Iterative plug-and-play methodology for constructing and modifying synthetic gene networks

Engineered live bacteria as disease detection and diagnosis tools

Sensitive and minimally invasive medical diagnostics are essential to the early detection of diseases, monitoring their progression and response to treatment. Engineered bacteria as live sensors are being developed as a new class of biosensors for sensitive, robust, noninvasive, and in situ detection of disease onset at low cost. Akin to microrobotic systems, a combination of simple genetic rules, basic logic gates, and complex synthetic bioengineering principles are used to program bacterial vectors as living machines for detecting biomarkers of diseases, some of which cannot be detected with other sensing technologies. Bacterial whole-cell biosensors (BWCBs) can have wide-ranging functions from detection only, to detection and recording, to closed-loop detection-regulated treatment. In this review article, we first summarize the unique benefits of bacteria as living sensors. We then describe the different bacteria-based diagnosis approaches and provide examples of diagnosing various diseases and disorders. We also discuss the use of bacteria as imaging vectors for disease detection and image-guided surgery. We conclude by highlighting current challenges and opportunities for further exploration toward clinical translation of these bacteria-based systems.

Unbalanced response to growth variations reshapes the cell fate decision landscape

The global regulation of cell growth rate on gene expression perturbs the performance of gene networks, which would impose complex variations on the cell-fate decision landscape. Here we use a simple synthetic circuit of mutual repression that allows a bistable landscape to examine how such global regulation would affect the stability of phenotypic landscape and the accompanying dynamics of cell-fate determination. We show that the landscape experiences a growth-rate-induced bifurcation between monostability and bistability. Theoretical and experimental analyses reveal that this bifurcating deformation of landscape arises from the unbalanced response of gene expression to growth variations. The path of growth transition across the bifurcation would reshape cell-fate decisions. These results demonstrate the importance of growth regulation on cell-fate determination processes, regardless of specific molecular signaling or regulation.

Automated design of gene circuits with optimal mushroom-bifurcation behavior

Recent advances in synthetic biology are enabling exciting technologies, including the next generation of biosensors, the rational design of cell memory, modulated synthetic cell differentiation, and generic multifunctional biocircuits. These novel applications require the design of gene circuits leading to sophisticated behaviors and functionalities. At the same time, designs need to be kept minimal to avoid compromising cell viability. Bifurcation theory addresses such challenges by associating circuit dynamical properties with molecular details of its design. Nevertheless, incorporating bifurcation analysis into automated design processes has not been accomplished yet. This work presents an optimization-based method for the automated design of synthetic gene circuits with specified bifurcation diagrams that employ minimal network topologies. Using this approach, we designed circuits exhibiting the mushroom bifurcation, distilled the most robust topologies, and explored its multifunctional behavior. We then outline potential applications in biosensors, memory devices, and synthetic cell differentiation.

Ratiometric control of cell phenotypes in monostrain microbial consortia

We address the problem of regulating and keeping at a desired balance the relative numbers between cells exhibiting a different phenotype within a monostrain microbial consortium. We propose a strategy based on the use of external control inputs, assuming each cell in the community is endowed with a reversible, bistable memory mechanism. Specifically, we provide a general analytical framework to guide the design of external feedback control strategies aimed at balancing the ratio between cells whose memory is stabilized at either one of two equilibria associated with different cell phenotypes. We demonstrate the stability and robustness properties of the control laws proposed and validate them in silico, implementing the memory element via a genetic toggle-switch. The proposed control framework may be used to allow long-term coexistence of different populations, with both industrial and biotechnological applications. As a representative example, we consider the realistic agent-based implementation of our control strategy to enable cooperative bioproduction of a dimer in a monostrain microbial consortium.

Rational design of complex phenotype via network models

We demonstrate a modeling and computational framework that allows for rapid screening of thousands of potential network designs for particular dynamic behavior. To illustrate this capability we consider the problem of hysteresis, a prerequisite for construction of robust bistable switches and hence a cornerstone for construction of more complex synthetic circuits. We evaluate and rank most three node networks according to their ability to robustly exhibit hysteresis where robustness is measured with respect to parameters over multiple dynamic phenotypes. Focusing on the highest ranked networks, we demonstrate how additional robustness and design constraints can be applied. We compare our results to more traditional methods based on specific parameterization of ordinary differential equation models and demonstrate a strong qualitative match at a small fraction of the computational cost.

A major challenge in the domains of systems and synthetic biology is an inability to efficiently predict function(s) of complex networks. This work demonstrates a modeling and computational framework that allows for a mathematically justifiable rigorous screening of thousands of potential network designs for a wide variety of dynamical behavior. We screen all 3-node genetic networks and rank them based on their ability to act as an inducible bistable switch. Our results are summarized in a searchable database that can be used to construct robust switches. The ability to quickly screen thousands of designs significantly reduces the set of viable designs and allows synthetic biologists to focus their experimental and more traditional modeling tools to this much smaller set.

An Automated Tabletop Continuous Culturing System with Multicolor Fluorescence Monitoring for Microbial Gene Expression and Long-Term Population Dynamics

Real-time monitoring of gene expression dynamics and population levels in a multispecies microbial community could enable the study of the role of changing gene expression patterns on eco-evolutionary outcomes. Here we report the design and validation of a unique experimental platform with an in situ fluorescence measurement system that has high dynamic range and temporal resolution and is capable of monitoring multiple fluorophores for long-term gene expression and population dynamics experiments. We demonstrate the capability of our system to capture gene expression dynamics in response to external perturbations in two synthetic genetic systems: a simple inducible genetic circuit and a bistable toggle switch. Finally, in exploring the population dynamics of a two species microbial community, we show that our system can capture the switch between competitive exclusion and long-term coexistence in response to different nutrient conditions.

A rationally engineered decoder of transient intracellular signals

Cells can encode information about their environment by modulating signaling dynamics and responding accordingly. Yet, the mechanisms cells use to decode these dynamics remain unknown when cells respond exclusively to transient signals. Here, we approach design principles underlying such decoding by rationally engineering a synthetic short-pulse decoder in budding yeast. A computational method for rapid prototyping, TopoDesign, allowed us to explore 4122 possible circuit architectures, design targeted experiments, and then rationally select a single circuit for implementation. This circuit demonstrates short-pulse decoding through incoherent feedforward and positive feedback. We predict incoherent feedforward to be essential for decoding transient signals, thereby complementing proposed design principles of temporal filtering, the ability to respond to sustained signals, but not to transient signals. More generally, we anticipate TopoDesign to help designing other synthetic circuits with non-intuitive dynamics, simply by assembling available biological components.

Controlling spatiotemporal pattern formation in a concentration gradient with a synthetic toggle switch

The formation of spatiotemporal patterns of gene expression is frequently guided by gradients of diffusible signaling molecules. The toggle switch subnetwork, composed of two cross-repressing transcription factors, is a common component of gene regulatory networks in charge of patterning, converting the continuous information provided by the gradient into discrete abutting stripes of gene expression. We present a synthetic biology framework to understand and characterize the spatiotemporal patterning properties of the toggle switch. To this end, we built a synthetic toggle switch controllable by diffusible molecules in Escherichia coli. We analyzed the patterning capabilities of the circuit by combining quantitative measurements with a mathematical reconstruction of the underlying dynamical system. The toggle switch can produce robust patterns with sharp boundaries, governed by bistability and hysteresis. We further demonstrate how the hysteresis, position, timing, and precision of the boundary can be controlled, highlighting the dynamical flexibility of the circuit.

Topology-dependent interference of synthetic gene circuit function by growth feedback

Growth-mediated feedback between synthetic gene circuits and host organisms leads to diverse emerged behaviors, including growth bistability and enhanced ultrasensitivity. However, the range of possible impacts of growth feedback on gene circuits remains underexplored. Here, we mathematically and experimentally demonstrated that growth feedback affects the functions of memory circuits in a network topology-dependent way. Specifically, the memory of the self-activation switch is quickly lost due to the growth-mediated dilution of the circuit products. Decoupling of growth feedback reveals its memory, manifested by its hysteresis property across a broad range of inducer concentration. On the contrary, the toggle switch is more refractory to growth-mediated dilution and can retrieve its memory after the fast-growth phase. The underlying principle lies in the different dependence of active and repressive regulations in these circuits on the growth-mediated dilution. Our results unveil the topology-dependent mechanism on how growth-mediated feedback influences the behaviors of gene circuits.

Engineering repressors with coevolutionary cues facilitates toggle switches with a master reset

Engineering allosteric transcriptional repressors containing an environmental sensing module (ESM) and a DNA recognition module (DRM) has the potential to unlock a combinatorial set of rationally designed biological responses. We demonstrated that constructing hybrid repressors by fusing distinct ESMs and DRMs provides a means to flexibly rewire genetic networks for complex signal processing. We have used coevolutionary traits among LacI homologs to develop a model for predicting compatibility between ESMs and DRMs. Our predictions accurately agree with the performance of 40 engineered repressors. We have harnessed this framework to develop a system of multiple toggle switches with a master OFF signal that produces a unique behavior: each engineered biological activity is switched to a stable ON state by different chemicals and returned to OFF in response to a common signal. One promising application of this design is to develop living diagnostics for monitoring multiple parameters in complex physiological environments and it represents one of many circuit topologies that can be explored with modular repressors designed with coevolutionary information.

Synthetic biology for improving cell fate decisions and tissue engineering outcomes

Synthetic biology is a relatively new field of science that combines aspects of biology and engineering to create novel tools for the construction of biological systems. Using tools within synthetic biology, stem cells can then be reprogrammed and differentiated into a specified cell type. Stem cells have already proven to be largely beneficial in many different therapies and have paved the way for tissue engineering and regenerative medicine. Although scientists have made great strides in tissue engineering, there still remain many questions to be answered in regard to regeneration. Presented here is an overview of synthetic biology, common tools built within synthetic biology, and the way these tools are being used in stem cells. Specifically, this review focuses on how synthetic biologists engineer genetic circuits to dynamically control gene expression while also introducing emerging topics such as genome engineering and synthetic transcription factors. The findings mentioned in this review show the diverse use of stem cells within synthetic biology and provide a foundation for future research in tissue engineering with the use of synthetic biology tools. Overall, the work done using synthetic biology in stem cells is in its early stages, however, this early work is leading to new approaches for repairing diseased and damaged tissues and organs, and further expanding the field of tissue engineering.

A synthetic system for asymmetric cell division in Escherichia coli

We describe a synthetic genetic circuit for controlling asymmetric cell division in E. coli in which a progenitor cell creates a differentiated daughter cell while retaining its original phenotype. Specifically, we engineered an inducible system that can bind and segregate plasmid DNA to a single position in the cell. Upon cell division, co-localized plasmids are kept by one and only one of the daughter cells. The other daughter cell receives no plasmid DNA and is hence irreversibly differentiated from its sibling. In this way, we achieved asymmetric cell division through asymmetric plasmid partitioning. We then used this system to achieve physical separation of genetically distinct cells by tying motility to differentiation. Finally, we characterized an orthogonal inducible circuit that enables the simultaneous asymmetric partitioning of two plasmid species, resulting in cells that have four distinct differentiated states. These results point the way towards engineering multicellular systems from prokaryotic hosts.

Intracellular Noise Level Determines Ratio Control Strategy Confined by Speed-Accuracy Trade-off

Robust and precise ratio control of heterogeneous phenotypes within an isogenic population is an essential task, especially in the development and differentiation of a large number of cells such as bacteria, sensory receptors, and blood cells. However, the mechanisms of such ratio control are poorly understood. Here, we employ experimental and mathematical techniques to understand the combined effects of signal induction and gene expression stochasticity on phenotypic multimodality. We identify two strategies to control phenotypic ratios from an initially homogeneous population, suitable roughly to high-noise and low-noise intracellular environments, and we show that both can be used to generate precise fractional differentiation. In noisy gene expression contexts, such as those found in bacteria, induction within the circuit's bistable region is enough to cause noise-induced bimodality within a feasible time frame. However, in less noisy contexts, such as tightly controlled eukaryotic systems, spontaneous state transitions are rare and hence bimodality needs to be induced with a controlled pulse of induction that falls outside the bistable region. Finally, we show that noise levels, system response time, and ratio tuning accuracy impose trade-offs and limitations on both ratio control strategies, which guide the selection of strategy alternatives.

An Optimized Bistable Metabolic Switch To Decouple Phenotypic States during Anaerobic Fermentation

Metabolic engineers aim to genetically modify microorganisms to improve their ability to produce valuable compounds. Despite the prevalence of growth-coupled production processes, these strategies can significantly limit production rates. Instead, rates can be improved by decoupling and optimizing growth and production independently, and operating with a growth stage followed by a production stage. Here, we implement a bistable transcriptional controller to decouple and switch between these two states. We optimize the controller in anaerobic conditions, typical of industrial fermentations, to ensure stability and tight expression control, while improving switching dynamics. The stability of this controller can be maintained through a simulated seed train scale-up from 5 mL to 500 000 L, indicating industrial feasibility. Finally, we demonstrate a two-stage production process using our optimal construct to improve the instantaneous rate of lactate production by over 50%, motivating the use of these systems in broad metabolic engineering applications.

Development of novel metabolite-responsive transcription factors via transposon-mediated protein fusion

Naturally evolved metabolite-responsive biosensors enable applications in metabolic engineering, ranging from screening large genetic libraries to dynamically regulating biosynthetic pathways. However, there are many metabolites for which a natural biosensor does not exist. To address this need, we developed a general method for converting metabolite-binding proteins into metabolite-responsive transcription factors-Biosensor Engineering by Random Domain Insertion (BERDI). This approach takes advantage of an in vitro transposon insertion reaction to generate all possible insertions of a DNA-binding domain into a metabolite-binding protein, followed by fluorescence activated cell sorting to isolate functional biosensors. To develop and evaluate the BERDI method, we generated a library of candidate biosensors in which a zinc finger DNA-binding domain was inserted into maltose binding protein, which served as a model well-studied metabolite-binding protein. Library diversity was characterized by several methods, a selection scheme was deployed, and ultimately several distinct and functional maltose-responsive transcriptional biosensors were identified. We hypothesize that the BERDI method comprises a generalizable strategy that may ultimately be applied to convert a wide range of metabolite-binding proteins into novel biosensors for applications in metabolic engineering and synthetic biology.

Towards an Aspect-Oriented Design and Modelling Framework for Synthetic Biology

Work on synthetic biology has largely used a component-based metaphor for system construction. While this paradigm has been successful for the construction of numerous systems, the incorporation of contextual design issues—either compositional, host or environmental—will be key to realising more complex applications. Here, we present a design framework that radically steps away from a purely parts-based paradigm by using aspect-oriented software engineering concepts. We believe that the notion of concerns is a powerful and biologically credible way of thinking about system synthesis. By adopting this approach, we can separate core concerns, which represent modular aims of the design, from cross-cutting concerns, which represent system-wide attributes. The explicit handling of cross-cutting concerns allows for contextual information to enter the design process in a modular way. As a proof-of-principle, we implemented the aspect-oriented approach in the Python tool, SynBioWeaver, which enables the combination, or weaving, of core and cross-cutting concerns. The power and flexibility of this framework is demonstrated through a number of examples covering the inclusion of part context, combining circuit designs in a context dependent manner, and the generation of rule, logic and reaction models from synthetic circuit designs.

Engineering a living biomaterial via bacterial surface capture of environmental molecules

Synthetic biology holds significant potential in biomaterials science as synthetically engineered cells can produce new biomaterials, or alternately, can function as living components of new biomaterials. Here, we describe the creation of a new biomaterial that incorporates living bacterial constituents that interact with their environment using engineered surface display. We first developed a gene construct that enabled simultaneous expression of cytosolic mCherry and a surface-displayed, catalytically active enzyme capable of covalently bonding with benzylguanine (BG) groups. We then created a functional living material within a microfluidic channel using these genetically engineered cells. The material forms when engineered cells covalently bond to ambient BG-modified molecules upon induction. Given the wide range of materials amenable to functionalization with BG-groups, our system provides a proof-of-concept for the sequestration and assembly of BG-functionalized molecules on a fluid-swept, living biomaterial surface.

Bottom-up approaches in synthetic biology and biomaterials for tissue engineering applications

Synthetic biologists use engineering principles to design and construct genetic circuits for programming cells with novel functions. A bottom-up approach is commonly used to design and construct genetic circuits by piecing together functional modules that are capable of reprogramming cells with novel behavior. While genetic circuits control cell operations through the tight regulation of gene expression, a diverse array of environmental factors within the extracellular space also has a significant impact on cell behavior. This extracellular space offers an addition route for synthetic biologists to apply their engineering principles to program cell-responsive modules within the extracellular space using biomaterials. In this review, we discuss how taking a bottom-up approach to build genetic circuits using DNA modules can be applied to biomaterials for controlling cell behavior from the extracellular milieu. We suggest that, by collectively controlling intrinsic and extrinsic signals in synthetic biology and biomaterials, tissue engineering outcomes can be improved.

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.

Programming Biomaterial Interactions Using Engineered Living Cells

We have developed a biomaterials interface that allows the properties of a functionalized surface to be controlled by a population of genetically engineered bacteria. This interface was engineered by linking a genetically modified E. coli strain with a chemically functionalized surface. Critically, the E. coli was engineered to upregulate the production of biotin when induced by a small signaling molecule. This biotin would then interact with the functionalized surface to modulate the surface's binding dynamics. In this chapter, we detail three protocols: one protocol for developing a population of biotin-producing genetically engineered cells, and two protocols for creating different types of functionalized surfaces. These methods will enable scientists to readily explore strategies for controlling surface-based material assembly and modification using a linked culture of engineered cells.

Design of Adjacent Transcriptional Regions to Tune Gene Expression and Facilitate Circuit Construction

Polycistronic architecture is common for synthetic gene circuits, however, it remains unknown how expression of one gene is affected by the presence of other genes/noncoding regions in the operon, termed adjacent transcriptional regions (ATR). Here, we constructed synthetic operons with a reporter gene flanked by different ATRs, and we found that ATRs with high GC content, small size, and low folding energy lead to high gene expression. Based on these results, we built a model of gene expression and generated a metric that takes into account ATRs. We used the metric to design and construct logic gates with low basal expression and high sensitivity and nonlinearity. Furthermore, we rationally designed synthetic 5'ATRs with different GC content and sizes to tune protein expression levels over a 300-fold range and used these to build synthetic toggle switches with varying basal expression and degrees of bistability. Our comprehensive model and gene expression metric could facilitate the future engineering of more complex synthetic gene circuits.

SynBioHub: A Standards-Enabled Design Repository for Synthetic Biology

The SynBioHub repository ( https://synbiohub.org ) is an open-source software project that facilitates the sharing of information about engineered biological systems. SynBioHub provides computational access for software and data integration, and a graphical user interface that enables users to search for and share designs in a Web browser. By connecting to relevant repositories (e.g., the iGEM repository, JBEI ICE, and other instances of SynBioHub), the software allows users to browse, upload, and download data in various standard formats, regardless of their location or representation. SynBioHub also provides a central reference point for other resources to link to, delivering design information in a standardized format using the Synthetic Biology Open Language (SBOL). The adoption and use of SynBioHub, a community-driven effort, has the potential to overcome the reproducibility challenge across laboratories by helping to address the current lack of information about published designs.

A Glucose-Sensing Toggle Switch for Autonomous, High Productivity Genetic Control

Many biosynthetic strategies are coupled to growth, which is inherently limiting, as (1) excess feedstock (e.g., sugar) may be converted to biomass, instead of product, (2) essential genes must be maintained, and (3) growth toxicity must be managed. A decoupled growth and production phase strategy could avoid these issues. We have developed a toggle switch that uses glucose sensing to enable this two-phase strategy. Temporary glucose starvation precisely and autonomously activates product pathway expression in rich or minimal media, obviating the requirement for expensive inducers. The switch remains stably in the new state even after reintroduction of glucose. In the context of polyhydroxybutyrate (PHB) biosynthesis, our system enables shorter growth phases and comparable titers to a constitutively expressing PHB strain. This two-phase production strategy, and specifically the glucose toggle switch, should be broadly useful to initiate many types of genetic program for metabolic engineering applications.

Engineering of a synthetic quadrastable gene network to approach Waddington landscape and cell fate determination

The process of cell fate determination has been depicted intuitively as cells travelling and resting on a rugged landscape, which has been probed by various theoretical studies. However, few studies have experimentally demonstrated how underlying gene regulatory networks shape the landscape and hence orchestrate cellular decision-making in the presence of both signal and noise. Here we tested different topologies and verified a synthetic gene circuit with mutual inhibition and auto-activations to be quadrastable, which enables direct study of quadruple cell fate determination on an engineered landscape. We show that cells indeed gravitate towards local minima and signal inductions dictate cell fates through modulating the shape of the multistable landscape. Experiments, guided by model predictions, reveal that sequential inductions generate distinct cell fates by changing landscape in sequence and hence navigating cells to different final states. This work provides a synthetic biology framework to approach cell fate determination and suggests a landscape-based explanation of fixed induction sequences for targeted differentiation.

DOI:http://dx.doi.org/10.7554/eLife.23702.001

Cells in animals use a process called differentiation to specialize into specific cell types such as skin cells and liver cells. Proteins called transcription factors drive particular steps in differentiation by controlling the activity of specific genes. Many transcription factors interact with each other to form complex networks that regulate gene activity to determine the fate of a cell and control the whole differentiation process. Some individual gene networks can program cells to become any one of several different cell fates, a feature known as multistability.

In the 1950s, a scientist called Conrad Waddington proposed the concept of an “epigenetic landscape” to describe how the fate of a cell is decided as an animal develops. The cell, depicted as a ball, rolls down a rugged landscape and has the option of taking several different routes. Each route will eventually lead to a distinct cell fate. As the ball moves down the hill, the choice of routes and final destinations becomes more limited. Theoretical approaches have been used to understand how gene regulatory networks shape the epigenetic landscape of an animal. However, few studies have experimentally tested the findings of the theoretical approaches and it is not clear how environmental inputs help to determine which path a cell will take.

Although bacteria cells do not generally specialize into particular cell types, bacteria cells can use multistability in transcription factor networks to switch between different behaviors or “states” in response to cues from the environment. Wu et al. used a bacterium called E. coli as a model to investigate how a gene network called MINPA from mammals, which is involved in differentiation and is believed to show multistability, can guide cells to adopt different states. The work combined experimental and mathematical approaches to design, construct and test an artificial version of the MINPA gene network in E. coli.

The experiments showed that MINPA could direct the cells to adopt four different stable states in which the cells produced fluorescent proteins of different colors. With the help of mathematical modeling, Wu et al. charted how the landscape of cell states changed when external chemical cues were applied. Exposing the cells to several cues in particular orders guided the cells to different final states.

The findings of Wu et al. shed new light on how the fate of a cell is determined and provide a theoretical framework for understanding the complex networks that control cell differentiation. This could help develop new ways of directing cell fate that could ultimately be used to generate cells to treat human diseases.

DOI:http://dx.doi.org/10.7554/eLife.23702.002

Using Synthetic Biology to Engineer Living Cells That Interface with Programmable Materials

We have developed an abiotic-biotic interface that allows engineered cells to control the material properties of a functionalized surface. This system is made by creating two modules: a synthetically engineered strain of E. coli cells and a functionalized material interface. Within this paper, we detail a protocol for genetically engineering selected behaviors within a strain of E. coli using molecular cloning strategies. Once developed, this strain produces elevated levels of biotin when exposed to a chemical inducer. Additionally, we detail protocols for creating two different functionalized surfaces, each of which is able to respond to cell-synthesized biotin. Taken together, we present a methodology for creating a linked, abiotic-biotic system that allows engineered cells to control material composition and assembly on nonliving substrates.

Engineering Modular Biosensors to Confer Metabolite-Responsive Regulation of Transcription

Efforts to engineer microbial factories have benefitted from mining biological diversity and high throughput synthesis of novel enzymatic pathways, yet screening and optimizing metabolic pathways remain rate-limiting steps. Metabolite-responsive biosensors may help to address these persistent challenges by enabling the monitoring of metabolite levels in individual cells and metabolite-responsive feedback control. We are currently limited to naturally evolved biosensors, which are insufficient for monitoring many metabolites of interest. Thus, a method for engineering novel biosensors would be powerful, yet we lack a generalizable approach that enables the construction of a wide range of biosensors. As a step toward this goal, we here explore several strategies for converting a metabolite-binding protein into a metabolite-responsive transcriptional regulator. By pairing a modular protein design approach with a library of synthetic promoters and applying robust statistical analyses, we identified strategies for engineering biosensor-regulated bacterial promoters and for achieving design-driven improvements of biosensor performance. We demonstrated the feasibility of this strategy by fusing a programmable DNA binding motif (zinc finger module) with a model ligand binding protein (maltose binding protein), to generate a novel biosensor conferring maltose-regulated gene expression. This systematic investigation provides insights that may guide the development of additional novel biosensors for diverse synthetic biology applications.

Development of a genetically programed vanillin-sensing bacterium for high-throughput screening of lignin-degrading enzyme libraries

BACKGROUND

Lignin is a potential biorefinery feedstock for the production of value-added chemicals including vanillin. A huge amount of lignin is produced as a by-product of the paper industry, while cellulosic components of plant biomass are utilized for the production of paper pulp. In spite of vast potential, lignin remains the least exploited component of plant biomass due to its extremely complex and heterogenous structure. Several enzymes have been reported to have lignin-degrading properties and could be potentially used in lignin biorefining if their catalytic properties could be improved by enzyme engineering. The much needed improvement of lignin-degrading enzymes by high-throughput selection techniques such as directed evolution is currently limited, as robust methods for detecting the conversion of lignin to desired small molecules are not available.

RESULTS

We identified a vanillin-inducible promoter by RNAseq analysis of Escherichia coli cells treated with a sublethal dose of vanillin and developed a genetically programmed vanillin-sensing cell by placing the 'very green fluorescent protein' gene under the control of this promoter. Fluorescence of the biosensing cell is enhanced significantly when grown in the presence of vanillin and is readily visualized by fluorescence microscopy. The use of fluorescence-activated cell sorting analysis further enhances the sensitivity, enabling dose-dependent detection of as low as 200 µM vanillin. The biosensor is highly specific to vanillin and no major response is elicited by the presence of lignin, lignin model compound, DMSO, vanillin analogues or non-specific toxic chemicals.

CONCLUSIONS

We developed an engineered E. coli cell that can detect vanillin at a concentration as low as 200 µM. The vanillin-sensing cell did not show cross-reactivity towards lignin or major lignin degradation products including vanillin analogues. This engineered E. coli cell could potentially be used as a host cell for screening lignin-degrading enzymes that can convert lignin to vanillin.

A computational method for the investigation of multistable systems and its application to genetic switches

Background

Genetic switches exhibit multistability, form the basis of epigenetic memory, and are found in natural decision making systems, such as cell fate determination in developmental pathways. Synthetic genetic switches can be used for recording the presence of different environmental signals, for changing phenotype using synthetic inputs and as building blocks for higher-level sequential logic circuits. Understanding how multistable switches can be constructed and how they function within larger biological systems is therefore key to synthetic biology.

Results

Here we present a new computational tool, called StabilityFinder, that takes advantage of sequential Monte Carlo methods to identify regions of parameter space capable of producing multistable behaviour, while handling uncertainty in biochemical rate constants and initial conditions. The algorithm works by clustering trajectories in phase space, and iteratively minimizing a distance metric. Here we examine a collection of models of genetic switches, ranging from the deterministic Gardner toggle switch to stochastic models containing different positive feedback connections. We uncover the design principles behind making bistable, tristable and quadristable switches, and find that rate of gene expression is a key parameter. We demonstrate the ability of the framework to examine more complex systems and examine the design principles of a three gene switch. Our framework allows us to relax the assumptions that are often used in genetic switch models and we show that more complex abstractions are still capable of multistable behaviour.

Conclusions

Our results suggest many ways in which genetic switches can be enhanced and offer designs for the construction of novel switches. Our analysis also highlights subtle changes in correlation of experimentally tunable parameters that can lead to bifurcations in deterministic and stochastic systems. Overall we demonstrate that StabilityFinder will be a valuable tool in the future design and construction of novel gene networks.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0375-z) contains supplementary material, which is available to authorized users.

Cell-Free Synthetic Biology: Engineering Beyond the Cell

Cell-free protein synthesis (CFPS) technologies have enabled inexpensive and rapid recombinant protein expression. Numerous highly active CFPS platforms are now available and have recently been used for synthetic biology applications. In this review, we focus on the ability of CFPS to expand our understanding of biological systems and its applications in the synthetic biology field. First, we outline a variety of CFPS platforms that provide alternative and complementary methods for expressing proteins from different organisms, compared with in vivo approaches. Next, we review the types of proteins, protein complexes, and protein modifications that have been achieved using CFPS systems. Finally, we introduce recent work on genetic networks in cell-free systems and the use of cell-free systems for rapid prototyping of in vivo networks. Given the flexibility of cell-free systems, CFPS holds promise to be a powerful tool for synthetic biology as well as a protein production technology in years to come.

An Implementation-Focused Bio/Algorithmic Workflow for Synthetic Biology

As synthetic biology moves away from trial and error and embraces more formal processes, workflows have emerged that cover the roadmap from conceptualization of a genetic device to its construction and measurement. This latter aspect (i.e., characterization and measurement of synthetic genetic constructs) has received relatively little attention to date, but it is crucial for their outcome. An end-to-end use case for engineering a simple synthetic device is presented, which is supported by information standards and computational methods and focuses on such characterization/measurement. This workflow captures the main stages of genetic device design and description and offers standardized tools for both population-based measurement and single-cell analysis. To this end, three separate aspects are addressed. First, the specific vector features are discussed. Although device/circuit design has been successfully automated, important structural information is usually overlooked, as in the case of plasmid vectors. The use of the Standard European Vector Architecture (SEVA) is advocated for selecting the optimal carrier of a design and its thorough description in order to unequivocally correlate digital definitions and molecular devices. A digital version of this plasmid format was developed with the Synthetic Biology Open Language (SBOL) along with a software tool that allows users to embed genetic parts in vector cargoes. This enables annotation of a mathematical model of the device's kinetic reactions formatted with the Systems Biology Markup Language (SBML). From that point onward, the experimental results and their in silico counterparts proceed alongside, with constant feedback to preserve consistency between them. A second aspect involves a framework for the calibration of fluorescence-based measurements. One of the most challenging endeavors in standardization, metrology, is tackled by reinterpreting the experimental output in light of simulation results, allowing us to turn arbitrary fluorescence units into relative measurements. Finally, integration of single-cell methods into a framework for multicellular simulation and measurement is addressed, allowing standardized inspection of the interplay between the carrier chassis and the culture conditions.

Synthetic biology platform technologies for antimicrobial applications

The growing prevalence of antibiotic resistance calls for new approaches in the development of antimicrobial therapeutics. Likewise, improved diagnostic measures are essential in guiding the application of targeted therapies and preventing the evolution of therapeutic resistance. Discovery platforms are also needed to form new treatment strategies and identify novel antimicrobial agents. By applying engineering principles to molecular biology, synthetic biologists have developed platforms that improve upon, supplement, and will perhaps supplant traditional broad-spectrum antibiotics. Efforts in engineering bacteriophages and synthetic probiotics demonstrate targeted antimicrobial approaches that can be fine-tuned using synthetic biology-derived principles. Further, the development of paper-based, cell-free expression systems holds promise in promoting the clinical translation of molecular biology tools for diagnostic purposes. In this review, we highlight emerging synthetic biology platform technologies that are geared toward the generation of new antimicrobial therapies, diagnostics, and discovery channels.

Tools and applications in synthetic biology

Advances in synthetic biology have enabled the engineering of cells with genetic circuits in order to program cells with new biological behavior, dynamic gene expression, and logic control. This cellular engineering progression offers an array of living sensors that can discriminate between cell states, produce a regulated dose of therapeutic biomolecules, and function in various delivery platforms. In this review, we highlight and summarize the tools and applications in bacterial and mammalian synthetic biology. The examples detailed in this review provide insight to further understand genetic circuits, how they are used to program cells with novel functions, and current methods to reliably interface this technology in vivo; thus paving the way for the design of promising novel therapeutic applications.

Programming Surface Chemistry with Engineered Cells

We have developed synthetic gene networks that enable engineered cells to selectively program surface chemistry. E. coli were engineered to upregulate biotin synthase, and therefore biotin synthesis, upon biochemical induction. Additionally, two different functionalized surfaces were developed that utilized binding between biotin and streptavidin to regulate enzyme assembly on programmable surfaces. When combined, the interactions between engineered cells and surfaces demonstrated that synthetic biology can be used to engineer cells that selectively control and modify molecular assembly by exploiting surface chemistry. Our system is highly modular and has the potential to influence fields ranging from tissue engineering to drug development and delivery.

Creating Single-Copy Genetic Circuits

Synthetic biology is increasingly used to develop sophisticated living devices for basic and applied research. Many of these genetic devices are engineered using multi-copy plasmids, but as the field progresses from proof-of-principle demonstrations to practical applications, it is important to develop single-copy synthetic modules that minimize consumption of cellular resources and can be stably maintained as genomic integrants. Here we use empirical design, mathematical modeling, and iterative construction and testing to build single-copy, bistable toggle switches with improved performance and reduced metabolic load that can be stably integrated into the host genome. Deterministic and stochastic models led us to focus on basal transcription to optimize circuit performance and helped to explain the resulting circuit robustness across a large range of component expression levels. The design parameters developed here provide important guidance for future efforts to convert functional multi-copy gene circuits into optimized single-copy circuits for practical, real-world use.

An innovative platform for quick and flexible joining of assorted DNA fragments

Successful synthetic biology efforts rely on conceptual and experimental designs in combination with testing of multi-gene constructs. Despite recent progresses, several limitations still hinder the ability to flexibly assemble and collectively share different types of DNA segments. Here, we describe an advanced system for joining DNA fragments from a universal library that automatically maintains open reading frames (ORFs) and does not require linkers, adaptors, sequence homology, amplification or mutation (domestication) of fragments in order to work properly. This system, which is enhanced by a unique buffer formulation, provides unforeseen capabilities for testing, and sharing, complex multi-gene circuitry assembled from different DNA fragments.

'Deadman' and 'Passcode' microbial kill switches for bacterial containment

Biocontainment systems that couple environmental sensing with circuit-based control of cell viability could be used to prevent escape of genetically modified microbes into the environment. Here we present two engineered safe-guard systems: the Deadman and Passcode kill switches. The Deadman kill switch uses unbalanced reciprocal transcriptional repression to couple a specific input signal with cell survival. The Passcode kill switch uses a similar two-layered transcription design and incorporates hybrid LacI/GalR family transcription factors to provide diverse and complex environmental inputs to control circuit function. These synthetic gene circuits efficiently kill Escherichia coli and can be readily reprogrammed to change their environmental inputs, regulatory architecture and killing mechanism.

Synthetic biology for pharmaceutical drug discovery

Synthetic biology (SB) is an emerging discipline, which is slowly reorienting the field of drug discovery. For thousands of years, living organisms such as plants were the major source of human medicines. The difficulty in resynthesizing natural products, however, often turned pharmaceutical industries away from this rich source for human medicine. More recently, progress on transformation through genetic manipulation of biosynthetic units in microorganisms has opened the possibility of in-depth exploration of the large chemical space of natural products derivatives. Success of SB in drug synthesis culminated with the bioproduction of artemisinin by microorganisms, a tour de force in protein and metabolic engineering. Today, synthetic cells are not only used as biofactories but also used as cell-based screening platforms for both target-based and phenotypic-based approaches. Engineered genetic circuits in synthetic cells are also used to decipher disease mechanisms or drug mechanism of actions and to study cell-cell communication within bacteria consortia. This review presents latest developments of SB in the field of drug discovery, including some challenging issues such as drug resistance and drug toxicity.

Targeted Large-Scale Deletion of Bacterial Genomes Using CRISPR-Nickases

Programmable CRISPR-Cas systems have augmented our ability to produce precise genome manipulations. Here we demonstrate and characterize the ability of CRISPR-Cas derived nickases to direct targeted recombination of both small and large genomic regions flanked by repetitive elements in Escherichia coli. While CRISPR directed double-stranded DNA breaks are highly lethal in many bacteria, we show that CRISPR-guided nickase systems can be programmed to make precise, nonlethal, single-stranded incisions in targeted genomic regions. This induces recombination events and leads to targeted deletion. We demonstrate that dual-targeted nicking enables deletion of 36 and 97 Kb of the genome. Furthermore, multiplex targeting enables deletion of 133 Kb, accounting for approximately 3% of the entire E. coli genome. This technology provides a framework for methods to manipulate bacterial genomes using CRISPR-nickase systems. We envision this system working synergistically with preexisting bacterial genome engineering methods.

A Highly Characterized Yeast Toolkit for Modular, Multipart Assembly

Saccharomyces cerevisiae is an increasingly attractive host for synthetic biology because of its long history in industrial fermentations. However, until recently, most synthetic biology systems have focused on bacteria. While there is a wealth of resources and literature about the biology of yeast, it can be daunting to navigate and extract the tools needed for engineering applications. Here we present a versatile engineering platform for yeast, which contains both a rapid, modular assembly method and a basic set of characterized parts. This platform provides a framework in which to create new designs, as well as data on promoters, terminators, degradation tags, and copy number to inform those designs. Additionally, we describe genome-editing tools for making modifications directly to the yeast chromosomes, which we find preferable to plasmids due to reduced variability in expression. With this toolkit, we strive to simplify the process of engineering yeast by standardizing the physical manipulations and suggesting best practices that together will enable more straightforward translation of materials and data from one group to another. Additionally, by relieving researchers of the burden of technical details, they can focus on higher-level aspects of experimental design.

Synthetic biosensors for precise gene control and real-time monitoring of metabolites

Characterization and standardization of inducible transcriptional regulators has transformed how scientists approach biology by allowing precise and tunable control of gene expression. Despite their utility, only a handful of well-characterized regulators exist, limiting the complexity of engineered biological systems. We apply a characterization pipeline to four genetically encoded sensors that respond to acrylate, glucarate, erythromycin and naringenin. We evaluate how the concentration of the inducing chemical relates to protein expression, how the extent of induction affects protein expression kinetics, and how the activation behavior of single cells relates to ensemble measurements. We show that activation of each sensor is orthogonal to the other sensors, and to other common inducible systems. We demonstrate independent control of three fluorescent proteins in a single cell, chemically defining eight unique transcriptional states. To demonstrate biosensor utility in metabolic engineering, we apply the glucarate biosensor to monitor product formation in a heterologous glucarate biosynthesis pathway and identify superior enzyme variants. Doubling the number of well-characterized inducible systems makes more complex synthetic biological circuits accessible. Characterizing sensors that transduce the intracellular concentration of valuable metabolites into fluorescent readouts enables high-throughput screening of biological catalysts and alleviates the primary bottleneck of the metabolic engineering design-build-test cycle.

Engineered Phagemids for Nonlytic, Targeted Antibacterial Therapies

The increasing incidence of antibiotic-resistant bacterial infections is creating a global public health threat. Because conventional antibiotic drug discovery has failed to keep pace with the rise of resistance, a growing need exists to develop novel antibacterial methodologies. Replication-competent bacteriophages have been utilized in a limited fashion to treat bacterial infections. However, this approach can result in the release of harmful endotoxins, leading to untoward side effects. Here, we engineer bacterial phagemids to express antimicrobial peptides (AMPs) and protein toxins that disrupt intracellular processes, leading to rapid, nonlytic bacterial death. We show that this approach is highly modular, enabling one to readily alter the number and type of AMPs and toxins encoded by the phagemids. Furthermore, we demonstrate the effectiveness of engineered phagemids in an in vivo murine peritonitis infection model. This work shows that targeted, engineered phagemid therapy can serve as a viable, nonantibiotic means to treat bacterial infections, while avoiding the health issues inherent to lytic and replicative bacteriophage use.

Accurate predictions of genetic circuit behavior from part characterization and modular composition

A long-standing goal of synthetic biology is to rapidly engineer new regulatory circuits from simpler devices. As circuit complexity grows, it becomes increasingly important to guide design with quantitative models, but previous efforts have been hindered by lack of predictive accuracy. To address this, we developed Empirical Quantitative Incremental Prediction (EQuIP), a new method for accurate prediction of genetic regulatory network behavior from detailed characterizations of their components. In EQuIP, precisely calibrated time-series and dosage-response assays are used to construct hybrid phenotypic/mechanistic models of regulatory processes. This hybrid method ensures that model parameters match observable phenomena, using phenotypic formulation where current hypotheses about biological mechanisms do not agree closely with experimental observations. We demonstrate EQuIP's precision at predicting distributions of cell behaviors for six transcriptional cascades and three feed-forward circuits in mammalian cells. Our cascade predictions have only 1.6-fold mean error over a 261-fold mean range of fluorescence variation, owing primarily to calibrated measurements and piecewise-linear models. Predictions for three feed-forward circuits had a 2.0-fold mean error on a 333-fold mean range, further demonstrating that EQuIP can scale to more complex systems. Such accurate predictions will foster reliable forward engineering of complex biological circuits from libraries of standardized devices.

Tunable protein degradation in bacteria

Tunable control of protein degradation in bacteria would provide a powerful research tool. We use components of the Mesoplasma florum tmRNA system to create a synthetic degradation system that provides both independent control of the steady-state protein level and inducible degradation of targeted proteins in Escherichia coli. We demonstrate application of this system in synthetic circuit development and control of core bacterial processes and antibacterial targets, and transfer the system to Lactococcus lactis to establish its broad functionality in bacteria. We create a 238-member library of tagged essential proteins in E. coli that can serve as both a research tool to study essential gene function and an applied system for antibiotic discovery. Our synthetic protein degradation system is modular, does not require disruption of host systems, and can be transferred to diverse bacteria with minimal modification.

Quorum-sensing crosstalk-driven synthetic circuits: from unimodality to trimodality

Widespread quorum-sensing (QS) enables bacteria to communicate and plays a critical role in controlling bacterial virulence. However, effects of promiscuous QS crosstalk and its implications for gene regulation and cell decision-making remain largely unknown. Here we systematically studied the crosstalk between LuxR/I and LasR/I systems and found that QS crosstalk can be dissected into signal crosstalk and promoter crosstalk. Further investigations using synthetic positive feedback circuits revealed that signal crosstalk significantly decreases a circuit's bistable potential while maintaining unimodality. Promoter crosstalk, however, reproducibly generates complex trimodal responses resulting from noise-induced state transitions and host-circuit interactions. A mathematical model that integrates the circuit's nonlinearity, stochasticity, and host-circuit interactions was developed, and its predictions of conditions for trimodality were verified experimentally. Combining synthetic biology and mathematical modeling, this work sheds light on the complex behaviors emerging from QS crosstalk, which could be exploited for therapeutics and biotechnology.

A unified design space of synthetic stripe-forming networks

Synthetic biology is a promising tool to study the function and properties of gene regulatory networks. Gene circuits with predefined behaviours have been successfully built and modelled, but largely on a case-by-case basis. Here we go beyond individual networks and explore both computationally and synthetically the design space of possible dynamical mechanisms for 3-node stripe-forming networks. First, we computationally test every possible 3-node network for stripe formation in a morphogen gradient. We discover four different dynamical mechanisms to form a stripe and identify the minimal network of each group. Next, with the help of newly established engineering criteria we build these four networks synthetically and show that they indeed operate with four fundamentally distinct mechanisms. Finally, this close match between theory and experiment allows us to infer and subsequently build a 2-node network that represents the archetype of the explored design space.

Unique nucleotide sequence-guided assembly of repetitive DNA parts for synthetic biology applications

Recombination-based DNA construction methods, such as Gibson assembly, have made it possible to easily and simultaneously assemble multiple DNA parts, and they hold promise for the development and optimization of metabolic pathways and functional genetic circuits. Over time, however, these pathways and circuits have become more complex, and the increasing need for standardization and insulation of genetic parts has resulted in sequence redundancies--for example, repeated terminator and insulator sequences--that complicate recombination-based assembly. We and others have recently developed DNA assembly methods, which we refer to collectively as unique nucleotide sequence (UNS)-guided assembly, in which individual DNA parts are flanked with UNSs to facilitate the ordered, recombination-based assembly of repetitive sequences. Here we present a detailed protocol for UNS-guided assembly that enables researchers to convert multiple DNA parts into sequenced, correctly assembled constructs, or into high-quality combinatorial libraries in only 2-3 d. If the DNA parts must be generated from scratch, an additional 2-5 d are necessary. This protocol requires no specialized equipment and can easily be implemented by a student with experience in basic cloning techniques.

Linear DNA for rapid prototyping of synthetic biological circuits in an Escherichia coli based TX-TL cell-free system

Accelerating the pace of synthetic biology experiments requires new approaches for rapid prototyping of circuits from individual DNA regulatory elements. However, current testing standards require days to weeks due to cloning and in vivo transformation. In this work, we first characterized methods to protect linear DNA strands from exonuclease degradation in an Escherichia coli based transcription-translation cell-free system (TX-TL), as well as mechanisms of degradation. This enabled the use of linear DNA PCR products in TX-TL. We then compared expression levels and binding dynamics of different promoters on linear DNA and plasmid DNA. We also demonstrated assembly technology to rapidly build circuits entirely in vitro from separate parts. Using this strategy, we prototyped a four component genetic switch in under 8 h entirely in vitro. Rapid in vitro assembly has future applications for prototyping multiple component circuits if combined with predictive computational models.