Programmable chemical controllers made from DNA

A Uniform Molecular Low-Density Parity Check Decoder

Error correction codes, such as low-density parity check (LDPC) codes, are required to be larger scale to meet the increasing demands for reliable and massive data transmission. However, the construction of such a large-scale decoder will result in high complexity and hinder its silicon implementation. Thanks to the advantages of natural computing in high parallelism and low power, we propose a method to synthesize a uniform molecular LDPC decoder by implementing the belief-propagation algorithm with chemical reaction networks (CRNs). This method enables us to flexibly design the LDPC decoder with arbitrary code length, code rate, and node degrees. Compared with existing methods, our proposal reduces the number of reactions to update the variable nodes by 42.86% and the check nodes by 47.37%. Numerical results are presented to show the feasibility and validity of our proposal.

Effective design principles for leakless strand displacement systems

Artificially designed molecular systems with programmable behaviors have become a valuable tool in chemistry, biology, material science, and medicine. Although information processing in biological regulatory pathways is remarkably robust to error, it remains a challenge to design molecular systems that are similarly robust. With functionality determined entirely by secondary structure of DNA, strand displacement has emerged as a uniquely versatile building block for cell-free biochemical networks. Here, we experimentally investigate a design principle to reduce undesired triggering in the absence of input (leak), a side reaction that critically reduces sensitivity and disrupts the behavior of strand displacement cascades. Inspired by error correction methods exploiting redundancy in electrical engineering, we ensure a higher-energy penalty to leak via logical redundancy. Our design strategy is, in principle, capable of reducing leak to arbitrarily low levels, and we experimentally test two levels of leak reduction for a core "translator" component that converts a signal of one sequence into that of another. We show that the leak was not measurable in the high-redundancy scheme, even for concentrations that are up to 100 times larger than typical. Beyond a single translator, we constructed a fast and low-leak translator cascade of nine strand displacement steps and a logic OR gate circuit consisting of 10 translators, showing that our design principle can be used to effectively reduce leak in more complex chemical systems.

Automated sequence-level analysis of kinetics and thermodynamics for domain-level DNA strand-displacement systems

As an engineering material, DNA is well suited for the construction of biochemical circuits and systems, because it is simple enough that its interactions can be rationally designed using Watson-Crick base pairing rules, yet the design space is remarkably rich. When designing DNA systems, this simplicity permits using functional sections of each strand, called domains, without considering particular nucleotide sequences. However, the actual sequences used may have interactions not predicted at the domain-level abstraction, and new rigorous analysis techniques are needed to determine the extent to which the chosen sequences conform to the system's domain-level description. We have developed a computational method for verifying sequence-level systems by identifying discrepancies between the domain-level and sequence-level behaviour. This method takes a DNA system, as specified using the domain-level tool Peppercorn, and analyses data from the stochastic sequence-level simulator Multistrand and sequence-level thermodynamic analysis tool NUPACK to estimate important aspects of the system, such as reaction rate constants and secondary structure formation. These techniques, implemented as the Python package KinDA, will allow researchers to predict the kinetic and thermodynamic behaviour of domain-level systems after sequence assignment, as well as to detect violations of the intended behaviour.

Nucleic Acid Strand Displacement with Synthetic mRNA Inputs in Living Mammalian Cells

Strand displacement reactions are widely used in DNA nanotechnology as a building block for engineering molecular computers and machines. Here, we demonstrate that strand displacement-based probes can be triggered by RNA expressed in mammalian cells, thus taking a step toward adapting the DNA nanotechnology toolbox to a cellular environment. We systematically compare different probe architectures in order to identify a design that works robustly in living cells. Our optimized strand displacement probe combines chemically modified nucleic acids that enhance stability to degradation by cellular nucleases with structural elements that improve probe retention in the cytoplasm. We visualize probe binding to individual mRNA carrying 96 repeats of a target sequence in the 3'UTR. We find that RNA counts based on live cell imaging using a strand displacement probe are comparable to counts from independent measurement based on fluorescence in situ hybridization experiments. We used probes with scrambled toeholds and scrambled binding domains to demonstrate that target recognition indeed occurs through toehold-mediated strand displacement. Our results demonstrate that strand displacement probes can work reliably in mammalian cells and lay the groundwork for future applications of such probes for live-cell imaging and molecular computing.

Information-based autonomous reconfiguration in systems of interacting DNA nanostructures

The dynamic interactions between complex molecular structures underlie a wide range of sophisticated behaviors in biological systems. In building artificial molecular machines out of DNA, an outstanding challenge is to develop mechanisms that can control the kinetics of interacting DNA nanostructures and that can compose the interactions together to carry out system-level functions. Here we show a mechanism of DNA tile displacement that follows the principles of toehold binding and branch migration similar to DNA strand displacement, but occurs at a larger scale between interacting DNA origami structures. Utilizing this mechanism, we show controlled reaction kinetics over five orders of magnitude and programmed cascades of reactions in multi-structure systems. Furthermore, we demonstrate the generality of tile displacement for occurring at any location in an array in any order, illustrated as a tic-tac-toe game. Our results suggest that tile displacement is a simple-yet-powerful mechanism that opens up the possibility for complex structural components in artificial molecular machines to undergo information-based reconfiguration in response to their environments.

Multifunctional sequence-defined macromolecules for chemical data storage

Sequence-defined macromolecules consist of a defined chain length (single mass), end-groups, composition and topology and prove promising in application fields such as anti-counterfeiting, biological mimicking and data storage. Here we show the potential use of multifunctional sequence-defined macromolecules as a storage medium. As a proof-of-principle, we describe how short text fragments (human-readable data) and QR codes (machine-readable data) are encoded as a collection of oligomers and how the original data can be reconstructed. The amide-urethane containing oligomers are generated using an automated protecting-group free, two-step iterative protocol based on thiolactone chemistry. Tandem mass spectrometry techniques have been explored to provide detailed analysis of the oligomer sequences. We have developed the generic software tools Chemcoder for encoding/decoding binary data as a collection of multifunctional macromolecules and Chemreader for reconstructing oligomer sequences from mass spectra to automate the process of chemical writing and reading.

Modular DNA strand-displacement controllers for directing material expansion

Soft materials that swell or change shape in response to external stimuli show extensive promise in regenerative medicine, targeted therapeutics, and soft robotics. Generally, a stimulus for shape change must interact directly with the material, limiting the types of stimuli that may be used and necessitating high stimulus concentrations. Here, we show how DNA strand-displacement controllers within hydrogels can mediate size change by interpreting, amplifying, and integrating stimuli and releasing signals that direct the response. These controllers tune the time scale and degree of DNA-crosslinked hydrogel swelling and can actuate dramatic material size change in response to <100 nM of a specific biomolecular input. Controllers can also direct swelling in response to small molecules or perform logic. The integration of these stimuli-responsive materials with biomolecular circuits is a major step towards autonomous soft robotic systems in which sensing and actuation are implemented by biomolecular reaction networks.

Materials which change shape in response to a trigger are of interest for soft robotics and targeted therapeutic delivery. Here, the authors report on the development of DNA-crosslinked hydrogels which can expand upon the detection of different biomolecular inputs mediated by DNA strand-displacement.

DNA Strand Buffers

A buffer reaction actively resists changes to the concentration of a chemical species. Typically, buffering reactions have only been able to regulate the concentration of hydronium (i.e., pH) and other ions. Here, we develop a new class of buffers that regulate the concentrations of short sequences of DNA (i.e., oligonucleotides). A buffer's behavior is determined by its set point concentration, capacity to resist disturbances, and response time after a disturbance. We provide simple mathematical formulas for selecting rate constants to tune each of these properties and show how to design DNA sequences and concentrations to implement the desired rate constants. We demonstrate several oligonucleotide buffers that maintain oligonucleotide set point concentrations between 10 and 80 nM in the presence of disturbances of 50 to 500 nM, with response times of less than 10 min to 1.5 h. Multiple buffers can regulate different sequences of DNA in parallel without crosstalk. Oligonucleotide buffers could stabilize and restore reactant concentrations in DNA circuits or in self-assembly processes, allowing such systems to operate reliably for extended durations. These buffers might also be coupled to other reactions to buffer molecules other than DNA. In general, an oligonucleotide buffer can be viewed as a chemical "battery" that maintains the total chemical potential of a buffered species in a closed system.

Sequential super-resolution imaging using DNA strand displacement

Sequential labeling and imaging in fluorescence microscopy allows the imaging of multiple structures in the same cell using a single fluorophore species. In super-resolution applications, the optimal dye suited to the method can be chosen, the optical setup can be simpler and there are no chromatic aberrations between images of different structures. We describe a method based on DNA strand displacement that can be used to quickly and easily perform the labeling and removal of the fluorophores during each sequence. Site-specific tags are conjugated with unique and orthogonal single stranded DNA. Labeling for a particular structure is achieved by hybridization of antibody-bound DNA with a complimentary dye-labeled strand. After imaging, the dye is removed using toehold-mediated strand displacement, in which an invader strand competes off the dye-labeled strand than can be subsequently washed away. Labeling and removal of each DNA-species requires only a few minutes. We demonstrate the concept using sequential dSTORM super-resolution for multiplex imaging of subcellular structures.

In Vitro Transcription Networks Based on Hairpin Promoter Switches

In vitro transcription networks are analogs of naturally occurring gene regulatory networks that consist of synthetic DNA templates that are cross-regulated by their own transcripts. This ability to design and execute in vitro transcription networks has allowed bottom-up construction of complex network topologies with predictable dynamic behavior. Here we describe the simplified design of an in vitro transcription network based on single-stranded synthetic DNA hairpin switches that function similar to molecular beacons, via toehold mediated strand displacement. Systematic construction of increasingly larger circuits was achieved by programming interactions between multiple switches through rational sequence design, and the dynamic behavior of networks was accurately predicted using a simple mathematical model. Ultimately, we engineered a cascade of switches that acted as a Boolean complete NAND gate capable of sensing both DNA and RNA inputs. The tools and framework that have been developed makes the execution of in vitro transcription circuits much simpler, which will enable them to more readily serve as testbeds for nucleic acid computations both in vitro and in vivo.

Period-amplitude co-variation in biomolecular oscillators

The period and amplitude of biomolecular oscillators are functionally important properties in multiple contexts. For a biomolecular oscillator, the overall constraints in how tuning of amplitude affects period, and vice versa, are generally unclear. Here, the authors investigate this co-variation of the period and amplitude in mathematical models of biomolecular oscillators using both simulations and analytical approximations. The authors computed the amplitude-period co-variation of 11 benchmark biomolecular oscillators as their parameters were individually varied around a nominal value, classifying the various co-variation patterns such as a simultaneous increase/decrease in period and amplitude. Next, the authors repeated the classification using a power norm-based amplitude metric, to account for the amplitudes of the many biomolecular species that may be part of the oscillations, finding largely similar trends. Finally, the authors calculate 'scaling laws' of period-amplitude co-variation for a subset of these benchmark oscillators finding that as the approximated period increases, the upper bound of the amplitude increases, or reaches a constant value. Based on these results, the authors discuss the effect of different parameters on the type of period-amplitude co-variation as well as the difficulty in achieving an oscillation with large amplitude and small period.

Programming Chemical Reaction Networks Using Intramolecular Conformational Motions of DNA

The programmable regulation of chemical reaction networks (CRNs) represents a major challenge toward the development of complex molecular devices performing sophisticated motions and functions. Nevertheless, regulation of artificial CRNs is generally energy- and time-intensive as compared to natural regulation. Inspired by allosteric regulation in biological CRNs, we herein develop an intramolecular conformational motion strategy (InCMS) for programmable regulation of DNA CRNs. We design a DNA switch as the regulatory element to program the distance between the toehold and branch migration domain. The presence of multiple conformational transitions leads to wide-range kinetic regulation spanning over 4 orders of magnitude. Furthermore, the process of energy-cost-free strand exchange accompanied by conformational change discriminates single base mismatches. Our strategy thus provides a simple yet effective approach for dynamic programming of complex CRNs.

Accelerating DNA-Based Computing on a Supramolecular Polymer

Dynamic DNA-based circuits represent versatile systems to perform complex computing operations at the molecular level. However, the majority of DNA circuits relies on freely diffusing reactants, which slows down their rate of operation substantially. Here we introduce the use of DNA-functionalized benzene-1,3,5-tricarboxamide (BTA) supramolecular polymers as dynamic scaffolds to template DNA-based molecular computing. By selectively recruiting DNA circuit components to a supramolecular BTA polymer functionalized with 10-nucleotide handle strands, the kinetics of strand displacement and strand exchange reactions were accelerated 100-fold. In addition, strand exchange reactions were also favored thermodynamically by bivalent interactions between the reaction product and the supramolecular polymer. The noncovalent assembly of the supramolecular polymers enabled straightforward optimization of the polymer composition to best suit various applications. The ability of supramolecular BTA polymers to increase the efficiency of DNA-based computing was demonstrated for three well-known and practically important DNA-computing operations: multi-input AND gates, Catalytic Hairpin Assembly and Hybridization Chain Reactions. This work thus establishes supramolecular BTA polymers as an efficient platform for DNA-based molecular operations, paving the way for the construction of autonomous bionanomolecular systems that confine and combine molecular sensing, computation, and actuation.

Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks

From bacteria following simple chemical gradients¹ to the brain distinguishing complex odour information², the ability to recognize molecular patterns is essential for biological organisms. This type of information-processing function has been implemented using DNA-based neural networks³, but has been limited to the recognition of a set of no more than four patterns, each composed of four distinct DNA molecules. Winner-take-all computation⁴ has been suggested^5,6 as a potential strategy for enhancing the capability of DNA-based neural networks. Compared to the linear-threshold circuits⁷ and Hopfield networks⁸ used previously³, winner-take-all circuits are computationally more powerful⁴, allow simpler molecular implementation and are not constrained by the number of patterns and their complexity, so both a large number of simple patterns and a small number of complex patterns can be recognized. Here we report a systematic implementation of winner-take-all neural networks based on DNA-strand-displacement^9,10 reactions. We use a previously developed seesaw DNA gate motif^3,11,12, extended to include a simple and robust component that facilitates the cooperative hybridization¹³ that is involved in the process of selecting a 'winner'. We show that with this extended seesaw motif DNA-based neural networks can classify patterns into up to nine categories. Each of these patterns consists of 20 distinct DNA molecules chosen from the set of 100 that represents the 100 bits in 10 × 10 patterns, with the 20 DNA molecules selected tracing one of the handwritten digits '1' to '9'. The network successfully classified test patterns with up to 30 of the 100 bits flipped relative to the digit patterns 'remembered' during training, suggesting that molecular circuits can robustly accomplish the sophisticated task of classifying highly complex and noisy information on the basis of similarity to a memory.

Noise control for molecular computing

Synthetic biology is a growing interdisciplinary field, with far-reaching applications, which aims to design biochemical systems that behave in a desired manner. With the advancement in nucleic-acid-based technology in general, and strand-displacement DNA computing in particular, a large class of abstract biochemical networks may be physically realized using nucleic acids. Methods for systematic design of the abstract systems with prescribed behaviours have been predominantly developed at the (less-detailed) deterministic level. However, stochastic effects, neglected at the deterministic level, are increasingly found to play an important role in biochemistry. In such circumstances, methods for controlling the intrinsic noise in the system are necessary for a successful network design at the (more-detailed) stochastic level. To bridge the gap, the noise-control algorithm for designing biochemical networks is developed in this paper. The algorithm structurally modifies any given reaction network under mass-action kinetics, in such a way that (i) controllable state-dependent noise is introduced into the stochastic dynamics, while (ii) the deterministic dynamics are preserved. The capabilities of the algorithm are demonstrated on a production-decay reaction system, and on an exotic system displaying bistability. For the production-decay system, it is shown that the algorithm may be used to redesign the network to achieve noise-induced multistability. For the exotic system, the algorithm is used to redesign the network to control the stochastic switching, and achieve noise-induced oscillations.

Computing with biological switches and clocks

The complex dynamics of biological systems is primarily driven by molecular interactions that underpin the regulatory networks of cells. These networks typically contain positive and negative feedback loops, which are responsible for switch-like and oscillatory dynamics, respectively. Many computing systems rely on switches and clocks as computational modules. While the combination of such modules in biological systems leads to a variety of dynamical behaviours, it is also driving development of new computing algorithms. Here we present a historical perspective on computation by biological systems, with a focus on switches and clocks, and discuss parallels between biology and computing. We also outline our vision for the future of biological computing.

Computing Mathematical Functions using DNA via Fractional Coding

This paper discusses the implementation of mathematical functions such as exponentials, trigonometric functions, the sigmoid function and the perceptron function with molecular reactions in general, and DNA strand displacement reactions in particular. The molecular constructs for these functions are predicated on a novel representation for input and output values: a fractional encoding, in which values are represented by the relative concentrations of two molecular types, denoted as type-1 and type-0. This representation is inspired by a technique from digital electronic design, termed stochastic logic, in which values are represented by the probability of 1's in a stream of randomly generated 0's and 1's. Research in the electronic realm has shown that a variety of complex functions can be computed with remarkably simple circuitry with this stochastic approach. This paper demonstrates how stochastic electronic designs can be translated to molecular circuits. It presents molecular implementations of mathematical functions that are considerably more complex than any shown to date. All designs are validated using mass-action simulations of the chemical kinetics of DNA strand displacement reactions.

DNA-Based Dynamic Reaction Networks

Deriving from logical and mechanical interactions between DNA strands and complexes, DNA-based artificial reaction networks (RNs) are attractive for their high programmability, as well as cascading and fan-out ability, which are similar to the basic principles of electronic logic gates. Arising from the dream of creating novel computing mechanisms, researchers have placed high hopes on the development of DNA-based dynamic RNs and have strived to establish the basic theories and operative strategies of these networks. This review starts by looking back on the evolution of DNA dynamic RNs; in particular' the most significant applications in biochemistry occurring in recent years. Finally, we discuss the perspectives of DNA dynamic RNs and give a possible direction for the development of DNA circuits.

A molecular multi-gene classifier for disease diagnostics

Despite its early promise as a diagnostic and prognostic tool, gene expression profiling remains cost-prohibitive and challenging to implement in a clinical setting. Here, we introduce a molecular computation strategy for analysing the information contained in complex gene expression signatures without the need for costly instrumentation. Our workflow begins by training a computational classifier on labelled gene expression data. This in silico classifier is then realized at the molecular level to enable expression analysis and classification of previously uncharacterized samples. Classification occurs through a series of molecular interactions between RNA inputs and engineered DNA probes designed to differentially weigh each input according to its importance. We validate our technology with two applications: a classifier for early cancer diagnostics and a classifier for differentiating viral and bacterial respiratory infections based on host gene expression. Together, our results demonstrate a general and modular framework for low-cost gene expression analysis.

Computer-aided biochemical programming of synthetic microreactors as diagnostic devices

Biological systems have evolved efficient sensing and decision-making mechanisms to maximize fitness in changing molecular environments. Synthetic biologists have exploited these capabilities to engineer control on information and energy processing in living cells. While engineered organisms pose important technological and ethical challenges, de novo assembly of non-living biomolecular devices could offer promising avenues toward various real-world applications. However, assembling biochemical parts into functional information processing systems has remained challenging due to extensive multidimensional parameter spaces that must be sampled comprehensively in order to identify robust, specification compliant molecular implementations. We introduce a systematic methodology based on automated computational design and microfluidics enabling the programming of synthetic cell-like microreactors embedding biochemical logic circuits, or protosensors, to perform accurate biosensing and biocomputing operations in vitro according to temporal logic specifications. We show that proof-of-concept protosensors integrating diagnostic algorithms detect specific patterns of biomarkers in human clinical samples. Protosensors may enable novel approaches to medicine and represent a step toward autonomous micromachines capable of precise interfacing of human physiology or other complex biological environments, ecosystems, or industrial bioprocesses.

Genetic programs can be compressed and autonomously decompressed in live cells

Fundamental computer science concepts have inspired novel information-processing molecular systems in test tubes 1-13 and genetically encoded circuits in live cells 14-21 . Recent research has shown that digital information storage in DNA, implemented using deep sequencing and conventional software, can approach the maximum Shannon information capacity 22 of two bits per nucleotide 23 . In nature, DNA is used to store genetic programs, but the information content of the encoding rarely approaches this maximum 24 . We hypothesize that the biological function of a genetic program can be preserved while reducing the length of its DNA encoding and increasing the information content per nucleotide. Here we support this hypothesis by describing an experimental procedure for compressing a genetic program and its subsequent autonomous decompression and execution in human cells. As a test-bed we choose an RNAi cell classifier circuit 25 that comprises redundant DNA sequences and is therefore amenable for compression, as are many other complex gene circuits 15,18,26-28 . In one example, we implement a compressed encoding of a ten-gene four-input AND gate circuit using only four genetic constructs. The compression principles applied to gene circuits can enable fitting complex genetic programs into DNA delivery vehicles with limited cargo capacity, and storing compressed and biologically inert programs in vivo for on-demand activation.

Petri-net-based 2D design of DNA walker circuits

We consider localised DNA computation, where a DNA strand walks along a binary decision graph to compute a binary function. One of the challenges for the design of reliable walker circuits consists in leakage transitions, which occur when a walker jumps into another branch of the decision graph. We automatically identify leakage transitions, which allows for a detailed qualitative and quantitative assessment of circuit designs, design comparison, and design optimisation. The ability to identify leakage transitions is an important step in the process of optimising DNA circuit layouts where the aim is to minimise the computational error inherent in a circuit while minimising the area of the circuit. Our 2D modelling approach of DNA walker circuits relies on coloured stochastic Petri nets which enable functionality, topology and dimensionality all to be integrated in one two-dimensional model. Our modelling and analysis approach can be easily extended to 3-dimensional walker systems.

Design and Analysis of Compact DNA Strand Displacement Circuits for Analog Computation Using Autocatalytic Amplifiers

A main goal in DNA computing is to build DNA circuits to compute designated functions using a minimal number of DNA strands. Here, we propose a novel architecture to build compact DNA strand displacement circuits to compute a broad scope of functions in an analog fashion. A circuit by this architecture is composed of three autocatalytic amplifiers, and the amplifiers interact to perform computation. We show DNA circuits to compute functions sqrt(x), ln(x) and exp(x) for x in tunable ranges with simulation results. A key innovation in our architecture, inspired by Napier's use of logarithm transforms to compute square roots on a slide rule, is to make use of autocatalytic amplifiers to do logarithmic and exponential transforms in concentration and time. In particular, we convert from the input that is encoded by the initial concentration of the input DNA strand, to time, and then back again to the output encoded by the concentration of the output DNA strand at equilibrium. This combined use of strand-concentration and time encoding of computational values may have impact on other forms of molecular computation.

Dynamic Control of Aptamer-Ligand Activity Using Strand Displacement Reactions

Nucleic acid aptamers are an expandable toolkit of sensors and regulators. To employ aptamer regulators within nonequilibrium molecular networks, the aptamer-ligand interactions should be tunable over time, so that functions within a given system can be activated or suppressed on demand. This is accomplished through complementary sequences to aptamers, which achieve programmable aptamer-ligand dissociation by displacing the aptamer from the ligand. We demonstrate the effectiveness of our simple approach on light-up aptamers as well as on aptamers inhibiting viral RNA polymerases, dynamically controlling the functionality of the aptamer-ligand complex. Mathematical models allow us to obtain estimates for the aptamer displacement kinetics. Our results suggest that aptamers, paired with their complement, could be used to build dynamic nucleic acid networks with direct control over a variety of aptamer-controllable enzymes and their downstream pathways.

DNA multi-bit non-volatile memory and bit-shifting operations using addressable electrode arrays and electric field-induced hybridization

DNA has been employed to either store digital information or to perform parallel molecular computing. Relatively unexplored is the ability to combine DNA-based memory and logical operations in a single platform. Here, we show a DNA tri-level cell non-volatile memory system capable of parallel random-access writing of memory and bit shifting operations. A microchip with an array of individually addressable electrodes was employed to enable random access of the memory cells using electric fields. Three segments on a DNA template molecule were used to encode three data bits. Rapid writing of data bits was enabled by electric field-induced hybridization of fluorescently labeled complementary probes and the data bits were read by fluorescence imaging. We demonstrated the rapid parallel writing and reading of 8 (2³) combinations of 3-bit memory data and bit shifting operations by electric field-induced strand displacement. Our system may find potential applications in DNA-based memory and computations.

DNA based technology holds promise for non-volatile memory and computational tasks, yet the relatively slow hybridization kinetics remain a bottleneck. Here, Song et al. have developed an electric field-induced hybridization platform that can speed up multi-bit memory and logic operations.

Probabilistic switching circuits in DNA

Biological organisms exhibit sophisticated control over the stochastic states of individual cells, but the understanding of underlying molecular mechanisms remains incomplete. It has been argued that unbiased choices are easy to achieve, but choices biased with specific probabilities are much harder. These natural phenomena raise an engineering challenge: Does there exist a simple method to program molecular systems that control arbitrary probabilities for individual molecular events? Here we show a molecular circuit architecture, using just a simple DNA strand displacement building block that functions as an unbiased switch, for creating a circuit output with any desired probability. We constructed several DNA circuits with multiple layers and feedback, demonstrating complex molecular information processing that exploits the inherent stochasticity of molecular interactions.

A natural feature of molecular systems is their inherent stochastic behavior. A fundamental challenge related to the programming of molecular information processing systems is to develop a circuit architecture that controls the stochastic states of individual molecular events. Here we present a systematic implementation of probabilistic switching circuits, using DNA strand displacement reactions. Exploiting the intrinsic stochasticity of molecular interactions, we developed a simple, unbiased DNA switch: An input signal strand binds to the switch and releases an output signal strand with probability one-half. Using this unbiased switch as a molecular building block, we designed DNA circuits that convert an input signal to an output signal with any desired probability. Further, this probability can be switched between 2ⁿ different values by simply varying the presence or absence of n distinct DNA molecules. We demonstrated several DNA circuits that have multiple layers and feedback, including a circuit that converts an input strand to an output strand with eight different probabilities, controlled by the combination of three DNA molecules. These circuits combine the advantages of digital and analog computation: They allow a small number of distinct input molecules to control a diverse signal range of output molecules, while keeping the inputs robust to noise and the outputs at precise values. Moreover, arbitrarily complex circuit behaviors can be implemented with just a single type of molecular building block.

Controlling protein activity by dynamic recruitment on a supramolecular polymer platform

Nature uses dynamic molecular platforms for the recruitment of weakly associating proteins into higher-order assemblies to achieve spatiotemporal control of signal transduction. Nanostructures that emulate this dynamic behavior require features such as plasticity, specificity and reversibility. Here we introduce a synthetic protein recruitment platform that combines the dynamics of supramolecular polymers with the programmability offered by DNA-mediated protein recruitment. Assembly of benzene-1,3,5-tricarboxamide (BTA) derivatives functionalized with a 10-nucleotide receptor strand into µm-long supramolecular BTA polymers is remarkably robust, even with high contents of DNA-functionalized BTA monomers and associated proteins. Specific recruitment of DNA-conjugated proteins on the supramolecular polymer results in a 1000-fold increase in protein complex formation, while at the same time enabling their rapid exchange along the BTA polymer. Our results establish supramolecular BTA polymers as a generic protein recruitment platform and demonstrate how assembly of protein complexes along the supramolecular polymer allows efficient and dynamic control of protein activity.

DNA-origami allows the precise recruitment of DNA-protein conjugates but lacks the dynamics found in natural protein assemblies. Here the authors present a synthetic polymer platform that combines the dynamics of supramolecular polymers with the programmability of DNA-mediated protein recruitment.

The importance of thermodynamics for molecular systems, and the importance of molecular systems for thermodynamics

Improved understanding of molecular systems has only emphasised the sophistication of networks within the cell. Simultaneously, the advance of nucleic acid nanotechnology, a platform within which reactions can be exquisitely controlled, has made the development of artificial architectures and devices possible. Vital to this progress has been a solid foundation in the thermodynamics of molecular systems. In this pedagogical review and perspective, we discuss how thermodynamics determines both the overall potential of molecular networks, and the minute details of design. We then argue that, in turn, the need to understand molecular systems is helping to drive the development of theories of thermodynamics at the microscopic scale.

DNA-Based Analog Computing

The field of DNA computation makes use of DNA reactions to do molecular-scale computation. Most works in DNA computation execute digital computations such as evaluation of Boolean circuits. This chapter surveys novel DNA computation methods that execute analog computations, where the inputs and outputs are real values specified by the concentrations of particular DNA strands.

Chemical reaction network designs for asynchronous logic circuits

Chemical reaction networks (CRNs) are a versatile language for describing the dynamical behaviour of chemical kinetics, capable of modelling a variety of digital and analogue processes. While CRN designs for synchronous sequential logic circuits have been proposed and their implementation in DNA demonstrated, a physical realisation of these devices is difficult because of their reliance on a clock. Asynchronous sequential logic, on the other hand, does not require a clock, and instead relies on handshaking protocols to ensure the temporal ordering of different phases of the computation. This paper provides novel CRN designs for the construction of asynchronous logic, arithmetic and control flow elements based on a bi-molecular reaction motif with catalytic reactions and uniform reaction rates. We model and validate the designs for the deterministic and stochastic semantics using Microsoft's GEC tool and the probabilistic model checker PRISM, demonstrating their ability to emulate the function of asynchronous components under low molecular count.

Enzyme-free nucleic acid dynamical systems

Chemistries exhibiting complex dynamics—from inorganic oscillators to gene regulatory networks—have been long known but either cannot be reprogrammed at will or rely on the sophisticated enzyme chemistry underlying the central dogma. Can simpler molecular mechanisms, designed from scratch, exhibit the same range of behaviors? Abstract chemical reaction networks have been proposed as a programming language for complex dynamics, along with their systematic implementation using short synthetic DNA molecules. We developed this technology for dynamical systems by identifying critical design principles and codifying them into a compiler automating the design process. Using this approach, we built an oscillator containing only DNA components, establishing that Watson-Crick base-pairing interactions alone suffice for complex chemical dynamics and that autonomous molecular systems can be designed via molecular programming languages.

Programming discrete distributions with chemical reaction networks

We explore the range of probabilistic behaviours that can be engineered with Chemical Reaction Networks (CRNs). We give methods to "program" CRNs so that their steady state is chosen from some desired target distribution that has finite support in [Formula: see text], with [Formula: see text]. Moreover, any distribution with countable infinite support can be approximated with arbitrarily small error under the [Formula: see text] norm. We also give optimized schemes for special distributions, including the uniform distribution. Finally, we formulate a calculus to compute on distributions that is complete for finite support distributions, and can be compiled to a restricted class of CRNs that at steady state realize those distributions.

Addressable configurations of DNA nanostructures for rewritable memory

DNA serves as nature's information storage molecule, and has been the primary focus of engineered systems for biological computing and data storage. Here we combine recent efforts in DNA self-assembly and toehold-mediated strand displacement to develop a rewritable multi-bit DNA memory system. The system operates by encoding information in distinct and reversible conformations of a DNA nanoswitch and decoding by gel electrophoresis. We demonstrate a 5-bit system capable of writing, erasing, and rewriting binary representations of alphanumeric symbols, as well as compatibility with 'OR' and 'AND' logic operations. Our strategy is simple to implement, requiring only a single mixing step at room temperature for each operation and standard gel electrophoresis to read the data. We envision such systems could find use in covert product labeling and barcoding, as well as secure messaging and authentication when combined with previously developed encryption strategies. Ultimately, this type of memory has exciting potential in biomedical sciences as data storage can be coupled to sensing of biological molecules.

CAGE: Chromatin Analogous Gene Expression

Self-assembled nucleic acids perform biological, chemical, and mechanical work at the nanoscale. DNA-based molecular machines have been designed here to perform work by reacting with cancer-specific miRNA mimics and then regulating gene expression in vitro by tuning RNA polymerase activity. Because RNA production is topologically restrained, the machines demonstrate chromatin analogous gene expression (CAGE). With modular and tunable design features, CAGE has potential for molecular biology, synthetic biology, and personalized medicine applications.

Photochemical Control over Oscillations in Chemical Reaction Networks

Systems chemistry aims to emulate the functional behavior observed in living systems by constructing chemical reaction networks (CRNs) with well-defined dynamic properties. Future expansion of the complexity of these systems would require external control to tune behavior and temporal organization of such CRNs. In this work, we design and implement a photolabile probe, which upon irradiation strengthens the negative feedback loop of a CRN that produces oscillations of trypsin under out-of-equilibrium conditions. By changing the timing and duration of irradiation, we can tailor the temporal response of the network.

DNA Implementation of Fuzzy Inference Engine: Towards DNA Decision-Making Systems

Decision-making systems are an integral part of any autonomous device. With the recent developments in bio-nanorobots, smart drugs, and engineered viruses, there is an immediate need of decision-making systems which are bio-compatible in nature. DNA is considered a perfect candidate for designing the computing systems in such decision-making systems because of their bio-compatibility and programmability. Complex biological systems can be easily modeled/controlled using fuzzy logic operations with the help of linguistic rules. In this paper, we propose an enzyme-free DNA strand displacement-based architecture of fuzzy inference engine using the fuzzy operators, such as fuzzy intersection and union. The basic building blocks of this architecture are minimum, maximum, and fan-out gates. All these gates are analog in nature, which means that the input/output values of the gates are represented by the concentration of the input/output DNA strands. To demonstrate the performance of the proposed architecture, a detailed design, analysis, and kinetic simulation of each gate were carried out. Finally, the minimum and maximum gates are cascaded according to the pre-defined rules to design the fuzzy inference engine. All these DNA circuits are implemented and simulated in Visual DSD software.

Design and Characterization of DNA Strand-Displacement Circuits in Serum-Supplemented Cell Medium

The functional stability and lifetimes of synthetic molecular circuits in biological environments are important for long-term, stable sensors or controllers of cell or tissue behavior. DNA-based molecular circuits, in particular DNA strand-displacement circuits, provide simple and effective biocompatible control mechanisms and sensors, but are vulnerable to digestion by nucleases present in living tissues and serum-supplemented cell culture. The stability of double-stranded and single-stranded DNA circuit components in serum-supplemented cell medium and the corresponding effect of nuclease-mediated degradation on circuit performance were characterized to determine the major routes of degradation and DNA strand-displacement circuit failure. Simple circuit design choices, such as the use of 5' toeholds within the DNA complexes used as reactants in the strand-displacement reactions and the termination of single-stranded components with DNA hairpin domains at the 3' termini, significantly increase the functional lifetime of the circuit components in the presence of nucleases. Simulations of multireaction circuits, guided by the experimentally measured operation of single-reaction circuits, enable predictive realization of multilayer and competitive-reaction circuit behavior. Together, these results provide a basic route to increased DNA circuit stability in cell culture environments.

Protein-driven RNA nanostructured devices that function in vitro and control mammalian cell fate

Nucleic acid nanotechnology has great potential for future therapeutic applications. However, the construction of nanostructured devices that control cell fate by detecting and amplifying protein signals has remained a challenge. Here we design and build protein-driven RNA-nanostructured devices that actuate in vitro by RNA-binding-protein-inducible conformational change and regulate mammalian cell fate by RNA–protein interaction-mediated protein assembly. The conformation and function of the RNA nanostructures are dynamically controlled by RNA-binding protein signals. The protein-responsive RNA nanodevices are constructed inside cells using RNA-only delivery, which may provide a safe tool for building functional RNA–protein nanostructures. Moreover, the designed RNA scaffolds that control the assembly and oligomerization of apoptosis-regulatory proteins on a nanometre scale selectively kill target cells via specific RNA–protein interactions. These findings suggest that synthetic RNA nanodevices could function as molecular robots that detect signals and localize target proteins, induce RNA conformational changes, and programme mammalian cellular behaviour.

Nucleic acid nanotechnology has great potential for future therapeutic applications. Here the authors build protein-driven RNA nanostructures that can function within mammalian cells and regulate the cell fate.

A spatially localized architecture for fast and modular DNA computing

Cells use spatial constraints to control and accelerate the flow of information in enzyme cascades and signalling networks. Synthetic silicon-based circuitry similarly relies on spatial constraints to process information. Here, we show that spatial organization can be a similarly powerful design principle for overcoming limitations of speed and modularity in engineered molecular circuits. We create logic gates and signal transmission lines by spatially arranging reactive DNA hairpins on a DNA origami. Signal propagation is demonstrated across transmission lines of different lengths and orientations and logic gates are modularly combined into circuits that establish the universality of our approach. Because reactions preferentially occur between neighbours, identical DNA hairpins can be reused across circuits. Co-localization of circuit elements decreases computation time from hours to minutes compared to circuits with diffusible components. Detailed computational models enable predictive circuit design. We anticipate our approach will motivate using spatial constraints for future molecular control circuit designs.

Grip on complexity in chemical reaction networks

A new discipline of "systems chemistry" is emerging, which aims to capture the complexity observed in natural systems within a synthetic chemical framework. Living systems rely on complex networks of chemical reactions to control the concentration of molecules in space and time. Despite the enormous complexity in biological networks, it is possible to identify network motifs that lead to functional outputs such as bistability or oscillations. To truly understand how living systems function, we need a complete understanding of how chemical reaction networks (CRNs) create function. We propose the development of a bottom-up approach to design and construct CRNs where we can follow the influence of single chemical entities on the properties of the network as a whole. Ultimately, this approach should allow us to not only understand such complex networks but also to guide and control their behavior.

Serotyping, antibiotic susceptibility, and virulence genes screening of Escherichia coli isolates obtained from diarrheic buffalo calves in Egyptian farms

AIM

In Egypt as in many other countries, river water buffalo (Bubalus bubalis) is considered an important source of high-quality milk and meat supply. The objective of this study was to investigate serotypes, virulence genes, and antibiotic resistance determinants profiles of Escherichia coli isolated from buffalo at some places in Egypt; noticibly, this issue was not discussed in the country yet.

MATERIALS AND METHODS

A number of 58 rectal samples were collected from diarrheic buffalo calves in different regions in Egypt, and bacteriological investigated for E. coli existence. The E. coli isolates were biochemically, serologicaly identified, tested for antibiotic susceptibility, and polymerase chain reaction (PCR) analyzed for the presence of antibiotic resistance determinants and virulence genes.

RESULTS

Overall 14 isolates typed as E. coli (24.1%); 6 were belonged to serogroup O78 (10.3%), followed by O125 (4 isolates, 6.9%), then O158 (3 isolates, 5.2%) and one isolate O8 (1.7%), among them, there were 5 E. coli isolates showed a picture of hemolysis (35.7%). The isolates exhibited a high resistance to β lactams over 60%, followed by sulfa (50%) and aminoglucoside (42.8%) group, in the same time the isolates were sensitive to quinolone, trimethoprim-sulfamethoxazole, tetracycline (100%), and cephalosporine groups (71.4%). A multiplex PCR was applied to the 14 E. coli isolates revealed that all were carrying at least one gene, as 10 carried blaTEM (71.4%), 8 Sul1 (57.1%), and 6 aadB (42.8%), and 9 isolates could be considered multidrug resistant (MDR) by an incidence of 64.3%. A PCR survey was stratified for the most important E. coli virulence genes, and showed the presence of Shiga toxins in 9 isolates carried either one or the two Stx genes (64.3%), 5 isolates carried hylA gene (35.7%), and eae in 2 isolates only (14.3%), all isolates carried at least one virulence gene except two (85.7%).

CONCLUSION

The obtained data displayed that in Egypt, buffalo as well as other ruminants could be a potential source of MDR pathogenic E. coli variants which have a public health importance.

Molecular Engineering of Robustness and Resilience in Enzymatic Reaction Networks

Living systems rely on complex networks of chemical reactions to control the concentrations of molecules in space and time. Despite the enormous complexity in biological networks, it is possible to identify network motifs that lead to functional outputs such as bistability or oscillations. One of the greatest challenges in chemistry is the creation of such functionality from chemical reactions. A key limitation is our lack of understanding of how molecular structure impacts on the dynamics of chemical reaction networks, preventing the design of networks that are robust (i.e., function in a large parameter space) and resilient (i.e., reach their out-of-equilibrium function rapidly). Here we demonstrate that reaction rates of individual reactions in the network can control the dynamics by which the system reaches limit cycle oscillations, thereby gaining information on the key parameters that govern the dynamics of these networks. We envision that these principles will be incorporated into the design of network motifs, enabling chemists to develop "molecular software" to create functional behavior in chemical systems.

Bacterial cell-free expression technology to in vitro systems engineering and optimization

Cell-free expression system is a technology for the synthesis of proteins in vitro. The system is a platform for several bioengineering projects, e.g. cell-free metabolic engineering, evolutionary design of experiments, and synthetic minimal cell construction. Bacterial cell-free protein synthesis system (CFPS) is a robust tool for synthetic biology. The bacteria lysate, the DNA, and the energy module, which are the three optimized sub-systems for in vitro protein synthesis, compose the integrated system. Currently, an optimized E. coli cell-free expression system can produce up to ∼2.3 mg/mL of a fluorescent reporter protein. Herein, I will describe the features of ATP-regeneration systems for in vitro protein synthesis, and I will present a machine-learning experiment for optimizing the protein yield of E. coli cell-free protein synthesis systems. Moreover, I will introduce experiments on the synthesis of a minimal cell using liposomes as dynamic containers, and E. coli cell-free expression system as biochemical platform for metabolism and gene expression. CFPS can be further integrated with other technologies for novel applications in environmental, medical and material science.