A population-based temporal logic gate for timing and recording chemical events

A history-dependent integrase recorder of plant gene expression with single-cell resolution

During development, most cells experience a progressive restriction of fate that ultimately results in a fully differentiated mature state. Understanding more about the gene expression patterns that underlie developmental programs can inform engineering efforts for new or optimized forms. Here, we present a four-state integrase-based recorder of gene expression history and demonstrate its use in tracking gene expression events in Arabidopsis thaliana in two developmental contexts: lateral root initiation and stomatal differentiation. The recorder uses two serine integrases to mediate sequential DNA recombination events, resulting in step-wise, history-dependent switching between expression of fluorescent reporters. By using promoters that express at different times along each of the two differentiation pathways to drive integrase expression, we tie fluorescent status to an ordered progression of gene expression along the developmental trajectory. In one snapshot of a mature tissue, our recorder is able to reveal past gene expression with single cell resolution. In this way, we are able to capture heterogeneity in stomatal development, confirming the existence of two alternate paths of differentiation.

Orthogonal Serine Integrases Enable Scalable Gene Storage Cascades in Bacterial Genome

Genome integration enables host organisms to stably carry heterologous DNA messages, introducing new genotypes and phenotypes for expanded applications. While several genome integration approaches have been reported, a scalable tool for DNA message storage within site-specific genome landing pads is still lacking. Here, we introduce an iterative genome integration method utilizing orthogonal serine integrases, enabling the stable storage of multiple heterologous genes in the chromosome of Escherichia coli MG1655. By leveraging serine integrases TP901-1, Bxb1, and PhiC31, along with engineered integration vectors, we demonstrate high-efficiency, marker-free integration of DNA fragments up to 13 kb in length. To further simplify the procedure, we then develop a streamlined integration method and showcase the system's versatility by constructing an engineered E. coli strain capable of storing and expressing multiple genes from diverse species. Additionally, we illustrate the potential utility of these engineered strains for synthetic biology applications, including in vivo and in vitro protein expression. Our work extends the application scope of serine integrases for scalable gene integration cascades, with implications for genome manipulation and gene storage applications in synthetic biology.

An Exo III-powered closed-loop DNA circuit architecture for biosensing/imaging

With their regulated Boolean logic operations in vitro and in vivo, DNA logic circuits have shown great promise for target recognition and disease diagnosis. However, significant obstacles must be overcome to improve their operational efficiency and broaden their range of applications. In this study, we propose an Exo III-powered closed-loop DNA circuit (ECDC) architecture that integrates four highly efficient AND logic gates. The ECDC utilizes Exo III as the sole enzyme-activated actuator, simplifying the circuit design and ensuring optimal performance. Moreover, the use of Exo III enables a self-feedback (autocatalytic) mechanism in the dynamic switching between AND logic gates within this circulating logic circuit. After validating the signal flow and examining the impact of each AND logic gate on the regulation of the circuit, we demonstrate the intelligent determination of miR-21 using the carefully designed ECDC architecture in vitro. The proposed ECDC exhibits a linear detection range for miR-21 from 0 to 300 nM, with a limit of detection (LOD) of approximately 0.01 nM, surpassing most reported methods. It also shows excellent selectivity for miR-21 detection and holds potential for identifying and imaging live cancer cells. This study presents a practical and efficient strategy for monitoring various nucleic acid-based biomarkers in vitro and in vivo through specific sequence modifications, offering significant potential for early cancer diagnosis, bioanalysis, and prognostic clinical applications.

DNA Reaction System That Acquires Classical Conditioning

Biochemical reaction networks can exhibit plastic adaptation to alter their functions in response to environmental changes. This capability is derived from the structure and dynamics of the reaction networks and the functionality of the biomolecule. This plastic adaptation in biochemical reaction systems is essentially related to memory and learning capabilities, which have been studied in DNA computing applications for the past decade. However, designing DNA reaction systems with memory and learning capabilities using the dynamic properties of biochemical reactions remains challenging. In this study, we propose a basic DNA reaction system design that acquires classical conditioning, a phenomenon underlying memory and learning, as a typical learning task. Our design is based on a simple mechanism of five DNA strand displacement reactions and two degradative reactions. The proposed DNA circuit can acquire or lose a new function under specific conditions, depending on the input history formed by repetitive stimuli, by exploiting the dynamic properties of biochemical reactions induced by different input timings.

Development of Cell-Free Transcription-Translation Systems in Three Soil Pseudomonads

In vitro transcription-translation (TX-TL) can enable faster engineering of biological systems. This speed-up can be significant, especially in difficult-to-transform chassis. This work shows the successful development of TX-TL systems using three soil-derived wild-type Pseudomonads known to promote plant growth: Pseudomonas synxantha, Pseudomonas chlororaphis, and Pseudomonas aureofaciens. All three species demonstrated multiple sonication, runoff, and salt conditions producing detectable protein synthesis. One of these new TX-TL systems, P. synxantha, demonstrated a maximum protein yield of 2.5 μM at 125 proteins per DNA template, a maximum protein synthesis rate of 20 nM/min, and a range of DNA concentrations with a linear correspondence with the resulting protein synthesis. A set of different constitutive promoters driving mNeonGreen expression were tested in TX-TL and integrated into the genome, showing similar normalized strengths for in vivo and in vitro fluorescence. This correspondence between the TX-TL-derived promoter strength and the in vivo promoter strength indicates that these lysate-based cell-free systems can be used to characterize and engineer biological parts without genomic integration, enabling a faster design-build-test cycle.

Temporal logic circuits implementation using a dual cross-inhibition mechanism based on DNA strand displacement

Molecular circuits crafted from DNA molecules harness the inherent programmability and biocompatibility of DNA to intelligently steer molecular machines in the execution of microscopic tasks. In comparison to combinational circuits, DNA-based temporal circuits boast supplementary capabilities, allowing them to proficiently handle the omnipresent temporal information within biochemical systems and life sciences. However, the lack of temporal mechanisms and components proficient in comprehending and processing temporal information presents challenges in advancing DNA circuits that excel in complex tasks requiring temporal control and time perception. In this study, we engineered temporal logic circuits through the design and implementation of a dual cross-inhibition mechanism, which enables the acceptance and processing of temporal information, serving as a fundamental building block for constructing temporal circuits. By incorporating the dual cross-inhibition mechanism, the temporal logic gates are endowed with cascading capabilities, significantly enhancing the inhibitory effect compared to a cross-inhibitor. Furthermore, we have introduced the annihilation mechanism into the circuit to further augment the inhibition effect. As a result, the circuit demonstrates sensitive time response characteristics, leading to a fundamental improvement in circuit performance. This architecture provides a means to efficiently process temporal signals in DNA strand displacement circuits. We anticipate that our findings will contribute to the design of complex temporal logic circuits and the advancement of molecular programming.

Temporally resolved transcriptional recording in E. coli DNA using a Retro-Cascorder

Biological signals occur over time in living cells. Yet most current approaches to interrogate biology, particularly gene expression, use destructive techniques that quantify signals only at a single point in time. A recent technological advance, termed the Retro-Cascorder, overcomes this limitation by molecularly logging a record of gene expression events in a temporally organized genomic ledger. The Retro-Cascorder works by converting a transcriptional event into a DNA barcode using a retron reverse transcriptase and then storing that event in a unidirectionally expanding clustered regularly interspaced short palindromic repeats (CRISPR) array via acquisition by CRISPR-Cas integrases. This CRISPR array-based ledger of gene expression can be retrieved at a later point in time by sequencing. Here we describe an implementation of the Retro-Cascorder in which the relative timing of transcriptional events from multiple promoters of interest is recorded chronologically in Escherichia coli populations over multiple days. We detail the molecular components required for this technology, provide a step-by-step guide to generate the recording and retrieve the data by Illumina sequencing, and give instructions for how to use custom software to infer the relative transcriptional timing from the sequencing data. The example recording is generated in 2 d, preparation of sequencing libraries and sequencing can be accomplished in 2-3 d, and analysis of data takes up to several hours. This protocol can be implemented by someone familiar with basic bacterial culture, molecular biology and bioinformatics. Analysis can be minimally run on a personal computer.

Untethered Soft Microrobots with Adaptive Logic Gates

Integrating adaptative logic computation directly into soft microrobots is imperative for the next generation of intelligent soft microrobots as well as for the smart materials to move beyond stimulus-response relationships and toward the intelligent behaviors seen in biological systems. Acquiring adaptivity is coveted for soft microrobots that can adapt to implement different works and respond to different environments either passively or actively through human intervention like biological systems. Here, a novel and simple strategy for constructing untethered soft microrobots based on stimuli-responsive hydrogels that can switch logic gates according to the surrounding stimuli of environment is introduced. Different basic logic gates and combinational logic gates are integrated into a microrobot via a straightforward method. Importantly, two kinds of soft microrobots with adaptive logic gates are designed and fabricated, which can smartly switch logic operation between AND gate and OR gate under different surrounding environmental stimuli. Furthermore, a same magnetic microrobot with adaptive logic gate is used to capture and release the specified objects through the change of the surrounding environmental stimuli based on AND or OR logic gate. This work contributes an innovative strategy to integrate computation into small-scale untethered soft robots with adaptive logic gates.

Molecular Computation for Molecular Classification

DNA as an informational polymer has, for the past 30 years, progressively become an essential molecule to rationally build chemical reaction networks endowed with powerful signal-processing capabilities. Whether influenced by the silicon world or inspired by natural computation, molecular programming has gained attention for diagnosis applications. Of particular interest for this review, molecular classifiers have shown promising results for disease pattern recognition and sample classification. Because both input integration and computation are performed in a single tube, at the molecular level, this low-cost approach may come as a complementary tool to molecular profiling strategies, where all biomarkers are quantified independently using high-tech instrumentation. After introducing the elementary components of molecular classifiers, some of their experimental implementations are discussed either using digital Boolean logic or analog neural network architectures.

Simple and Rapid Site-Specific Integration of Multiple Heterologous DNAs into the Escherichia coli Chromosome

Escherichia coli is the most studied and well understood microorganism, but research in this system can still be limited by available genetic tools, including the ability to rapidly integrate multiple DNA constructs efficiently into the chromosome. Site-specific, large serine-recombinases can be useful tools, catalyzing a single, unidirectional recombination event between 2 specific DNA sequences, attB and attP, without requiring host proteins for functionality. Using these recombinases, we have developed a system to integrate up to 12 genetic constructs sequentially and stably into in the E. coli chromosome. A cassette of attB sites was inserted into the chromosome and the corresponding recombinases were cloned onto temperature sensitive plasmids to mediate recombination between a non-replicating, attP-containing "cargo" plasmid and the corresponding attB site on the chromosome. The efficiency of DNA insertion into the E. coli chromosome was approximately 10⁷ CFU/μg DNA for six of the recombinases when the competent cells already contained the recombinase-expressing plasmid and approximately 10⁵ CFU/μg DNA or higher when the recombinase-expressing plasmid and "cargo" plasmid were co-transformed. The "cargo" plasmid contains ΦC31 recombination sites flanking the antibiotic gene, allowing for resistance markers to be removed and reused following transient expression of the ΦC31 recombinase. As an example of the utility of this system, eight DNA methyltransferases from Clostridium clariflavum 4-2a were inserted into the E. coli chromosome to methylate plasmid DNA for evasion of the C. clariflavum restriction systems, enabling the first demonstration of transformation of this cellulose-degrading species. IMPORTANCE More rapid genetic tools can help accelerate strain engineering, even in advanced hosts like Escherichia coli. Here, we adapt a suite of site-specific recombinases to enable simple, rapid, and highly efficient site-specific integration of heterologous DNA into the chromosome. This utility of this system was demonstrated by sequential insertion of eight DNA methyltransferases into the E. coli chromosome, allowing plasmid DNA to be protected from restriction in Clostridium clariflavum and enabling genetic transformation of this organism. This integration system should also be highly portable into non-model organisms.

Characterization of Integrase and Excisionase Activity in a Cell-Free Protein Expression System Using a Modeling and Analysis Pipeline

We present a full-stack modeling, analysis, and parameter identification pipeline to guide the modeling and design of biological systems starting from specifications to circuit implementations and parametrizations. We demonstrate this pipeline by characterizing the integrase and excisionase activity in a cell-free protein expression system. We build on existing Python tools─BioCRNpyler, AutoReduce, and Bioscrape─to create this pipeline. For enzyme-mediated DNA recombination in a cell-free system, we create detailed chemical reaction network models from simple high-level descriptions of the biological circuits and their context using BioCRNpyler. We use Bioscrape to show that the output of the detailed model is sensitive to many parameters. However, parameter identification is infeasible for this high-dimensional model; hence, we use AutoReduce to automatically obtain reduced models that have fewer parameters. This results in a hierarchy of reduced models under different assumptions to finally arrive at a minimal ODE model for each circuit. Then, we run sensitivity analysis-guided Bayesian inference using Bioscrape for each circuit to identify the model parameters. This process allows us to quantify integrase and excisionase activity in cell extracts enabling complex-circuit designs that depend on accurate control over protein expression levels through DNA recombination. The automated pipeline presented in this paper opens up a new approach to complex circuit design, modeling, reduction, and parametrization.

Molecular recording: transcriptional data collection into the genome

Advances in regenerative medicine depend upon understanding the complex transcriptional choreography that guides cellular development. Transcriptional molecular recorders, tools that record different transcriptional events into the genome of cells, hold promise to elucidate both the intensity and timing of transcriptional activity at single-cell resolution without requiring destructive multitime point assays. These technologies are dependent on DNA writers, which translate transcriptional signals into stable genomic mutations that encode the duration, intensity, and order of transcriptional events. In this review, we highlight recent progress toward more informative and multiplexable transcriptional recording through the use of three different types of DNA writing - recombineering, Cas1-Cas2 acquisition, and prime editing - and the architecture of the genomic data generated.

Integrase-mediated differentiation circuits improve evolutionary stability of burdensome and toxic functions in E. coli

Advances in synthetic biology, bioengineering, and computation allow us to rapidly and reliably program cells with increasingly complex and useful functions. However, because the functions we engineer cells to perform are typically burdensome to cell growth, they can be rapidly lost due to the processes of mutation and natural selection. Here, we show that a strategy of terminal differentiation improves the evolutionary stability of burdensome functions in a general manner by realizing a reproductive and metabolic division of labor. To implement this strategy, we develop a genetic differentiation circuit in Escherichia coli using unidirectional integrase-recombination. With terminal differentiation, differentiated cells uniquely express burdensome functions driven by the orthogonal T7 RNA polymerase, but their capacity to proliferate is limited to prevent the propagation of advantageous loss-of-function mutations that inevitably occur. We demonstrate computationally and experimentally that terminal differentiation increases duration and yield of high-burden expression and that its evolutionary stability can be improved with strategic redundancy. Further, we show this strategy can even be applied to toxic functions. Overall, this study provides an effective, generalizable approach for protecting burdensome engineered functions from evolutionary degradation.

DNA Strand-Displacement Temporal Logic Circuits

Molecular circuits capable of processing temporal information are essential for complex decision making in response to both the presence and history of a molecular environment. A particular type of temporal information that has been recognized to be important is the relative timing of signals. Here we demonstrate the strategy of temporal memory combined with logic computation in DNA strand-displacement circuits capable of making decisions based on specific combinations of inputs as well as their relative timing. The circuit encodes the timing information on inputs in a set of memory strands, which allows for the construction of logic gates that act on current and historical signals. We show that mismatches can be employed to reduce the complexity of circuit design and that shortening specific toeholds can be useful for improving the robustness of circuit behavior. We also show that a detailed model can provide critical insights for guiding certain aspects of experimental investigations that an abstract model cannot. We envision that the design principles explored in this study can be generalized to more complex temporal logic circuits and incorporated into other types of circuit architectures, including DNA-based neural networks, enabling the implementation of timing-dependent learning rules and opening up new opportunities for embedding intelligent behaviors into artificial molecular machines.

The Use of Bacteriophages in Biotechnology and Recent Insights into Proteomics

Phages have certain features, such as their ability to form protein-protein interactions, that make them good candidates for use in a variety of beneficial applications, such as in human or animal health, industry, food science, food safety, and agriculture. It is essential to identify and characterize the proteins produced by particular phages in order to use these viruses in a variety of functional processes, such as bacterial detection, as vehicles for drug delivery, in vaccine development, and to combat multidrug resistant bacterial infections. Furthermore, phages can also play a major role in the design of a variety of cheap and stable sensors as well as in diagnostic assays that can either specifically identify specific compounds or detect bacteria. This article reviews recently developed phage-based techniques, such as the use of recombinant tempered phages, phage display and phage amplification-based detection. It also encompasses the application of phages as capture elements, biosensors and bioreceptors, with a special emphasis on novel bacteriophage-based mass spectrometry (MS) applications.

Probabilistic Inference with Polymerizing Biochemical Circuits

Probabilistic inference-the process of estimating the values of unobserved variables in probabilistic models-has been used to describe various cognitive phenomena related to learning and memory. While the study of biological realizations of inference has focused on animal nervous systems, single-celled organisms also show complex and potentially "predictive" behaviors in changing environments. Yet, it is unclear how the biochemical machinery found in cells might perform inference. Here, we show how inference in a simple Markov model can be approximately realized, in real-time, using polymerizing biochemical circuits. Our approach relies on assembling linear polymers that record the history of environmental changes, where the polymerization process produces molecular complexes that reflect posterior probabilities. We discuss the implications of realizing inference using biochemistry, and the potential of polymerization as a form of biological information-processing.

Ultrasound-controllable engineered bacteria for cancer immunotherapy

Rapid advances in synthetic biology are driving the development of genetically engineered microbes as therapeutic agents for a multitude of human diseases, including cancer. The immunosuppressive microenvironment of solid tumors, in particular, creates a favorable niche for systemically administered bacteria to engraft and release therapeutic payloads. However, such payloads can be harmful if released outside the tumor in healthy tissues where the bacteria also engraft in smaller numbers. To address this limitation, we engineer therapeutic bacteria to be controlled by focused ultrasound, a form of energy that can be applied noninvasively to specific anatomical sites such as solid tumors. This control is provided by a temperature-actuated genetic state switch that produces lasting therapeutic output in response to briefly applied focused ultrasound hyperthermia. Using a combination of rational design and high-throughput screening we optimize the switching circuits of engineered cells and connect their activity to the release of immune checkpoint inhibitors. In a clinically relevant cancer model, ultrasound-activated therapeutic microbes successfully turn on in situ and induce a marked suppression of tumor growth. This technology provides a critical tool for the spatiotemporal targeting of potent bacterial therapeutics in a variety of biological and clinical scenarios.

Data-driven network models for genetic circuits from time-series data with incomplete measurements

Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro, due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements of state dynamics. We develop a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a previously reported in vitro genetic circuit and a new E. coli-based transcriptional event detector.

Magnetic induction inspires a schematic theory for crosstalk-driven relaxation dynamics in cells

Establishing formal mathematical analogies between disparate physical systems can be a powerful tool, allowing for the well studied behavior of one system to be directly translated into predictions about the behavior of another that may be harder to probe. In this paper we lay the foundation for such an analogy between the macroscale electrodynamics of simple magnetic circuits and the microscale chemical kinetics of transcriptional regulation in cells. By artificially allowing the inductor coils of the former to elastically expand under the action of their Lorentz pressure, we introduce nonlinearities into the system that we interpret through the lens of our analogy as a schematic model for the impact of crosstalk on the rates of gene expression near steady state. Synthetic plasmids introduced into a cell must compete for a finite pool of metabolic and enzymatic resources against a maelstrom of crisscrossing biological processes, and our theory makes sensible predictions about how this noisy background might impact the expression profiles of synthetic constructs without explicitly modeling the kinetics of numerous interconnected regulatory interactions. We conclude the paper with a discussion of how our theory might be expanded to a broader class of plasmid circuits and how our predictions might be tested experimentally.

Synthetic biology in the clinic: engineering vaccines, diagnostics, and therapeutics

Synthetic biology is a design-driven discipline centered on engineering novel biological functions through the discovery, characterization, and repurposing of molecular parts. Several synthetic biological solutions to critical biomedical problems are on the verge of widespread adoption and demonstrate the burgeoning maturation of the field. Here, we highlight applications of synthetic biology in vaccine development, molecular diagnostics, and cell-based therapeutics, emphasizing technologies approved for clinical use or in active clinical trials. We conclude by drawing attention to recent innovations in synthetic biology that are likely to have a significant impact on future applications in biomedicine.

Recording of elapsed time and temporal information about biological events using Cas9

DNA has not been utilized to record temporal information, although DNA has been used to record biological information and to compute mathematical problems. Here, we found that indel generation by Cas9 and guide RNA can occur at steady rates, in contrast to typical dynamic biological reactions, and the accumulated indel frequency can be a function of time. By measuring indel frequencies, we developed a method for recording and measuring absolute time periods over hours to weeks in mammalian cells. These time-recordings were conducted in several cell types, with different promoters and delivery vectors for Cas9, and in both cultured cells and cells of living mice. As applications, we recorded the duration of chemical exposure and the lengths of elapsed time since the onset of biological events (e.g., heat exposure and inflammation). We propose that our systems could serve as synthetic "DNA clocks."

A MATLAB toolbox for modeling genetic circuits in cell-free systems

We introduce a MATLAB-based simulation toolbox, called txtlsim, for an Escherichia coli-based Transcription-Translation (TX-TL) system. This toolbox accounts for several cell-free-related phenomena, such as resource loading, consumption and degradation, and in doing so, models the dynamics of TX-TL reactions for the entire duration of solution phase batch-mode experiments. We use a Bayesian parameter inference approach to characterize the reaction rate parameters associated with the core transcription, translation and mRNA degradation mechanics of the toolbox, allowing it to reproduce constitutive mRNA and protein-expression trajectories. We demonstrate the use of this characterized toolbox in a circuit behavior prediction case study for an incoherent feed-forward loop.

A mechanistic model of the BLADE platform predicts performance characteristics of 256 different synthetic DNA recombination circuits

Boolean logic and arithmetic through DNA excision (BLADE) is a recently developed platform for implementing inducible and logical control over gene expression in mammalian cells, which has the potential to revolutionise cell engineering for therapeutic applications. This 2-input 2-output platform can implement 256 different logical circuits that exploit the specificity and stability of DNA recombination. Here, we develop the first mechanistic mathematical model of the 2-input BLADE platform based on Cre- and Flp-mediated DNA excision. After calibrating the model on experimental data from two circuits, we demonstrate close agreement between model outputs and data on the other 111 circuits that have so far been experimentally constructed using the 2-input BLADE platform. Model simulations of the remaining 143 circuits that have yet to be tested experimentally predict excellent performance of the 2-input BLADE platform across the range of possible circuits. Circuits from both the tested and untested subsets that perform less well consist of a disproportionally high number of STOP sequences. Model predictions suggested that circuit performance declines with a decrease in recombinase expression and new experimental data was generated that confirms this relationship.

Rational programming of history-dependent logic in cellular populations

Genetic programs operating in a history-dependent fashion are ubiquitous in nature and govern sophisticated processes such as development and differentiation. The ability to systematically and predictably encode such programs would advance the engineering of synthetic organisms and ecosystems with rich signal processing abilities. Here we implement robust, scalable history-dependent programs by distributing the computational labor across a cellular population. Our design is based on standardized recombinase-driven DNA scaffolds expressing different genes according to the order of occurrence of inputs. These multicellular computing systems are highly modular, do not require cell-cell communication channels, and any program can be built by differential composition of strains containing well-characterized logic scaffolds. We developed automated workflows that researchers can use to streamline program design and optimization. We anticipate that the history-dependent programs presented here will support many applications using cellular populations for material engineering, biomanufacturing and healthcare.

From Microbial Communities to Distributed Computing Systems

A distributed biological system can be defined as a system whose components are located in different subpopulations, which communicate and coordinate their actions through interpopulation messages and interactions. We see that distributed systems are pervasive in nature, performing computation across all scales, from microbial communities to a flock of birds. We often observe that information processing within communities exhibits a complexity far greater than any single organism. Synthetic biology is an area of research which aims to design and build synthetic biological machines from biological parts to perform a defined function, in a manner similar to the engineering disciplines. However, the field has reached a bottleneck in the complexity of the genetic networks that we can implement using monocultures, facing constraints from metabolic burden and genetic interference. This makes building distributed biological systems an attractive prospect for synthetic biology that would alleviate these constraints and allow us to expand the applications of our systems into areas including complex biosensing and diagnostic tools, bioprocess control and the monitoring of industrial processes. In this review we will discuss the fundamental limitations we face when engineering functionality with a monoculture, and the key areas where distributed systems can provide an advantage. We cite evidence from natural systems that support arguments in favor of distributed systems to overcome the limitations of monocultures. Following this we conduct a comprehensive overview of the synthetic communities that have been built to date, and the components that have been used. The potential computational capabilities of communities are discussed, along with some of the applications that these will be useful for. We discuss some of the challenges with building co-cultures, including the problem of competitive exclusion and maintenance of desired community composition. Finally, we assess computational frameworks currently available to aide in the design of microbial communities and identify areas where we lack the necessary tools.

Development of an epigenetic tetracycline sensor system based on DNA methylation

Bacterial live cell sensors are potentially powerful tools for the detection of environmental toxins. In this work, we have established and validated a flow cytometry readout for an existing bacterial arabinose sensor system with DNA methylation based memory function (Maier et al., 2017, Nat. Comm., 8:15336). Flow cytometry readout is convenient and enables a multiparameter analysis providing information about single-cell variability, which is beneficial for further development of sensor systems of this type in the future. We then designed a tetracycline sensor system, because of the importance of antibiotics pollution in the light of multi-resistant pathogens. To this end, a tetracycline trigger plasmid was constructed by replacing the araC repressor gene and the ara operator of the arabinose trigger plasmid with the tetR gene coding for the tetracycline repressor and the tet operon. After combination with the memory plasmid, the tetracycline sensor system was shown to be functional in E. coli allowing to detect and memorize the presence of tetracycline. Due to a positive feedback between the trigger and memory systems, the combined whole-cell biosensor showed a very high sensitivity for tetracycline with a detection threshold at 0.1 ng/ml tetracycline, which may be a general property of sensors of this type. Moreover, acute presence of tetracycline and past exposure can be detected by this sensor using the dual readout of two reporter fluorophores.

Mechanistic modelling of tyrosine recombination reveals key parameters determining the performance of a CAR T cell switching circuit

Inducible genetic switches based on tyrosine recombinase-based DNA excision are a promising platform for the regulation and control of chimeric antigen receptor (CAR) T cell activity in cancer immunotherapy. These switches exploit the increased stability of DNA excision in tyrosine recombinases through an inversion-excision circuit design. Here, the authors develop the first mechanistic mathematical model of switching dynamics in tyrosine recombinases and validate it against experimental data through both global optimisation and statistical approximation approaches. Analysis of this model provides guidelines regarding which system parameters are best suited to experimental tuning in order to establish optimal switch performance in vivo. In particular, they find that the switching response can be made significantly faster by increasing the concentration of the inducer drug 4-OHT and/or by using promoters generating higher expression levels of the FlpO recombinase.

A broad-host-range event detector: expanding and quantifying performance between Escherichia coli and Pseudomonas species

Modern microbial biodesign relies on the principle that well-characterized genetic parts can be reused and reconfigured for different functions. However, this paradigm has only been successful in a limited set of hosts, mostly comprised from common lab strains of Escherichia coli. It is clear that new applications such as chemical sensing and event logging in complex environments will benefit from new host chassis. This study quantitatively compared how the same chemical event logger performed across four strains and three different microbial species. An integrase-based sensor and memory device was operated by two representative soil Pseudomonads—Pseudomonas fluorescens SBW25 and Pseudomonas putida DSM 291. Quantitative comparisons were made between these two non-traditional hosts and two benchmark E. coli chassis including the probiotic Nissle 1917 and common cloning strain DH5α. The performance of sensor and memory components changed according to each host, such that a clear chassis effect was observed and quantified. These results were obtained via fluorescence from reporter proteins that were transcriptionally fused to the integrase and downstream recombinant region and via data-driven kinetic models. The Pseudomonads proved to be acceptable chassis for the operation of this event logger, which outperformed the common E. coli DH5α in many ways. This study advances an emerging frontier in synthetic biology that aims to build broad-host-range devices and understand the context by which different species can execute programmable genetic operations.

Synthetic genetic circuits for programmable biological functionalities

Living organisms evolve complex genetic networks to interact with the environment. Due to the rapid development of synthetic biology, various modularized genetic parts and units have been identified from these networks. They have been employed to construct synthetic genetic circuits, including toggle switches, oscillators, feedback loops and Boolean logic gates. Building on these circuits, complex genetic machines with capabilities in programmable decision-making could be created. Consequently, these accomplishments have led to novel applications, such as dynamic and autonomous modulation of metabolic networks, directed evolution of biological units, remote and targeted diagnostics and therapies, as well as biological containment methods to prevent release of engineered microorganisms and genetic materials. Herein, we outline the principles in genetic circuit design that have initiated a new chapter in transforming concepts to realistic applications. The features of modularized building blocks and circuit architecture that facilitate realization of circuits for a variety of novel applications are discussed. Furthermore, recent advances and challenges in employing genetic circuits to impart microorganisms with distinct and programmable functionalities are highlighted. We envision that this review gives new insights into the design of synthetic genetic circuits and offers a guideline for the implementation of different circuits in various aspects of biotechnology and bioengineering.

Temporal Pattern Recognition through Analog Molecular Computation

Living cells communicate information about physiological conditions by producing signaling molecules in a specific timed manner. Different conditions can result in the same total amount of a signaling molecule, differing only in the pattern of the molecular concentration over time. Such temporally coded information can be completely invisible to even state-of-the-art molecular sensors with high chemical specificity that respond only to the total amount of the signaling molecule. Here, we demonstrate design principles for circuits with temporal specificity, that is, molecular circuits that respond to specific temporal patterns in a molecular concentration. We consider pulsatile patterns in a molecular concentration characterized by three fundamental temporal features: time period, duty fraction, and number of pulses. We develop circuits that respond to each one of these features while being insensitive to the others. We demonstrate our design principles using general chemical reaction networks and with explicit simulations of DNA strand displacement reactions. In this way, our work develops building blocks for temporal pattern recognition through molecular computation.

Hierarchical composition of reliable recombinase logic devices

A major goal of synthetic biology is to reprogram living organisms to solve pressing challenges in manufacturing, environmental remediation, and healthcare. Recombinase devices can efficiently encode complex logic in many species, yet current designs are performed on a case-by-case basis, limiting their scalability and requiring time-consuming optimization. Here we provide a systematic framework for engineering reliable recombinase logic devices by hierarchical composition of well-characterized, optimized recombinase switches. We apply this framework to build a recombinase logic device family supporting up to 4-input Boolean logic within a multicellular system. This work enables straightforward implementation of multicellular recombinase logic and will support the predictable engineering of several classes of recombinase devices to reliably control cellular behavior.

Genetic logic devices allow the host cell to incorporate multiple inputs to determine output behaviour. Here the authors provide a framework for engineering reliable recombinase-based devices and demonstrate 4-input logic in a multicellular system.

DNA-based memory devices for recording cellular events

Measuring biological data across time and space is critical for understanding complex biological processes and for various biosurveillance applications. However, such data are often inaccessible or difficult to directly obtain. Less invasive, more robust and higher-throughput biological recording tools are needed to profile cells and their environments. DNA-based cellular recording is an emerging and powerful framework for tracking intracellular and extracellular biological events over time across living cells and populations. Here, we review and assess DNA recorders that utilize CRISPR nucleases, integrases and base-editing strategies, as well as recombinase and polymerase-based methods. Quantitative characterization, modelling and evaluation of these DNA-recording modalities can guide their design and implementation for specific application areas.

Scaling up genetic circuit design for cellular computing: advances and prospects

Synthetic biology aims to engineer and redesign biological systems for useful real-world applications in biomanufacturing, biosensing and biotherapy following a typical design-build-test cycle. Inspired from computer science and electronics, synthetic gene circuits have been designed to exhibit control over the flow of information in biological systems. Two types are Boolean logic inspired TRUE or FALSE digital logic and graded analog computation. Key principles for gene circuit engineering include modularity, orthogonality, predictability and reliability. Initial circuits in the field were small and hampered by a lack of modular and orthogonal components, however in recent years the library of available parts has increased vastly. New tools for high throughput DNA assembly and characterization have been developed enabling rapid prototyping, systematic in situ characterization, as well as automated design and assembly of circuits. Recently implemented computing paradigms in circuit memory and distributed computing using cell consortia will also be discussed. Finally, we will examine existing challenges in building predictable large-scale circuits including modularity, context dependency and metabolic burden as well as tools and methods used to resolve them. These new trends and techniques have the potential to accelerate design of larger gene circuits and result in an increase in our basic understanding of circuit and host behaviour.

Phage-Based Applications in Synthetic Biology

Bacteriophage research has been instrumental to advancing many fields of biology, such as genetics, molecular biology, and synthetic biology. Many phage-derived technologies have been adapted for building gene circuits to program biological systems. Phages also exhibit significant medical potential as antibacterial agents and bacterial diagnostics due to their extreme specificity for their host, and our growing ability to engineer them further enhances this potential. Phages have also been used as scaffolds for genetically programmable biomaterials that have highly tunable properties. Furthermore, phages are central to powerful directed evolution platforms, which are being leveraged to enhance existing biological functions and even produce new ones. In this review, we discuss recent examples of how phage research is influencing these next-generation biotechnologies.

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.

An Automated Design Framework for Multicellular Recombinase Logic

Tools to systematically reprogram cellular behavior are crucial to address pressing challenges in manufacturing, environment, or healthcare. Recombinases can very efficiently encode Boolean and history-dependent logic in many species, yet current designs are performed on a case-by-case basis, limiting their scalability and requiring time-consuming optimization. Here we present an automated workflow for designing recombinase logic devices executing Boolean functions. Our theoretical framework uses a reduced library of computational devices distributed into different cellular subpopulations, which are then composed in various manners to implement all desired logic functions at the multicellular level. Our design platform called CALIN (Composable Asynchronous Logic using Integrase Networks) is broadly accessible via a web server, taking truth tables as inputs and providing corresponding DNA designs and sequences as outputs (available at http://synbio.cbs.cnrs.fr/calin). We anticipate that this automated design workflow will streamline the implementation of Boolean functions in many organisms and for various applications.

Rewritable multi-event analog recording in bacterial and mammalian cells

We present two CRISPR-mediated analog multi-event recording apparatus (CAMERA) systems that use base editors and Cas9 nucleases to record cellular events in bacteria and mammalian cells. The devices record signal amplitude or duration as changes in the ratio of mutually exclusive DNA sequences (CAMERA 1) or as single-base modifications (CAMERA 2). We achieved recording of multiple stimuli in bacteria or mammalian cells, including exposure to antibiotics, nutrients, viruses, light, and changes in Wnt signaling. When recording to multicopy plasmids, reliable readout requires as few as 10 to 100 cells. The order of stimuli can be recorded through an overlapping guide RNA design, and memories can be erased and re-recorded over multiple cycles. CAMERA systems serve as "cell data recorders" that write a history of endogenous or exogenous signaling events into permanent DNA sequence modifications in living cells.

Serine Integrases: Advancing Synthetic Biology

Serine integrases catalyze precise rearrangement of DNA through site-specific recombination of small sequences of DNA called attachment (att) sites. Unlike other site-specific recombinases, the recombination reaction driven by serine integrases is highly directional and can only be reversed in the presence of an accessory protein called a recombination directionality factor (RDF). The ability to control reaction directionality has led to the development of serine integrases as tools for controlled rearrangement and modification of DNA in synthetic biology, gene therapy, and biotechnology. This review discusses recent advances in serine integrase technologies focusing on their applications in genome engineering, DNA assembly, and logic and data storage devices.

A novel optogenetically tunable frequency modulating oscillator

Synthetic biology has enabled the creation of biological reconfigurable circuits, which perform multiple functions monopolizing a single biological machine; Such a system can switch between different behaviours in response to environmental cues. Previous work has demonstrated switchable dynamical behaviour employing reconfigurable logic gate genetic networks. Here we describe a computational framework for reconfigurable circuits in E.coli using combinations of logic gates, and also propose the biological implementation. The proposed system is an oscillator that can exhibit tunability of frequency and amplitude of oscillations. Further, the frequency of operation can be changed optogenetically. Insilico analysis revealed that two-component light systems, in response to light within a frequency range, can be used for modulating the frequency of the oscillator or stopping the oscillations altogether. Computational modelling reveals that mixing two colonies of E.coli oscillating at different frequencies generates spatial beat patterns. Further, we show that these oscillations more robustly respond to input perturbations compared to the base oscillator, to which the proposed oscillator is a modification. Compared to the base oscillator, the proposed system shows faster synchronization in a colony of cells for a larger region of the parameter space. Additionally, the proposed oscillator also exhibits lesser synchronization error in the transient period after input perturbations. This provides a strong basis for the construction of synthetic reconfigurable circuits in bacteria and other organisms, which can be scaled up to perform functions in the field of time dependent drug delivery with tunable dosages, and sets the stage for further development of circuits with synchronized population level behaviour.

Optimization-based synthesis of stochastic biocircuits with statistical specifications

Model-guided design has become a standard approach to engineering biomolecular circuits in synthetic biology. However, the stochastic nature of biomolecular reactions is often overlooked in the design process. As a result, cell-cell heterogeneity causes unexpected deviation of biocircuit behaviours from model predictions and requires additional iterations of design-build-test cycles. To enhance the design process of stochastic biocircuits, this paper presents a computational framework to systematically specify the level of intrinsic noise using well-defined metrics of statistics and design highly heterogeneous biocircuits based on the specifications. Specifically, we use descriptive statistics of population distributions as an intuitive specification language of stochastic biocircuits and develop an optimization-based computational tool that explores parameter configurations satisfying design requirements. Sensitivity analysis methods are also performed to ensure the robustness of a biocircuit design against extrinsic perturbations. These design tools are formulated with convex optimization programs to enable rigorous and efficient quantification of the statistics. We demonstrate these features by designing a stochastic negative feedback biocircuit that satisfies multiple statistical constraints and perform an in-depth study of noise propagation and regulation in negative feedback pathways.

Multiplex recording of cellular events over time on CRISPR biological tape

Although dynamics underlie many biological processes, our ability to robustly and accurately profile time-varying biological signals and regulatory programs remains limited. Here we describe a framework for storing temporal biological information directly in the genomes of a cell population. We developed a "biological tape recorder" in which biological signals trigger intracellular DNA production that is then recorded by the CRISPR-Cas adaptation system. This approach enables stable recording over multiple days and accurate reconstruction of temporal and lineage information by sequencing CRISPR arrays. We further demonstrate a multiplexing strategy to simultaneously record the temporal availability of three metabolites (copper, trehalose, and fucose) in the environment of a cell population over time. This work enables the temporal measurement of dynamic cellular states and environmental changes and suggests new applications for chronicling biological events on a large scale.

Engineering Mammalian Designer Cells for the Treatment of Metabolic Diseases

Synthetic biology applies engineering principles to biological systems and has significantly advanced the design of synthetic gene circuits that can reprogram cell activities to perform new functions. The ability to engineer mammalian designer cells with robust therapeutic behaviors has brought new opportunities for treating metabolic diseases. In this review, the authors highlight the most recent advances in the development of synthetic designer cells uploaded with open- or closed-loop gene circuits for the treatment of metabolic disorders including diabetes, hypertension, hyperuricemia, and obesity, and discuss the current technologies and future perspectives in applying these designer cells for clinical applications. In the future, more and more rationally designed cells will be constructed and revolutionized to treat a number of metabolic disorders in an intelligent manner.

Modeling the architecture of the regulatory system controlling methylenomycin production in Streptomyces coelicolor

Background

The antibiotic methylenomycin A is produced naturally by Streptomyces coelicolor A3(2), a model organism for streptomycetes. This compound is of particular interest to synthetic biologists because all of the associated biosynthetic, regulatory and resistance genes are located on a single cluster on the SCP1 plasmid, making the entire module easily transferable between different bacterial strains. Understanding further the regulation and biosynthesis of the methylenomycin producing gene cluster could assist in the identification of motifs that can be exploited in synthetic regulatory systems for the rational engineering of novel natural products and antibiotics.

Results

We identify and validate a plausible architecture for the regulatory system controlling methylenomycin production in S. coelicolor using mathematical modeling approaches. Model selection via an approximate Bayesian computation (ABC) approach identifies three candidate model architectures that are most likely to produce the available experimental data, from a set of 48 possible candidates. Subsequent global optimization of the parameters of these model architectures identifies a single model that most accurately reproduces the dynamical response of the system, as captured by time series data on methylenomycin production. Further analyses of variants of this model architecture that capture the effects of gene knockouts also reproduce qualitative experimental results observed in mutant S. coelicolor strains.

Conclusions

The mechanistic mathematical model developed in this study recapitulates current biological knowledge of the regulation and biosynthesis of the methylenomycin producing gene cluster, and can be used in future studies to make testable predictions and formulate experiments to further improve our understanding of this complex regulatory system.

A Synthetic Recombinase-Based Feedback Loop Results in Robust Expression

Accurate control of a biological process is essential for many critical functions in biology, from the cell cycle to proteome regulation. To achieve this, negative feedback is frequently employed to provide a highly robust and reliable output. Feedback is found throughout biology and technology, but due to challenges posed by its implementation, it is yet to be widely adopted in synthetic biology. In this paper we design a synthetic feedback network using a class of recombinase proteins called integrases, which can be re-engineered to flip the orientation of DNA segments in a digital manner. This system is highly orthogonal, and demonstrates a strong capability for regulating and reducing the expression variability of genes being transcribed under its control. An excisionase protein provides the negative feedback signal to close the loop in this system, by flipping DNA segments in the reverse direction. Our integrase/excisionase negative feedback system thus provides a modular architecture that can be tuned to suit applications throughout synthetic biology and biomanufacturing that require a highly robust and orthogonally controlled output.

Cell-free extract based optimization of biomolecular circuits with droplet microfluidics

Engineering an efficient biomolecular circuit often requires time-consuming iterations of optimization. Cell-free protein expression systems allow rapid testing of biocircuits in vitro, speeding the design-build-test cycle of synthetic biology. In this paper, we combine this with droplet microfluidics to densely scan a transcription-translation biocircuit space. Our system assays millions of parameter combinations per hour, providing a detailed map of function. The ability to comprehensively map biocircuit parameter spaces allows accurate modeling to predict circuit function and identify optimal circuits and conditions.

CRISPR-Cas encoding of a digital movie into the genomes of a population of living bacteria

DNA is an excellent medium for archiving data. Recent efforts have illustrated the potential for information storage in DNA using synthesized oligonucleotides assembled in vitro. A relatively unexplored avenue of information storage in DNA is the ability to write information into the genome of a living cell by the addition of nucleotides over time. Using the Cas1-Cas2 integrase, the CRISPR-Cas microbial immune system stores the nucleotide content of invading viruses to confer adaptive immunity. When harnessed, this system has the potential to write arbitrary information into the genome. Here we use the CRISPR-Cas system to encode the pixel values of black and white images and a short movie into the genomes of a population of living bacteria. In doing so, we push the technical limits of this information storage system and optimize strategies to minimize those limitations. We also uncover underlying principles of the CRISPR-Cas adaptation system, including sequence determinants of spacer acquisition that are relevant for understanding both the basic biology of bacterial adaptation and its technological applications. This work demonstrates that this system can capture and stably store practical amounts of real data within the genomes of populations of living cells.

Making serine integrases work for us

DNA site-specific recombinases are enzymes (often associated with mobile DNA elements) that catalyse breaking and rejoining of DNA strands at specific points, thereby bringing about precise genetic rearrangements. Serine integrases are a group of recombinases derived from bacteriophages. Their unusual properties, including directionality of recombination and simple site requirements, are leading to their development as efficient, versatile tools for applications in experimental biology, biotechnology, synthetic biology and gene therapy. This article summarizes our current knowledge of serine integrase structure and mechanism, then outlines key factors that affect the performance of these phage recombination systems. Recently published studies, that have expanded the repertoire of available systems and reveal system-specific characteristics, will help us to choose the best integrases for envisaged applications.

Recursively constructing analytic expressions for equilibrium distributions of stochastic biochemical reaction networks

Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture its dynamics. The chemical master equation (CME) is a commonly used stochastic model of Kolmogorov forward equations that describe how the probability distribution of a chemically reacting system varies with time. Finding analytic solutions to the CME can have benefits, such as expediting simulations of multiscale biochemical reaction networks and aiding the design of distributional responses. However, analytic solutions are rarely known. A recent method of computing analytic stationary solutions relies on gluing simple state spaces together recursively at one or two states. We explore the capabilities of this method and introduce algorithms to derive analytic stationary solutions to the CME. We first formally characterize state spaces that can be constructed by performing single-state gluing of paths, cycles or both sequentially. We then study stochastic biochemical reaction networks that consist of reversible, elementary reactions with two-dimensional state spaces. We also discuss extending the method to infinite state spaces and designing the stationary behaviour of stochastic biochemical reaction networks. Finally, we illustrate the aforementioned ideas using examples that include two interconnected transcriptional components and biochemical reactions with two-dimensional state spaces.