated. It is shown that asynchronous tissue P systems with symport/antiport rules and with locally synchronous sets of rules, channels, or cells are all Turing universal. By comparing the computational power of asynchronous tissue P systems with or without local synchronization, it can be found that the local synchronization is a useful tool to achieve a desired computational power.

A Subregion Division-Based Evolutionary Algorithm With Effective Mating Selection for Many-Objective Optimization.

A variety of evolutionary algorithms have been proposed for many-objective optimization in recent years. However, the difficulties in balancing the convergence and diversity of the population and selecting promising parents for offspring reproduction remain. In this paper, we propose a subregion division-based evolutionary algorithm with an effective mating selection strategy, termed SdEA, for many-objective optimization. In SdEA, a subregion division approach is proposed to divide the objective space into different subregions for balancing the diversity and convergence of the population. Besides, an effective mating selection strategy is proposed to enhance the diversity of the mating pool solutions, aimed at enhancing the selection probability of solutions in the sparse subregions. The proposed SdEA is compared with five state-of-the-art many-objective evolutionary algorithms on 23 test problems from DTLZ, WFG, and MaF test suites. Experimental results on these problems demonstrate that the proposed algorithm is competitive in solving many-objective problems. Furthermore, the proposed mating selection strategy is embedded in several evolutionary algorithms and experimental results demonstrate its effectiveness on improving the performance of the embedded algorithms.

Nicking-Assisted Reactant Recycle To Implement Entropy-Driven DNA Circuit.

Synthetic catalytic DNA circuits are important signal amplification tools for molecular programming due to their robust and modular properties. In catalytic circuits, the reactant recycling operation is essential to facilitate continuous processes. Therefore, it is desirable to develop new methods for the recycling of reactants and to improve the recyclability in entropy-driven DNA circuit reactions. Here, we describe the implementation of a nicking-assisted recycling strategy for reactants in entropy-driven DNA circuits, in which duplex DNA waste products are able to revert into active components that could participate in the next reaction cycle. Both a single-layered circuit and multiple two-layered circuits of different designs were constructed and analyzed. During the reaction, the single-layered catalytic circuit can consume excess fuel DNA strands without depleting the gate components. The recycling of the two-layered circuits occurs during the fuel DNA digestion but not during the release of the downstream trigger. This strategy provides a simple yet versatile method for creating more efficient entropy-driven DNA circuits for molecular programming and synthetic biology.

Aptamer-based regulation of transcription circuits.

We propose synthetic DNA/RNA transcription circuits based on specific aptamer recognition. By mimicking transcription factor regulation, combined with specific enzyme/DNA aptamer binding, multiple biomolecules including DNA, RNA, polymerase, restriction enzymes and methylase were used as regulators. In addition, multi-level cascading networks and methylation-switch circuits were also established. This regulation strategy has the potential to expand the toolkit of in vitro synthetic biology.