Foundations for the design and implementation of synthetic genetic circuits

Customizable gene sensing and response without altering endogenous coding sequences

Synthetic biology aims to modify cellular behaviors by implementing genetic circuits that respond to changes in cell state. Integrating genetic biosensors into endogenous gene coding sequences using clustered regularly interspaced short palindromic repeats and Cas9 enables interrogation of gene expression dynamics in the appropriate chromosomal context. However, embedding a biosensor into a gene coding sequence may unpredictably alter endogenous gene regulation. To address this challenge, we developed an approach to integrate genetic biosensors into endogenous genes without modifying their coding sequence by inserting into their terminator region single-guide RNAs that activate downstream circuits. Sensor dosage responses can be fine-tuned and predicted through a mathematical model. We engineered a cell stress sensor and actuator in CHO-K1 cells that conditionally activates antiapoptotic protein BCL-2 through a downstream circuit, thereby increasing cell survival under stress conditions. Our gene sensor and actuator platform has potential use for a wide range of applications that include biomanufacturing, cell fate control and cell-based therapeutics.

CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution

Predicting the strength of promoters and guiding their directed evolution is a crucial task in synthetic biology. This approach significantly reduces the experimental costs in conventional promoter engineering. Previous studies employing machine learning or deep learning methods have shown some success in this task, but their outcomes were not satisfactory enough, primarily due to the neglect of evolutionary information. In this paper, we introduce the Chaos-Attention net for Promoter Evolution (CAPE) to address the limitations of existing methods. We comprehensively extract evolutionary information within promoters using merged chaos game representation and process the overall information with modified DenseNet and Transformer structures. Our model achieves state-of-the-art results on two kinds of distinct tasks related to prokaryotic promoter strength prediction. The incorporation of evolutionary information enhances the model's accuracy, with transfer learning further extending its adaptability. Furthermore, experimental results confirm CAPE's efficacy in simulating in silico directed evolution of promoters, marking a significant advancement in predictive modeling for prokaryotic promoter strength. Our paper also presents a user-friendly website for the practical implementation of in silico directed evolution on promoters. The source code implemented in this study and the instructions on accessing the website can be found in our GitHub repository https://github.com/BobYHY/CAPE.

Development of transcriptional factor-based whole-cell biosensors to monitor and degrade antibiotics using mutant cells obtained via adaptive laboratory evolution

With the widespread use of antibiotics and increasing environmental concerns regarding antibiotic abuse, the detection and degradation of antibiotic residues in various samples has become a pressing issue. Transcriptional factor (TF)-based whole-cell biosensors are low-cost, easy-to-use, and flexible tools for detecting chemicals and controlling bioprocesses. However, because of cytotoxicity caused by antibiotics, the application of such biosensors is limited in the presence of antibiotics. In this study, we used antibiotic-tolerant mutants obtained via adaptive laboratory evolution (ALE) to develop TF-based whole-cell biosensors for antibiotic monitoring and degradation. The biosensors had high performance and stability in detecting relatively high concentrations of tetracycline (Tc) and nisin. The ALE mutant-based Tc biosensor exhibited a 10-fold larger linear detection range than the wild-type strain-based biosensor. Then, the Tc biosensor was employed to detect residual amounts of Tc in mouse stool, serum, and urine samples and facilitate Tc biodegradation in mouse stool, demonstrating its high utility. Considering that ALE has been demonstrated to enhance cell tolerance to various toxic chemicals, our strategy might facilitate the development of whole-cell biosensors for most antibiotics and other toxic ligands.

In vitro generation of genetic diversity for directed evolution by error-prone artificial DNA synthesis

Generating genetic diversity lies at the heart of directed evolution which has been widely used to engineer genetic parts and gene circuits in synthetic biology. With the ever-expanding application of directed evolution, different approaches of generating genetic diversity are required to enrich the traditional toolbox. Here we show in vitro generation of genetic diversity for directed evolution by error-prone artificial DNA synthesis (epADS). This approach comprises a three-step process which incorporates base errors randomly generated during chemical synthesis of oligonucleotides under specific conditions into the target DNA. Through this method, 200 ~ 4000 folds of diversification in fluorescent strength have been achieved in genes encoding fluorescent proteins. EpADS has also been successfully used to diversify regulatory genetic parts, synthetic gene circuits and even increase microbial tolerance to carbenicillin in a short time period. EpADS would be an alternative tool for directed evolution which may have useful applications in synthetic biology.

A molecular proximity sensor based on an engineered, dual-component guide RNA

One of the goals of synthetic biology is to enable the design of arbitrary molecular circuits with programmable inputs and outputs. Such circuits bridge the properties of electronic and natural circuits, processing information in a predictable manner within living cells. Genome editing is a potentially powerful component of synthetic molecular circuits, whether for modulating the expression of a target gene or for stably recording information to genomic DNA. However, programming molecular events such as protein-protein interactions or induced proximity as triggers for genome editing remains challenging. Here we demonstrate a strategy termed "P3 editing", which links protein-protein proximity to the formation of a functional CRISPRCas9 dual-component guide RNA. By engineering the crRNA:tracrRNA interaction, we demonstrate that various known protein-protein interactions, as well as the chemically-induced dimerization of protein domains, can be used to activate prime editing or base editing in human cells. Additionally, we explore how P3 editing can incorporate outputs from ADAR-based RNA sensors, potentially allowing specific RNAs to induce specific genome edits within a larger circuit. Our strategy enhances the controllability of CRISPR-based genome editing, facilitating its use in synthetic molecular circuits deployed in living cells.

Synthetic microbe-to-plant communication channels

Plants and microbes communicate to collaborate to stop pests, scavenge nutrients, and react to environmental change. Microbiota consisting of thousands of species interact with each other and plants using a large chemical language that is interpreted by complex regulatory networks. In this work, we develop modular interkingdom communication channels, enabling bacteria to convey environmental stimuli to plants. We introduce a "sender device" in Pseudomonas putida and Klebsiella pneumoniae, that produces the small molecule p-coumaroyl-homoserine lactone (pC-HSL) when the output of a sensor or circuit turns on. This molecule triggers a "receiver device" in the plant to activate gene expression. We validate this system in Arabidopsis thaliana and Solanum tuberosum (potato) grown hydroponically and in soil, demonstrating its modularity by swapping bacteria that process different stimuli, including IPTG, aTc and arsenic. Programmable communication channels between bacteria and plants will enable microbial sentinels to transmit information to crops and provide the building blocks for designing artificial consortia.

Tailored bacteria tackling with environmental mercury: Inspired by natural mercuric detoxification operons

Mercury (Hg) and its inorganic and organic compounds significantly threaten the ecosystem and human health. However, the natural and anthropogenic Hg environmental inputs exceed 5000 metric tons annually. Hg is usually discharged in elemental or ionic forms, accumulating in surface water and sediments where Hg-methylating microbes-mediated biotransformation occurs. Microbial genetic factors such as the mer operon play a significant role in the complex Hg biogeochemical cycle. Previous reviews summarize the fate of environmental Hg, its biogeochemistry, and the mechanism of bacterial Hg resistance. This review mainly focuses on the mer operon and its components in detecting, absorbing, bioaccumulating, and detoxifying environmental Hg. Four components of the mer operon, including the MerR regulator, divergent mer promoter, and detoxification factors MerA and MerB, are rare bio-parts for assembling synthetic bacteria, which tackle pollutant Hg. Bacteria are designed to integrate synthetic biology, protein engineering, and metabolic engineering. In summary, this review highlights that designed bacteria based on the mer operon can potentially sense and bioremediate pollutant Hg in a green and low-cost manner.

A Genetic Circuit Design for Targeted Viral RNA Degradation

Advances in synthetic biology have led to the design of biological parts that can be assembled in different ways to perform specific functions. For example, genetic circuits can be designed to execute specific therapeutic functions, including gene therapy or targeted detection and the destruction of invading viruses. Viral infections are difficult to manage through drug treatment. Due to their high mutation rates and their ability to hijack the host's ribosomes to make viral proteins, very few therapeutic options are available. One approach to addressing this problem is to disrupt the process of converting viral RNA into proteins, thereby disrupting the mechanism for assembling new viral particles that could infect other cells. This can be done by ensuring precise control over the abundance of viral RNA (vRNA) inside host cells by designing biological circuits to target vRNA for degradation. RNA-binding proteins (RBPs) have become important biological devices in regulating RNA processing. Incorporating naturally upregulated RBPs into a gene circuit could be advantageous because such a circuit could mimic the natural pathway for RNA degradation. This review highlights the process of viral RNA degradation and different approaches to designing genetic circuits. We also provide a customizable template for designing genetic circuits that utilize RBPs as transcription activators for viral RNA degradation, with the overall goal of taking advantage of the natural functions of RBPs in host cells to activate targeted viral RNA degradation.

Engineering E. coli strains using antibiotic-resistance-gene-free plasmids

We created a generalizable pipeline for antibiotic-resistance-gene-free plasmid (ARGFP)-based cloning using a dual auxotrophic- and essential-gene-based selection strategy. We use auxotrophic selection to construct plasmids in engineered E. coli DH10B cloning strains and both auxotrophic- and essential-gene-based selection to (1) select for recombinant strains and (2) maintain a plasmid in E. coli Nissle 1917, a common chassis for engineered probiotic applications, and E. coli MG1655, the laboratory "wild-type" E. coli strain. We show that our approach has comparable efficiency to that of antibiotic-resistance-gene-based cloning. We also show that the double-knockout Nissle and MG1655 strains are simple to transform with plasmids of interest. Notably, we show that the engineered Nissle strains are amenable to long-term plasmid maintenance in repeated culturing as well as in the mouse gut, demonstrating the potential for broad applications while minimizing the risk of antibiotic resistance spread via horizontal gene transfer.

Engineering synthetic phosphorylation signaling networks in human cells

Protein phosphorylation signaling networks play a central role in how cells sense and respond to their environment. Here, we describe the engineering of artificial phosphorylation networks in which "push-pull" motifs-reversible enzymatic phosphorylation cycles consisting of opposing kinase and phosphatase activities-are assembled from modular protein domain parts and then wired together to create synthetic phosphorylation circuits in human cells. We demonstrate that the composability of our design scheme enables model-guided tuning of circuit function and the ability to make diverse network connections; synthetic phosphorylation circuits can be coupled to upstream cell surface receptors to enable fast-timescale sensing of extracellular ligands, while downstream connections can regulate gene expression. We leverage these capabilities to engineer cell-based cytokine controllers that dynamically sense and suppress activated T cells. Our work introduces a generalizable approach for designing and building phosphorylation signaling circuits that enable user-defined sense-and-respond function for diverse biosensing and therapeutic applications.

Construction of stable microbial consortia for effective biochemical synthesis

Microbial consortia can complete otherwise arduous tasks through the cooperation of multiple microbial species. This concept has been applied to produce commodity chemicals, natural products, and biofuels. However, metabolite incompatibility and growth competition can make the microbial composition unstable, and fluctuating microbial populations reduce the efficiency of chemical production. Thus, controlling the populations and regulating the complex interactions between different strains are challenges in constructing stable microbial consortia. This Review discusses advances in synthetic biology and metabolic engineering to control social interactions within microbial cocultures, including substrate separation, byproduct elimination, crossfeeding, and quorum-sensing circuit design. Additionally, this Review addresses interdisciplinary strategies to improve the stability of microbial consortia and provides design principles for microbial consortia to enhance chemical production.

Applications of synthetic biology in medical and pharmaceutical fields

Synthetic biology aims to design or assemble existing bioparts or bio-components for useful bioproperties. During the past decades, progresses have been made to build delicate biocircuits, standardized biological building blocks and to develop various genomic/metabolic engineering tools and approaches. Medical and pharmaceutical demands have also pushed the development of synthetic biology, including integration of heterologous pathways into designer cells to efficiently produce medical agents, enhanced yields of natural products in cell growth media to equal or higher than that of the extracts from plants or fungi, constructions of novel genetic circuits for tumor targeting, controllable releases of therapeutic agents in response to specific biomarkers to fight diseases such as diabetes and cancers. Besides, new strategies are developed to treat complex immune diseases, infectious diseases and metabolic disorders that are hard to cure via traditional approaches. In general, synthetic biology brings new capabilities to medical and pharmaceutical researches. This review summarizes the timeline of synthetic biology developments, the past and present of synthetic biology for microbial productions of pharmaceutics, engineered cells equipped with synthetic DNA circuits for diagnosis and therapies, live and auto-assemblied biomaterials for medical treatments, cell-free synthetic biology in medical and pharmaceutical fields, and DNA engineering approaches with potentials for biomedical applications.

Integration of multiple stress signals in plants using synthetic Boolean logic gates

As photosynthetic organisms, plants have a potential role in the sustainable production of high-value products such as medicines, biofuels, and chemical feedstocks. With effective engineering using synthetic biology approaches, plant-based platforms could conceivably be designed to minimize the costs and waste of production for materials that would otherwise be uneconomical. Additionally, modern agricultural crops could be engineered to be more productive, resilient, or restorative in different or rapidly changing environments and climates. Information-processing genetic devices and circuits containing multiple interacting parts that behave predictably must be developed to achieve these complex goals. A genetic Boolean AND logic gate is a device that computes the presence or absence of two inputs (signals, stimuli) and produces an output (response) only when both inputs are present. We optimized individual genetic components and used synthetic protein heterodimerizing domains to rationally assemble genetic AND logic gates that integrate two hormonal inputs in transgenic Arabidopsis thaliana plants. These AND gates produce an output only in the presence of both abscisic acid and auxin but not when either or neither hormone is present. The AND logic gate can also integrate signals resulting from two plant stresses, cold temperature and bacterial infection, to produce a response. The design principles used here are generalizable, and, therefore, multiple orthogonal AND gates could be assembled and rationally layered to process complex genetic information in plants. These layered logic gates may be used in genetic circuits to probe fundamental questions in plant biology, such as hormonal crosstalk, in addition to plant engineering for bioproduction.

Genetic circuits in microbial biosensors for heavy metal detection in soil and water

With the rapid population growth, the world is witnessing an ever-increasing demand for energy and natural resources. Consequently, soil, air, and water are polluted with diverse pollutants, including heavy metals (HM). The detection of heavy metals is necessary to remediate them, which is achieved with biosensors. Initially, these HM were detected using atomic absorption spectroscopy (AAS), emission spectroscopy, mass spectrometry, gas chromatography etc., but these were costly and time consuming which further paved a way for microbe-based biosensors. The development of genetic circuits for microbe-based biosensors has become more popular in recent years for heavy metal detection. In this review, we have especially discussed the various types of genetic circuits such as toggle switches, logic gates, and amplification modules used in these biosensors as they are used to enhance sensitivity and specificity. Genetic circuits also allow for rapid and multiple analyte detection at the same time. The use of microbial biosensors for the detection of HM in the soil as well as the water is also described below. Although with a higher success rate than classical biosensors, these microbial biosensors still have some drawbacks like bioavailability and size of the analyte which are needed to be addressed.

Ultra-high throughput mapping of genetic design space

Massively parallel genetic screens have been used to map sequence-to-function relationships for a variety of genetic elements. However, because these approaches only interrogate short sequences, it remains challenging to perform high throughput (HT) assays on constructs containing combinations of sequence elements arranged across multi-kb length scales. Overcoming this barrier could accelerate synthetic biology; by screening diverse gene circuit designs, "composition-to-function" mappings could be created that reveal genetic part composability rules and enable rapid identification of behavior-optimized variants. Here, we introduce CLASSIC, a generalizable genetic screening platform that combines long- and short-read next-generation sequencing (NGS) modalities to quantitatively assess pooled libraries of DNA constructs of arbitrary length. We show that CLASSIC can measure expression profiles of >10 ⁵ drug-inducible gene circuit designs (ranging from 6-9 kb) in a single experiment in human cells. Using statistical inference and machine learning (ML) approaches, we demonstrate that data obtained with CLASSIC enables predictive modeling of an entire circuit design landscape, offering critical insight into underlying design principles. Our work shows that by expanding the throughput and understanding gained with each design-build-test-learn (DBTL) cycle, CLASSIC dramatically augments the pace and scale of synthetic biology and establishes an experimental basis for data-driven design of complex genetic systems.

Bacteria-based bioactive materials for cancer imaging and therapy

Owing to the unique biological functions, bacteria as biological materials have been widely used in biomedical field. With advances in biotechnology and nanotechnology, various bacteria-based bioactive materials were developed for cancer imaging and therapy. In this review, different types of bacteria-based bioactive materials and their construction strategies were summarized. The advantages and property-function relationship of bacteria-based bioactive materials were described. Representative researches of bacteria-based bioactive materials in cancer imaging and therapy were illustrated, revealing general ideas for their construction. Also, limitation and challenges of bacteria-based bioactive materials in cancer research were discussed.

Overreact, an in silico lab: Automative quantum chemical microkinetic simulations for complex chemical reactions

Today's demand for precisely predicting chemical reactions from first principles requires research to go beyond Gibbs' free energy diagrams and consider other effects such as concentrations and quantum tunneling. The present work introduces overreact, a novel Python package for propagating chemical reactions over time using data from computational chemistry only. The overreact code infers all differential equations and parameters from a simple input that consists of a set of chemical equations and quantum chemistry package outputs for each chemical species. We evaluate some applications from the literature: gas-phase eclipsed-staggered isomerization of ethane, gas-phase umbrella inversion of ammonia, gas-phase degradation of methane by chlorine radical, and three solvation-phase reactions. Furthermore, we comment on a simple solvation-phase acid-base equilibrium. We show how it is possible to achieve reaction profiles and information matching experiments.

Function-based classification of hazardous biological sequences: Demonstration of a new paradigm for biohazard assessments

Bioengineering applies analytical and engineering principles to identify functional biological building blocks for biotechnology applications. While these building blocks are leveraged to improve the human condition, the lack of simplistic, machine-readable definition of biohazards at the function level is creating a gap for biosafety practices. More specifically, traditional safety practices focus on the biohazards of known pathogens at the organism-level and may not accurately consider novel biodesigns with engineered functionalities at the genetic component-level. This gap is motivating the need for a paradigm shift from organism-centric procedures to function-centric biohazard identification and classification practices. To address this challenge, we present a novel methodology for classifying biohazards at the individual sequence level, which we then compiled to distinguish the biohazardous property of pathogenicity at the whole genome level. Our methodology is rooted in compilation of hazardous functions, defined as a set of sequences and associated metadata that describe coarse-level functions associated with pathogens (e.g., adherence, immune subversion). We demonstrate that the resulting database can be used to develop hazardous “fingerprints” based on the functional metadata categories. We verified that these hazardous functions are found at higher levels in pathogens compared to non-pathogens, and hierarchical clustering of the fingerprints can distinguish between these two groups. The methodology presented here defines the hazardous functions associated with bioengineering functional building blocks at the sequence level, which provide a foundational framework for classifying biological hazards at the organism level, thus leading to the improvement and standardization of current biosecurity and biosafety practices.

Engineering genetic circuits: advancements in genetic design automation tools and standards for synthetic biology

Synthetic biology (SynBio) is a field at the intersection of biology and engineering. Inspired by engineering principles, researchers use defined parts to build functionally defined biological circuits. Genetic design automation (GDA) allows scientists to design, model, and analyze their genetic circuits in silico before building them in the lab, saving time, and resources in the process. Establishing SynBio's future is dependent on GDA, since the computational approach opens the field to a broad, interdisciplinary community. However, challenges with part libraries, standards, and software tools are currently stalling progress in the field. This review first covers recent advancements in GDA, followed by an assessment of the challenges ahead, and a proposed automated genetic design workflow for the future.

Multiscale engineering of microbial cell factories: A step forward towards sustainable natural products industry

Microbial cell factories (bacteria and fungi) are the leading producers of beneficial natural products such as lycopene, carotene, herbal medicine, and biodiesel etc. These microorganisms are considered efficient due to their effective bioprocessing strategy (monoculture- and consortial-based approach) under distinct processing conditions. Meanwhile, the advancement in genetic and process optimization techniques leads to enhanced biosynthesis of natural products that are known functional ingredients with numerous applications in the food, cosmetic and medical industries. Natural consortia and monoculture thrive in nature in a small proportion, such as wastewater, food products, and soils. In similitude to natural consortia, it is possible to engineer artificial microbial consortia and program their behaviours via synthetic biology tools. Therefore, this review summarizes the optimization of genetic and physicochemical parameters of the microbial system for improved production of natural products. Also, this review presents a brief history of natural consortium and describes the functional properties of monocultures. This review focuses on synthetic biology tools that enable new approaches to design synthetic consortia; and highlights the syntropic interactions that determine the performance and stability of synthetic consortia. In particular, the effect of processing conditions and advanced genetic techniques to improve the productibility of both monoculture and consortial based systems have been greatly emphasized. In this context, possible strategies are also discussed to give an insight into microbial engineering for improved production of natural products in the future. In summary, it is concluded that the coupling of genomic modifications with optimum physicochemical factors would be promising for producing a robust microbial cell factory that shall contribute to the increased production of natural products.

A modular RNA interference system for multiplexed gene regulation

The rational design and realisation of simple-to-use genetic control elements that are modular, orthogonal and robust is essential to the construction of predictable and reliable biological systems of increasing complexity. To this effect, we introduce modular Artificial RNA interference (mARi), a rational, modular and extensible design framework that enables robust, portable and multiplexed post-transcriptional regulation of gene expression in Escherichia coli. The regulatory function of mARi was characterised in a range of relevant genetic contexts, demonstrating its independence from other genetic control elements and the gene of interest, and providing new insight into the design rules of RNA based regulation in E. coli, while a range of cellular contexts also demonstrated it to be independent of growth-phase and strain type. Importantly, the extensibility and orthogonality of mARi enables the simultaneous post-transcriptional regulation of multi-gene systems as both single-gene cassettes and poly-cistronic operons. To facilitate adoption, mARi was designed to be directly integrated into the modular BASIC DNA assembly framework. We anticipate that mARi-based genetic control within an extensible DNA assembly framework will facilitate metabolic engineering, layered genetic control, and advanced genetic circuit applications.

Mathematical Modelling in Plant Synthetic Biology

Mathematical modelling techniques are integral to current research in plant synthetic biology. Modelling approaches can provide mechanistic understanding of a system, allowing predictions of behaviour and thus providing a tool to help design and analyse biological circuits. In this chapter, we provide an overview of mathematical modelling methods and their significance for plant synthetic biology. Starting with the basics of dynamics, we describe the process of constructing a model over both temporal and spatial scales and highlight crucial approaches, such as stochastic modelling and model-based design. Next, we focus on the model parameters and the techniques required in parameter analysis. We then describe the process of selecting a model based on tests and criteria and proceed to methods that allow closer analysis of the system's behaviour. Finally, we highlight the importance of uncertainty in modelling approaches and how to deal with a lack of knowledge, noisy data, and biological variability; all aspects that play a crucial role in the cooperation between the experimental and modelling components. Overall, this chapter aims to illustrate the importance of mathematical modelling in plant synthetic biology, providing an introduction for those researchers who are working with or working on modelling techniques.

A single layer artificial neural network type architecture with molecular engineered bacteria for reversible and irreversible computing

Here, we adapted the basic concept of artificial neural networks (ANNs) and experimentally demonstrate a broadly applicable single layer ANN type architecture with molecular engineered bacteria to perform complex irreversible computing like multiplexing, de-multiplexing, encoding, decoding, majority functions, and reversible computing like Feynman and Fredkin gates. The encoder and majority functions and reversible computing were experimentally implemented within living cells for the first time. We created cellular devices, which worked as artificial neuro-synapses in bacteria, where input chemical signals were linearly combined and processed through a non-linear activation function to produce fluorescent protein outputs. To create such cellular devices, we established a set of rules by correlating truth tables, mathematical equations of ANNs, and cellular device design, which unlike cellular computing, does not require a circuit diagram and the equation directly correlates the design of the cellular device. To our knowledge this is the first adaptation of ANN type architecture with engineered cells. This work may have significance in establishing a new platform for cellular computing, reversible computing and in transforming living cells as ANN-enabled hardware.

Acoustofluidic medium exchange for preparation of electrocompetent bacteria using channel wall trapping

Comprehensive integration of process steps into a miniaturised version of synthetic biology workflows remains a crucial task in automating the design of biosystems. However, each of these process steps has specific demands with respect to the environmental conditions, including in particular the composition of the surrounding fluid, which makes integration cumbersome. As a case in point, transformation, i.e. reprogramming of bacteria by delivering exogenous genetic material (such as DNA) into the cytoplasm, is a key process in molecular engineering and modern biotechnology in general. Transformation is often performed by electroporation, i.e. creating pores in the membrane using electric shocks in a low conductivity environment. However, cell preparation for electroporation can be cumbersome as it requires the exchange of growth medium (high-conductivity) for low-conductivity medium, typically performed via multiple time-intensive centrifugation steps. To simplify and miniaturise this step, we developed an acoustofluidic device capable of trapping the bacterium Escherichia coli non-invasively for subsequent exchange of medium, which is challenging in acoustofluidic devices due to detrimental acoustic streaming effects. With an improved etching process, we were able to produce a thin wall between two microfluidic channels, which, upon excitation, can generate streaming fields that complement the acoustic radiation force and therefore can be utilised for trapping of bacteria. Our novel design robustly traps Escherichia coli at a flow rate of 10 μL min^-1 and has a cell recovery performance of 47 ± 3% after washing the trapped cells. To verify that the performance of the medium exchange device is sufficient, we tested the electrocompetence of the recovered cells in a standard transformation procedure and found a transformation efficiency of 8 × 10⁵ CFU per μg of plasmid DNA. Our device is a low-volume alternative to centrifugation-based methods and opens the door for miniaturisation of a plethora of microbiological and molecular engineering protocols.

Microbial production of small peptide: pathway engineering and synthetic biology

Small peptides are a group of natural products with low molecular weights and complex structures. The diverse structures of small peptides endow them with broad bioactivities and suggest their potential therapeutic use in the medical field. The remaining challenge is methods to address the main limitations, namely (i) the low amount of available small peptides from natural sources, and (ii) complex processes required for traditional chemical synthesis. Therefore, harnessing microbial cells as workhorse appears to be a promising approach to synthesize these bioactive peptides. As an emerging engineering technology, synthetic biology aims to create standard, well-characterized and controllable synthetic systems for the biosynthesis of natural products. In this review, we describe the recent developments in the microbial production of small peptides. More importantly, synthetic biology approaches are considered for the production of small peptides, with an emphasis on chassis cells, the evolution of biosynthetic pathways, strain improvements and fermentation.

Engineering living therapeutics with synthetic biology

The steadfast advance of the synthetic biology field has enabled scientists to use genetically engineered cells, instead of small molecules or biologics, as the basis for the development of novel therapeutics. Cells endowed with synthetic gene circuits can control the localization, timing and dosage of therapeutic activities in response to specific disease biomarkers and thus represent a powerful new weapon in the fight against disease. Here, we conceptualize how synthetic biology approaches can be applied to programme living cells with therapeutic functions and discuss the advantages that they offer over conventional therapies in terms of flexibility, specificity and predictability, as well as challenges for their development. We present notable advances in the creation of engineered cells that harbour synthetic gene circuits capable of biological sensing and computation of signals derived from intracellular or extracellular biomarkers. We categorize and describe these developments based on the cell scaffold (human or microbial) and the site at which the engineered cell exerts its therapeutic function within its human host. The design of cell-based therapeutics with synthetic biology is a rapidly growing strategy in medicine that holds great promise for the development of effective treatments for a wide variety of human diseases.

Control of mammalian cell-based devices with genetic programming

Synthetic biology increasingly enables the construction of sophisticated functions in mammalian cells. A particularly promising frontier combines concepts drawn from industrial process control engineering-which is used to confer and balance properties such as stability and efficiency-with understanding as to how living systems have evolved to perform similar tasks with biological components. In this review, we first survey the state-of-the-art for both technologies and strategies available for genetic programming in mammalian cells. We then discuss recent progress in implementing programming objectives inspired by engineered and natural control mechanisms. Finally, we consider the transformative role of model-guided design in the present and future construction of customized mammalian cell functions for applications in biotechnology, medicine, and fundamental research.

The Tryptophan-Induced tnaC Ribosome Stalling Sequence Exposes High Amino Acid Cross-Talk That Can Be Mitigated by Removal of NusB for Higher Orthogonality

A growing number of engineered synthetic circuits have employed biological parts coupling transcription and translation in bacterial systems to control downstream gene expression. One such example, the leader sequence of the tryptophanase (tna) operon, is a transcription-translation system commonly employed as an l-tryptophan inducible circuit controlled by ribosome stalling. While induction of the tna operon has been well-characterized in response to l-tryptophan, cross-talk of this modular component with other metabolites in the cell, such as other naturally occurring amino acids, has been less explored. In this study, we investigated the impact of natural metabolites and E. coli host factors on induction of the tna leader sequence. To do so, we constructed and biochemically validated an experimental assay using the tna operon leader sequence to assess differential regulation of transcription elongation and translation in response to l-tryptophan. Operon induction was then assessed following addition of each of the 20 naturally occurring amino acids to discover that several additional amino acids (e.g., l-alanine, l-cysteine, l-glycine, l-methionine, and l-threonine) also induce expression of the tna leader sequence. Following characterization of dose-dependent induction by l-cysteine relative to l-tryptophan, the effect on induction by single gene knockouts of protein factors associated with transcription and/or translation were interrogated. Our results implicate the endogenous cellular protein, NusB, as an important factor associated with induction of the operon by the alternative amino acids. As such, removal of the nusB gene from strains intended for tryptophan-sensing utilizing the tna leader region reduces amino acid cross-talk, resulting in enhanced orthogonal control of this commonly used synthetic system.

Recent advances in tuning the expression and regulation of genes for constructing microbial cell factories

To overcome environmental problems caused by the use of fossil resources, microbial cell factories have become a promising technique for the sustainable and eco-friendly development of valuable products from renewable resources. Constructing microbial cell factories with high titers, yields, and productivity requires a balance between growth and production; to this end, tuning gene expression and regulation is necessary to optimise and precisely control complicated metabolic fluxes. In this article, we review the current trends and advances in tuning gene expression and regulation and consider their engineering at each of the three stages of gene regulation: genomic, mRNA, and protein. In particular, the technological approaches utilised in a diverse range of genetic-engineering-based tools for the construction of microbial cell factories are reviewed and representative applications of these strategies are presented. Finally, the prospects for strategies and systems for tuning gene expression and regulation are discussed.

Living Things Are Not (20th Century) Machines: Updating Mechanism Metaphors in Light of the Modern Science of Machine Behavior

One of the most useful metaphors for driving scientific and engineering progress has been that of the “machine.” Much controversy exists about the applicability of this concept in the life sciences. Advances in molecular biology have revealed numerous design principles that can be harnessed to understand cells from an engineering perspective, and build novel devices to rationally exploit the laws of chemistry, physics, and computation. At the same time, organicists point to the many unique features of life, especially at larger scales of organization, which have resisted decomposition analysis and artificial implementation. Here, we argue that much of this debate has focused on inessential aspects of machines – classical properties which have been surpassed by advances in modern Machine Behavior and no longer apply. This emerging multidisciplinary field, at the interface of artificial life, machine learning, and synthetic bioengineering, is highlighting the inadequacy of existing definitions. Key terms such as machine, robot, program, software, evolved, designed, etc., need to be revised in light of technological and theoretical advances that have moved past the dated philosophical conceptions that have limited our understanding of both evolved and designed systems. Moving beyond contingent aspects of historical and current machines will enable conceptual tools that embrace inevitable advances in synthetic and hybrid bioengineering and computer science, toward a framework that identifies essential distinctions between fundamental concepts of devices and living agents. Progress in both theory and practical applications requires the establishment of a novel conception of “machines as they could be,” based on the profound lessons of biology at all scales. We sketch a perspective that acknowledges the remarkable, unique aspects of life to help re-define key terms, and identify deep, essential features of concepts for a future in which sharp boundaries between evolved and designed systems will not exist.

2D printed multicellular devices performing digital and analogue computation

Much effort has been expended on building cellular computational devices for different applications. Despite the significant advances, there are still several addressable restraints to achieve the necessary technological transference. These improvements will ease the development of end-user applications working out of the lab. In this study, we propose a methodology for the construction of printable cellular devices, digital or analogue, for different purposes. These printable devices are designed to work in a 2D surface, in which the circuit information is encoded in the concentration of a biological signal, the so-called carrying signal. This signal diffuses through the 2D surface and thereby interacts with different device components. These components are distributed in a specific spatial arrangement and perform the computation by modulating the level of the carrying signal in response to external inputs, determining the final output. For experimental validation, 2D cellular circuits are printed on a paper surface by using a set of cellular inks. As a proof-of-principle, we have printed and analysed both digital and analogue circuits using the same set of cellular inks but with different spatial topologies. The proposed methodology can open the door to a feasible and reliable industrial production of cellular circuits for multiple applications.

Synthetic biology circuits are finding application in a wide range of computational devices, such as contaminant detection. Here, the authors design 2D paper circuits in which the spatial orientation of the cellular components specifies function.

Model-guided design of mammalian genetic programs

Genetically engineering cells to perform customizable functions is an emerging frontier with numerous technological and translational applications. However, it remains challenging to systematically engineer mammalian cells to execute complex functions. To address this need, we developed a method enabling accurate genetic program design using high-performing genetic parts and predictive computational models. We built multifunctional proteins integrating both transcriptional and posttranslational control, validated models for describing these mechanisms, implemented digital and analog processing, and effectively linked genetic circuits with sensors for multi-input evaluations. The functional modularity and compositional versatility of these parts enable one to satisfy a given design objective via multiple synonymous programs. Our approach empowers bioengineers to predictively design mammalian cellular functions that perform as expected even at high levels of biological complexity.

Plasmid Vectors for in Vivo Selection-Free Use with the Probiotic E. coli Nissle 1917

Escherichia coli Nissle 1917 (EcN) is a probiotic bacterium, commonly employed to treat certain gastrointestinal disorders. It is fast emerging as an important target for the development of therapeutic engineered bacteria, benefiting from the wealth of knowledge of E. coli biology and ease of manipulation. Bacterial synthetic biology projects commonly utilize engineered plasmid vectors, which are simple to engineer and can reliably achieve high levels of protein expression. However, plasmids typically require antibiotics for maintenance, and the administration of an antibiotic is often incompatible with in vivo experimentation or treatment. EcN natively contains plasmids pMUT1 and pMUT2, which have no known function but are stable within the bacteria. Here, we describe the development of the pMUT plasmids into a robust platform for engineering EcN for in vivo experimentation, alongside a CRISPR-Cas9 system to remove the native plasmids. We systematically engineered both pMUT plasmids to contain selection markers, fluorescent markers, temperature sensitive expression, and curli secretion systems to export a customizable functional material into the extracellular space. We then demonstrate that the engineered plasmids were maintained in bacteria as the engineered bacteria pass through the mouse GI tract without selection, and that the secretion system remains functional, exporting functionalized curli proteins into the gut. Our plasmid system presents a platform for the rapid development of therapeutic EcN bacteria.

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.

A Practical Step-by-Step Guide for Quantifying Retroactivity in Gene Networks

One of the fundamental properties of engineered large-scale complex systems is modularity. In synthetic biology, genetic parts exhibit context-dependent behavior. Here, we describe and quantify a major source of such behavior: retroactivity. In particular, we provide a step-by-step guide for characterizing retroactivity to restore the modular description of genetic modules. Additionally, we also discuss how retroactivity can be leveraged to quantify and maximize robustness to perturbations due to interconnection of genetic modules.

Re-engineering Plant Phenylpropanoid Metabolism With the Aid of Synthetic Biosensors

Phenylpropanoids comprise a large class of specialized plant metabolites with many important applications, including pharmaceuticals, food nutrients, colorants, fragrances, and biofuels. Therefore, much effort has been devoted to manipulating their biosynthesis to produce high yields in a more controlled manner in microbial and plant systems. However, current strategies are prone to significant adverse effects due to pathway complexity, metabolic burden, and metabolite bioactivity, which still hinder the development of tailor-made phenylpropanoid biofactories. This gap could be addressed by the use of biosensors, which are molecular devices capable of sensing specific metabolites and triggering a desired response, as a way to sense the pathway's metabolic status and dynamically regulate its flux based on specific signals. Here, we provide a brief overview of current research on synthetic biology and metabolic engineering approaches to control phenylpropanoid synthesis and phenylpropanoid-related biosensors, advocating for the use of biosensors and genetic circuits as a step forward in plant synthetic biology to develop autonomously-controlled phenylpropanoid-producing plant biofactories.

Pair of Residue Substitutions at the Outer Mouth of the Channel Pore Act as Inputs for a Boolean Logic "OR" Gate Based on the Glycine Receptor

The glycine receptor (GlyR) is a ligand-activated chloride channel, whose mutations are the major cause of hereditary hyperekplexia. The hyperekplexia-causing R271Q mutation, which is located at the extracellular outer mouth of the channel pore, dramatically impairs the GlyR function manifesting a reduced sensitivity toward glycine. This study reports that a second mutation, S273D, rescues the function of the R271Q GlyR to that of the wild-type (WT) GlyR. Surprisingly, the S273D mutation, when introduced to the WT GlyR, does not further increase the receptor function. In other words, the compromised function of the 271Q 273S GlyR (i.e., the R271Q GlyR) can be rescued to WT levels by the introduction of either, or both, of the Q271R and S273D substitutions. From the perspective of Boolean logic gates, the Q271R and S273D substitutions act as inputs for an OR gate based on the GlyR. Further experiments revealed that the negative-charge carried by the 273 residue is essential for the expression of the OR gate and that the expression of the OR gate is residue-position-specific. In addition, mechanistic investigation implied that the 273 residue influences the 271 residue, which might underpin the unique nonadditive OR gate relationship between these two residues. Such an ion-channel-based OR gate, expressing output in the form of electrical current, could potentially be developed to digitally manipulate neuronal activity.

Quantitative and Predictive Genetic Parts for Plant Synthetic Biology

Plant synthetic biology aims to harness the natural abilities of plants and to turn them to new purposes. A primary goal of plant synthetic biology is to produce predictable and programmable genetic circuits from simple regulatory elements and well-characterized genetic components. The number of available DNA parts for plants is increasing, and the methods for rapid quantitative characterization are being developed, but the field of plant synthetic biology is still in its early stages. We here describe methods used to describe the quantitative properties of genetic components needed for plant synthetic biology. Once the quantitative properties and transfer function of a variety of genetic parts are known, computers can select the optimal components to assemble into functional devices, such as toggle switches and positive feedback circuits. However, while the variety of circuits and traits that can be put into plants are limitless, doing synthetic biology in plants poses unique challenges. Plants are composed of differentiated cells and tissues, each representing potentially unique regulatory or developmental contexts to introduced synthetic genetic circuits. Further, plants have evolved to be highly sensitive to environmental influences, such as light or temperature, any of which can affect the quantitative function of individual parts or whole circuits. Measuring the function of plant components within the context of a plant cell and, ideally, in a living plant, will be essential to using these components in gene circuits with predictable function. Mathematical modeling will be needed to account for the variety of contexts a genetic part will experience in different plant tissues or environments. With such understanding in hand, it may be possible to redesign plant traits to serve human and environmental needs.

Could We Really Be Made of Swiss Cheese? Xenobiology as an Engineering Epistemology for Biological Realization

Besides having potential medical and biosafety applications, as well as challenging the foundations of biological engineering, xenobiology can also shed light on the epistemological and metaphysical questions that puzzle philosophers of science. This paper reviews this philosophical aspect of xenobiology, focusing on the possible multiple realizability of life. According to this hypothesis, what ultimately matters in understanding life is its function, not its particular building blocks. This is because there should, in theory, be many different ways to build the same function. The possibility of multiple realizability was originally raised in the context of AI's hypothesized capacity to realize mental functions. Because we still do not have any incontrovertible examples of digital minds, not to mention alien life of foreign biochemistry, the best way to test this philosophical idea is to examine the recent results and practices of synthetic biology and xenobiology.

For the sake of the Bioeconomy: define what a Synthetic Biology Chassis is!

At the onset of the 4th Industrial Revolution, the role of synthetic biology (SynBio) as a fuel for the bioeconomy requires clarification of the terms typically adopted by this growing scientific-technical field. The concept of the chassis as a defined, reusable biological frame where non-native components can be plugged in and out to create new functionalities lies at the boundary between frontline bioengineering and more traditional recombinant DNA technology. As synthetic biology leaves academic laboratories and starts penetrating industrial and environmental realms regulatory agencies demand clear definitions and descriptions of SynBio constituents, processes and products. In this article, the state of the ongoing discussion on what is a chassis is reviewed, a non-equivocal nomenclature for the jargon used is proposed and objective criteria are recommended for distinguishing SynBio agents from traditional GMOs. The use of genomic barcodes as unique identifiers is strongly advocated. Finally the soil bacterium Pseudomonas putida is shown as an example of the roadmap that one environmental isolate may go through to become a bona fide SynBio chassis.

Programmable CRISPR-Cas transcriptional activation in bacteria

Programmable gene activation enables fine-tuned regulation of endogenous and synthetic gene circuits to control cellular behavior. While CRISPR-Cas-mediated gene activation has been extensively developed for eukaryotic systems, similar strategies have been difficult to implement in bacteria. Here, we present a generalizable platform for screening and selection of functional bacterial CRISPR-Cas transcription activators. Using this platform, we identified a novel CRISPR activator, dCas9-AsiA, that could activate gene expression by more than 200-fold across genomic and plasmid targets with diverse promoters after directed evolution. The evolved dCas9-AsiA can simultaneously mediate activation and repression of bacterial regulons in E. coli. We further identified hundreds of promoters with varying basal expression that could be induced by dCas9-AsiA, which provides a rich resource of genetic parts for inducible gene activation. Finally, we show that dCas9-AsiA can be ported to other bacteria of clinical and bioindustrial relevance, thus enabling bacterial CRISPRa in more application areas. This work expands the toolbox for programmable gene regulation in bacteria and provides a useful resource for future engineering of other bacterial CRISPR-based gene regulators.

Nano-enabled cellular engineering for bioelectric studies

Engineered cells have opened up a new avenue for scientists and engineers to achieve specialized biological functions. Nanomaterials, such as silicon nanowires and quantum dots, can establish tight interfaces with cells either extra- or intracellularly, and they have already been widely used to control cellular functions. The future exploration of nanomaterials in cellular engineering may reveal numerous opportunities in both fundamental bioelectric studies and clinic applications. In this review, we highlight several nanomaterials-enabled non-genetic approaches to fabricating engineered cells. First, we briefly review the latest progress in engineered or synthetic cells, such as protocells that create cell-like behaviors from nonliving building blocks, and cells made by genetic or chemical modifications. Next, we illustrate the need for non-genetic cellular engineering with semiconductors and present some examples where chemical synthesis yields complex morphology or functions needed for biointerfaces. We then provide discussions in detail about the semiconductor nanostructure-enabled neural, cardiac, and microbial modulations. We also suggest the need to integrate tissue engineering with semiconductor devices to carry out more complex functions. We end this review by providing our perspectives for future development in non-genetic cellular engineering.

Biofilm architecture: An emerging synthetic biology target

Synthetic biologists are exploiting biofilms as an effective mechanism for producing various outputs. Metabolic optimization has become commonplace as a method of maximizing system output. In addition to production pathways, the biofilm itself contributes to the efficacy of production. The purpose of this review is to highlight opportunities that might be leveraged to further enhance production in preexisting biofilm production systems. These opportunities may be used with previously established production systems as a method of improving system efficiency further. This may be accomplished through the reduction in the cost of establishing and maintaining biofilms, and maintenance of the enhancement of product yield per unit of time, per unit of area, or per unit of required input.

Tinkering Cis Motifs Jigsaw Puzzle Led to Root-Specific Drought-Inducible Novel Synthetic Promoters

Following an in-depth transcriptomics-based approach, we first screened out and analyzed (in silico) cis motifs in a group of 63 drought-inducible genes (in soybean). Six novel synthetic promoters (SynP14-SynP19) were designed by concatenating 11 cis motifs, ABF, ABRE, ABRE-Like, CBF, E2F-VARIANT, G-box, GCC-Box, MYB1, MYB4, RAV1-A, and RAV1-B (in multiple copies and various combination) with a minimal 35s core promoter and a 222 bp synthetic intron sequence. In order to validate their drought-inducibility and root-specificity, the designed synthetic assemblies were transformed in soybean hairy roots to drive GUS gene using pCAMBIA3301. Through GUS histochemical assay (after a 72 h 6% PEG6000 treatment), we noticed higher glucuronidase activity in transgenic hairy roots harboring SynP15, SynP16, and SynP18. Further screening through GUS fluorometric assay flaunted SynP16 as the most appropriate combination of efficient drought-responsive cis motifs. Afterwards, we stably transformed SynP15, SynP16, and SynP18 in Arabidopsis and carried out GUS staining as well as fluorometric assays of the transgenic plants treated with simulated drought stress. Consistently, SynP16 retained higher transcriptional activity in Arabidopsis roots in response to drought. Thus the root-specific drought-inducible synthetic promoters designed using stimulus-specific cis motifs in a definite fashion could be exploited in developing drought tolerance in soybean and other crops as well. Moreover, the rationale of design extends our knowledge of trial-and-error based cis engineering to construct synthetic promoters for transcriptional upgradation against other stresses.

Gene networks that compensate for crosstalk with crosstalk

Crosstalk is a major challenge to engineering sophisticated synthetic gene networks. A common approach is to insulate signal-transduction pathways by minimizing molecular-level crosstalk between endogenous and synthetic genetic components, but this strategy can be difficult to apply in the context of complex, natural gene networks and unknown interactions. Here, we show that synthetic gene networks can be engineered to compensate for crosstalk by integrating pathway signals, rather than by pathway insulation. We demonstrate this principle using reactive oxygen species (ROS)-responsive gene circuits in Escherichia coli that exhibit concentration-dependent crosstalk with non-cognate ROS. We quantitatively map the degree of crosstalk and design gene circuits that introduce compensatory crosstalk at the gene network level. The resulting gene network exhibits reduced crosstalk in the sensing of the two different ROS. Our results suggest that simple network motifs that compensate for pathway crosstalk can be used by biological networks to accurately interpret environmental signals.

Crosstalk between genetic circuits is a major challenge for engineering sophisticated networks. Here the authors design networks that compensate for crosstalk by integrating, not insulating, pathways.

Genetic Circuit-Assisted Smart Microbial Engineering

Rapid advances in DNA synthesis, genetic manipulation, and biosensors have greatly improved the ability to engineer microorganisms with complex functions. By accurately integrating quality biosensors and complex genetic circuits, recently emerged smart microorganisms have enabled exciting opportunities for dissecting complex signaling networks and making responses without artificial intervention. However, because of the lack of design principles, developing such smart microorganisms remains challenging. In this review, we propose the concept of smart microbial engineering (SME) and describe the general features of basic SME, including the circuit architecture, components, and design process. We also summarize the latest SME achievements, remaining challenges, and potential solutions in this growing field.