Programming Escherichia coli to function as a digital display

Light-inducible protein degradation in E. coli with the LOVdeg tag

Molecular tools for optogenetic control allow for spatial and temporal regulation of cell behavior. In particular, light-controlled protein degradation is a valuable mechanism of regulation because it can be highly modular, used in tandem with other control mechanisms, and maintain functionality throughout growth phases. Here, we engineered LOVdeg, a tag that can be appended to a protein of interest for inducible degradation in Escherichia coli using blue light. We demonstrate the modularity of LOVdeg by using it to tag a range of proteins, including the LacI repressor, CRISPRa activator, and the AcrB efflux pump. Additionally, we demonstrate the utility of pairing the LOVdeg tag with existing optogenetic tools to enhance performance by developing a combined EL222 and LOVdeg system. Finally, we use the LOVdeg tag in a metabolic engineering application to demonstrate post-translational control of metabolism. Together, our results highlight the modularity and functionality of the LOVdeg tag system and introduce a powerful new tool for bacterial optogenetics.

Systematic mining of the human microbiome identifies antimicrobial peptides with diverse activity spectra

Human-associated bacteria secrete modified peptides to control host physiology and remodel the microbiota species composition. Here we scanned 2,229 Human Microbiome Project genomes of species colonizing skin, gastrointestinal tract, urogenital tract, mouth and trachea for gene clusters encoding RiPPs (ribosomally synthesized and post-translationally modified peptides). We found 218 lanthipeptides and 25 lasso peptides, 70 of which were synthesized and expressed in E. coli and 23 could be purified and functionally characterized. They were tested for activity against bacteria associated with healthy human flora and pathogens. New antibiotics were identified against strains implicated in skin, nasal and vaginal dysbiosis as well as from oral strains selectively targeting those in the gut. Extended- and narrow-spectrum antibiotics were found against methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococci. Mining natural products produced by human-associated microbes will enable the elucidation of ecological relationships and may be a rich resource for antimicrobial discovery.

Investigating and Modeling the Factors That Affect Genetic Circuit Performance

Over the past 2 decades, synthetic biology has yielded ever more complex genetic circuits that are able to perform sophisticated functions in response to specific signals. Yet, genetic circuits are not immediately transferable to an outside-the-lab setting where their performance is highly compromised. We propose introducing a broader test step to the design-build-test-learn workflow to include factors that might contribute to unexpected genetic circuit performance. As a proof of concept, we have designed and evaluated a genetic circuit in various temperatures, inducer concentrations, nonsterilized soil exposure, and bacterial growth stages. We determined that the circuit's performance is dramatically altered when these factors differ from the optimal lab conditions. We observed significant changes in the time for signal detection as well as signal intensity when the genetic circuit was tested under nonoptimal lab conditions. As a learning effort, we then proceeded to generate model predictions in untested conditions, which is currently lacking in synthetic biology application design. Furthermore, broader test and learn steps uncovered a negative correlation between the time it takes for a gate to turn ON and the bacterial growth phases. As the synthetic biology discipline transitions from proof-of-concept genetic programs to appropriate and safe application implementations, more emphasis on test and learn steps (i.e., characterizing parts and circuits for a broad range of conditions) will provide missing insights on genetic circuit behavior outside the lab.

Advancing reproducibility can ease the 'hard truths' of synthetic biology

Reproducibility has been identified as an outstanding challenge in science, and the field of synthetic biology is no exception. Meeting this challenge is critical to allow the transformative technological capabilities emerging from this field to reach their full potential to benefit the society. We discuss the current state of reproducibility in synthetic biology and how improvements can address some of the central shortcomings in the field. We argue that the successful adoption of reproducibility as a routine aspect of research and development requires commitment spanning researchers and relevant institutions via education, incentivization and investment in related infrastructure. The urgency of this topic pervades synthetic biology as it strives to advance fundamental insights and unlock new capabilities for safe, secure and scalable applications of biotechnology. Graphical Abstract.

Algorithmic Programming of Sequential Logic and Genetic Circuits for Recording Biochemical Concentration in a Probiotic Bacterium

Through the implementation of designable genetic circuits, engineered probiotic microorganisms could be used as noninvasive diagnostic tools for the gastrointestinal tract. For these living cells to report detected biomarkers or signals after exiting the gut, the genetic circuits must be able to record these signals by using genetically encoded memory. Complex memory register circuits could enable multiplex interrogation of biomarkers and signals. A theory-based approach to create genetic circuits containing memory, known as sequential logic circuits, was previously established for a model laboratory strain of Escherichia coli, yet how circuit component performance varies for nonmodel and clinically relevant bacterial strains is poorly understood. Here, we develop a scalable computational approach to design robust sequential logic circuits in probiotic strain Escherichia coli Nissle 1917 (EcN). In this work, we used TetR-family transcriptional repressors to build genetic logic gates that can be composed into sequential logic circuits, along with a set of engineered sensors relevant for use in the gut environment. Using standard methods, 16 genetic NOT gates and nine sensors were experimentally characterized in EcN. These data were used to design and predict the performance of circuit designs. We present a set of genetic circuits encoding both combinational logic and sequential logic and show that the circuit outputs are in close agreement with our quantitative predictions from the design algorithm. Furthermore, we demonstrate an analog-like concentration recording circuit that detects and reports three input concentration ranges of a biochemical signal using sequential logic.

Redesign of an Escherichia coli Nissle treatment for phenylketonuria using insulated genomic landing pads and genetic circuits to reduce burden

To build therapeutic strains, Escherichia coli Nissle (EcN) have been engineered to express antibiotics, toxin-degrading enzymes, immunoregulators, and anti-cancer chemotherapies. For efficacy, the recombinant genes need to be highly expressed, but this imposes a burden on the cell, and plasmids are difficult to maintain in the body. To address these problems, we have developed landing pads in the EcN genome and genetic circuits to control therapeutic gene expression. These tools were applied to EcN SYNB1618, undergoing clinical trials as a phenylketonuria treatment. The pathway for converting phenylalanine to trans-cinnamic acid was moved to a landing pad under the control of a circuit that keeps the pathway off during storage. The resulting strain (EcN SYN8784) achieved higher activity than EcN SYNB1618, reaching levels near when the pathway is carried on a plasmid. This work demonstrates a simple system for engineering EcN that aids quantitative strain design for therapeutics.

Living electronics: A catalogue of engineered living electronic components

Biology leverages a range of electrical phenomena to extract and store energy, control molecular reactions and enable multicellular communication. Microbes, in particular, have evolved genetically encoded machinery enabling them to utilize the abundant redox-active molecules and minerals available on Earth, which in turn drive global-scale biogeochemical cycles. Recently, the microbial machinery enabling these redox reactions have been leveraged for interfacing cells and biomolecules with electrical circuits for biotechnological applications. Synthetic biology is allowing for the use of these machinery as components of engineered living materials with tuneable electrical properties. Herein, we review the state of such living electronic components including wires, capacitors, transistors, diodes, optoelectronic components, spin filters, sensors, logic processors, bioactuators, information storage media and methods for assembling these components into living electronic circuits.

Parallel engineering of environmental bacteria and performance over years under jungle-simulated conditions

Engineered bacteria could perform many functions in the environment, for example, to remediate pollutants, deliver nutrients to crops or act as in-field biosensors. Model organisms can be unreliable in the field, but selecting an isolate from the thousands that naturally live there and genetically manipulating them to carry the desired function is a slow and uninformed process. Here, we demonstrate the parallel engineering of isolates from environmental samples by using the broad-host-range XPORT conjugation system (Bacillus subtilis mini-ICEBs1) to transfer a genetic payload to many isolates in parallel. Bacillus and Lysinibacillus species were obtained from seven soil and water samples from different locations in Israel. XPORT successfully transferred a genetic function (reporter expression) into 25 of these isolates. They were then screened to identify the best-performing chassis based on the expression level, doubling time, functional stability in soil, and environmentally-relevant traits of its closest annotated reference species, such as the ability to sporulate and temperature tolerance. From this library, we selected Bacillus frigoritolerans A3E1, re-introduced it to soil, and measured function and genetic stability in a contained environment that replicates jungle conditions. After 21 months of storage, the engineered bacteria were viable, could perform their function, and did not accumulate disruptive mutations.

Implementing re-configurable biological computation with distributed multicellular consortia

The use of synthetic biological circuits to deal with numerous biological challenges has been proposed in several studies, but its implementation is still remote. A major problem encountered is the complexity of the cellular engineering needed to achieve complex biological circuits and the lack of general-purpose biological systems. The generation of re-programmable circuits can increase circuit flexibility and the scalability of complex cell-based computing devices. Here we present a new architecture to produce reprogrammable biological circuits that allow the development of a variety of different functions with minimal cell engineering. We demonstrate the feasibility of creating several circuits using only a small set of engineered cells, which can be externally reprogrammed to implement simple logics in response to specific inputs. In this regard, depending on the computation needs, a device composed of a number of defined cells can generate a variety of circuits without the need of further cell engineering or rearrangements. In addition, the inclusion of a memory module in the circuits strongly improved the digital response of the devices. The reprogrammability of biological circuits is an intrinsic capacity that is not provided in electronics and it may be used as a tool to solve complex biological problems.

Toward predictive engineering of gene circuits

Many synthetic biology applications rely on programming living cells using gene circuits - the assembly and wiring of genetic elements to control cellular behaviors. Extensive progress has been made in constructing gene circuits with diverse functions and applications. For many circuit functions, however, it remains challenging to ensure that the circuits operate in a predictable manner. Although the notion of predictability may appear intuitive, close inspection suggests that it is not always clear what constitutes predictability. We dissect this concept and how it can be confounded by the complexity of a circuit, the complexity of the context, and the interplay between the two. We discuss circuit engineering strategies, in both computation and experiment, that have been used to improve the predictability of gene circuits.

Accurate characterization of dynamic microbial gene expression and growth rate profiles

Genetic circuits are subject to variability due to cellular and compositional contexts. Cells face changing internal states and environments, the cellular context, to which they sense and respond by changing their gene expression and growth rates. Furthermore, each gene in a genetic circuit operates in a compositional context of genes which may interact with each other and the host cell in complex ways. The context of genetic circuits can, therefore, change gene expression and growth rates, and measuring their dynamics is essential to understanding natural and synthetic regulatory networks that give rise to functional phenotypes. However, reconstruction of microbial gene expression and growth rate profiles from typical noisy measurements of cell populations is difficult due to the effects of noise at low cell densities among other factors. We present here a method for the estimation of dynamic microbial gene expression rates and growth rates from noisy measurement data. Compared to the current state-of-the-art, our method significantly reduced the mean squared error of reconstructions from simulated data of growth and gene expression rates, improving the estimation of timing and magnitude of relevant shapes of profiles. We applied our method to characterize a triple-reporter plasmid library combining multiple transcription units in different compositional and cellular contexts in Escherichia coli. Our analysis reveals cellular and compositional context effects on microbial growth and gene expression rate dynamics and suggests a method for the dynamic ratiometric characterization of constitutive promoters relative to an in vivo reference.

4-bit adhesion logic enables universal multicellular interface patterning

Multicellular systems, from bacterial biofilms to human organs, form interfaces (or boundaries) between different cell collectives to spatially organize versatile functions^1,2. The evolution of sufficiently descriptive genetic toolkits probably triggered the explosion of complex multicellular life and patterning^3,4. Synthetic biology aims to engineer multicellular systems for practical applications and to serve as a build-to-understand methodology for natural systems^5–8. However, our ability to engineer multicellular interface patterns^2,9 is still very limited, as synthetic cell–cell adhesion toolkits and suitable patterning algorithms are underdeveloped^5,7,10–13. Here we introduce a synthetic cell–cell adhesin logic with swarming bacteria and establish the precise engineering, predictive modelling and algorithmic programming of multicellular interface patterns. We demonstrate interface generation through a swarming adhesion mechanism, quantitative control over interface geometry and adhesion-mediated analogues of developmental organizers and morphogen fields. Using tiling and four-colour-mapping concepts, we identify algorithms for creating universal target patterns. This synthetic 4-bit adhesion logic advances practical applications such as human-readable molecular diagnostics, spatial fluid control on biological surfaces and programmable self-growing materials^5–8,14. Notably, a minimal set of just four adhesins represents 4 bits of information that suffice to program universal tessellation patterns, implying a low critical threshold for the evolution and engineering of complex multicellular systems^3,5.

A synthetic cell-cell adhesion logic using swarming E. coli with 4 bits of information is introduced, enabling the programming of interfaces that combine to form universal tessellation patterns over a large scale.

Digital models in biotechnology: Towards multi-scale integration and implementation

Industrial biotechnology encompasses a large area of multi-scale and multi-disciplinary research activities. With the recent megatrend of digitalization sweeping across all industries, there is an increased focus in the biotechnology industry on developing, integrating and applying digital models to improve all aspects of industrial biotechnology. Given the rapid development of this field, we systematically classify the state-of-art modelling concepts applied at different scales in industrial biotechnology and critically discuss their current usage, advantages and limitations. Further, we critically analyzed current strategies to couple cell models with computational fluid dynamics to study the performance of industrial microorganisms in large-scale bioprocesses, which is of crucial importance for the bio-based production industries. One of the most challenging aspects in this context is gathering intracellular data under industrially relevant conditions. Towards comprehensive models, we discuss how different scale-down concepts combined with appropriate analytical tools can capture intracellular states of single cells. We finally illustrated how the efforts could be used to develop digitals models suitable for both cell factory design and process optimization at industrial scales in the future.

LOICA: Integrating Models with Data for Genetic Network Design Automation

Genetic design automation tools are necessary to expand the scale and complexity of possible synthetic genetic networks. These tools are enabled by abstraction of a hierarchy of standardized components and devices. Abstracted elements must be parametrized from data derived from relevant experiments, and these experiments must be related to the part composition of the abstract components. Here we present Logical Operators for Integrated Cell Algorithms (LOICA), a Python package for designing, modeling, and characterizing genetic networks based on a simple object-oriented design abstraction. LOICA uses classes to represent different biological and experimental components, which generate models through their interactions. These models can be parametrized by direct connection to data contained in Flapjack so that abstracted components of designs can characterize themselves. Models can be simulated using continuous or stochastic methods and the data published and managed using Flapjack. LOICA also outputs SBOL3 descriptions and generates graph representations of genetic network designs.

High-Throughput Regulatory Part Prototyping and Analysis by Cell-Free Protein Synthesis and Droplet Microfluidics

Engineering regulatory parts for improved performance in genetic programs has played a pivotal role in the development of the synthetic biology cell programming toolbox. Here, we report the development of a novel high-throughput platform for regulatory part prototyping and analysis that leverages the advantages of engineered DNA libraries, cell-free protein synthesis (CFPS), high-throughput emulsion droplet microfluidics, standard flow sorting adapted to screen droplet reactions, and next-generation sequencing (NGS). With this integrated platform, we screened the activity of millions of genetic parts within hours, followed by NGS retrieval of the improved designs. This in vitro platform is particularly valuable for engineering regulatory parts of nonmodel organisms, where in vivo high-throughput screening methods are not readily available. The platform can be extended to multipart screening of complete genetic programs to optimize yield and stability.

New synthetic biology tools for metabolic control

In industrial bioprocesses, microbial metabolism dictates the product yields, and therefore, our capacity to control it has an enormous potential to help us move towards a bio-based economy. The rapid development of multiomics data has accelerated our systematic understanding of complex metabolic regulatory mechanisms, which allow us to develop tools to manipulate them. In the last few years, machine learning-based metabolic modeling, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) derived synthetic biology tools, and synthetic genetic circuits have been widely used to control the metabolism of microorganisms, manipulate gene expression, and build synthetic pathways for bioproduction. This review describes the latest developments for metabolic control, and focuses on the trends and challenges of metabolic engineering strategies.

Visualizing the pH in Escherichia coli Colonies via the Sensor Protein mCherryEA Allows High-Throughput Screening of Mutant Libraries

Cytoplasmic pH in bacteria is tightly regulated by diverse active mechanisms and interconnected regulatory processes. Many processes and regulators underlying pH homeostasis have been identified via phenotypic screening of strain libraries for nongrowth at low or high pH values. Direct screens with respect to changes of the internal pH in mutant strain collections are limited by laborious methods, which include fluorescent dyes and radioactive probes. Genetically encoded biosensors equip single organisms or strain libraries with an internal sensor molecule during the generation of the strain. Here, we used the pH-sensitive mCherry variant mCherryEA as a ratiometric pH biosensor. We visualized the internal pH of Escherichia coli colonies on agar plates by the use of a GelDoc imaging system. Combining this imaging technology with robot-assisted colony picking and spotting allowed us to screen and select mutants with altered internal pH values from a small transposon mutagenesis-derived E. coli library. Identification of the transposon (Tn) insertion sites in strains with altered internal pH levels revealed that the transposon was inserted into trkH (encoding a transmembrane protein of the potassium uptake system) or rssB (encoding the adaptor protein RssB, which mediates the proteolytic degradation of the general stress response regulator RpoS), two genes known to be associated with pH homeostasis and pH stress adaptation. This successful screening approach demonstrates that the pH sensor-based analysis of arrayed colonies on agar plates is a sensitive approach for the rapid identification of genes involved in pH homeostasis or pH stress adaptation in E. coli.

IMPORTANCE Phenotypic screening of strain libraries on agar plates has become a versatile tool to understand gene functions and to optimize biotechnological platform organisms. Screening is supported by genetically encoded biosensors that allow to easily measure intracellular processes. For this purpose, transcription factor-based biosensors have emerged as the sensor type of choice. Here, the target stimulus initiates the activation of a response gene (e.g., a fluorescent protein), followed by transcription, translation, and maturation. Due to this mechanistic principle, biosensor readouts are delayed and cannot report the actual intracellular state of the cell in real time. To capture rapid intracellular processes adequately, fluorescent reporter proteins are extensively applied. However, these sensor types have not previously been used for phenotypic screenings. To take advantage of their properties, we established here an imaging method that allows application of a rapid ratiometric sensor protein for assessing the internal pH of colonies in a high-throughput manner.

Genetic circuit design automation with Cello 2.0

Cells interact with their environment, communicate among themselves, track time and make decisions through functions controlled by natural regulatory genetic circuits consisting of interacting biological components. Synthetic programmable circuits used in therapeutics and other applications can be automatically designed by computer-aided tools. The Cello software designs the DNA sequences for programmable circuits based on a high-level software description and a library of characterized DNA parts representing Boolean logic gates. This process allows for design specification reuse, modular DNA part library curation and formalized circuit transformations based on experimental data. This protocol describes Cello 2.0, a freely available cross-platform software written in Java. Cello 2.0 enables flexible descriptions of the logic gates' structure and their mathematical models representing dynamic behavior, new formal rules for describing the placement of gates in a genome, a new graphical user interface, support for Verilog 2005 syntax and a connection to the SynBioHub parts repository software environment. Collectively, these features expand Cello's capabilities beyond Escherichia coli plasmids to new organisms and broader genetic contexts, including the genome. Designing circuits with Cello 2.0 produces an abstract Boolean network from a Verilog file, assigns biological parts to each node in the Boolean network, constructs a DNA sequence and generates highly structured and annotated sequence representations suitable for downstream processing and fabrication, respectively. The result is a sequence implementing the specified Boolean function in the organism and predictions of circuit performance. Depending on the size of the design space and users' expertise, jobs may take minutes or hours to complete.

Exploiting Noise, Non-Linearity, and Feedback for Differential Control of Multiple Synthetic Cells with a Single Optogenetic Input

Synthetic biology seeks to develop modular biocircuits that combine to produce complex, controllable behaviors. These designs are often subject to noisy fluctuations and uncertainties, and most modern synthetic biology design processes have focused to create robust components to mitigate the noise of gene expression and reduce the heterogeneity of single-cell responses. However, a deeper understanding of noise can achieve control goals that would otherwise be impossible. We explore how an "Optogenetic Maxwell Demon" could selectively amplify noise to control multiple cells using single-input-multiple-output (SIMO) feedback. Using data-constrained stochastic model simulations and theory, we show how an appropriately selected stochastic SIMO controller can drive multiple different cells to different user-specified configurations irrespective of initial conditions. We explore how controllability depends on cells' regulatory structures, the amount of information available to the controller, and the accuracy of the model used. Our results suggest that gene regulation noise, when combined with optogenetic feedback and non-linear biochemical auto-regulation, can achieve synergy to enable precise control of complex stochastic processes.

Automated design and implementation of a NOR gate in Pseudomonas putida

Boolean NOR gates have been widely implemented in Escherichia coli as transcriptional regulatory devices for building complex genetic circuits. Yet, their portability to other bacterial hosts/chassis is generally hampered by frequent changes in the parameters of the INPUT/OUTPUT response functions brought about by new genetic and biochemical contexts. Here, we have used the circuit design tool CELLO for assembling a NOR gate in the soil bacterium and the metabolic engineering platform Pseudomonas putida with components tailored for E. coli. To this end, we capitalized on the functional parameters of 20 genetic inverters for each host and the resulting compatibility between NOT pairs. Moreover, we added to the gate library three inducible promoters that are specific to P. putida, thus expanding cross-platform assembly options. While the number of potential connectable inverters decreased drastically when moving the library from E. coli to P. putida, the CELLO software was still able to find an effective NOR gate in the new chassis. The automated generation of the corresponding DNA sequence and in vivo experimental verification accredited that some genetic modules initially optimized for E. coli can indeed be reused to deliver NOR logic in P. putida as well. Furthermore, the results highlight the value of creating host-specific collections of well-characterized regulatory inverters for the quick assembly of genetic circuits to meet complex specifications.

Stochastic Hazard Analysis of Genetic Circuits in iBioSim and STAMINA

In synthetic biology, combinational circuits are used to program cells for various new applications like biosensors, drug delivery systems, and biofuels. Similar to asynchronous electronic circuits, some combinational genetic circuits may show unwanted switching variations (glitches) caused by multiple input changes. Depending on the biological circuit, glitches can cause irreversible effects and jeopardize the circuit's functionality. This paper presents a stochastic analysis to predict glitch propensities for three implementations of a genetic circuit with known glitching behavior. The analysis uses STochastic Approximate Model-checker for INfinite-state Analysis (STAMINA), a tool for stochastic verification. The STAMINA results were validated by comparison to stochastic simulation in iBioSim resulting in further improvements of STAMINA. This paper demonstrates that stochastic verification can be utilized by genetic designers to evaluate design choices and input restrictions to achieve a desired reliability of operation.

Development of a N-Acetylneuraminic Acid-Based Sensing and Responding Switch for Orthogonal Gene Regulation in Cyanobacterial Synechococcus Strains

Advances in synthetic biology have allowed photosynthetic cyanobacteria as promising "green cell factories" for sustainable production of biofuels and biochemicals. However, a limited of genetic switches developed in cyanobacteria restrict the complex and orthogonal metabolic regulation. In addition, suitable and controllable switches sensing and responding to specific inducers would allow for the separation of cellular growth and expression of exogenous genes or pathways that cause metabolic burden or toxicity. Here in this study, we developed a genetic switch repressed by NanR and induced by N-acetylneuraminic acid (Neu5Ac) in a fast-growing cyanobacterium Synechococcus elongatus UTEX 2973 along with its highly homologous strain S. elongatus PCC 7942. First, nanR from Escherichia coli and a previously optimized cognate promoter P_J23119H10 were introduced into Syn2973 to control the expression of the reporter gene lacZ encoding β-galactosidase, achieving induction with negligible leakage. Second, the switch was systemically optimized to reach ∼738-fold induction by fine-tuning the expression level of NanR and introducing additional transporter of Neu5Ac. Finally, the orthogonality between the NanR/Neu5Ac switch and theophylline-responsive riboregulator was investigated, achieving a coordinated regulation or binary regulation toward the target gene. Our work here provided a new switch for transcriptional control and orthogonal regulation strategies in cyanobacteria, which would promote the metabolic regulation for the cyanobacterial chassis in the future.

Vertical and horizontal quorum-sensing-based multicellular communications

Quorum sensing (QS) plays an important role in both natural and synthetic microbial systems. The complexity of QS entails multilayer controls, biomolecular crosstalk, and population-based interactions. In this review, we divide complex QS-based interactions into vertical and horizontal interactions. With respect to the former, we discuss QS-based interactions among phages, bacteria, and hosts in natural microbial systems, which are based on various QS signals and hormones. With regard to the latter, we highlight manipulations of QS-based interactions for multicomponent synthetic microbial consortia. We further present the recent and emerging applications of manipulating these interactions (collectively referred to as 'QS communication networks') in natural and synthetic microbiota. Finally, we identify key challenges in engineering diverse QS communication networks for various future applications.

Collective colony growth is optimized by branching pattern formation in Pseudomonas aeruginosa

Branching pattern formation is common in many microbes. Extensive studies have focused on addressing how such patterns emerge from local cell-cell and cell-environment interactions. However, little is known about whether and to what extent these patterns play a physiological role. Here, we consider the colonization of bacteria as an optimization problem to find the colony patterns that maximize colony growth efficiency under different environmental conditions. We demonstrate that Pseudomonas aeruginosa colonies develop branching patterns with characteristics comparable to the prediction of modeling; for example, colonies form thin branches in a nutrient-poor environment. Hence, the formation of branching patterns represents an optimal strategy for the growth of Pseudomonas aeruginosa colonies. The quantitative relationship between colony patterns and growth conditions enables us to develop a coarse-grained model to predict diverse colony patterns under more complex conditions, which we validated experimentally. Our results offer new insights into branching pattern formation as a problem-solving social behavior in microbes and enable fast and accurate predictions of complex spatial patterns in branching colonies.

Feasibility and reliability of sequential logic with gene regulatory networks

Gene regulatory networks exhibiting Boolean behaviour, e.g. AND, OR or XOR, have been routinely designed for years. However, achieving more sophisticated functions, such as control or computation, usually requires sequential circuits or so-called state machines. For such a circuit, outputs depend both on inputs and the current state of the system. Although it is still possible to design such circuits by analogy with digital electronics, some particularities of biology make the task trickier. The impact of two of them, namely the stochasticity of biological processes and the inhomogeneity in the response of regulation mechanisms, are assessed in this paper. Numerical simulations performed in two use cases point out high risks of malfunctions even for designed GRNs functional from a theoretical point of view. Several solutions to improve reliability of such systems are also discussed.

CRISPRi-Mediated NIMPLY Logic Gate for Fine-Tuning the Whole-Cell Sensing toward Simple Urine Glucose Detection

Whole-cell biosensors have been regarded as a prominent alternative to chemical and physical biosensors due to their renewability, environmental friendliness, and biocompatibility. However, there is still a lack of noninvasive measurements of urine glucose, which plays a vital role in monitoring the risk of diabetes in the healthcare system, via whole-cell biosensors. In this study, we characterized a glucose-inducible promoter and further enhanced the sensing performance using three genetic effectors, which encompassed ribozyme regulator (RiboJ), clustered regularly interspaced short palindromic repeat interference (CRISPRi), and plasmid-based T7RNA polymerase (PDT7), to develop the noninvasive glucose biosensor by fluorescent signal. As a result, RiboJ increased dynamic range to 2989 au, but declined signal-to-noise (S/N) to 1.59, while CRISPRi-mediated NIMPLY gate intensified both dynamic range to 5720 au and S/N to 4.58. The use of single PDT7 orthogonal with T7 promoter in cells (i.e., P strain) achieved a 44 180 au of dynamic range with S/N at 3.08. By coupling the PDT7 and NIMPLY-mediated CRISPRi, we constructed an optimum PIGAS strain with the highest S/N value of 4.95. Finally, we adopted the synthetic bacteria into a microdevice to afford an integrative and portable system for daily urine glucose inspection, which would be an alternative approach for medical diagnosis in the future.

A Standardized Inverter Package Borne by Broad Host Range Plasmids for Genetic Circuit Design in Gram-Negative Bacteria

Genetically encoded logic gates, especially inverters-NOT gates-are the building blocks for designing circuits, engineering biosensors, or decision-making devices in synthetic biology. However, the repertoire of inverters readily available for different species is rather limited. In this work, a large whole of NOT gates that was shown to function previously in a specific strain of Escherichia coli, was recreated as broad host range (BHR) collection of constructs assembled in low, medium, and high copy number plasmid backbones of the SEVA (Standard European Vector Architecture) collection. The input/output function of each of the gates was characterized and parametrized in the environmental bacterium and metabolic engineering chassis Pseudomonas putida. Comparisons of the resulting fluorescence cytometry data with those published for the same gates in Escherichia coli provided useful hints on the portability of the corresponding gates. The hereby described inverter package (20 different versions of 12 distinct gates) borne by BHR plasmids thus becomes a toolbox of choice for designing genetic circuitries in a variety of Gram-negative species other than E. coli.

Synthetic Biology Approaches in The Development of Engineered Therapeutic Microbes

Since the intimate relationship between microbes and human health has been uncovered, microbes have been in the spotlight as therapeutic targets for several diseases. Microbes contribute to a wide range of diseases, such as gastrointestinal disorders, diabetes and cancer. However, as host-microbiome interactions have not been fully elucidated, treatments such as probiotic administration and fecal transplantations that are used to modulate the microbial community often cause nonspecific results with serious safety concerns. As an alternative, synthetic biology can be used to rewire microbial networks such that the microbes can function as therapeutic agents. Genetic sensors can be transformed to detect biomarkers associated with disease occurrence and progression. Moreover, microbes can be reprogrammed to produce various therapeutic molecules from the host and bacterial proteins, such as cytokines, enzymes and signaling molecules, in response to a disturbed physiological state of the host. These therapeutic treatment systems are composed of several genetic parts, either identified in bacterial endogenous regulation systems or developed through synthetic design. Such genetic components are connected to form complex genetic logic circuits for sophisticated therapy. In this review, we discussed the synthetic biology strategies that can be used to construct engineered therapeutic microbes for improved microbiome-based treatment.

Architectures for Combined Transcriptional and Translational Resource Allocation Controllers

Recent work on engineering synthetic cellular circuitry has shown that non-regulatory interactions mediated by competition for gene expression resources can result in degraded performance or even failure. Transcriptional and translational resource allocation controllers based on orthogonal circuit-specific gene expression machinery have separately been shown to improve modularity and circuit performance. Here, we investigate the potential advantages, challenges, and design trade-offs involved in combining transcriptional and translational controllers into a "dual resource allocation control system." We show that separately functional, translational, and transcriptional controllers cannot generally be combined without extensive redesign. We analyze candidate architectures for direct design of dual resource allocation controllers and propose modifications to improve their performance (in terms of decoupling and expression level) and robustness. We show that dual controllers can be built that are composed only of orthogonal gene expression resources and demonstrate that such designs offer both superior performance and robustness characteristics.

Genetic Circuit Dynamics: Hazard and Glitch Analysis

Multiple input changes can cause unwanted switching variations, or glitches, in the output of genetic combinational circuits. These glitches can have drastic effects if the output of the circuit causes irreversible changes within or with other cells such as a cascade of responses, apoptosis, or the release of a pharmaceutical in an off-target tissue. Therefore, avoiding unwanted variation of a circuit's output can be crucial for the safe operation of a genetic circuit. This paper investigates what causes unwanted switching variations in combinational genetic circuits using hazard analysis and a new dynamic model generator. The analysis is done in previously built and modeled genetic circuits with known glitching behavior. The dynamic models generated not only predict the same steady states as previous models but can also predict the unwanted switching variations that have been observed experimentally. Multiple input changes may cause glitches due to propagation delays within the circuit. Modifying the circuit's layout to alter these delays may change the likelihood of certain glitches, but it cannot eliminate the possibility that the glitch may occur. In other words, function hazards cannot be eliminated. Instead, they must be avoided by restricting the allowed input changes to the system. Logic hazards, on the other hand, can be avoided using hazard-free logic synthesis. This paper demonstrates this by showing how a circuit designed using a popular genetic design automation tool can be redesigned to eliminate logic hazards.

Distributed Implementation of Boolean Functions by Transcriptional Synthetic Circuits

Starting in the early 2000s, sophisticated technologies have been developed for the rational construction of synthetic genetic networks that implement specified logical functionalities. Despite impressive progress, however, the scaling necessary in order to achieve greater computational power has been hampered by many constraints, including repressor toxicity and the lack of large sets of mutually orthogonal repressors. As a consequence, a typical circuit contains no more than roughly seven repressor-based gates per cell. A possible way around this scalability problem is to distribute the computation among multiple cell types, each of which implements a small subcircuit, which communicate among themselves using diffusible small molecules (DSMs). Examples of DSMs are those employed by quorum sensing systems in bacteria. This paper focuses on systematic ways to implement this distributed approach, in the context of the evaluation of arbitrary Boolean functions. The unique characteristics of genetic circuits and the properties of DSMs require the development of new Boolean synthesis methods, distinct from those classically used in electronic circuit design. In this work, we propose a fast algorithm to synthesize distributed realizations for any Boolean function, under constraints on the number of gates per cell and the number of orthogonal DSMs. The method is based on an exact synthesis algorithm to find the minimal circuit per cell, which in turn allows us to build an extensive database of Boolean functions up to a given number of inputs. For concreteness, we will specifically focus on circuits of up to 4 inputs, which might represent, for example, two chemical inducers and two light inputs at different frequencies. Our method shows that, with a constraint of no more than seven gates per cell, the use of a single DSM increases the total number of realizable circuits by at least 7.58-fold compared to centralized computation. Moreover, when allowing two DSM's, one can realize 99.995% of all possible 4-input Boolean functions, still with at most 7 gates per cell. The methodology introduced here can be readily adapted to complement recent genetic circuit design automation software. A toolbox that uses the proposed algorithm was created and made available at https://github.com/sontaglab/DBC/.

Precision design of stable genetic circuits carried in highly-insulated E. coli genomic landing pads

Genetic circuits have many applications, from guiding living therapeutics to ordering process in a bioreactor, but to be useful they have to be genetically stable and not hinder the host. Encoding circuits in the genome reduces burden, but this decreases performance and can interfere with native transcription. We have designed genomic landing pads in Escherichia coli at high-expression sites, flanked by ultrastrong double terminators. DNA payloads >8 kb are targeted to the landing pads using phage integrases. One landing pad is dedicated to carrying a sensor array, and two are used to carry genetic circuits. NOT/NOR gates based on repressors are optimized for the genome and characterized in the landing pads. These data are used, in conjunction with design automation software (Cello 2.0), to design circuits that perform quantitatively as predicted. These circuits require fourfold less RNA polymerase than when carried on a plasmid and are stable for weeks in a recA+ strain without selection. This approach enables the design of synthetic regulatory networks to guide cells in environments or for applications where plasmid use is infeasible.

Multiplexing cell-cell communication

The engineering of advanced multicellular behaviors, such as the programmed growth of biofilms or tissues, requires cells to communicate multiple aspects of physiological information. Unfortunately, few cell-cell communication systems have been developed for synthetic biology. Here, we engineer a genetically encoded channel selector device that enables a single communication system to transmit two separate intercellular conversations. Our design comprises multiplexer and demultiplexer sub-circuits constructed from a total of 12 CRISPRi-based transcriptional logic gates, an acyl homoserine lactone-based communication module, and three inducible promoters that enable small molecule control over the conversations. Experimentally parameterized mathematical models of the sub-components predict the steady state and dynamical performance of the full system. Multiplexed cell-cell communication has applications in synthetic development, metabolic engineering, and other areas requiring the coordination of multiple pathways among a community of cells.

Programming Escherichia coli to function as a digital display

Synthetic genetic circuits offer the potential to wield computational control over biology, but their complexity is limited by the accuracy of mathematical models. Here, we present advances that enable the complete encoding of an electronic chip in the DNA carried by Escherichia coli (E. coli). The chip is a binary-coded digit (BCD) to 7-segment decoder, associated with clocks and calculators, to turn on segments to visualize 0-9. Design automation is used to build seven strains, each of which contains a circuit with up to 12 repressors and two activators (totaling 63 regulators and 76,000 bp DNA). The inputs to each circuit represent the digit to be displayed (encoded in binary by four molecules), and output is the segment state, reported as fluorescence. Implementation requires an advanced gate model that captures dynamics, promoter interference, and a measure of total power usage (RNAP flux). This project is an exemplar of design automation pushing engineering beyond that achievable "by hand", essential for realizing the potential of biology.