Construction of a genetic multiplexer to toggle between chemosensory pathways in Escherichia coli

SynMADE: synthetic microbiota across diverse ecosystems

The past two decades have witnessed rapid advances in engineering individual microbial strains to produce biochemicals and biomaterials. However, engineering microbial consortia has been relatively slow. Using systems and synthetic biology approaches, researchers have been developing tools for engineering complex microbiota. In this opinion article, I discuss future directions and visions regarding developing microbiota as a biomanufacturing host. Specifically, I propose that we can develop the soil microbial community itself as a huge bioreactor. Ultimately, researchers will provide a generalizable system that enables us to understand a microbial consortium's interaction and metabolism on diverse temporal and spatial scales to address global problems, including the climate crisis, food inequality, the issue of waste, and sustainable bioproduction.

Achieving spatially precise diagnosis and therapy in the mammalian gut using synthetic microbial gene circuits

The mammalian gut and its microbiome form a temporally dynamic and spatially heterogeneous environment. The inaccessibility of the gut and the spatially restricted nature of many gut diseases translate into difficulties in diagnosis and therapy for which novel tools are needed. Engineered bacterial whole-cell biosensors and therapeutics have shown early promise at addressing these challenges. Natural and engineered sensing systems can be repurposed in synthetic genetic circuits to detect spatially specific biomarkers during health and disease. Heat, light, and magnetic signals can also activate gene circuit function with externally directed spatial precision. The resulting engineered bacteria can report on conditions in situ within the complex gut environment or produce biotherapeutics that specifically target host or microbiome activity. Here, we review the current approaches to engineering spatial precision for in vivo bacterial diagnostics and therapeutics using synthetic circuits, and the challenges and opportunities this technology presents.

Modelling co-translational dimerization for programmable nonlinearity in synthetic biology

Nonlinearity plays a fundamental role in the performance of both natural and synthetic biological networks. Key functional motifs in living microbial systems, such as the emergence of bistability or oscillations, rely on nonlinear molecular dynamics. Despite its core importance, the rational design of nonlinearity remains an unmet challenge. This is largely due to a lack of mathematical modelling that accounts for the mechanistic basis of nonlinearity. We introduce a model for gene regulatory circuits that explicitly simulates protein dimerization-a well-known source of nonlinear dynamics. Specifically, our approach focuses on modelling co-translational dimerization: the formation of protein dimers during-and not after-translation. This is in contrast to the prevailing assumption that dimer generation is only viable between freely diffusing monomers (i.e. post-translational dimerization). We provide a method for fine-tuning nonlinearity on demand by balancing the impact of co- versus post-translational dimerization. Furthermore, we suggest design rules, such as protein length or physical separation between genes, that may be used to adjust dimerization dynamics in vivo. The design, build and test of genetic circuits with on-demand nonlinear dynamics will greatly improve the programmability of synthetic biological systems.

Reinforcement learning in synthetic gene circuits

Synthetic gene circuits allow programming in DNA the expression of a phenotype at a given environmental condition. The recent integration of memory systems with gene circuits opens the door to their adaptation to new conditions and their re-programming. This lays the foundation to emulate neuromorphic behaviour and solve complex problems similarly to artificial neural networks. Cellular products such as DNA or proteins can be used to store memory in both digital and analog formats, allowing cells to be turned into living computing devices able to record information regarding their previous states. In particular, synthetic gene circuits with memory can be engineered into living systems to allow their adaptation through reinforcement learning. The development of gene circuits able to adapt through reinforcement learning moves Sciences towards the ambitious goal: the bottom-up creation of a fully fledged living artificial intelligence.

Characterization and synthetic biology of lipase from Bacillus amyloliquefaciens strain

Lipases with high tolerance to temperature play a significant role in industry from food manufacturing to waste management systems. Thus, there is a need to investigate these enzymes from different geographical areas to look out for a more thermo-stable one. Characterization of lipases through experimental approaches is time consuming process and sometimes the results are ambiguous due to errors. However, integration of computational technologies is quite useful for prediction of optimized conditions. Such technologies can be applied as synthetic biology, which has many major applications in engineered biological approaches for accurate prediction of effects of different physical and chemical parameters on the system. In this study, cloning and expression of a lipase gene from Bacillus amyloliquefaciens, isolated from a novel geographical region of Pakistan, in Escherichia coli DH5α cells followed by sequencing was carried out. To isolate thermostable lipase producing strains, all the samples were kept at 50 °C. Genomic DNA was isolated and signal peptide (1-32 residues) sequence was chopped (ΔSPLipase). The ΔSPLipase was amplified and expressed in Linearized p15TV-L vector. The purified lipase appeared as single band of approximately 26 kDa. Suitable conditions of factors required for maximum lipase activity such as temperature, pH, substrate, organic solvent, detergents and metal ions were predicted through synthetic biology approach and further confirmed in wet lab. The predicted suitable factors for enzyme were almost similar to those determined experimentally. The optimum enzyme activity was recorded at pH 8 and 50 °C temperature. Interestingly, the activity of enzyme was found on a number of solvents, metal ions, detergents, and surfactants. The predicted optimum values and their experimental confirmations highlights the importance of integrated synthetic biology approaches in wet lab experiments. The characterized lipase of B. amyloliquefaciens at molecular level from Pakistani strains displayed good activity on a range of factors that implies this strain to be used for application in industrial level production.

A Model for the Spatiotemporal Design of Gene Regulatory Circuits †

Mathematical modeling assists the design of synthetic regulatory networks by providing a detailed mechanistic understanding of biological systems. Models that can predict the performance of a design are fundamental for synthetic biology since they minimize iterations along the design-build-test lifecycle. Such predictability depends crucially on what assumptions (i.e., biological simplifications) the model considers. Here, we challenge a common assumption when it comes to the modeling of bacterial-based gene regulation: considering negligible the effects of intracellular physical space. It is commonly assumed that molecules, such as transcription factors (TF), are homogeneously distributed inside a cell, so there is no need to model their diffusion. We describe a mathematical model that accounts for molecular diffusion and show how simulations of network performance are decisively affected by the distance between its components. Specifically, the model focuses on the search by a TF for its target promoter. The combination of local searches, via one-dimensional sliding along the chromosome, and global searches, via three-dimensional diffusion through the cytoplasm, determine TF-promoter interplay. Previous experimental results with engineered bacteria in which the distance between TF source and target was minimized or enlarged were successfully reproduced by the spatially resolved model we introduce here. This suggests that the spatial specification of the circuit alone can be exploited as a design parameter in synthetic biology to select programmable output levels.

High-Performance Biocomputing in Synthetic Biology-Integrated Transcriptional and Metabolic Circuits

Biocomputing uses molecular biology parts as the hardware to implement computational devices. By following pre-defined rules, often hard-coded into biological systems, these devices are able to process inputs and return outputs—thus computing information. Key to the success of any biocomputing endeavor is the availability of a wealth of molecular tools and biological motifs from which functional devices can be assembled. Synthetic biology is a fabulous playground for such purpose, offering numerous genetic parts that allow for the rational engineering of genetic circuits that mimic the behavior of electronic functions, such as logic gates. A grand challenge, as far as biocomputing is concerned, is to expand the molecular hardware available beyond the realm of genetic parts by tapping into the host metabolism. This objective requires the formalization of the interplay of genetic constructs with the rest of the cellular machinery. Furthermore, the field of metabolic engineering has had little intersection with biocomputing thus far, which has led to a lack of definition of metabolic dynamics as computing basics. In this perspective article, we advocate the conceptualization of metabolism and its motifs as the way forward to achieve whole-cell biocomputations. The design of merged transcriptional and metabolic circuits will not only increase the amount and type of information being processed by a synthetic construct, but will also provide fundamental control mechanisms for increased reliability.

Dynamic control of endogenous metabolism with combinatorial logic circuits

Controlling gene expression during a bioprocess enables real-time metabolic control, coordinated cellular responses, and staging order-of-operations. Achieving this with small molecule inducers is impractical at scale and dynamic circuits are difficult to design. Here, we show that the same set of sensors can be integrated by different combinatorial logic circuits to vary when genes are turned on and off during growth. Three Escherichia coli sensors that respond to the consumption of feedstock (glucose), dissolved oxygen, and by-product accumulation (acetate) are constructed and optimized. By integrating these sensors, logic circuits implement temporal control over an 18-h period. The circuit outputs are used to regulate endogenous enzymes at the transcriptional and post-translational level using CRISPRi and targeted proteolysis, respectively. As a demonstration, two circuits are designed to control acetate production by matching their dynamics to when endogenous genes are expressed (pta or poxB) and respond by turning off the corresponding gene. This work demonstrates how simple circuits can be implemented to enable customizable dynamic gene regulation.

Spatial interference scale as a determinant of microbial range expansion

In microbial communities, social interactions such as competition occur ubiquitously across multiple spatial scales from local proximity to remote distance. However, it remains unclear how such a spatial variation of interaction contributes to the structural development of microbial populations. Here, we developed synthetic consortia, biophysical theory, and simulations to elucidate the role of spatial interference scale in governing ecosystem organization during range expansion. For consortia with unidirectional interference, we discovered that, at growing fronts, the extinction time of toxin-sensitive species is reciprocal to the spatial interference scale. In contrast, for communities with bidirectional interference, their structures diverge into distinct monoculture colonies under different initial conditions, with the corresponding separatrix set by the spatial scale of interference. Near the separatrix, ecosystem development becomes noise-driven and yields opposite structures. Our results establish spatial interaction scale as a key determinant for microbial range expansion, providing insights into microbial spatial organization and synthetic ecosystem engineering.

Cellular checkpoint control using programmable sequential logic

Biological processes that require orderly progression, such as growth and differentiation, proceed via regulatory checkpoints where the cell waits for signals before continuing to the next state. Implementing such control would allow genetic engineers to divide complex tasks into stages. We present genetic circuits that encode sequential logic to instruct Escherichia coli to proceed through a linear or cyclical sequence of states. These are built with 11 set-reset latches, designed with repressor-based NOR gates, which can connect to each other and sensors. The performance of circuits with up to three latches and four sensors, including a gated D latch, closely match predictions made by using nonlinear dynamics. Checkpoint control is demonstrated by switching cells between multiple circuit states in response to external signals over days.

An intelligent 1:2 demultiplexer as an intracellular theranostic device based on DNA/Ag cluster-gated nanovehicles

The logic device demultiplexer can convey a single input signal into one of multiple output channels. The choice of the output channel is controlled by a selector. Several molecules and biomolecules have been used to mimic the function of a demultiplexer. However, the practical application of logic devices still remains a big challenge. Herein, we design and construct an intelligent 1:2 demultiplexer as a theranostic device based on azobenzene (azo)-modified and DNA/Ag cluster-gated nanovehicles. The configuration of azo and the conformation of the DNA ensemble can be regulated by light irradiation and pH, respectively. The demultiplexer which uses light as the input and acid as the selector can emit red fluorescence or a release drug under different conditions. Depending on different cells, the intelligent logic device can select the mode of cellular imaging in healthy cells or tumor therapy in tumor cells. The study incorporates the logic gate with the theranostic device, paving the way for tangible applications of logic gates in the future.

Cellular Biosensors with Engineered Genetic Circuits

An increasing interest in building novel biological devices with designed cellular functionalities has triggered the search of innovative tools for biocomputation. Utilizing the tools of synthetic biology, numerous genetic circuits have been implemented such as engineered logic operation in analog and digital circuits. Whole cell biosensors are widely used biological devices that employ several biocomputation tools to program cells for desired functions. Up to the present date, a wide range of whole-cell biosensors have been designed and implemented for disease theranostics, biomedical applications, and environmental monitoring. In this review, we investigated the recent developments in biocomputation tools such as analog, digital, and mix circuits, logic gates, switches, and state machines. Additionally, we stated the novel applications of biological devices with computing functionalities for diagnosis and therapy of various diseases such as infections, cancer, or metabolic diseases, as well as the detection of environmental pollutants such as heavy metals or organic toxic compounds. Current whole-cell biosensors are innovative alternatives to classical biosensors; however, there is still a need to advance decision making capabilities by developing novel biocomputing devices.

Riboswitch-Based Reversible Dual Color Sensor

Riboswitches are RNA-based "sensors" that utilize chemically induced structural changes in the 5'-untranslated region of mRNA to regulate expression of downstream genes. Coupling a specific riboswitch with a reporter gene system translates chemical detection by the cell into a quantifiable reporter protein signal. For the majority of reporter gene systems, the readout signal is only expressed in the presence of the target analyte. This makes it difficult to determine the viability and localization of the uninduced biosensor when it is used for "real-word" applications. To address this problem, we developed a dual-color reporter comprising elements of the E. coli fimbriae phase variation system: recombinase FimE controlled by a synthetic riboswitch and an invertible DNA segment (fimS) containing a constitutively active promoter placed between two fluorescent protein genes. Without an analyte, the fluorescent reporter constitutively expressed green fluorescent protein (GFPa1). Addition of the analyte initiated translation of fimE causing unidirectional inversion of the fimS segment and constitutive expression of red fluorescent protein (mKate2). Thus, the sensor is always fluorescent, but its color is determined by detection of a specific analyte. We demonstrate that the recombinase-based dual-color reporter can be successfully applied to monitor the activation of a theophylline synthetic riboswitch that was used as our model system. To show the feasibility of the FimE recombinase-based system to serve as a reporter for monitoring activation of multiple synthetic riboswitches and, therefore, expand the applicability of the system, we tested a number of previously developed synthetic riboswitches responsive to different analytes. We show that the dual-color reporter system can be successfully used to monitor activation of M6 and M6″ riboswitches responsive to ammeline and pyrimido[4,5-d]pyrimidine-2,4-diamine, respectively, and a 2,4,6-trinitrotoluene-responsive riboswitch developed in this study. We also demonstrate that the system can be reversed by HbiF recombinase-mediated fimS inversion to the initial state of the fluorescent reporter, creating a resettable and reusable cell-based sensor.

Permutation Machines

We define a new inversion-based machine called a permuton of n genetic elements, which allows the n elements to be rearranged in any of the n·(n - 1)·(n - 2)···2 = n! distinct orderings. We present two design algorithms for architecting such a machine. We define a notion of a feasible design and use the framework to discuss the feasibility of the permuton architectures. We have implemented our design algorithms in a freely usable web-accessible software for exploration of these machines. Permutation machines could be used as memory elements or state machines and explicitly illustrate a rational approach to designing biological systems.

Molecular recordings by directed CRISPR spacer acquisition

The ability to write a stable record of identified molecular events into a specific genomic locus would enable the examination of long cellular histories and have many applications, ranging from developmental biology to synthetic devices. We show that the type I-E CRISPR (clustered regularly interspaced short palindromic repeats)-Cas system of Escherichia coli can mediate acquisition of defined pieces of synthetic DNA. We harnessed this feature to generate records of specific DNA sequences into a population of bacterial genomes. We then applied directed evolution so as to alter the recognition of a protospacer adjacent motif by the Cas1-Cas2 complex, which enabled recording in two modes simultaneously. We used this system to reveal aspects of spacer acquisition, fundamental to the CRISPR-Cas adaptation process. These results lay the foundations of a multimodal intracellular recording device.

Novel Utilization of Terminators in the Design of Biologically Adjustable Synthetic Filters

Terminators, which signal the end of transcription processes, are typically placed behind the last coding sequence of an operon to prevent interference between transcript units in most biologically synthetic systems. Here, we seek to extend the usability of terminators in genetic system design by using terminators as regulatory genetic parts. Terminators with different impacts on their upstream and downstream genes are characterized in detail via dynamic modeling to predict the behavior of the overall genetic system. Some nonlinear effects of terminators observed in our terminator measurements potentially facilitate regulation of gene expression. Through dynamic modeling in silico, we find that such genetic systems may behave like genetic filters. In agreement with the simulations, we successfully implement genetic high-pass and bandpass filters in vivo, demonstrating the potential of using terminators as regulatory parts. The genetic bandpass filter in this work is implemented through the interdependence between genetic parts, in which the termination efficiency of a terminator varies with the strength of the upstream promoter. This design strategy for a bandpass filter requires fewer base pairs than the conventional strategy of concatenating high-pass and low-pass filters. Our results show that this novel utilization of terminators as regulatory parts may provide a new perspective for efficient design of genetic circuits. We believe that further exploration of the complicated dynamics of terminators is important in the development of synthetic biology.

Engineering microbial consortia for controllable outputs

Much research has been invested into engineering microorganisms to perform desired biotransformations; nonetheless, these efforts frequently fall short of expected results due to the unforeseen effects of biofeedback regulation and functional incompatibility. In nature, metabolic function is compartmentalized into diverse organisms assembled into robust consortia, in which the division of labor is thought to lead to increased community efficiency and productivity. Here we consider whether and how consortia can be designed to perform bioprocesses of interest beyond the metabolic flexibility limitations of a single organism. Advances in post-genomic analysis of microbial consortia and application of high-resolution global measurements now offer the promise of systems-level understanding of how microbial consortia adapt to changes in environmental variables and inputs of carbon and energy. We argue that, when combined with appropriate modeling frameworks, systems-level knowledge can markedly improve our ability to predict the fate and functioning of consortia. Here we articulate our collective perspective on the current and future state of microbial community engineering and control while placing specific emphasis on ecological principles that promote control over community function and emergent properties.

Synthetic biology: insights into biological computation

Synthetic biology attempts to rationally engineer biological systems in order to perform desired functions. Our increasing understanding of biological systems guides this rational design, while the huge background in electronics for building circuits defines the methodology.

Memory and Combinatorial Logic Based on DNA Inversions: Dynamics and Evolutionary Stability

Genetic memory can be implemented using enzymes that catalyze DNA inversions, where each orientation corresponds to a "bit". Here, we use two DNA invertases (FimE and HbiF) that reorient DNA irreversibly between two states with opposite directionality. First, we construct memory that is set by FimE and reset by HbiF. Next, we build a NOT gate where the input promoter drives FimE and in the absence of signal the reverse state is maintained by the constitutive expression of HbiF. The gate requires ∼3 h to turn on and off. The evolutionary stabilities of these circuits are measured by passaging cells while cycling function. The memory switch is stable over 400 h (17 days, 14 state changes); however, the gate breaks after 54 h (>2 days) due to continuous invertase expression. Genome sequencing reveals that the circuit remains intact, but the host strain evolves to reduce invertase expression. This work highlights the need to evaluate the evolutionary robustness and failure modes of circuit designs, especially as more complex multigate circuits are implemented.

Artificial cell-cell communication as an emerging tool in synthetic biology applications

Cell-cell communication is a widespread phenomenon in nature, ranging from bacterial quorum sensing and fungal pheromone communication to cellular crosstalk in multicellular eukaryotes. These communication modes offer the possibility to control the behavior of an entire community by modifying the performance of individual cells in specific ways. Synthetic biology, i.e., the implementation of artificial functions within biological systems, is a promising approach towards the engineering of sophisticated, autonomous devices based on specifically functionalized cells. With the growing complexity of the functions performed by such systems, both the risk of circuit crosstalk and the metabolic burden resulting from the expression of numerous foreign genes are increasing. Therefore, systems based on a single type of cells are no longer feasible. Synthetic biology approaches with multiple subpopulations of specifically functionalized cells, wired by artificial cell-cell communication systems, provide an attractive and powerful alternative. Here we review recent applications of synthetic cell-cell communication systems with a specific focus on recent advances with fungal hosts.

Slow and steady wins the race: a bacterial exploitative competition strategy in fluctuating environments

One promising frontier for synthetic biology is the development of synthetic ecologies, whereby interacting species form an additional layer of connectivity for engineered gene circuits. Toward this goal, an important step is to understand different types of bacterial interactions in natural settings, among which competition is the most prevalent. By constructing a two-species population dynamics model, here, we mimicked bacterial growth in nature with resource-limited fluctuating environments and searched for optimal strategies for bacterial exploitative competition. In a simple game with two strategy options (constant or susceptible growth), we found that the species playing the constant growth strategy always outplays or is evenly matched with its competitor, suggesting that constant growth is a "no-loss" good bet. We also showed that adoption of sophisticated strategies enables a species to maximize its fitness when its competitor grows susceptibly. The pursuit of fitness maximization is, however, associated with potential loss if both species are capable of strategy adjustment, indicating an intrinsic risk-return trade-off. These findings offer new insights into bacterial competition and may also facilitate the engineering of microbial consortia for synthetic biology applications.

Avoidance of truncated proteins from unintended ribosome binding sites within heterologous protein coding sequences

Genetic sequences ported into non-native hosts for synthetic biology applications can gain unexpected properties. In this study, we explored sequences functioning as ribosome binding sites (RBSs) within protein coding DNA sequences (CDSs) that cause internal translation, resulting in truncated proteins. Genome-wide prediction of bacterial RBSs, based on biophysical calculations employed by the RBS calculator, suggests a selection against internal RBSs within CDSs in Escherichia coli, but not those in Saccharomyces cerevisiae. Based on these calculations, silent mutations aimed at removing internal RBSs can effectively reduce truncation products from internal translation. However, a solution for complete elimination of internal translation initiation is not always feasible due to constraints of available coding sequences. Fluorescence assays and Western blot analysis showed that in genes with internal RBSs, increasing the strength of the intended upstream RBS had little influence on the internal translation strength. Another strategy to minimize truncated products from an internal RBS is to increase the relative strength of the upstream RBS with a concomitant reduction in promoter strength to achieve the same protein expression level. Unfortunately, lower transcription levels result in increased noise at the single cell level due to stochasticity in gene expression. At the low expression regimes desired for many synthetic biology applications, this problem becomes particularly pronounced. We found that balancing promoter strengths and upstream RBS strengths to intermediate levels can achieve the target protein concentration while avoiding both excessive noise and truncated protein.

Permanent genetic memory with >1-byte capacity

Genetic memory enables the recording of information in the DNA of living cells. Memory can record a transient environmental signal or cell state that is then recalled at a later time. Permanent memory is implemented using irreversible recombinases that invert the orientation of a unit of DNA, corresponding to the [0,1] state of a bit. To expand the memory capacity, we have applied bioinformatics to identify 34 phage integrases (and their cognate attB and attP recognition sites), from which we build 11 memory switches that are perfectly orthogonal to each other and the FimE and HbiF bacterial invertases. Using these switches, a memory array is constructed in Escherichia coli that can record 1.375 bytes of information. It is demonstrated that the recombinases can be layered and used to permanently record the transient state of a transcriptional logic gate.

Directed acyclic graph-based technology mapping of genetic circuit models

As engineering foundations such as standards and abstraction begin to mature within synthetic biology, it is vital that genetic design automation (GDA) tools be developed to enable synthetic biologists to automatically select standardized DNA components from a library to meet the behavioral specification for a genetic circuit. To this end, we have developed a genetic technology mapping algorithm that builds on the directed acyclic graph (DAG) based mapping techniques originally used to select parts for digital electronic circuit designs and implemented it in our GDA tool, iBioSim. It is among the first genetic technology mapping algorithms to adapt techniques from electronic circuit design, in particular the use of a cost function to guide the search for an optimal solution, and perhaps that which makes the greatest use of standards for describing genetic function and structure to represent design specifications and component libraries. This paper demonstrates the use of our algorithm to map the specifications for three different genetic circuits against four randomly generated libraries of increasing size to evaluate its performance against both exhaustive search and greedy variants for finding optimal and near-optimal solutions.

Synthetic biology approaches to improve biocatalyst identification in metagenomic library screening

There is a growing demand for enzymes with improved catalytic performance or tolerance to process-specific parameters, and biotechnology plays a crucial role in the development of biocatalysts for use in industry, agriculture, medicine and energy generation. Metagenomics takes advantage of the wealth of genetic and biochemical diversity present in the genomes of microorganisms found in environmental samples, and provides a set of new technologies directed towards screening for new catalytic activities from environmental samples with potential biotechnology applications. However, biased and low level of expression of heterologous proteins in Escherichia coli together with the use of non-optimal cloning vectors for the construction of metagenomic libraries generally results in an extremely low success rate for enzyme identification. The bottleneck arising from inefficient screening of enzymatic activities has been addressed from several perspectives; however, the limitations related to biased expression in heterologous hosts cannot be overcome by using a single approach, but rather requires the synergetic implementation of multiple methodologies. Here, we review some of the principal constraints regarding the discovery of new enzymes in metagenomic libraries and discuss how these might be resolved by using synthetic biology methods.

Modulating the frequency and bias of stochastic switching to control phenotypic variation

Mechanisms that control cell-to-cell variation in gene expression ('phenotypic variation') can determine a population's growth rate, robustness, adaptability and capacity for complex behaviours. Here we describe a general strategy (termed FABMOS) for tuning the phenotypic variation and mean expression of cell populations by modulating the frequency and bias of stochastic transitions between 'OFF' and 'ON' expression states of a genetic switch. We validated the strategy experimentally using a synthetic fim switch in Escherichia coli. Modulating the frequency of switching can generate a bimodal (low frequency) or a unimodal (high frequency) population distribution with the same mean expression. Modulating the bias as well as the frequency of switching can generate a spectrum of bimodal and unimodal distributions with the same mean expression. This remarkable control over phenotypic variation, which cannot be easily achieved with standard gene regulatory mechanisms, has many potential applications for synthetic biology, engineered microbial ecosystems and experimental evolution.

Principles of genetic circuit design

Cells navigate environments, communicate and build complex patterns by initiating gene expression in response to specific signals. Engineers seek to harness this capability to program cells to perform tasks or create chemicals and materials that match the complexity seen in nature. This Review describes new tools that aid the construction of genetic circuits. Circuit dynamics can be influenced by the choice of regulators and changed with expression 'tuning knobs'. We collate the failure modes encountered when assembling circuits, quantify their impact on performance and review mitigation efforts. Finally, we discuss the constraints that arise from circuits having to operate within a living cell. Collectively, better tools, well-characterized parts and a comprehensive understanding of how to compose circuits are leading to a breakthrough in the ability to program living cells for advanced applications, from living therapeutics to the atomic manufacturing of functional materials.

Digital and analog gene circuits for biotechnology

Biotechnology offers the promise of valuable chemical production via microbial processing of renewable and inexpensive substrates. Thus far, static metabolic engineering strategies have enabled this field to advance industrial applications. However, the industrial scaling of statically engineered microbes inevitably creates inefficiencies due to variable conditions present in large-scale microbial cultures. Synthetic gene circuits that dynamically sense and regulate different molecules can resolve this issue by enabling cells to continuously adapt to variable conditions. These circuits also have the potential to enable next-generation production programs capable of autonomous transitioning between steps in a bioprocess. Here, we review the design and application of two main classes of dynamic gene circuits, digital and analog, for biotechnology. Within the context of these classes, we also discuss the potential benefits of digital-analog interconversion, memory, and multi-signal integration. Though synthetic gene circuits have largely been applied for cellular computation to date, we envision that utilizing them in biotechnology will enhance the efficiency and scope of biochemical production with living cells.

How to make a synthetic multicellular computer

Biological systems perform computations at multiple scales and they do so in a robust way. Engineering metaphors have often been used in order to provide a rationale for modeling cellular and molecular computing networks and as the basis for their synthetic design. However, a major constraint in this mapping between electronic and wet computational circuits is the wiring problem. Although wires are identical within electronic devices, they must be different when using synthetic biology designs. Moreover, in most cases the designed molecular systems cannot be reused for other functions. A new approximation allows us to simplify the problem by using synthetic cellular consortia where the output of the computation is distributed over multiple engineered cells. By evolving circuits in silico, we can obtain the minimal sets of Boolean units required to solve the given problem at the lowest cost using cellular consortia. Our analysis reveals that the basic set of logic units is typically non-standard. Among the most common units, the so called inverted IMPLIES (N-Implies) appears to be one of the most important elements along with the NOT and AND functions. Although NOR and NAND gates are widely used in electronics, evolved circuits based on combinations of these gates are rare, thus suggesting that the strategy of combining the same basic logic gates might be inappropriate in order to easily implement synthetic computational constructs. The implications for future synthetic designs, the general view of synthetic biology as a standard engineering domain, as well as potencial drawbacks are outlined.

Biological signal processing with a genetic toggle switch

Complex gene regulation requires responses that depend not only on the current levels of input signals but also on signals received in the past. In digital electronics, logic circuits with this property are referred to as sequential logic, in contrast to the simpler combinatorial logic without such internal memory. In molecular biology, memory is implemented in various forms such as biochemical modification of proteins or multistable gene circuits, but the design of the regulatory interface, which processes the input signals and the memory content, is often not well understood. Here, we explore design constraints for such regulatory interfaces using coarse-grained nonlinear models and stochastic simulations of detailed biochemical reaction networks. We test different designs for biological analogs of the most versatile memory element in digital electronics, the JK-latch. Our analysis shows that simple protein-protein interactions and protein-DNA binding are sufficient, in principle, to implement genetic circuits with the capabilities of a JK-latch. However, it also exposes fundamental limitations to its reliability, due to the fact that biological signal processing is asynchronous, in contrast to most digital electronics systems that feature a central clock to orchestrate the timing of all operations. We describe a seemingly natural way to improve the reliability by invoking the master-slave concept from digital electronics design. This concept could be useful to interpret the design of natural regulatory circuits, and for the design of synthetic biological systems.

Multicellular computing using conjugation for wiring

Recent efforts in synthetic biology have focussed on the implementation of logical functions within living cells. One aim is to facilitate both internal "re-programming" and external control of cells, with potential applications in a wide range of domains. However, fundamental limitations on the degree to which single cells may be re-engineered have led to a growth of interest in multicellular systems, in which a "computation" is distributed over a number of different cell types, in a manner analogous to modern computer networks. Within this model, individual cell type perform specific sub-tasks, the results of which are then communicated to other cell types for further processing. The manner in which outputs are communicated is therefore of great significance to the overall success of such a scheme. Previous experiments in distributed cellular computation have used global communication schemes, such as quorum sensing (QS), to implement the "wiring" between cell types. While useful, this method lacks specificity, and limits the amount of information that may be transferred at any one time. We propose an alternative scheme, based on specific cell-cell conjugation. This mechanism allows for the direct transfer of genetic information between bacteria, via circular DNA strands known as plasmids. We design a multi-cellular population that is able to compute, in a distributed fashion, a Boolean XOR function. Through this, we describe a general scheme for distributed logic that works by mixing different strains in a single population; this constitutes an important advantage of our novel approach. Importantly, the amount of genetic information exchanged through conjugation is significantly higher than the amount possible through QS-based communication. We provide full computational modelling and simulation results, using deterministic, stochastic and spatially-explicit methods. These simulations explore the behaviour of one possible conjugation-wired cellular computing system under different conditions, and provide baseline information for future laboratory implementations.

SBROME: a scalable optimization and module matching framework for automated biosystems design

The development of a scalable framework for biodesign automation is a formidable challenge given the expected increase in part availability and the ever-growing complexity of synthetic circuits. To allow for (a) the use of previously constructed and characterized circuits or modules and (b) the implementation of designs that can scale up to hundreds of nodes, we here propose a divide-and-conquer Synthetic Biology Reusable Optimization Methodology (SBROME). An abstract user-defined circuit is first transformed and matched against a module database that incorporates circuits that have previously been experimentally characterized. Then the resulting circuit is decomposed to subcircuits that are populated with the set of parts that best approximate the desired function. Finally, all subcircuits are subsequently characterized and deposited back to the module database for future reuse. We successfully applied SBROME toward two alternative designs of a modular 3-input multiplexer that utilize pre-existing logic gates and characterized biological parts.

Genetic circuit performance under conditions relevant for industrial bioreactors

Synthetic genetic programs promise to enable novel applications in industrial processes. For such applications, the genetic circuits that compose programs will require fidelity in varying and complex environments. In this work, we report the performance of two synthetic circuits in Escherichia coli under industrially relevant conditions, including the selection of media, strain, and growth rate. We test and compare two transcriptional circuits: an AND and a NOR gate. In E. coli DH10B, the AND gate is inactive in minimal media; activity can be rescued by supplementing the media and transferring the gate into the industrial strain E. coli DS68637 where normal function is observed in minimal media. In contrast, the NOR gate is robust to media composition and functions similarly in both strains. The AND gate is evaluated at three stages of early scale-up: 100 mL shake flask experiments, a 1 mL MTP microreactor, and a 10 L bioreactor. A reference plasmid that constitutively produces a GFP reporter is used to make comparisons of circuit performance across conditions. The AND gate function is quantitatively different at each scale. The output deteriorates late in fermentation after the shift from exponential to constant feed rates, which induces rapid resource depletion and changes in growth rate. In addition, one of the output states of the AND gate failed in the bioreactor, effectively making it only responsive to a single input. Finally, cells carrying the AND gate show considerably less accumulation of biomass. Overall, these results highlight challenges and suggest modified strategies for developing and characterizing genetic circuits that function reliably during fermentation.

Engineering robust control of two-component system phosphotransfer using modular scaffolds

Synthetic biology applies engineering principles to facilitate the predictable design of biological systems. Biological systems composed of modular parts with clearly defined interactions are generally easier to manipulate than complex systems exhibiting a large number of subtle interactions. However, recreating the function of a naturally complex system with simple modular parts can increase fragility. Here, inspired by scaffold-directed signaling in higher organisms, we modularize prokaryotic signal transduction to allow programmable redirection of phosphate flux from a histidine kinase to response regulators based on targeting by eukaryotic protein-protein interaction domains. Although scaffold-directed colocalization alone was sufficient to direct signaling between components, this minimal system suffered from high sensitivity to changing expression levels of each component. To address this fragility, we demonstrate how to engineer autoinhibition into the kinase so that phosphotransfer is possible only upon binding to the scaffold. This system, in which scaffold performs the dual functions of activating this autoinhibited kinase and directing flux to the cotargeted response regulator, was significantly more robust to varying component concentrations. Thus, we demonstrate that design principles inspired by the complex signal-transduction pathways of eukaryotes may be generalized, abstracted, and applied to prokaryotes using well-characterized parts.

Modular control of multiple pathways using engineered orthogonal T7 polymerases

Synthetic genetic sensors and circuits enable programmable control over the timing and conditions of gene expression. They are being increasingly incorporated into the control of complex, multigene pathways and cellular functions. Here, we propose a design strategy to genetically separate the sensing/circuitry functions from the pathway to be controlled. This separation is achieved by having the output of the circuit drive the expression of a polymerase, which then activates the pathway from polymerase-specific promoters. The sensors, circuits and polymerase are encoded together on a 'controller' plasmid. Variants of T7 RNA polymerase that reduce toxicity were constructed and used as scaffolds for the construction of four orthogonal polymerases identified via part mining that bind to unique promoter sequences. This set is highly orthogonal and induces cognate promoters by 8- to 75-fold more than off-target promoters. These orthogonal polymerases enable four independent channels linking the outputs of circuits to the control of different cellular functions. As a demonstration, we constructed a controller plasmid that integrates two inducible systems, implements an AND logic operation and toggles between metabolic pathways that change Escherichia coli green (deoxychromoviridans) and red (lycopene). The advantages of this organization are that (i) the regulation of the pathway can be changed simply by introducing a different controller plasmid, (ii) transcription is orthogonal to host machinery and (iii) the pathway genes are not transcribed in the absence of a controller and are thus more easily carried without invoking evolutionary pressure.