Evolution and synthetic biology

Evolutionary observations have often served as an inspiration for biological design. Decoding of the central dogma of life at a molecular level and understanding of the cellular biochemistry have been elegantly used to engineer various synthetic biology applications, including building genetic circuits in vitro and in cells, building synthetic translational systems, and metabolic engineering in cells to biosynthesize and even bioproduce complex high-value molecules. Here, we review three broad areas of synthetic biology that are inspired by evolutionary observations: (i) combinatorial approaches toward cell-based biomolecular evolution, (ii) engineering interdependencies to establish microbial consortia, and (iii) synthetic immunology. In each of the areas, we will highlight the evolutionary premise that was central toward designing these platforms. These are only a subset of the examples where evolution and natural phenomena directly or indirectly serve as a powerful source of inspiration in shaping synthetic biology and biotechnology.

Progress and prospect: Biosynthesis of plant natural products based on plant chassis

Plant-derived natural products are a specific class of active substances with numerous applications in the medical, energy, and industrial fields. Many of these substances are in high demand and have become the fundamental materials for various purposes. Recently, the use of synthetic biology to produce plant-derived natural products has become a significant trend. Plant chassis, in particular, offer unique advantages over microbial chassis in terms of cell structure, product affinity, safety, and storage. The development of the plant hairy root tissue culture system has accelerated the commercialization and industrialization of synthetic biology in the production of plant-derived natural products. This paper will present recent progress in the synthesis of various plant natural products using plant chassis, organized by the types of different structures. Additionally, we will summarize the four primary types of plant chassis used for synthesizing natural products from plant sources and review the enabling technologies that have contributed to the development of synthetic biology in recent years. Finally, we will present the role of isolated and combined use of different optimization strategies in breaking the upper limit of natural product production in plant chassis. This review aims to provide practical references for synthetic biologists and highlight the great commercial potential of plant chassis biosynthesis, such as hairy roots.

Recent advances in systems metabolic engineering

Systems metabolic engineering, which integrates metabolic engineering with systems biology, synthetic biology, and evolutionary engineering, has revolutionized the sustainable production of fuels and materials through the creation of efficient microbial cell factories. Recent advancements in systems metabolic engineering targeting different biological components of the host cell have enabled the creation of highly productive microbial cell factories. This article provides a review of the recent tools and strategies used for enzyme-, genetic module-, pathway-, flux-, genome-, and cell-level engineering, supported by illustrative examples. Furthermore, we highlight recent trends in systems metabolic engineering, which involve the application of multiple tools discussed in this review. Finally, the paper addresses the challenges and perspectives of transitioning academic-level metabolic engineering studies to commercial-scale production.

A Self-Regenerating Artificial Cell, that is One Step Closer to Living Cells: Challenges and Perspectives

Controllable, self-regenerating artificial cells (SRACs) can be a vital advancement in the field of synthetic biology, which seeks to create living cells by recombining various biological molecules in the lab. This represents, more importantly, the first step on a long journey toward creating reproductive cells from rather fragmentary biochemical mimics. However, it is still a difficult task to replicate the complex processes involved in cell regeneration, such as genetic material replication and cell membrane division, in artificially created spaces. This review highlights recent advances in the field of controllable, SRACs and the strategies to achieve the goal of creating such cells. Self-regenerating cells start by replicating DNA and transferring it to a location where proteins can be synthesized. Functional but essential proteins must be synthesized for sustained energy generation and survival needs and function in the same liposomal space. Finally, self-division and repeated cycling lead to autonomous, self-regenerating cells. The pursuit of controllable, SRACs will enable authors to make bold advances in understanding life at the cellular level, ultimately providing an opportunity to use this knowledge to understand the nature of life.

Design principles for synthetic control systems to engineer plants

KEY MESSAGE

Synthetic control systems have led to significant advancement in the study and engineering of unicellular organisms, but it has been challenging to apply these tools to multicellular organisms like plants. The ability to predictably engineer plants will enable the development of novel traits capable of alleviating global problems, such as climate change and food insecurity. Engineering predictable multicellular phenotypes will require the development of synthetic control systems that can precisely regulate how the information encoded in genomes is translated into phenotypes. Many efficient control systems have been developed for unicellular organisms. However, it remains challenging to use such tools to study or engineer multicellular organisms. Plants are a good chassis within which to develop strategies to overcome these challenges, thanks to their capacity to withstand large-scale reprogramming without lethality. Additionally, engineered plants have great potential for solving major societal problems. Here we briefly review the progress of control system development in unicellular organisms, and how that information can be leveraged to characterize control systems in plants. Further, we discuss strategies for developing control systems designed to regulate the expression of transgenes or endogenous loci and generate dosage-dependent or discrete traits. Finally, we discuss the utility that mathematical models of biological processes have for control system deployment.

Media representations of synthetic biology in China

Media representations of emerging biotechnologies in the media can influence public attitudes and have the potential to impact on policy decisions and law-making. We discuss the unbalanced portrayal of synthetic biology in Chinese news media and how it might affect the perceptions of the public, the scientific community, and decision-makers.

[Biomanufacturing of bioactive compounds: current situation, challenges, and future perspectives]

Biomanufacturing uses biological systems, including cells, microorganisms, and enzymes, to produce natural or synthetic molecules with biological activities for use in various industries, such as pharmaceuticals, cosmetics, and agriculture. These bioactive compounds are expected to play important roles in improving the quality of life and prolonging its length. Fortunately, recent advances in synthetic biology and automation technologies have accelerated the development of biomanufacturing, enabling us to create new products and replace conventional methods in a more sustainable manner. As of now, the role of biomanufacturing in the growth and innovation of bioeconomy is steadily increasing, and this techbology becomes a prevalent technology in global markets. To gain a comprehensive understanding of this field, this article presents a retrospective review of Bloomage Biotechnology's Research and Development and briefly reviews the developments of biomanufacturing and offers insights into the futre prospects. In conclusion, biomanufacturing will continue to be an important, environmentally friendly, and sustainable production mode in the ongoing development of bioeconomy.

Design, construction, and functional characterization of a tRNA neochromosome in yeast

Here, we report the design, construction, and characterization of a tRNA neochromosome, a designer chromosome that functions as an additional, de novo counterpart to the native complement of Saccharomyces cerevisiae. Intending to address one of the central design principles of the Sc2.0 project, the ∼190-kb tRNA neochromosome houses all 275 relocated nuclear tRNA genes. To maximize stability, the design incorporates orthogonal genetic elements from non-S. cerevisiae yeast species. Furthermore, the presence of 283 rox recombination sites enables an orthogonal tRNA SCRaMbLE system. Following construction in yeast, we obtained evidence of a potent selective force, manifesting as a spontaneous doubling in cell ploidy. Furthermore, tRNA sequencing, transcriptomics, proteomics, nucleosome mapping, replication profiling, FISH, and Hi-C were undertaken to investigate questions of tRNA neochromosome behavior and function. Its construction demonstrates the remarkable tractability of the yeast model and opens up opportunities to directly test hypotheses surrounding these essential non-coding RNAs.

Debugging and consolidating multiple synthetic chromosomes reveals combinatorial genetic interactions

The Sc2.0 project is building a eukaryotic synthetic genome from scratch. A major milestone has been achieved with all individual Sc2.0 chromosomes assembled. Here, we describe the consolidation of multiple synthetic chromosomes using advanced endoreduplication intercrossing with tRNA expression cassettes to generate a strain with 6.5 synthetic chromosomes. The 3D chromosome organization and transcript isoform profiles were evaluated using Hi-C and long-read direct RNA sequencing. We developed CRISPR Directed Biallelic URA3-assisted Genome Scan, or "CRISPR D-BUGS," to map phenotypic variants caused by specific designer modifications, known as "bugs." We first fine-mapped a bug in synthetic chromosome II (synII) and then discovered a combinatorial interaction associated with synIII and synX, revealing an unexpected genetic interaction that links transcriptional regulation, inositol metabolism, and tRNASerCGA abundance. Finally, to expedite consolidation, we employed chromosome substitution to incorporate the largest chromosome (synIV), thereby consolidating >50% of the Sc2.0 genome in one strain.

Navigating the Frontier of Synthetic Biology: An AI-Driven Analytics Platform for Exploring Research Trends and Relationships

The field of synthetic biology has experienced rapid growth in recent years, leading to an overwhelming amount of literature that can make it difficult to comprehend the scope and trends of the discipline. In this study, we employ topic modeling to comprehensively map research topics within synthetic biology, revealing subtopics and their relationships, as well as trends over time. We utilize metadata to identify the most significant journals and countries in the field and discuss potential policy impact on the research output. In addition, we investigate co-authorship networks to analyze collaborations among authors, institutions, and countries. We believe that our findings could serve as a valuable resource for gaining a deeper understanding of synthetic biology and provide a foundation for analyzing other disciplines.

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.

Physical Laboratory Automation in Synthetic Biology

Synthetic Biology has overcome many of the early challenges facing the field and is entering a systems era characterized by adoption of Design-Build-Test-Learn (DBTL) approaches. The need for automation and standardization to enable reproducible, scalable, and translatable research has become increasingly accepted in recent years, and many of the hardware and software tools needed to address these challenges are now in place or under development. However, the lack of connectivity between DBTL modules and barriers to access and adoption remain significant challenges to realizing the full potential of lab automation. In this review, we characterize and classify the state of automation in synthetic biology with a focus on the physical automation of experimental workflows. Though fully autonomous scientific discovery is likely a long way off, impressive progress has been made toward automating critical elements of experimentation by combining intelligent hardware and software tools. It is worth questioning whether total automation that removes humans entirely from the loop should be the ultimate goal, and considerations for appropriate automation versus total automation are discussed in this light while emphasizing areas where further development is needed in both contexts.

Enabling AI in synthetic biology through Construction File specification

The Construction File (CF) specification establishes a standardized interface for molecular biology operations, laying a foundation for automation and enhanced efficiency in experiment design. It is implemented across three distinct software projects: PyDNA_CF_Simulator, a Python project featuring a ChatGPT plugin for interactive parsing and simulating experiments; ConstructionFileSimulator, a field-tested Java project that showcases 'Experiment' objects expressed as flat files; and C6-Tools, a JavaScript project integrated with Google Sheets via Apps Script, providing a user-friendly interface for authoring and simulation of CF. The CF specification not only standardizes and modularizes molecular biology operations but also promotes collaboration, automation, and reuse, significantly reducing potential errors. The potential integration of CF with artificial intelligence, particularly GPT-4, suggests innovative automation strategies for synthetic biology. While challenges such as token limits, data storage, and biosecurity remain, proposed solutions promise a way forward in harnessing AI for experiment design. This shift from human-driven design to AI-assisted workflows, steered by high-level objectives, charts a potential future path in synthetic biology, envisioning an environment where complexities are managed more effectively.

Engineering a scalable and orthogonal platform for synthetic communication in mammalian cells

The rational design and implementation of synthetic mammalian communication systems can unravel fundamental design principles of cell communication circuits and offer a framework for engineering of designer cell consortia with potential applications in cell therapeutics. Here, we develop the foundations of an orthogonal, and scalable mammalian synthetic communication platform that exploits the programmability of synthetic receptors and selective affinity and tunability of diffusing coiled-coil peptides. Leveraging the ability of coiled-coils to exclusively bind to a cognate receptor, we demonstrate orthogonal receptor activation and Boolean logic operations at the receptor level. We show intercellular communication based on synthetic receptors and secreted multidomain coiled-coils and demonstrate a three-cell population system that can perform AND gate logic. Finally, we show CC-GEMS receptor-dependent therapeutic protein expression. Our work provides a modular and scalable framework for the engineering of complex cell consortia, with the potential to expand the aptitude of cell therapeutics and diagnostics.

A synthetic biology approach to engineering circuits in immune cells

A synthetic circuit in a biological system involves the designed assembly of genetic elements, biomolecules, or cells to create a defined function. These circuits are central in synthetic biology, enabling the reprogramming of cellular behavior and the engineering of cells with customized responses. In cancer therapeutics, engineering T cells with circuits have the potential to overcome the challenges of current approaches, for example, by allowing specific recognition and killing of cancer cells. Recent advances also facilitate engineering integrated circuits for the controlled release of therapeutic molecules at specified locations, for example, in a solid tumor. In this review, we discuss recent strategies and applications of synthetic receptor circuits aimed at enhancing immune cell functions for cancer immunotherapy. We begin by introducing the concept of circuits in networks at the molecular and cellular scales and provide an analysis of the development and implementation of several synthetic circuits in T cells that have the goal to overcome current challenges in cancer immunotherapy. These include specific targeting of cancer cells, increased T-cell proliferation, and persistence in the tumor microenvironment. By harnessing the power of synthetic biology, and the characteristics of certain circuit architectures, it is now possible to engineer a new generation of immune cells that recognize cancer cells, while minimizing off-target toxicities. We specifically discuss T-cell circuits for antigen density sensing. These circuits allow targeting of solid tumors that share antigens with normal tissues. Additionally, we explore designs for synthetic circuits that could control T-cell differentiation or T-cell fate as well as the concept of synthetic multicellular circuits that leverage cellular communication and division of labor to achieve improved therapeutic efficacy. As our understanding of cell biology expands and novel tools for genome, protein, and cell engineering are developed, we anticipate further innovative approaches to emerge in the design and engineering of circuits in immune cells.

Biosensors in microalgae: A roadmap for new opportunities in synthetic biology and biotechnology

Biosensors are powerful tools to investigate, phenotype, improve and prototype microbial strains, both in fundamental research and in industrial contexts. Genetic and biotechnological developments now allow the implementation of synthetic biology approaches to novel different classes of microbial hosts, for example photosynthetic microalgae, which offer unique opportunities. To date, biosensors have not yet been implemented in phototrophic eukaryotic microorganisms, leaving great potential for novel biological and technological advancements untapped. Here, starting from selected biosensor technologies that have successfully been implemented in heterotrophic organisms, we project and define a roadmap on how these could be applied to microalgae research. We highlight novel opportunities for the development of new biosensors, identify critical challenges, and finally provide a perspective on the impact of their eventual implementation to tackle research questions and bioengineering strategies. From studying metabolism at the single-cell level to genome-wide screen approaches, and assisted laboratory evolution experiments, biosensors will greatly impact the pace of progress in understanding and engineering microalgal metabolism. We envision how this could further advance the possibilities for unraveling their ecological role, evolutionary history and accelerate their domestication, to further drive them as resource-efficient production hosts.

Advancement of Engineered Bacteria for Orally Delivered Therapeutics

The use of bacteria and their biotic components as therapeutics has shown great potential in the treatment of diseases. Orally delivered bacteria improve patient compliance compared with injection-administered bacteria and are considered the preferred mode. However, due to the harsh gastrointestinal environment, the viability and therapeutic efficacy of orally delivered bacteria are significantly reduced in vivo. In recent years, with the rapid development of synthetic biology and nanotechnology, bacteria and biotic components have been engineered to achieve directed genetic reprogramming for construction and precise spatiotemporal control in the gastrointestinal tract, which can improve viability and therapeutic efficiency. Herein, a state-of-the-art review on the current progress of engineered bacterial systems for oral delivery is provided. The different types of bacterial and biotic components for oral administration are first summarized. The engineering strategies of these bacteria and biotic components and their treatment of diseases are next systematically summarized. Finally, the current challenges and prospects of these bacterial therapeutics are highlighted that will contribute to the development of next-generation orally delivered bacteriotherapy.

Synthetic biology tools for environmental protection

Synthetic biology transforms the way we perceive biological systems. Emerging technologies in this field affect many disciplines of science and engineering. Traditionally, synthetic biology approaches were commonly aimed at developing cost-effective microbial cell factories to produce chemicals from renewable sources. Based on this, the immediate beneficial impact of synthetic biology on the environment came from reducing our oil dependency. However, synthetic biology is starting to play a more direct role in environmental protection. Toxic chemicals released by industries and agriculture endanger the environment, disrupting ecosystem balance and biodiversity loss. This review highlights synthetic biology approaches that can help environmental protection by providing remediation systems capable of sensing and responding to specific pollutants. Remediation strategies based on genetically engineered microbes and plants are discussed. Further, an overview of computational approaches that facilitate the design and application of synthetic biology tools in environmental protection is presented.

The technology, opportunities, and challenges of Synthetic Biological Intelligence

Integrating neural cultures developed through synthetic biology methods with digital computing has enabled the early development of Synthetic Biological Intelligence (SBI). Recently, key studies have emphasized the advantages of biological neural systems in some information processing tasks. However, neither the technology behind this early development, nor the potential ethical opportunities or challenges, have been explored in detail yet. Here, we review the key aspects that facilitate the development of SBI and explore potential applications. Considering these foreseeable use cases, various ethical implications are proposed. Ultimately, this work aims to provide a robust framework to structure ethical considerations to ensure that SBI technology can be both researched and applied responsibly.

Photosynthesis and food security: the evolving story of C4 rice

Traditional "Green Revolution" cereal breeding strategies to improve yield are now reaching a plateau in our principal global food crop rice. Photosynthesis has now become a major target of international consortia to increase yield potential. Synthetic biology is being used across multiple large projects to improve photosynthetic efficiency. This review follows the genesis and progress of one of the first of these consortia projects, now in its 13th year; the Bill and Melinda Gates funded C4 Rice Project. This project seeks to install the biochemical and anatomical attributes necessary to support C4 photosynthesis in the C3 crop rice. Here we address the advances made thus far in installing the biochemical pathway and some of the key targets yet to be reached.

IDESS: a toolbox for identification and automated design of stochastic gene circuits

MOTIVATION

One of the main causes hampering predictability during the model identification and automated design of gene circuits in synthetic biology is the effect of molecular noise. Stochasticity may significantly impact the dynamics and function of gene circuits, specially in bacteria and yeast due to low mRNA copy numbers. Standard stochastic simulation methods are too computationally costly in realistic scenarios to be applied to optimization-based design or parameter estimation.

RESULTS

In this work, we present IDESS (Identification and automated Design of Stochastic gene circuitS), a software toolbox for optimization-based design and model identification of gene regulatory circuits in the stochastic regime. This software incorporates an efficient approximation of the Chemical Master Equation as well as a stochastic simulation algorithm-both with GPU and CPU implementations-combined with global optimization algorithms capable of solving Mixed Integer Nonlinear Programming problems. The toolbox efficiently addresses two types of problems relevant in systems and synthetic biology: the automated design of stochastic synthetic gene circuits, and the parameter estimation for model identification of stochastic gene regulatory networks.

AVAILABILITY AND IMPLEMENTATION

IDESS runs under the MATLAB environment and it is available under GPLv3 license at https://doi.org/10.5281/zenodo.7788692.

Eras of designer Tregs: Harnessing synthetic biology for immune suppression

Since their discovery, CD4+ CD25hi FOXP3hi regulatory T cells (Tregs) have been firmly established as a critical cell type for regulating immune homeostasis through a plethora of mechanisms. Due to their immunoregulatory power, delivery of polyclonal Tregs has been explored as a therapy to dampen inflammation in the settings of transplantation and autoimmunity. Evidence shows that Treg therapy is safe and well-tolerated, but efficacy remains undefined and could be limited by poor persistence in vivo and lack of antigen specificity. With the advent of new genetic engineering tools, it is now possible to create bespoke "designer" Tregs that not only overcome possible limitations of polyclonal Tregs but also introduce new features. Here, we review the development of designer Tregs through the perspective of three 'eras': (1) the era of FOXP3 engineering, in which breakthroughs in the biological understanding of this transcription factor enabled the conversion of conventional T cells to Tregs; (2) the antigen-specificity era, in which transgenic T-cell receptors and chimeric antigen receptors were introduced to create more potent and directed Treg therapies; and (3) the current era, which is harnessing advanced genome-editing techniques to introduce and refine existing and new engineering approaches. The year 2022 marked the entry of "designer" Tregs into the clinic, with exciting potential for application and efficacy in a wide variety of immune-mediated diseases.

Metabolic engineering and synthetic biology strategies for producing high-value natural pigments in Microalgae

Microalgae are microorganisms capable of producing bioactive compounds using photosynthesis. Microalgae contain a variety of high value-added natural pigments such as carotenoids, phycobilins, and chlorophylls. These pigments play an important role in many areas such as food, pharmaceuticals, and cosmetics. Natural pigments have a health value that is unmatched by synthetic pigments. However, the current commercial production of natural pigments from microalgae is not able to meet the growing market demand. The use of metabolic engineering and synthetic biological strategies to improve the production performance of microalgal cell factories is essential to promote the large-scale production of high-value pigments from microalgae. This paper reviews the health and economic values, the applications, and the synthesis pathways of microalgal pigments. Overall, this review aims to highlight the latest research progress in metabolic engineering and synthetic biology in constructing engineered strains of microalgae with high-value pigments and the application of CRISPR technology and multi-omics in this context. Finally, we conclude with a discussion on the bottlenecks and challenges of microalgal pigment production and their future development prospects.

Generative Artificial Intelligence GPT-4 Accelerates Knowledge Mining and Machine Learning for Synthetic Biology

Knowledge mining from synthetic biology journal articles for machine learning (ML) applications is a labor-intensive process. The development of natural language processing (NLP) tools, such as GPT-4, can accelerate the extraction of published information related to microbial performance under complex strain engineering and bioreactor conditions. As a proof of concept, we proposed prompt engineering for a GPT-4 workflow pipeline to extract knowledge from 176 publications on two oleaginous yeasts (Yarrowia lipolytica and Rhodosporidium toruloides). After human intervention, the pipeline obtained a total of 2037 data instances. The structured data sets and feature selections enabled ML approaches (e.g., a random forest model) to predict Yarrowia fermentation titers with decent accuracy (R2 of 0.86 for unseen test data). Via transfer learning, the trained model could assess the production potential of the engineered nonconventional yeast, R. toruloides, for which there are fewer published reports. This work demonstrated the potential of generative artificial intelligence to streamline information extraction from research articles, thereby facilitating fermentation predictions and biomanufacturing development.

Special Issue "Synthetic Biology for Biosensing in Health and Environmental Applications"

Biosensors are analytical devices that utilize biological sensing elements, such as enzymes, antibodies, nucleic acids, or cells, to detect a given analyte [...].

Advancements in synthetic biology-based bacterial cancer therapy: A modular design approach

Synthetic biology aims to program living bacteria cells with artificial genetic circuits for user-defined functions, transforming them into powerful tools with numerous applications in various fields, including oncology. Cancer treatments have serious side effects on patients due to the systemic action of the drugs involved. To address this, new systems that provide localized antitumoral action while minimizing damage to healthy tissues are required. Bacteria, often considered pathogenic agents, have been used as cancer treatments since the early 20th century. Advances in genetic engineering, synthetic biology, microbiology, and oncology have improved bacterial therapies, making them safer and more effective. Here we propose six modules for a successful synthetic biology-based bacterial cancer therapy, the modules include Payload, Release, Tumor-targeting, Biocontainment, Memory, and Genetic Circuit Stability Module. These will ensure antitumor activity, safety for the environment and patient, prevent bacterial colonization, maintain cell stability, and prevent loss or defunctionalization of the genetic circuit.

Examining expertise: Synthetic biology experts' perceptions of risk, benefit, and the public for research and applications regulation

Scientific experts can play an important role in decision-making surrounding policy for technical and value-laden issues, often in contexts that directly affect lay publics. Yet little is known about what characterizes scientific experts who want lay public involvement in decision-making. In this study, we examine how synthetic biology experts' perceptions of risks, benefits, and ambivalence for synthetic biology relate to views of lay publics, deference to scientific authority, and regulations. We analyzed survey data of researchers in the United States, who published academic articles relating to synthetic biology from 2000 to 2015. Scientific experts who see less risk and are more deferent to scientific authority appear to favor a more closed system in which regulations are sufficient, citizens should not be involved, and scientists know best. Conversely, scientific experts who see more potential for risk and see the public as bringing a valuable perspective appear to favor a more open, inclusive system.

Running the numbers on plant synthetic biology solutions to global problems

Synthetic biology and metabolic engineering promise to deliver sustainable solutions to global problems such as phasing out fossil fuels and replacing industrial nitrogen fixation. While this promise is real, scale matters, and so do knock-on effects of implementing solutions. Both scale and knock-on effects can be estimated by 'Fermi calculations' (aka 'back-of-envelope calculations') that use uncontroversial input data plus simple arithmetic to reach rough but reliable conclusions. Here, we illustrate how this is done and how informative it can be using two cases: oilcane (sugarcane engineered to accumulate triglycerides instead of sugar) as a source of bio-jet fuel, and nitrogen fixation by bacteria in mucilage secreted by maize aerial roots. We estimate that oilcane could meet no more than about 1% of today's U.S. jet fuel demand if grown on all current U.S. sugarcane land and that, if cane land were expanded to meet two-thirds of this demand, the fertilizer and refinery requirements would create a large carbon footprint. Conversely, we estimate that nitrogen fixation in aerial-root mucilage could replace up to 10% of the fertilizer nitrogen applied to U.S. maize, that 2% of plant carbon income used for growth would suffice to fuel the fixation, and that this extra carbon consumption would likely reduce grain yield only slightly.

Rational design of small-molecule responsive protein switches

Small-molecule responsive protein switches are powerful tools for controlling cellular processes. These switches are designed to respond rapidly and specifically to their inducer. They have been used in numerous applications, including the regulation of gene expression, post-translational protein modification, and signal transduction. Typically, small-molecule responsive protein switches consist of two proteins that interact with each other in the presence or absence of a small molecule. Recent advances in computational protein design already contributed to the development of protein switches with an expanded range of small-molecule inducers and increasingly sophisticated switch mechanisms. Further progress in the engineering of small-molecule responsive switches is fueled by cutting-edge computational design approaches, which will enable more complex and precise control over cellular processes and advance synthetic biology applications in biotechnology and medicine. Here, we discuss recent milestones and how technological advances are impacting the development of chemical switches.

Advances in automated and reactive flow cytometry for synthetic biotechnology

Automated flow cytometry (FC) has been initially considered for bioprocess monitoring and optimization. More recently, new physical and software interfaces have been made available, facilitating the access to this technology for labs and industries. It also comes with new capabilities, such as being able to act on the cultivation conditions based on population data. This approach, known as reactive FC, extended the range of applications of automated FC to bioprocess control and the stabilization of cocultures, but also to the broad field of synthetic and systems biology for the characterization of gene circuits. However, several issues must be addressed before automated and reactive FC can be considered standard and modular technologies.

Noise properties of adaptation-conferring biochemical control modules

A key goal of synthetic biology is to develop functional biochemical modules with network-independent properties. Antithetic integral feedback (AIF) is a recently developed control module in which two control species perfectly annihilate each other's biological activity. The AIF module confers robust perfect adaptation to the steady-state average level of a controlled intracellular component when subjected to sustained perturbations. Recent work has suggested that such robustness comes at the unavoidable price of increased stochastic fluctuations around average levels. We present theoretical results that support and quantify this trade-off for the commonly analyzed AIF variant in the idealized limit with perfect annihilation. However, we also show that this trade-off is a singular limit of the control module: Even minute deviations from perfect adaptation allow systems to achieve effective noise suppression as long as cells can pay the corresponding energetic cost. We further show that a variant of the AIF control module can achieve significant noise suppression even in the idealized limit with perfect adaptation. This atypical configuration may thus be preferable in synthetic biology applications.

Chemical Systems for Wetware Artificial Life: Selected Perspectives in Synthetic Cell Research

The recent and important advances in bottom-up synthetic biology (SB), in particular in the field of the so-called "synthetic cells" (SCs) (or "artificial cells", or "protocells"), lead us to consider the role of wetware technologies in the "Sciences of Artificial", where they constitute the third pillar, alongside the more well-known pillars hardware (robotics) and software (Artificial Intelligence, AI). In this article, it will be highlighted how wetware approaches can help to model life and cognition from a unique perspective, complementary to robotics and AI. It is suggested that, through SB, it is possible to explore novel forms of bio-inspired technologies and systems, in particular chemical AI. Furthermore, attention is paid to the concept of semantic information and its quantification, following the strategy recently introduced by Kolchinsky and Wolpert. Semantic information, in turn, is linked to the processes of generation of "meaning", interpreted here through the lens of autonomy and cognition in artificial systems, emphasizing its role in chemical ones.

Making the Next Generation of Therapeutics: mRNA Meets Synthetic Biology

The development of mRNA-based therapeutics centers around the natural functioning of mRNA molecules to provide the genetic information required for protein translation. To improve the efficacy of these therapeutics and minimize side effects, researchers can focus on the features of mRNA itself or the properties of the delivery agent to achieve the desired response. The tools considered for mRNA manipulation can be improved in terms of targetability, tunability, and translatability to medicine. While ongoing studies are dedicated to improving conventional approaches, innovative approaches can also be considered to unleash the full potential of mRNA-based therapeutics. Here, we discuss the opportunities that emerged from introducing synthetic biology to mRNA therapeutics. It includes a discussion of modular self-assembled mRNA nanoparticles, logic gates on a single mRNA molecule, and other possibilities.

Tailored Synthetic sRNAs Dynamically Tune Multilayer Genetic Circuits

Predictable and controllable tuning of genetic circuits to regulate gene expression, including modulation of existing circuits or constructs without the need for redesign or rebuilding, is a persistent challenge in synthetic biology. Here, we propose rationally designed new small RNAs (sRNAs) that dynamically modulate gene expression of genetic circuits with a broad range (high, medium, and low) of repression. We designed multiple multilayer genetic circuits in which the variable effector element is a transcription factor (TF) controlling downstream the production of a reporter protein. The sRNAs target TFs instead of a reporter gene, and harnessing the intrinsic RNA-interference pathway in E. coli allowed for a wide range of expression modulation of the reporter protein, including the most difficult to achieve dynamic switch to an OFF state. The synthetic sRNAs are expressed independently of the circuit(s), thus allowing for repression without modifying the circuit itself. Our work provides a frame for achieving independent modulation of gene expression and dynamic and modular control of the multilayer genetic circuits by only including an independent control circuit expressing synthetic sRNAs, without altering the structure of existing genetic circuits.

Establishing a Cell-Free Transcription-Translation Platform for Cutibacterium acnes to Prototype Engineered Metabolic and Synthetic Biology

In the past few years, new bacterial-cell-free transcription-translation systems have emerged as potent and quick platforms for protein production as well as for prototyping of DNA regulatory elements, genetic circuits, and metabolic pathways. The Gram-positive commensal Cutibacterium acnes is one of the most abundant bacteria present in the human skin microbiome. However, it has recently been reported that some C. acnes phylotypes can be associated with common inflammatory skin conditions, such as acne vulgaris, whereas others seem to play a protective role, acting as possible "skin probiotics". This fact has made C. acnes become a bacterial model of interest for the cosmetic industry. In the present study we report for the first time the development and optimization of a C. acnes-based cell-free system (CFS) that is able to produce 85 μg/mL firefly luciferase. We highlight the importance of harvesting the bacterial pellet in mid log phase and maintaining CFS reactions at 30 °C and physiological pH to obtain the optimal yield. Additionally, a C. acnes promoter library was engineered to compare coupled in vitro TX-TL activities, and a temperature biosensor was tested, demonstrating the wide range of applications of this toolkit in the synthetic biology field.

Spatial biology of Ising-like synthetic genetic networks

BACKGROUND

Understanding how spatial patterns of gene expression emerge from the interaction of individual gene networks is a fundamental challenge in biology. Developing a synthetic experimental system with a common theoretical framework that captures the emergence of short- and long-range spatial correlations (and anti-correlations) from interacting gene networks could serve to uncover generic scaling properties of these ubiquitous phenomena.

RESULTS

Here, we combine synthetic biology, statistical mechanics models, and computational simulations to study the spatial behavior of synthetic gene networks (SGNs) in Escherichia coli quasi-2D colonies growing on hard agar surfaces. Guided by the combined mechanisms of the contact process lattice simulation and two-dimensional Ising model (CPIM), we describe the spatial behavior of bi-stable and chemically coupled SGNs that self-organize into patterns of long-range correlations with power-law scaling or short-range anti-correlations. These patterns, resembling ferromagnetic and anti-ferromagnetic configurations of the Ising model near critical points, maintain their scaling properties upon changes in growth rate and cell shape.

CONCLUSIONS

Our findings shed light on the spatial biology of coupled and bistable gene networks in growing cell populations. This emergent spatial behavior could provide insights into the study and engineering of self-organizing gene patterns in eukaryotic tissues and bacterial consortia.

Synthetic biology for plant genetic engineering and molecular farming

Many efforts have been put into engineering plants to improve crop yields and stress tolerance and boost the bioproduction of valuable molecules. Yet, our capabilities are still limited due to the lack of well-characterized genetic building blocks and resources for precise manipulation and given the inherently challenging properties of plant tissues. Advancements in plant synthetic biology can overcome these bottlenecks and release the full potential of engineered plants. In this review, we first discuss the recently developed plant synthetic elements from single parts to advanced circuits, software, and hardware tools expediting the engineering cycle. Next, we survey the advancements in plant biotechnology enabled by these recent resources. We conclude the review with outstanding challenges and future directions of plant synthetic biology.

Cell factory engineering: Challenges and opportunities for synthetic biology applications

The production of high-quality recombinant proteins is critical to maintaining a continuous supply of biopharmaceuticals, such as therapeutic antibodies. Engineering mammalian cell factories presents a number of limitations typically associated with the proteotoxic stress induced upon aberrant accumulation of off-pathway protein folding intermediates, which eventually culminate in the induction of apoptosis. In this review, we will discuss advances in cell engineering and their applications at different hierarchical levels of control of the expression of recombinant proteins, from transcription and translational to posttranslational modifications and subcellular trafficking. We also highlight challenges and unique opportunities to apply modern synthetic biology tools to the design of programmable cell factories for improved biomanufacturing of therapeutic proteins.

Synthetic epigenetics-assisted microbial chassis engineering

Microbial chassis engineering is the milestone of efficient biotechnological applications. However, microbial chassis cell engineering is adversely affected by (i) regulatory tool orthogonality, (ii) host metabolic fitness, and (iii) cell population heterogeneity. Herein, we explore how synthetic epigenetics can potentially address these limitations and offer insights into prospects in this field.

Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes

Engineering biology relies on the accurate prediction of cell responses. However, making these predictions is challenging for a variety of reasons, including the stochasticity of biochemical reactions, variability between cells, and incomplete information about underlying biological processes. Machine learning methods, which can model diverse input-output relationships without requiring a priori mechanistic knowledge, are an ideal tool for this task. For example, such approaches can be used to predict gene expression dynamics given time-series data of past expression history. To explore this application, we computationally simulated single-cell responses, incorporating different sources of noise and alternative genetic circuit designs. We showed that deep neural networks trained on these simulated data were able to correctly infer the underlying dynamics of a cell response even in the presence of measurement noise and stochasticity in the biochemical reactions. The training set size and the amount of past data provided as inputs both affected prediction quality, with cascaded genetic circuits that introduce delays requiring more past data. We also tested prediction performance on a bistable auto-activation circuit, finding that our initial method for predicting a single trajectory was fundamentally ill-suited for multimodal dynamics. To address this, we updated the network architecture to predict the entire distribution of future states, showing it could accurately predict bimodal expression distributions. Overall, these methods can be readily applied to the diverse prediction tasks necessary to predict and control a variety of biological circuits, a key aspect of many synthetic biology applications.

Spontaneous Emergence of Multicellular Heritability

The major transitions in evolution include events and processes that result in the emergence of new levels of biological individuality. For collectives to undergo Darwinian evolution, their traits must be heritable, but the emergence of higher-level heritability is poorly understood and has long been considered a stumbling block for nascent evolutionary transitions. Using analytical models, synthetic biology, and biologically-informed simulations, we explored the emergence of trait heritability during the evolution of multicellularity. Prior work on the evolution of multicellularity has asserted that substantial collective-level trait heritability either emerges only late in the transition or requires some evolutionary change subsequent to the formation of clonal multicellular groups. In a prior analytical model, we showed that collective-level heritability not only exists but is usually more heritable than the underlying cell-level trait upon which it is based, as soon as multicellular groups form. Here, we show that key assumptions and predictions of that model are borne out in a real engineered biological system, with important implications for the emergence of collective-level heritability.

Artificial Small Molecules as Cofactors and Biomacromolecular Building Blocks in Synthetic Biology: Design, Synthesis, Applications, and Challenges

Enzymes are essential catalysts for various chemical reactions in biological systems and often rely on metal ions or cofactors to stabilize their structure or perform functions. Improving enzyme performance has always been an important direction of protein engineering. In recent years, various artificial small molecules have been successfully used in enzyme engineering. The types of enzymatic reactions and metabolic pathways in cells can be expanded by the incorporation of these artificial small molecules either as cofactors or as building blocks of proteins and nucleic acids, which greatly promotes the development and application of biotechnology. In this review, we summarized research on artificial small molecules including biological metal cluster mimics, coenzyme analogs (mNADs), designer cofactors, non-natural nucleotides (XNAs), and non-natural amino acids (nnAAs), focusing on their design, synthesis, and applications as well as the current challenges in synthetic biology.

Modularized synthetic biology enabled intelligent biosensors

Biosensors that sense the concentration of a specified target and produce a specific signal output have become important technology for biological analysis. Recently, intelligent biosensors have received great interest due to their adaptability to meet sophisticated demands. Advances in developing standard modules and carriers in synthetic biology have shed light on intelligent biosensors that can implement advanced analytical processing to better accommodate practical applications. This review focuses on intelligent synthetic biology-enabled biosensors (SBBs). First, we illustrate recent progress in intelligent SBBs with the capability of computation, memory storage, and self-calibration. Then, we discuss emerging applications of SBBs in point-of-care testing (POCT) and wearable monitoring. Finally, future perspectives on intelligent SBBs are proposed.

Enabling technology and core theory of synthetic biology

Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.

CSI: Contrastive data Stratification for Interaction prediction and its application to compound-protein interaction prediction

MOTIVATION

Accurately predicting the likelihood of interaction between two objects (compound-protein sequence, user-item, author-paper, etc.) is a fundamental problem in Computer Science. Current deep-learning models rely on learning accurate representations of the interacting objects. Importantly, relationships between the interacting objects, or features of the interaction, offer an opportunity to partition the data to create multi-views of the interacting objects. The resulting congruent and non-congruent views can then be exploited via contrastive learning techniques to learn enhanced representations of the objects.

RESULTS

We present a novel method, Contrastive Stratification for Interaction Prediction (CSI), to stratify (partition) a dataset in a manner that can be exploited via Contrastive Multiview Coding to learn embeddings that maximize the mutual information across congruent data views. CSI assigns a key and multiple views to each data point, where data partitions under a particular key form congruent views of the data. We showcase the effectiveness of CSI by applying it to the compound-protein sequence interaction prediction problem, a pressing problem whose solution promises to expedite drug delivery (drug-protein interaction prediction), metabolic engineering, and synthetic biology (compound-enzyme interaction prediction) applications. Comparing CSI with a baseline model that does not utilize data stratification and contrastive learning, and show gains in average precision ranging from 13.7% to 39% using compounds and sequences as keys across multiple drug-target and enzymatic datasets, and gains ranging from 16.9% to 63% using reaction features as keys across enzymatic datasets.

AVAILABILITY AND IMPLEMENTATION

Code and dataset available at https://github.com/HassounLab/CSI.

libSBOLj3: a graph-based library for design and data exchange in synthetic biology

SUMMARY

The Synthetic Biology Open Language version 3 data standard provides a graph-based approach to exchange information about biological designs. The new data model has major updates and offers several features for software tools. Here, we present libSBOLj3 to facilitate data exchange and provide interoperability between computer-aided design and automation tools using this standard. The library adopts a graph-based approach. Tool developers can extend these graphs with application-specific information and use detailed validation reports to identify errors and interoperability issues and apply best practice rules.

AVAILABILITY AND IMPLEMENTATION

The libSBOLj3 library is implemented in Java and can be downloaded or used as a Maven dependency. The open-source project, code examples and documentation about accessing and using the library are available via GitHub at https://github.com/SynBioDex/libSBOLj3.

A Synthetic Signaling Network Imitating the Action of Immune Cells in Response to Bacterial Metabolism

State-of-the-art bottom-up synthetic biology allows to replicate many basic biological functions in artificial-cell-like devices. To mimic more complex behaviors, however, artificial cells would need to perform many of these functions in a synergistic and coordinated fashion, which remains elusive. Here, a sophisticated biological response is considered, namely the capture and deactivation of pathogens by neutrophil immune cells, through the process of netosis. A consortium consisting of two synthetic agents is designed-responsive DNA-based particles and antibiotic-loaded lipid vesicles-whose coordinated action mimics the sought immune-like response when triggered by bacterial metabolism. The artificial netosis-like response emerges from a series of interlinked sensing and communication pathways between the live and synthetic agents, and translates into both physical and chemical antimicrobial actions, namely bacteria immobilization and exposure to antibiotics. The results demonstrate how advanced life-like responses can be prescribed with a relatively small number of synthetic molecular components, and outlines a new strategy for artificial-cell-based antimicrobial solutions.

Synthetic developmental biology: molecular tools to re-design plant shoots and roots

Plant morphology and anatomy strongly influence agricultural yield. Crop domestication has strived for desirable growth and developmental traits, such as larger and more fruits and semi-dwarf architecture. Genetic engineering has accelerated rational, purpose-driven engineering of plant development, but it can be unpredictable. Developmental pathways are complex and riddled with environmental and hormonal inputs, as well as feedback and feedforward interactions, which occur at specific times and places in a growing multicellular organism. Rational modification of plant development would probably benefit from precision engineering based on synthetic biology approaches. This review outlines recently developed synthetic biology technologies for plant systems and highlights their potential for engineering plant growth and development. Streamlined and high-capacity genetic construction methods (Golden Gate DNA Assembly frameworks and toolkits) allow fast and variation-series cloning of multigene transgene constructs. This, together with a suite of gene regulation tools (e.g. cell type-specific promoters, logic gates, and multiplex regulation systems), is starting to enable developmental pathway engineering with predictable outcomes in model plant and crop species.