High-resolution mapping of bifurcations in nonlinear biochemical circuits

DNA-empowered synthetic cells as minimalistic life forms

Cells, the fundamental units of life, orchestrate intricate functions - motility, adaptation, replication, communication, and self-organization within tissues. Originating from spatiotemporally organized structures and machinery, coupled with information processing in signalling networks, cells embody the 'sensor-processor-actuator' paradigm. Can we glean insights from these processes to construct primitive artificial systems with life-like properties? Using de novo design approaches, what can we uncover about the evolutionary path of life? This Review discusses the strides made in crafting synthetic cells, utilizing the powerful toolbox of structural and dynamic DNA nanoscience. We describe how DNA can serve as a versatile tool for engineering entire synthetic cells or subcellular entities, and how DNA enables complex behaviour, including motility and information processing for adaptive and interactive processes. We chart future directions for DNA-empowered synthetic cells, envisioning interactive systems wherein synthetic cells communicate within communities and with living cells.

Artificial Homeostasis Systems Based on Feedback Reaction Networks: Design Principles and Future Promises

Feedback-controlled chemical reaction networks (FCRNs) are indispensable for various biological processes, such as cellular mechanisms, patterns, and signaling pathways. Through the intricate interplay of many feedback loops (FLs), FCRNs maintain a stable internal cellular environment. Currently, creating minimalistic synthetic cells is the long-term objective of systems chemistry, which is motivated by such natural integrity. The design, kinetic optimization, and analysis of FCRNs to exhibit functions akin to those of a cell still pose significant challenges. Indeed, reaching synthetic homeostasis is essential for engineering synthetic cell components. However, maintaining homeostasis in artificial systems against various agitations is a difficult task. Several biological events can provide us with guidelines for a conceptual understanding of homeostasis, which can be further applicable in designing artificial synthetic systems. In this regard, we organize our review with artificial homeostasis systems driven by FCRNs at different length scales, including homogeneous, compartmentalized, and soft material systems. First, we stretch a quick overview of FCRNs in different molecular and supramolecular systems, which are the essential toolbox for engineering different nonlinear functions and homeostatic systems. Moreover, the existing history of synthetic homeostasis in chemical and material systems and their advanced functions with self-correcting, and regulating properties are also emphasized.

DNA as a universal chemical substrate for computing and data storage

DNA computing and DNA data storage are emerging fields that are unlocking new possibilities in information technology and diagnostics. These approaches use DNA molecules as a computing substrate or a storage medium, offering nanoscale compactness and operation in unconventional media (including aqueous solutions, water-in-oil microemulsions and self-assembled membranized compartments) for applications beyond traditional silicon-based computing systems. To build a functional DNA computer that can process and store molecular information necessitates the continued development of strategies for computing and data storage, as well as bridging the gap between these fields. In this Review, we explore how DNA can be leveraged in the context of DNA computing with a focus on neural networks and compartmentalized DNA circuits. We also discuss emerging approaches to the storage of data in DNA and associated topics such as the writing, reading, retrieval and post-synthesis editing of DNA-encoded data. Finally, we provide insights into how DNA computing can be integrated with DNA data storage and explore the use of DNA for near-memory computing for future information technology and health analysis applications.

Programming and monitoring surface-confined DNA computing

DNA-based molecular computing has evolved to encompass a diverse range of functions, demonstrating substantial promise for both highly parallel computing and various biomedical applications. Recent advances in DNA computing systems based on surface reactions have demonstrated improved levels of specificity and computational speed compared to their solution-based counterparts that depend on three-dimensional molecular collisions. Herein, computational biomolecular interactions confined by various surfaces such as DNA origamis, nanoparticles, lipid membranes and chips are systematically reviewed, along with their manipulation methodologies. Monitoring techniques and applications for these surface-based computing systems are also described. The advantages and challenges of surface-confined DNA computing are discussed.

DNA droplets for intelligent and dynamical artificial cells: from the viewpoint of computation and non-equilibrium systems

Living systems are molecular assemblies whose dynamics are maintained by non-equilibrium chemical reactions. To date, artificial cells have been studied from such physical and chemical viewpoints. This review briefly gives a perspective on using DNA droplets in constructing artificial cells. A DNA droplet is a coacervate composed of DNA nanostructures, a novel category of synthetic DNA self-assembled systems. The DNA droplets have programmability in physical properties based on DNA base sequence design. The aspect of DNA as an information molecule allows physical and chemical control of nanostructure formation, molecular assembly and molecular reactions through the design of DNA base pairing. As a result, the construction of artificial cells equipped with non-equilibrium behaviours such as dynamical motions, phase separations, molecular sensing and computation using chemical energy is becoming possible. This review mainly focuses on such dynamical DNA droplets for artificial cell research in terms of computation and non-equilibrium chemical reactions.

Micro-compartmentalized strand displacement reactions with a random pool background

Toehold-mediated strand displacement (TMSD) is a widely used process in dynamic DNA nanotechnology, which has been applied for the actuation of molecular devices, in biosensor applications, and for DNA-based molecular computation. Similar processes also occur in a biological context, when RNA strands invade secondary structures or duplexes of other RNA or DNA molecules. Complex reaction environments-inside cells or synthetic cells-potentially contain a large number of competing nucleic acid molecules that transiently bind to the components of the strand displacement reaction of interest and thus slow down its kinetics. Here, we investigate the kinetics of TMSD reactions compartmentalized into water-in-oil emulsion droplets-in both the presence and absence of a random sequence background-using a droplet microfluidic 'stopped flow' set-up. The set-up enables one to determine the kinetics within thousands of droplets and easily vary experimental parameters such as the stoichiometry of the TMSD components. While the average kinetics in the droplets coincides precisely with the bulk behaviour, we observe considerable variability among the droplets. This variability is partially explained by the encapsulation procedure itself, but appears to be more pronounced in reactions involving a random pool background.

DNA-based programmable gate arrays for general-purpose DNA computing

The past decades have witnessed the evolution of electronic and photonic integrated circuits, from application specific to programmable1,2. Although liquid-phase DNA circuitry holds the potential for massive parallelism in the encoding and execution of algorithms3,4, the development of general-purpose DNA integrated circuits (DICs) has yet to be explored. Here we demonstrate a DIC system by integration of multilayer DNA-based programmable gate arrays (DPGAs). We find that the use of generic single-stranded oligonucleotides as a uniform transmission signal can reliably integrate large-scale DICs with minimal leakage and high fidelity for general-purpose computing. Reconfiguration of a single DPGA with 24 addressable dual-rail gates can be programmed with wiring instructions to implement over 100 billion distinct circuits. Furthermore, to control the intrinsically random collision of molecules, we designed DNA origami registers to provide the directionality for asynchronous execution of cascaded DPGAs. We exemplify this by a quadratic equation-solving DIC assembled with three layers of cascade DPGAs comprising 30 logic gates with around 500 DNA strands. We further show that integration of a DPGA with an analog-to-digital converter can classify disease-related microRNAs. The ability to integrate large-scale DPGA networks without apparent signal attenuation marks a key step towards general-purpose DNA computing.

Tailoring a Minimal Self-Replicate DNA Circuit for Highly Efficient Intracellular Imaging of microRNA

Trace analyte detection in complex intracellular environment requires the development of simple yet robust self-sufficient molecular circuits with high signal-gain and anti-interference features. Herein, a minimal non-enzymatic self-replicate DNA circuitry (SDC) system is proposed with high-signal-gain for highly efficient biosensing in living cells. It is facilely engineered through the self-stacking of only one elementary cascade hybridization reaction (CHR), thus is encoding with more economic yet effective amplification pathways and reactants. Trigger (T) stimulates the activation of CHR for producing numerous T replica that reversely motivate new CHR reaction cycles, thus achieving the successive self-replication of CHR system with an exponentially magnified readout signal. The intrinsic self-replicate circuity design and the self-accelerated reaction format of SDC system is experimentally demonstrated and theoretically simulated. With simple circuitry configuration and low reactant complexity, the SDC amplifier enables the high-contrast and accurate visualization of microRNA (miRNA), ascribing to its robust molecular recognition and self-sufficient signal amplification, thus offering a promising strategy for monitoring these clinically significant analytes.

Droplet Encoding-Pairing Enabled Multiplexed Digital Loop-Mediated Isothermal Amplification for Simultaneous Quantitative Detection of Multiple Pathogens

Despite the advantages of digital nucleic acid analysis (DNAA) in terms of sensitivity, precision, and resolution, current DNAA methods commonly suffer a limitation in multiplexing capacity. To address this issue, a droplet encoding-pairing enabled DNAA multiplexing strategy is developed, wherein unique tricolor combinations are deployed to index individual primer droplets. The template droplets and primer droplets are sequentially introduced into a microfluidic chip with a calabash-shaped microwell array and are pairwise trapped and merged in the microwells. Pre-merging and post-amplification image analysis with a machine learning algorithm is used to identify, enumerate, and address the droplets. By incorporating the amplification signals with droplet encoding information, simultaneous quantitative detection of multiple targets is achieved. This strategy allows for the establishment of flexible multiplexed DNAA by simply adjusting the primer droplet library. Its flexibility is demonstrated by establishing two multiplexed (8-plex) droplet digital loop-mediated isothermal amplification (mddLAMP) assays for individually detecting lower respiratory tract infection and urinary tract infection causative pathogens. Clinical sample analysis shows that the microbial detection outcomes of the mddLAMP assays are consistent with those of the conventional assay. This DNAA multiplexing strategy can achieve flexible high-order multiplexing on demand, making it a desirable tool for high-content pathogen detection.

Stochastic approximations of higher-molecular by bi-molecular reactions

Reactions involving three or more reactants, called higher-molecular reactions, play an important role in mathematical modelling in systems and synthetic biology. In particular, such reactions underpin a variety of important bio-dynamical phenomena, such as multi-stability/multi-modality, oscillations, bifurcations, and noise-induced effects. However, as opposed to reactions involving at most two reactants, called bi-molecular reactions, higher-molecular reactions are biochemically improbable. To bridge the gap, in this paper we put forward an algorithm for systematically approximating arbitrary higher-molecular reactions with bi-molecular ones, while preserving the underlying stochastic dynamics. Properties of the algorithm and convergence are established via singular perturbation theory. The algorithm is applied to a variety of higher-molecular biochemical networks, and is shown to play an important role in synthetic biology.

Cell-Free Production Systems in Droplet Microfluidics

The use of cell-free production systems in droplet microfluidic devices has gained significant interest during the last decade. Encapsulating DNA replication, RNA transcription, and protein expression systems in water-in-oil drops allows for the interrogation of unique molecules and high-throughput screening of libraries of industrial and biomedical interest. Furthermore, the use of such systems in closed compartments enables the evaluation of various properties of novel synthetic or minimal cells. In this chapter, we review the latest advances in the usage of the cell-free macromolecule production toolbox in droplets, with a special emphasis on new on-chip technologies for the amplification, transcription, expression, screening, and directed evolution of biomolecules.

Nonlinear decision-making with enzymatic neural networks

Artificial neural networks have revolutionized electronic computing. Similarly, molecular networks with neuromorphic architectures may enable molecular decision-making on a level comparable to gene regulatory networks^1,2. Non-enzymatic networks could in principle support neuromorphic architectures, and seminal proofs-of-principle have been reported^3,4. However, leakages (that is, the unwanted release of species), as well as issues with sensitivity, speed, preparation and the lack of strong nonlinear responses, make the composition of layers delicate, and molecular classifications equivalent to a multilayer neural network remain elusive (for example, the partitioning of a concentration space into regions that cannot be linearly separated). Here we introduce DNA-encoded enzymatic neurons with tuneable weights and biases, and which are assembled in multilayer architectures to classify nonlinearly separable regions. We first leverage the sharp decision margin of a neuron to compute various majority functions on 10 bits. We then compose neurons into a two-layer network and synthetize a parametric family of rectangular functions on a microRNA input. Finally, we connect neural and logical computations into a hybrid circuit that recursively partitions a concentration plane according to a decision tree in cell-sized droplets. This computational power and extreme miniaturization open avenues to query and manage molecular systems with complex contents, such as liquid biopsies or DNA databases.

Standardized excitable elements for scalable engineering of far-from-equilibrium chemical networks

Engineered far-from-equilibrium synthetic chemical networks that pulse or switch states in response to environmental signals could precisely regulate the kinetics of chemical synthesis or self-assembly. Currently, such networks must be extensively tuned to compensate for the different activities of and unintended reactions between a network's various chemical components. Modular elements with standardized performance could be used to rapidly construct networks with designed functions. Here we develop standardized excitable chemical regulatory elements, termed genelets, and use them to construct complex in vitro transcriptional networks. We develop a protocol for identifying >15 interchangeable genelet elements with uniform performance and minimal crosstalk. These elements can be combined to engineer feedforward and feedback modules whose dynamics match those predicted by a simple kinetic model. Modules can then be rationally integrated and organized into networks that produce tunable temporal pulses and act as multistate switchable memories. Standardized genelet elements, and the workflow to identify more, should make engineering complex far-from-equilibrium chemical dynamics routine.

Programmable synthetic cell networks regulated by tuneable reaction rates

Coupled compartmentalised information processing and communication via molecular diffusion underpin network based population dynamics as observed in biological systems. Understanding how both compartmentalisation and communication can regulate information processes is key to rational design and control of compartmentalised reaction networks. Here, we integrate PEN DNA reactions into semi-permeable proteinosomes and characterise the effect of compartmentalisation on autocatalytic PEN DNA reactions. We observe unique behaviours in the compartmentalised systems which are not accessible under bulk conditions; for example, rates of reaction increase by an order of magnitude and reaction kinetics are more readily tuneable by enzyme concentrations in proteinosomes compared to buffer solution. We exploit these properties to regulate the reaction kinetics in two node compartmentalised reaction networks comprised of linear and autocatalytic reactions which we establish by bottom-up synthetic biology approaches.

Complex dynamics in a synchronized cell-free genetic clock

Complex dynamics such as period doubling and chaos occur in a wide variety of non-linear dynamical systems. In the context of biological circadian clocks, such phenomena have been previously found in computational models, but their experimental study in biological systems has been challenging. Here, we present experimental evidence of period doubling in a forced cell-free genetic oscillator operated in a microfluidic reactor, where the system is periodically perturbed by modulating the concentration of one of the oscillator components. When the external driving matches the intrinsic period, we experimentally find period doubling and quadrupling in the oscillator dynamics. Our results closely match the predictions of a theoretical model, which also suggests conditions under which our system would display chaotic dynamics. We show that detuning of the external and intrinsic period leads to more stable entrainment, suggesting a simple design principle for synchronized synthetic and natural genetic clocks.

In theory, driven biological oscillators can display complex dynamic behaviors, but these are experimentally difficult to observe. Here the authors, using microfluidics, show that a synthetic cell-free gene oscillator displays period doubling and even quadrupling.

Controlling The Synchronization of Molecular Oscillators Through Indirect Coupling

In this article, we study the coupling of a collection of molecular oscillators, called repressilators, interacting indirectly through enzymatic saturation. We extended a measure of autocorrelation to identify the period of the whole system and to detect coupling behaviors. We explored the parameter space of concentrations of molecular species in each oscillator versus enzymatic saturation, and observed regions of uncoupled, partially, or fully coupled systems. In particular, we found a region that provided a sharp transition between no coupling, two coupled oscillators, and full coupling. In practical applications, signals from the environment can directly affect parameters such as local enzymatic saturation, and thus switch the system from a coupled to an uncoupled regime and vice-versa. Our parameter exploration can be used to guide the design of complex molecular systems, such as active materials or molecular robot controllers.

Investigation of Different Free Image Analysis Software for High-Throughput Droplet Detection

Droplet microfluidics has revealed innovative strategies in biology and chemistry. This advancement has delivered novel quantification methods, such as droplet digital polymerase chain reaction (ddPCR) and an antibiotic heteroresistance analysis tool. For droplet analysis, researchers often use image-based detection techniques. Unfortunately, the analysis of images may require specific tools or programming skills to produce the expected results. In order to address the issue, we explore the potential use of standalone freely available software to perform image-based droplet detection. We select the four most popular software and classify them into rule-based and machine learning-based types after assessing the software's modules. We test and evaluate the software's (i) ability to detect droplets, (ii) accuracy and precision, and (iii) overall components and supporting material. In our experimental setting, we find that the rule-based type of software is better suited for image-based droplet detection. The rule-based type of software also has a simpler workflow or pipeline, especially aimed for non-experienced users. In our case, CellProfiler (CP) offers the most user-friendly experience for both single image and batch processing analyses.

A small-molecule chemical interface for molecular programs

In vitro molecular circuits, based on DNA-programmable chemistries, can perform an increasing range of high-level functions, such as molecular level computation, image or chemical pattern recognition and pattern generation. Most reported demonstrations, however, can only accept nucleic acids as input signals. Real-world applications of these programmable chemistries critically depend on strategies to interface them with a variety of non-DNA inputs, in particular small biologically relevant chemicals. We introduce here a general strategy to interface DNA-based circuits with non-DNA signals, based on input-translating modules. These translating modules contain a DNA response part and an allosteric protein sensing part, and use a simple design that renders them fully tunable and modular. They can be repurposed to either transmit or invert the response associated with the presence of a given input. By combining these translating-modules with robust and leak-free amplification motifs, we build sensing circuits that provide a fluorescent quantitative time-response to the concentration of their small-molecule input, with good specificity and sensitivity. The programmability of the DNA layer can be leveraged to perform DNA based signal processing operations, which we demonstrate here with logical inversion, signal modulation and a classification task on two inputs. The DNA circuits are also compatible with standard biochemical conditions, and we show the one-pot detection of an enzyme through its native metabolic activity. We anticipate that this sensitive small-molecule-to-DNA conversion strategy will play a critical role in the future applications of molecular-level circuitry.

Dynamic self-assembly of compartmentalized DNA nanotubes

Bottom-up synthetic biology aims to engineer artificial cells capable of responsive behaviors by using a minimal set of molecular components. An important challenge toward this goal is the development of programmable biomaterials that can provide active spatial organization in cell-sized compartments. Here, we demonstrate the dynamic self-assembly of nucleic acid (NA) nanotubes inside water-in-oil droplets. We develop methods to encapsulate and assemble different types of DNA nanotubes from programmable DNA monomers, and demonstrate temporal control of assembly via designed pathways of RNA production and degradation. We examine the dynamic response of encapsulated nanotube assembly and disassembly with the support of statistical analysis of droplet images. Our study provides a toolkit of methods and components to build increasingly complex and functional NA materials to mimic life-like functions in synthetic cells.

Novel nucleic acid origami structures and conventional molecular beacon-based platforms: a comparison in biosensing applications

Nucleic acid nanotechnology designs and develops synthetic nucleic acid strands to fabricate nanosized functional systems. Structural properties and the conformational polymorphism of nucleic acid sequences are inherent characteristics that make nucleic acid nanostructures attractive systems in biosensing. This review critically discusses recent advances in biosensing derived from molecular beacon and DNA origami structures. Molecular beacons belong to a conventional class of nucleic acid structures used in biosensing, whereas DNA origami nanostructures are fabricated by fully exploiting possibilities offered by nucleic acid nanotechnology. We present nucleic acid scaffolds divided into conventional hairpin molecular beacons and DNA origami, and discuss some relevant examples by focusing on peculiar aspects exploited in biosensing applications. We also critically evaluate analytical uses of the synthetic nucleic acid structures in biosensing to point out similarities and differences between traditional hairpin nucleic acid sequences and DNA origami.

Quasi-robust control of biochemical reaction networks via stochastic morphing

One of the main objectives of synthetic biology is the development of molecular controllers that can manipulate the dynamics of a given biochemical network that is at most partially known. When integrated into smaller compartments, such as living or synthetic cells, controllers have to be calibrated to factor in the intrinsic noise. In this context, biochemical controllers put forward in the literature have focused on manipulating the mean (first moment) and reducing the variance (second moment) of the target molecular species. However, many critical biochemical processes are realized via higher-order moments, particularly the number and configuration of the probability distribution modes (maxima). To bridge the gap, we put forward the stochastic morpher controller that can, under suitable timescale separations, morph the probability distribution of the target molecular species into a predefined form. The morphing can be performed at a lower-resolution, allowing one to achieve desired multi-modality/multi-stability, and at a higher-resolution, allowing one to achieve arbitrary probability distributions. Properties of the controller, such as robustness and convergence, are rigorously established, and demonstrated on various examples. Also proposed is a blueprint for an experimental implementation of stochastic morpher.

Morphological Manipulation of DNA Gel Microbeads with Biomolecular Stimuli

Hydrogels are essential in many fields ranging from tissue engineering and drug delivery to food sciences or cosmetics. Hydrogels that respond to specific biomolecular stimuli such as DNA, mRNA, miRNA and small molecules are highly desirable from the perspective of medical applications, however interfacing classical hydrogels with nucleic acids is still challenging. Here were demonstrate the generation of microbeads of DNA hydrogels with droplet microfluidic, and their morphological actuation with DNA strands. Using strand displacement and the specificity of DNA base pairing, we selectively dissolved gel beads, and reversibly changed their size on-the-fly with controlled swelling and shrinking. Lastly, we performed a complex computing primitive-A Winner-Takes-All competition between two populations of gel beads. Overall, these results show that strand responsive DNA gels have tantalizing potentials to enhance and expand traditional hydrogels, in particular for applications in sequencing and drug delivery.

Accelerating the Finite-Element Method for Reaction-Diffusion Simulations on GPUs with CUDA

DNA nanotechnology offers a fine control over biochemistry by programming chemical reactions in DNA templates. Coupled to microfluidics, it has enabled DNA-based reaction-diffusion microsystems with advanced spatio-temporal dynamics such as traveling waves. The Finite Element Method (FEM) is a standard tool to simulate the physics of such systems where boundary conditions play a crucial role. However, a fine discretization in time and space is required for complex geometries (like sharp corners) and highly nonlinear chemistry. Graphical Processing Units (GPUs) are increasingly used to speed up scientific computing, but their application to accelerate simulations of reaction-diffusion in DNA nanotechnology has been little investigated. Here we study reaction-diffusion equations (a DNA-based predator-prey system) in a tortuous geometry (a maze), which was shown experimentally to generate subtle geometric effects. We solve the partial differential equations on a GPU, demonstrating a speedup of ∼100 over the same resolution on a 20 cores CPU.

Machine-driven parameter screen of biochemical reactions

The development of complex methods in molecular biology is a laborious, costly, iterative and often intuition-bound process where optima are sought in a multidimensional parameter space through step-by-step optimizations. The difficulty of miniaturizing reactions under the microliter volumes usually handled in multiwell plates by robots, plus the cost of the experiments, limit the number of parameters and the dynamic ranges that can be explored. Nevertheless, because of non-linearities of the response of biochemical systems to their reagent concentrations, broad dynamic ranges are necessary. Here we use a high-performance nanoliter handling platform and computer generation of liquid transfer programs to explore in quadruplicates 648 combinations of 4 parameters of a biochemical reaction, the reverse-transcription, which lead us to uncover non-linear responses, parameter interactions and novel mechanistic insights. With the increased availability of computer-driven laboratory platforms for biotechnology, our results demonstrate the feasibility and advantage of methods development based on reproducible, computer-aided exhaustive characterization of biochemical systems.

Bottom-Up Construction of Complex Biomolecular Systems With Cell-Free Synthetic Biology

Cell-free systems offer a promising approach to engineer biology since their open nature allows for well-controlled and characterized reaction conditions. In this review, we discuss the history and recent developments in engineering recombinant and crude extract systems, as well as breakthroughs in enabling technologies, that have facilitated increased throughput, compartmentalization, and spatial control of cell-free protein synthesis reactions. Combined with a deeper understanding of the cell-free systems themselves, these advances improve our ability to address a range of scientific questions. By mastering control of the cell-free platform, we will be in a position to construct increasingly complex biomolecular systems, and approach natural biological complexity in a bottom-up manner.

Bottom-Gate Approach for All Basic Logic Gates Implementation by a Single-Type IGZO-Based MOS Transistor with Reduced Footprint

Logic functions are the key backbone in electronic circuits for computing applications. Complementary metal-oxide-semiconductor (CMOS) logic gates, with both n-type and p-type channel transistors, have been to date the dominant building blocks of logic circuitry as they carry obvious advantages over other technologies. Important physical limits are however starting to arise, as the transistor-processing technology has begun to meet scaling-down difficulties. To address this issue, there is the crucial need for a next-generation electronics era based on new concepts and designs. In this respect, a single-type channel multigate MOS transistor (SMG-MOS) is introduced holding the two important aspects of processing adaptability and low static dissipation of CMOS. Furthermore, the SMG-MOS approach strongly reduces the footprint down to 40% or even less area needed for current CMOS logic function in the same processing technology node. Logic NAND, NOT, AND, NOR, and OR gates, which typically require a large number of CMOS transistors, can be realized by a single SMG-MOS transistor. Two functional examples of SMG-MOS are reported here with their analysis based both on simulations and experiments. The results strongly suggest that SMG-MOS can represent a facile approach to scale down complex integrated circuits, enabling design flexibility and production rates ramp-up.

Streamlined digital bioassays with a 3D printed sample changer

Off-chip sample changer device increase the sample throughput of droplet digital bioassays.

A chemically fueled non-enzymatic bistable network

One of the grand challenges in contemporary systems chemistry research is to mimic life-like functions using simple synthetic molecular networks. This is particularly true for systems that are out of chemical equilibrium and show complex dynamic behaviour, such as multi-stability, oscillations and chaos. We report here on thiodepsipeptide-based non-enzymatic networks propelled by reversible replication processes out of equilibrium, displaying bistability. Accordingly, we present quantitative analyses of the bistable behaviour, featuring a phase transition from the simple equilibration processes taking place in reversible dynamic chemistry into the bistable region. This behaviour is observed only when the system is continuously fueled by a reducing agent that keeps it far from equilibrium, and only when operating within a specifically defined parameter space. We propose that the development of biomimetic bistable systems will pave the way towards the study of more elaborate functions, such as information transfer and signalling.

Nonlinear Regulation of Enzyme-Free DNA Circuitry with Ultrasensitive Switches

DNA is used to construct synthetic chemical reaction networks (CRNs), such as inorganic oscillators and gene regulatory networks. Nonlinear regulation with a simpler molecular mechanism is particularly important in large-scale CRNs with complex dynamics, such as bistability, adaptation, and oscillation of cellular functions. Here we introduce a new approach based on ultrasensitive switches as modular regulatory elements to nonlinearly regulate DNA-based CRNs. The nonlinear behavior of the systems can be finely tuned by programmable regulation of the linker length and the ligand binding sites, of which the Hill coefficients (n_H) are in the range of 1.00-2.32. By integrating two different strand displacement reactions with low-order nonlinearities (n_H ≈ 1.44 and 1.54), we could construct CRNs exhibiting high-order nonlinearities with Hill coefficients of up to ∼2.70. In addition, this could provide an efficient approach for designing CRNs at will with complex chemical dynamics by incorporating our design with previously developed enzyme-free DNA circuits.

DNA Origami Nanoplate-Based Emulsion with Nanopore Function

Bio-inspired functional microcapsules have attracted increasing attention in many fields from physical/chemical science to artificial-cell engineering. Although particle-stabilised microcapsules are advantageous for their stability and functionalisation potential, versatile methods for their functionalisation are desired to expand their possibilities. This study reports a water-in-oil microdroplet stabilised with amphiphilic DNA origami nanoplates. By utilising DNA nanotechnology, DNA nanoplates were designed as a nanopore device for ion transportation and to stabilise the oil-water interface. Microscopic examination revealed the microcapsule formed by the accumulation of amphiphilic DNA nanoplates at the oil-water interface. Ion current measurements revealed the nanoplate pores functioned as channel to transport ions. These findings provide a general strategy for the programmable design of microcapsules to engineer artificial cells and molecular robots.

On the importance of reaction networks for synthetic living systems

The goal of creating a synthetic cell necessitates the development of reaction networks which will underlie all of its behaviours. Recent developments in in vitro systems, based upon both DNA and enzymes, have created networks capable of a range of behaviours e.g. information processing, adaptation and diffusive signalling. These networks are based upon reaction motifs that when combined together produce more complex behaviour. We highlight why it is inevitable that networks, based on enzymes or enzyme-like catalysts, will be required for the construction of a synthetic cell. We outline several of the challenges, including (a) timing, (b) regulation and (c) energy distribution, that must be overcome in order to transition from the simple networks we have today to much more complex networks capable of a variety of behaviours and which could find application one day within a synthetic cell.

Building in vitro transcriptional regulatory networks by successively integrating multiple functional circuit modules

The regulation of cellular dynamics and responses to stimuli by genetic regulatory networks suggests how in vitro chemical reaction networks might analogously direct the dynamics of synthetic materials or chemistries. A key step in developing genetic regulatory network analogues capable of this type of sophisticated regulation is the integration of multiple coordinated functions within a single network. Here, we demonstrate how such functional integration can be achieved using in vitro transcriptional genelet circuits that emulate essential features of cellular genetic regulatory networks. By successively incorporating functional genelet modules into a bistable circuit, we construct an integrated regulatory network that dynamically changes its state in response to upstream stimuli and coordinates the timing of downstream signal expression. We use quantitative models to guide module integration and develop strategies to mitigate undesired interactions between network components that arise as the size of the network increases. This approach could enable the construction of in vitro networks capable of multifaceted chemical and material regulation.

Building Dynamic Cellular Machineries in Droplet-Based Artificial Cells with Single-Droplet Tracking and Analysis

Although the application of droplet microfluidics has grown exponentially in chemistry and biology over the past decades, robust universal platforms for the routine generation and comprehensive analysis of droplet-based artificial cells are still rare. Here we report using microfluidic droplets to reproduce a variety of types of cellular machinery in in vitro artificial cells. In combination with a unique image-based analysis method, the system enables full automation in tracking single droplets with high accuracy, high throughput, and high sensitivity. These powerful performances allow broad applicability evident in three representative droplet-based analytical prototypes that we develop for (i) droplet digital detection, (ii) in vitro transcription and translation reactions, and (iii) spatiotemporal dynamics of cell-cycle oscillations. The capacities of this platform to generate, incubate, track, and analyze individual microdroplets via real-time, long-term imaging unleash its great potential in accelerating cell-free synthetic biology. Moreover, the wide scope covering from digital to analog to morphological detections makes this droplet analysis technique adaptable for many other divergent types of droplet-based chemical and biological assays.

Design Principles for Compartmentalization and Spatial Organization of Synthetic Genetic Circuits

Compartmentalization is a hallmark of cellular systems and an ingredient actively exploited in evolution. It is also being engineered and exploited in synthetic biology, in multiple ways. While these have demonstrated important experimental capabilities, understanding design principles underpinning compartmentalization of genetic circuits has been elusive. We develop a systems framework to elucidate the interplay between the nature of the genetic circuit, the spatial organization of compartments, and their operational state (well-mixed or otherwise). In so doing, we reveal a number of unexpected features associated with compartmentalizing synthetic and template-based circuits. These include (i) the consequences of distributing circuits including trade-offs and how they may be circumvented, (ii) hidden constraints in realizing a distributed circuit, and (iii) appealing new features of compartmentalized circuits. We build on this to examine exemplar applications, which consolidate and extend the design principles we have obtained. Our insights, which emerge from the most basic and general considerations of compartmentalizing genetic circuits, are relevant in a broad range of settings.

Consecutive feedback-driven constitutional dynamic networks

Cellular transformations are driven by environmentally triggered complex dynamic networks, which include signal-triggered feedback processes, cascaded reactions, and switchable transformations. We apply the structural and functional information encoded in the sequences of nucleic acids to construct signal-triggered constitutional dynamic networks (CDNs) that mimic the functions of natural networks. Using predesigned hairpin structures as triggers, the network generates functional strands, which stabilize one or the other of the constituents of the network, leading to feedback-driven reconfiguration and time-dependent equilibration of the networks. Using structurally designed hairpins, positive-feedback or negative-feedback mechanisms operated by the CDNs are demonstrated. With two predesigned hairpins, the coupled consecutive operations of negative/positive- or positive/positive- feedback cascades are accomplished. The time-dependent composition changes of the networks are well reproduced by chemical kinetics simulations that provide predictive behaviors of the network, under variable auxiliary conditions. Beyond mimicking natural network properties and functions by means of the synthetic nucleic-acid-based CDNs, the systems introduce versatile perspectives for the design of amplified sensors (sensing of miRNA-376a) and the development of logic gate circuits.

Coding of Experimental Conditions in Microfluidic Droplet Assays Using Colored Beads and Machine Learning Supported Image Analysis

To efficiently exploit the potential of several millions of droplets that can be considered as individual bioreactors in microfluidic experiments, methods to encode different experimental conditions in droplets are needed. The approach presented here is based on coencapsulation of colored polystyrene beads with biological samples. The decoding of the droplets, as well as content quantification, are performed by automated analysis of triggered images of individual droplets in-flow using bright-field microscopy. The decoding strategy combines bead classification using a random forest classifier and Bayesian inference to identify different codes and thus experimental conditions. Antibiotic susceptibility testing of nine different antibiotics and the determination of the minimal inhibitory concentration of a specific antibiotic against a laboratory strain of Escherichia coli are presented as a proof-of-principle. It is demonstrated that this method allows successful encoding and decoding of 20 different experimental conditions within a large droplet population of more than 10⁵ droplets per condition. The decoding strategy correctly assigns 99.6% of droplets to the correct condition and a method for the determination of minimal inhibitory concentration using droplet microfluidics is established. The current encoding and decoding pipeline can readily be extended to more codes by adding more bead colors or color combinations.

Signalling and differentiation in emulsion-based multi-compartmentalized in vitro gene circuits

Multicellularity enables the growth of complex life forms as it allows for specialization of cell types, differentiation, and large scale spatial organization. In a similar way, modular construction of synthetic multicellular systems will lead to dynamic biomimetic materials that can respond to their environment in complex ways. In order to achieve this goal, artificial cellular communication and developmental programs still have to be established. Here, we create geometrically controlled spatial arrangements of emulsion-based artificial cellular compartments containing synthetic in vitro gene circuitry, separated by lipid bilayer membranes. We quantitatively determine the membrane pore-dependent response of the circuits to artificial morphogen gradients, which are established via diffusion from dedicated organizer cells. Utilizing different types of feed-forward and feedback in vitro gene circuits, we then implement artificial signaling and differentiation processes, demonstrating the potential for the realization of complex spatiotemporal dynamics in artificial multicellular systems.

Hybrid cell reactor system from Escherichia coli protoplast cells and arrayed lipid bilayer chamber device

We developed a novel hybrid cell reactor system via functional fusion of single Escherichia coli protoplast cells, that are deficient in cell wall and expose plasma membrane, with arrayed lipid bilayer chambers on a device in order to incorporate the full set of cytosolic and membrane constituents into the artificial chambers. We investigated gene expression activity to represent the viability of the hybrid cell reactors: over 20% of hybrid cells showed gene expression activity from plasmid or mRNA. This suggests that the hybrid cell reactors retained fundamental activity of genetic information transduction. To expand the applicability of the hybrid cell reactors, we also developed the E. coli-in-E. coli cytoplasm system as an artificial parasitism system. Over 30% of encapsulated E. coli cells exhibited normal cell division, showing that hybrid cells can accommodate and cultivate living cells. This novel artificial cell reactor technology would enable unique approaches for synthetic cell researches such as reconstruction of living cell, artificial parasitism/symbiosis system, or physical simulation to test functionality of synthetic genome.

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.

Out-of-equilibrium microcompartments for the bottom-up integration of metabolic functions

Self-sustained metabolic pathways in microcompartments are the corner-stone for living systems. From a technological viewpoint, such pathways are a mandatory prerequisite for the reliable design of artificial cells functioning out-of-equilibrium. Here we develop a microfluidic platform for the miniaturization and analysis of metabolic pathways in man-made microcompartments formed of water-in-oil droplets. In a modular approach, we integrate in the microcompartments a nicotinamide adenine dinucleotide (NAD)-dependent enzymatic reaction and a NAD-regeneration module as a minimal metabolism. We show that the microcompartments sustain a metabolically active state until the substrate is fully consumed. Reversibly, the external addition of the substrate reboots the metabolic activity of the microcompartments back to an active state. We therefore control the metabolic state of thousands of independent monodisperse microcompartments, a step of relevance for the construction of large populations of metabolically active artificial cells.