Integrating biological redesign: where synthetic biology came from and where it needs to go

Resource allocation in mammalian systems

Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.

The Crucial Role of Molecular Biology in Cancer Therapy: A Comprehensive Review

Molecular biology shines a light of hope amid the complex terrain of cancer, bringing revolutionary approaches to cancer treatment. Instead of providing a synopsis, this review presents an engaging story that sheds light on the genetic nuances controlling the course of cancer. This review goes beyond just listing genetic alterations to examine the complex interactions that lead to oncogene activation, exploring particular triggers such as viral infections or proto-oncogene mutations. A comprehensive grasp of the significant influence of oncogenes is possible through the classification and clarification of their function in various types of cancer. Furthermore, the role of tumor suppressor genes in controlling cell division and preventing tumor growth is fully explained, providing concrete examples and case studies to ground the conversation and create a stronger story. This study highlights the practical applications of molecular biology and provides a comprehensive overview of various detection and treatment modalities. It emphasizes the effectiveness of RNA analysis, immunohistochemistry, and next-generation sequencing (NGS) in cancer diagnosis and prognosis prediction. Examples include the individualized classification of breast cancers through RNA profiling, the use of NGS to identify actionable mutations such as epidermal growth factor receptor and anaplastic lymphoma kinase in lung cancer, and the use of immunohistochemical staining for proteins such as Kirsten rat sarcoma viral oncogene to guide treatment decisions in colorectal cancer. This paper carefully examines how molecular biology is essential to creating new strategies to fight this difficult and widespread illness. It highlights the exciting array of available therapeutic approaches, offering concrete instances of how clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein 9 (CRISPR-Cas9), targeted pharmaceuticals, immunotherapy, and treatments that induce apoptosis are driving a paradigm shift in cancer care. The revolutionary CRISPR-Cas9 system takes center stage, showcasing how precise gene editing could transform cancer therapy. This study concludes by fervently highlighting the critical role that molecular biology plays in reducing the complexity of cancer and changing the treatment landscape. It lists accomplishments but also thoughtfully examines cases and findings that progress our search for more precisely customized and effective cancer therapies.

Deciphering cell wall sensors enabling the construction of robust P. pastoris for single-cell protein production

Single-cell protein (SCP) production in the methylotrophic yeast Pichia pastoris has the potential to achieve a sustainable protein supply. However, improving the methanol fermentation efficiency and reducing carbon loss has been a long-standing challenge with far-reaching scientific and practical implications. Here, comparative transcriptomics revealed that PAS_0305, a gene directly associated with cell wall thickness under methanol stress, can be used as a target for unlocking cell wall sensors. Intracellular trehalose accumulation confirmed that cell wall sensors were activated after knocking out PAS_0305, which resulted in increased cell wall permeability. Genome-wide signal perturbations were transduced through the HOG module and the CWI pathway, which was confirmed to connected by Pbs2-Mkk. As a consequence of CWI pathway activation, ΔPAS_0305 elicited a rescue response of cell wall remodeling by increasing the β-1,3-glucan content and decreasing the chitin/mannose content. Remarkably, perturbations in global stress signals led to a fine-tuning of the metabolic network of ΔPAS_0305, resulting in a superior phenotype with highest crude protein and methanol conversion rate of 67.21% and 0.46 gDCW/g. Further genome-scale metabolic models were constructed to validate the experimental results, confirming that unlocking cell wall sensors resulted in maximized flux from methanol towards SCP and effectively addressing the issue of carbon loss in methanol fermentation. This work sheds new light on the potential of manipulating cellular signaling pathways to optimize metabolic networks and achieve exceptional phenotypic characteristics, providing new strategies for constructing versatile cell factories in P. pastoris.

Visible Light-Induced Specific Protein Reaction Delineates Early Stages of Cell Adhesion

Light is well-established for control of bond breakage but not for control of specific bond formation in complex environments. We previously engineered the diffusion-limited reactivity of the SpyTag003 peptide with its protein partner SpyCatcher003 through spontaneous isopeptide bond formation. This system enables precise and irreversible assembly of biological building blocks with applications from biomaterials to vaccines. Here we establish a system for the rapid control of this amide bond formation with visible light. We have generated a caged SpyCatcher003, which allows light triggering of covalent bond formation to SpyTag003 in mammalian cells. Photocaging is achieved through site-specific incorporation of an unnatural coumarin-lysine at the reactive site of SpyCatcher003. We showed a uniform specific reaction in cell lysate upon light activation. We then used the spatiotemporal precision of a 405 nm confocal laser for uncaging in seconds, probing the earliest events in mechanotransduction by talin, the key force sensor between the cytoskeleton and the extracellular matrix. Reconstituting talin induced rapid biphasic extension of lamellipodia, revealing the kinetics of talin-regulated cell spreading and polarization. Thereafter we determined the hierarchy of the recruitment of key components for cell adhesion. Precise control over site-specific protein reaction with visible light creates diverse opportunities for cell biology and nanoassembly.

A robust yeast biocontainment system with two-layered regulation switch dependent on unnatural amino acid

Synthetic auxotrophy in which cell viability depends on the presence of an unnatural amino acid (unAA) provides a powerful strategy to restrict unwanted propagation of genetically modified organisms (GMOs) in open environments and potentially prevent industrial espionage. Here, we describe a generic approach for robust biocontainment of budding yeast dependent on unAA. By understanding escape mechanisms, we specifically optimize our strategies by introducing designed "immunity" to the generation of amber-suppressor tRNAs and developing the transcriptional- and translational-based biocontainment switch. We further develop a fitness-oriented screening method to easily obtain multiplex safeguard strains that exhibit robust growth and undetectable escape frequency (<~10-9) on solid media for 14 days. Finally, we show that employing our multiplex safeguard system could restrict the proliferation of strains of interest in a real fermentation scenario, highlighting the great potential of our yeast biocontainment strategy to protect the industrial proprietary strains.

CRISPR-Cas9 assisted non-homologous end joining genome editing system of Halomonas bluephagenesis for large DNA fragment deletion

BACKGROUND

Halophiles possess several unique properties and have broad biotechnological applications including industrial biotechnology production. Halomonas spp., especially Halomonas bluephagenesis, have been engineered to produce various biopolyesters such as polyhydroxyalkanoates (PHA), some proteins, small molecular compounds, organic acids, and has the potential to become a chassis cell for the next-generation of industrial biotechnology (NGIB) owing to its simple culture, fast growth, contamination-resistant, low production cost, and high production value. An efficient genome editing system is the key for its engineering and application. However, the efficiency of the established CRISPR-Cas-homologous recombination (HR) gene editing tool for large DNA fragments was still relatively low. In this study, we firstly report a CRISPR-Cas9 gene editing system combined with a non-homologous end joining (NHEJ) repair system for efficient large DNA fragment deletion in Halomonas bluephagenesis.

RESULTS

Three different NHEJ repair systems were selected and functionally identified in Halomonas bluephagenesis TD01. The NHEJ system from M. tuberculosis H37Rv (Mt-NHEJ) can functionally work in H. bluephagenesis TD01, resulting in base deletion of different lengths for different genes and some random base insertions. Factors affecting knockout efficiencies, such as the number and position of sgRNAs on the DNA double-strands, the Cas9 protein promoter, and the interaction between the HR and the NHEJ repair system, were further investigated. Finally, the optimized CRISPR-Cas9-NHEJ editing system was able to delete DNA fragments up to 50 kb rapidly with high efficiency of 31.3%, when three sgRNAs on the Crick/Watson/Watson DNA double-strands and the arabinose-induced promoter Para for Cas9 were used, along with the background expression of the HR repair system.

CONCLUSIONS

This was the first report of CRISPR-Cas9 gene editing system combined with a non-homologous end joining (NHEJ) repair system for efficient large DNA fragment deletion in Halomonas spp. These results not only suggest that this editing system is a powerful genome engineering tool for constructing chassis cells in Halomonas, but also extend the application of the NHEJ repair system.

Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering

Combinatorial pathway optimization is an important tool in metabolic flux optimization. Simultaneous optimization of a large number of pathway genes often leads to combinatorial explosions. Strain optimization is therefore often performed using iterative design-build-test-learn (DBTL) cycles. The aim of these cycles is to develop a product strain iteratively, every time incorporating learning from the previous cycle. Machine learning methods provide a potentially powerful tool to learn from data and propose new designs for the next DBTL cycle. However, due to the lack of a framework for consistently testing the performance of machine learning methods over multiple DBTL cycles, evaluating the effectiveness of these methods remains a challenge. In this work, we propose a mechanistic kinetic model-based framework to test and optimize machine learning for iterative combinatorial pathway optimization. Using this framework, we show that gradient boosting and random forest models outperform the other tested methods in the low-data regime. We demonstrate that these methods are robust for training set biases and experimental noise. Finally, we introduce an algorithm for recommending new designs using machine learning model predictions. We show that when the number of strains to be built is limited, starting with a large initial DBTL cycle is favorable over building the same number of strains for every cycle.

Characterization of Hsp17, a Novel Small Heat Shock Protein, in Sphingomonas melonis TY under Heat Stress

Bacteria are constantly exposed to a variety of environmental stresses. Temperature is considered one of the most important environmental factors affecting microbial growth and survival. As ubiquitous environmental microorganisms, Sphingomonas species play essential roles in the biodegradation of organic contaminants, plant protection, and environmental remediation. Understanding the mechanism by which they respond to heat shock will help further improve cell resistance by applying synthetic biological strategies. Here, we assessed the transcriptomic and proteomic responses of Sphingomonas melonis TY to heat shock and found that stressful conditions caused significant changes in functional genes related to protein synthesis at the transcriptional level. The most notable changes observed were increases in the transcription (1,857-fold) and protein expression (11-fold) of Hsp17, which belongs to the small heat shock protein family, and the function of Hsp17 in heat stress was further investigated in this study. We found that the deletion of hsp17 reduced the capacity of the cells to tolerate high temperatures, whereas the overexpression of hsp17 significantly enhanced the ability of the cells to withstand high temperatures. Moreover, the heterologous expression of hsp17 in Escherichia coli DH5α conferred to the bacterium the ability to resist heat stress. Interestingly, its cells were elongated and formed connected cells following the increase in temperature, while hsp17 overexpression restored their normal morphology under high temperature. In general, these results indicate that the novel small heat shock protein Hsp17 greatly contributes to maintaining cell viability and morphology under stress conditions. IMPORTANCE Temperature is generally considered the most important factor affecting metabolic functions and the survival of microbes. As molecular chaperones, small heat shock proteins can prevent damaged protein aggregation during abiotic stress, especially heat stress. Sphingomonas species are widely distributed in nature, and they can frequently be found in various extreme environments. However, the role of small heat shock proteins in Sphingomonas under high-temperature stress has not been elucidated. This study greatly enhances our understanding of a novel identified protein, Hsp17, in S. melonis TY in terms of its ability to resist heat stress and maintain cell morphology under high temperature, leading to a broader understanding of how microbes adapt to environmental extremes. Furthermore, our study will provide potential heat resistance elements for further enhancing cellular resistance as well as the synthetic biological applications of Sphingomonas.

Visible light-induced specific protein reaction delineates early stages of cell adhesion

Light is well established for control of bond breakage, but not for control of specific bond formation in complex environments. We previously engineered diffusion-limited reactivity of SpyTag003 peptide with its protein partner SpyCatcher003 through spontaneous transamidation. This system enables precise and irreversible assembly of biological building blocks, with applications from biomaterials to vaccines. Here, we establish a system for rapid control of this amide bond formation with visible light. We have generated a caged SpyCatcher003, which allows light triggering of covalent bond formation to SpyTag003 in mammalian cells. Photocaging is achieved through site-specific incorporation of an unnatural coumarin-lysine at the reactive site of SpyCatcher003. We showed uniform specific reaction in cell lysate upon light activation. We then used the spatiotemporal precision of a 405 nm confocal laser for uncaging in seconds, probing the earliest events in mechanotransduction by talin, the key force sensor between the cytoskeleton and extracellular matrix. Reconstituting talin induced rapid biphasic extension of lamellipodia, revealing the kinetics of talin-regulated cell spreading and polarization. Thereafter we determined the hierarchy of recruitment of key components for cell adhesion. Precise control over site-specific protein reaction with visible light creates diverse opportunities for cell biology and nanoassembly.

Design and Evolution of Enhanced Peptide-Peptide Ligation for Modular Transglutaminase Assembly

Robust and precise tools are needed to enhance the functionality and resilience of synthetic nanoarchitectures. Here, we have employed directed evolution and rational design to build a fast-acting molecular superglue from a bacterial adhesion protein. We have generated the SnoopLigase2 coupling system, a genetically encoded route for efficient transamidation between SnoopTag2 and DogTag2 peptides. Each peptide was selected for rapid reaction by phage display screening. The optimized set allows more than 99% completion and is compatible with diverse buffers, pH values, and temperatures, accelerating the reaction over 1000-fold. SnoopLigase2 directs a specific reaction in the mammalian secretory pathway, allowing covalent display on the plasma membrane. Transglutaminase 2 (TG2) has a network of interactions and substrates amidst the mammalian cell surface and extracellular matrix. We expressed a modified TG2 with resistance to oxidative inactivation and minimal self-reactivity. SnoopLigase2 enables TG2 functionalization with transforming growth factor alpha (TGFα) in routes that would be impossible through genetic fusion. The TG2:TGFα conjugate retained transamidase activity, stably anchored TGFα for signal activation in the extracellular environment, and reprogrammed cell behavior. This modular toolbox should create new opportunities for molecular assembly, both for novel biomaterials and complex cellular environments.

Self-Regulated and Bidirectional Communication in Synthetic Cell Communities

Cell-to-cell communication is not limited to a sender releasing a signaling molecule and a receiver perceiving it but is often self-regulated and bidirectional. Yet, in communities of synthetic cells, such features that render communication efficient and adaptive are missing. Here, we report the design and implementation of adaptive two-way signaling with lipid-vesicle-based synthetic cells. The first layer of self-regulation derives from coupling the temporal dynamics of the signal, H₂O₂, production in the sender to adhesions between sender and receiver cells. This way the receiver stays within the signaling range for the duration sender produces the signal and detaches once the signal fades. Specifically, H₂O₂ acts as both a forward signal and a regulator of the adhesions by activating photoswitchable proteins at the surface for the duration of the chemiluminescence. The second layer of self-regulation arises when the adhesions render the receiver permeable and trigger the release of a backward signal, resulting in bidirectional exchange. These design rules provide a concept for engineering multicellular systems with adaptive communication.

Light and carbon: Synthetic biology toward new cyanobacteria-based living biomaterials

Cyanobacteria are ideal candidates to use in developing carbon neutral and carbon negative technologies; they are efficient photosynthesizers and amenable to genetic manipulation. Over the past two decades, researchers have demonstrated that cyanobacteria can make sustainable, useful biomaterials, many of which are engineered living materials. However, we are only beginning to see such technologies applied at an industrial scale. In this review, we explore the ways in which synthetic biology tools enable the development of cyanobacteria-based biomaterials. First we give an overview of the ecological and biogeochemical importance of cyanobacteria and the work that has been done using cyanobacteria to create biomaterials so far. This is followed by a discussion of commonly used cyanobacteria strains and synthetic biology tools that exist to engineer cyanobacteria. Then, three case studies-bioconcrete, biocomposites, and biophotovoltaics-are explored as potential applications of synthetic biology in cyanobacteria-based materials. Finally, challenges and future directions of cyanobacterial biomaterials are discussed.

SpySwitch enables pH- or heat-responsive capture and release for plug-and-display nanoassembly

Proteins can be empowered via SpyTag for anchoring and nanoassembly, through covalent bonding to SpyCatcher partners. Here we generate a switchable version of SpyCatcher, allowing gentle purification of SpyTagged proteins. We introduce numerous histidines adjacent to SpyTag’s binding site, giving moderate pH-dependent release. After phage-based selection, our final SpySwitch allows purification of SpyTag- and SpyTag003-fusions from bacterial or mammalian culture by capture at neutral pH and release at pH 5, with purity far beyond His-tag methods. SpySwitch is also thermosensitive, capturing at 4 °C and releasing at 37 °C. With flexible choice of eluent, SpySwitch-purified proteins can directly assemble onto multimeric scaffolds. 60-mer multimerization enhances immunogenicity and we use SpySwitch to purify receptor-binding domains from SARS-CoV-2 and 11 other sarbecoviruses. For these receptor-binding domains we determine thermal resilience (for mosaic vaccine development) and cross-recognition by antibodies. Antibody EY6A reacts across all tested sarbecoviruses, towards potential application against new coronavirus pandemic threats.

The SpyCatcher-SpyTag system allows protein anchoring and nanoassembly. Here, the authors engineer SpySwitch, a dually switchable Catcher which allows gentle purification of SpyTagged proteins prior to downstream applications such as the assembly of virus-like particles.

Premature translation termination mediated non-ER stress induced ATF6 activation by a ligand-dependent ribosomal frameshifting circuit

The −1 programmed ribosomal frameshifting (−1 PRF) has been explored as a gene regulatory circuit for synthetic biology applications. The −1 PRF usually uses an RNA pseudoknot structure as the frameshifting stimulator. Finding a ligand-responsive pseudoknot with efficient −1 PRF activity is time consuming and is becoming a bottleneck for its development. Inserting a guanine to guanine (GG)–mismatch pair in the 5′-stem of a small frameshifting pseudoknot could attenuate −1 PRF activity by reducing stem stability. Thus, a ligand-responsive frameshifting pseudoknot can be built using GG-mismatch–targeting small molecules to restore stem stability. Here, a pseudoknot requiring stem–loop tertiary interactions for potent frameshifting activity was used as the engineering template. This considerably amplified the effect of mismatch destabilization, and led to creation of a mammalian −1 PRF riboswitch module capable of mediating premature translation termination as a synthetic regulatory mode. Application of the synthetic circuit allowed ligand-dependent ATF6N mimic formation for the activation of protein folding–related genes involved in the unfolded protein response without an ER-stress inducing agent. With the availability of mismatch-targeting molecules, the tailored module thus paves the way for various mismatch plug-ins to streamline highly efficient orthogonal ligand-dependent −1 PRF stimulator development in the synthetic biology toolbox.

Parameterized Computational Framework for the Description and Design of Genetic Circuits of Morphogenesis Based on Contact-Dependent Signaling and Changes in Cell-Cell Adhesion

Synthetic development is a nascent field of research that uses the tools of synthetic biology to design genetic programs directing cellular patterning and morphogenesis in higher eukaryotic cells, such as mammalian cells. One specific example of such synthetic genetic programs was based on cell-cell contact-dependent signaling using synthetic Notch pathways and was shown to drive the formation of multilayered spheroids by modulating cell-cell adhesion via differential expression of cadherin family proteins in a mouse fibroblast cell line (L929). The design method for these genetic programs relied on trial and error, which limited the number of possible circuits and parameter ranges that could be explored. Here, we build a parameterized computational framework that, given a cell-cell communication network driving changes in cell adhesion and initial conditions as inputs, predicts developmental trajectories. We first built a general computational framework where contact-dependent cell-cell signaling networks and changes in cell-cell adhesion could be designed in a modular fashion. We then used a set of available in vitro results (that we call the "training set" in analogy to similar pipelines in the machine learning field) to parameterize the computational model with values for adhesion and signaling. We then show that this parameterized model can qualitatively predict experimental results from a "testing set" of available in vitro data that varied the genetic network in terms of adhesion combinations, initial number of cells, and even changes to the network architecture. Finally, this parameterized model is used to recommend novel network implementation for the formation of a four-layered structure that has not been reported previously. The framework that we develop here could function as a testing ground to identify the reachable space of morphologies that can be obtained by controlling contact-dependent cell-cell communications and adhesion with these molecular tools and in this cellular system. Additionally, we discuss how the model could be expanded to include other forms of communication or effectors for the computational design of the next generation of synthetic developmental trajectories.

Bioengineered microbial platforms for biomass-derived biofuel production - A review

Global warming issues, rapid fossil fuel diminution, and increasing worldwide energy demands have diverted accelerated attention in finding alternate sources of biofuels and energy to combat the energy crisis. Bioconversion of lignocellulosic biomass has emerged as a prodigious way to produce various renewable biofuels such as biodiesel, bioethanol, biogas, and biohydrogen. Ideal microbial hosts for biofuel synthesis should be capable of using high substrate quantity, tolerance to inhibiting substances and end-products, fast sugar transportation, and amplified metabolic fluxes to yielding enhanced fermentative bioproduct. Genetic manipulation and microbes' metabolic engineering are fascinating strategies for the economical production of next-generation biofuel from lignocellulosic feedstocks. Metabolic engineering is a rapidly developing approach to construct robust biofuel-producing microbial hosts and an important component for future bioeconomy. This approach has been widely adopted in the last decade for redirecting and revamping the biosynthetic pathways to obtain a high titer of target products. Biotechnologists and metabolic scientists have produced a wide variety of new products with industrial relevance through metabolic pathway engineering or optimizing native metabolic pathways. This review focuses on exploiting metabolically engineered microbes as promising cell factories for the enhanced production of advanced biofuels.

Theranostic cells: emerging clinical applications of synthetic biology

Synthetic biology seeks to redesign biological systems to perform novel functions in a predictable manner. Recent advances in bacterial and mammalian cell engineering include the development of cells that function in biological samples or within the body as minimally invasive diagnostics or theranostics for the real-time regulation of complex diseased states. Ex vivo and in vivo cell-based biosensors and therapeutics have been developed to target a wide range of diseases including cancer, microbiome dysbiosis and autoimmune and metabolic diseases. While probiotic therapies have advanced to clinical trials, chimeric antigen receptor (CAR) T cell therapies have received regulatory approval, exemplifying the clinical potential of cellular therapies. This Review discusses preclinical and clinical applications of bacterial and mammalian sensing and drug delivery platforms as well as the underlying biological designs that could enable new classes of cell diagnostics and therapeutics. Additionally, we describe challenges that must be overcome for more rapid and safer clinical use of engineered systems.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Advancing Undergraduate Synthetic Biology Education: Insights from a Canadian iGEM Student Perspective

The last two decades have seen vigorous activity in synthetic biology research and ever-increasing applications of its technologies. However, pedagogical research pertaining to teaching synthetic biology is scarce, especially when compared to other science and engineering disciplines. Within Canada there are only three universities that offer synthetic biology programs; two of which are at the undergraduate level. Rather than take place in formal academic settings, many Canadian undergraduate students are introduced to synthetic biology through participation in the annual International Genetically Engineered Machine (iGEM) competition. Although the iGEM competition has had a transformative impact on synthetic biology training in other nations, the impact in Canada has been relatively modest. Consequently, the iGEM competition is still a major setting for synthetic biology education in Canada. To promote further development of synthetic biology education, we surveyed undergraduate students from the Canadian iGEM design teams of 2019. We extracted insights from these data using qualitative analysis to provide recommendations for best teaching practices in synthetic biology undergraduate education, which we describe through our proposed Framework for Transdisciplinary Synthetic Biology Education (FTSBE).

Bioinformational trends in grape and wine biotechnology

The creative destruction caused by the coronavirus pandemic is yielding immense opportunity for collaborative innovation networks. The confluence of biosciences, information sciences, and the engineering of biology, is unveiling promising bioinformational futures for a vibrant and sustainable bioeconomy. Bioinformational engineering, underpinned by DNA reading, writing, and editing technologies, has become a beacon of opportunity in a world paralysed by uncertainty. This article draws on lessons from the current pandemic and previous agricultural blights, and explores bioinformational research directions aimed at future-proofing the grape and wine industry against biological shocks from global blights and climate change.

Hybrid Living Capsules Autonomously Produced by Engineered Bacteria

Bacterial cellulose (BC) has excellent material properties and can be produced sustainably through simple bacterial culture, but BC-producing bacteria lack the extensive genetic toolkits of model organisms such as Escherichia coli (E. coli). Here, a simple approach is reported for producing highly programmable BC materials through incorporation of engineered E. coli. The acetic acid bacterium Gluconacetobacter hansenii is cocultured with engineered E. coli in droplets of glucose-rich media to produce robust cellulose capsules, which are then colonized by the E. coli upon transfer to selective lysogeny broth media. It is shown that the encapsulated E. coli can produce engineered protein nanofibers within the cellulose matrix, yielding hybrid capsules capable of sequestering specific biomolecules from the environment and enzymatic catalysis. Furthermore, capsules are produced which can alter their own bulk physical properties through enzyme-induced biomineralization. This novel system uses a simple fabrication process, based on the autonomous activity of two bacteria, to significantly expand the functionality of BC-based living materials.

The number of catalytic cycles in an enzyme's lifetime and why it matters to metabolic engineering

The continuous replacement of enzymes and other proteins appropriates up to half the maintenance energy budget in microorganisms and plants. High enzyme replacement rates therefore cut the productivity of biosystems ranging from microbial fermentations to crops. However, yardsticks to assess what drives enzyme protein replacement and guidelines on how to reduce it are lacking. Accordingly, we compared enzymes’ life spans across kingdoms using a new yardstick (catalytic cycles until replacement [CCR]) and related CCR to enzyme reaction chemistry. We concluded that 1) many enzymes fail due to collateral damage from the reaction they catalyze, and 2) such damage and its attendant enzyme replacement costs are mitigable by engineering and are therefore promising targets for synthetic biology.

Metabolic engineering uses enzymes as parts to build biosystems for specified tasks. Although a part’s working life and failure modes are key engineering performance indicators, this is not yet so in metabolic engineering because it is not known how long enzymes remain functional in vivo or whether cumulative deterioration (wear-out), sudden random failure, or other causes drive replacement. Consequently, enzymes cannot be engineered to extend life and cut the high energy costs of replacement. Guided by catalyst engineering, we adopted catalytic cycles until replacement (CCR) as a metric for enzyme functional life span in vivo. CCR is the number of catalytic cycles that an enzyme mediates in vivo before failure or replacement, i.e., metabolic flux rate/protein turnover rate. We used estimated fluxes and measured protein turnover rates to calculate CCRs for ∼100–200 enzymes each from Lactococcus lactis, yeast, and Arabidopsis. CCRs in these organisms had similar ranges (<10³ to >10⁷) but different median values (3–4 × 10⁴ in L. lactis and yeast versus 4 × 10⁵ in Arabidopsis). In all organisms, enzymes whose substrates, products, or mechanisms can attack reactive amino acid residues had significantly lower median CCR values than other enzymes. Taken with literature on mechanism-based inactivation, the latter finding supports the proposal that 1) random active-site damage by reaction chemistry is an important cause of enzyme failure, and 2) reactive noncatalytic residues in the active-site region are likely contributors to damage susceptibility. Enzyme engineering to raise CCRs and lower replacement costs may thus be both beneficial and feasible.

Bioprospecting of microbial strains for biofuel production: metabolic engineering, applications, and challenges

The issues of global warming, coupled with fossil fuel depletion, have undoubtedly led to renewed interest in other sources of commercial fuels. The search for renewable fuels has motivated research into the biological degradation of lignocellulosic biomass feedstock to produce biofuels such as bioethanol, biodiesel, and biohydrogen. The model strain for biofuel production needs the capability to utilize a high amount of substrate, transportation of sugar through fast and deregulated pathways, ability to tolerate inhibitory compounds and end products, and increased metabolic fluxes to produce an improved fermentation product. Engineering microbes might be a great approach to produce biofuel from lignocellulosic biomass by exploiting metabolic pathways economically. Metabolic engineering is an advanced technology for the construction of highly effective microbial cell factories and a key component for the next-generation bioeconomy. It has been extensively used to redirect the biosynthetic pathway to produce desired products in several native or engineered hosts. A wide range of novel compounds has been manufactured through engineering metabolic pathways or endogenous metabolism optimizations by metabolic engineers. This review is focused on the potential utilization of engineered strains to produce biofuel and gives prospects for improvement in metabolic engineering for new strain development using advanced technologies.

Understanding the Targeting Mechanisms of Multi-Specific Biologics in Immunotherapy with Multiscale Modeling

Immunotherapeutics are frequently associated with adverse side effects due to the elicitation of global immune modulation. To lower the risk of these side effects, recombinant DNA technology is employed to enhance the selectivity of cell targeting by genetically fusing different biomolecules, yielding new species referred to as multi-specific biologics. The design of new multi-specific biologics is a central challenge for the realization of new immunotherapies. To understand the molecular determinants responsible for regulating the binding between multi-specific biologics and surface-bound membrane receptors, we developed a multiscale computational framework that integrates various simulation approaches covering different timescales and spatial resolutions. Our model system of multi-specific biologics contains two natural ligands of immune receptors, which are covalently tethered by a peptide linker. Using this method, a number of interesting features of multi-specific biologics were identified. Our study therefore provides an important strategy to design the next-generation biologics for immunotherapy.

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Two proteins are connected by different linkers as a model of bispecific biologics

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Conformational dynamics of biologics are captured by microsecond MD simulations

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Coarse-grained simulations are used to test binding between biologics and receptors

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Biologics with long and flexible linkers are more efficient in targeting receptors

SEVA 3.1: enabling interoperability of DNA assembly among the SEVA, BioBricks and Type IIS restriction enzyme standards

Robust synthetic biology applications rely heavily on the design and assembly of DNA parts with specific functionalities based on engineering principles. However, the assembly standards adopted by different communities vary considerably, thus limiting the interoperability of parts, vectors and methods. We hereby introduce the SEVA 3.1 platform consisting of the SEVA 3.1 vectors and the Golden Gate-based 'SevaBrick Assembly'. This platform enables the convergence of standard processes between the SEVA platform, the BioBricks and the Type IIs-mediated DNA assemblies to reduce complexity and optimize compatibility between parts and methods. It features a wide library of cloning vectors along with a core set of standard SevaBrick primers that allow multipart assembly and exchange of short functional genetic elements (promoters, RBSs) with minimal cloning and design effort. As proof of concept, we constructed, among others, multiple sfGFP expression vectors under the control of eight RBSs, eight promoters and four origins of replication as well as an inducible four-gene operon expressing the biosynthetic genes for the black pigment proviolacein. To demonstrate the interoperability of the SEVA 3.1 vectors, all constructs were characterized in both Pseudomonas putida and Escherichia coli. In summary, the SEVA 3.1 platform optimizes compatibility and modularity of inserts and backbones with a cost- and time-friendly DNA assembly method, substantially expanding the toolbox for successful synthetic biology applications in Gram-negative bacteria.

Computer-Aided Whole-Cell Design: Taking a Holistic Approach by Integrating Synthetic With Systems Biology

Computer-aided design (CAD) for synthetic biology promises to accelerate the rational and robust engineering of biological systems. It requires both detailed and quantitative mathematical and experimental models of the processes to (re)design biology, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. CAD strategies require quantitative models of cells that can capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems. We also discuss several challenges for the realization of our vision. The possibility to describe and build whole-cells in silico offers an opportunity to develop increasingly automatized, precise and accessible CAD tools and strategies.

Power to the protein: enhancing and combining activities using the Spy toolbox

Proteins span an extraordinary range of shapes, sizes and functionalities. Therefore generic approaches are needed to overcome this diversity and stream-line protein analysis or application. Here we review SpyTag technology, now used in hundreds of publications or patents, and its potential for detecting and controlling protein behaviour. SpyTag forms a spontaneous and irreversible isopeptide bond upon binding its protein partner SpyCatcher, where both parts are genetically-encoded. New variants of this pair allow reaction at a rate approaching the diffusion limit, while reversible versions allow purification of SpyTagged proteins or tuned dynamic interaction inside cells. Anchoring of SpyTag-linked proteins has been established to diverse nanoparticles or surfaces, including gold, graphene and the air/water interface. SpyTag/SpyCatcher is mechanically stable, so is widely used for investigating protein folding and force sensitivity. A toolbox of scaffolds allows SpyTag-fusions to be assembled into defined multimers, from dimers to 180-mers, or unlimited 1D, 2D or 3D networks. Icosahedral multimers are being evaluated for vaccination against malaria, HIV and cancer. For enzymes, Spy technology has increased resilience, promoted substrate channelling, and assembled hydrogels for continuous flow biocatalysis. Combinatorial increase in functionality has been achieved through modular derivatisation of antibodies, light-emitting diodes or viral vectors. In living cells, SpyTag allowed imaging of protein trafficking, retargeting of CAR-T cell killing, investigation of heart contraction, and control of nucleosome position. The simple genetic encoding and rapid irreversible reaction provide diverse opportunities to enhance protein function. We describe limitations as well as future directions.

Scale-Free Biology: Integrating Evolutionary and Developmental Thinking

When the history of life on earth is viewed as a history of cell division, all of life becomes a single cell lineage. The growth and differentiation of this lineage in reciprocal interaction with its environment can be viewed as a developmental process; hence the evolution of life on earth can also be seen as the development of life on earth. Here, in reviewing this field, some potentially fruitful research directions suggested by this change in perspective are highlighted. Variation and selection become, for example, bidirectional information flows between scales, while the notions of "cooperation" and "competition" become scale relative. The language of communication, inference, and information processing becomes more useful than the language of causation to describe the interactions of both homogeneous and heterogeneous living systems at any scale. Emerging scale-free theoretical frameworks such as predictive coding and active inference provide conceptual tools for reconceptualizing biology as the study of a unified, multiscale dynamical system.

Synthetic Biology Speeds Up Drug Target Discovery

As a rising emerging field, synthetic biology intends to realize precise regulations of cellular network by constructing artificial synthetic circuits, and it brings great opportunities to treat diseases and discover novel drug targets. Depending on the combination mode of different logic gates, various synthetic circuits are created to carry out multilevel regulations. In given synthetic circuits, drugs often act as inputs to drive circuits operation. It is becoming available to construct drug-responsive gene circuits for experimentally treating various disease models, including metabolic disease, immunity disease, cancer and bacterial infection. Synthetic biology works well in association with the CRISPR system for drug target functional screening. Remarkably, more and more well-designed circuits are developed to discover novel drug targets and precisely regulate drug therapy for diseases.

Principles of Information Storage in Small-Molecule Mixtures

Molecular data systems have the potential to store information at dramatically higher density than existing electronic media. Some of the first experimental demonstrations of this idea have used DNA, but nature also uses a wide diversity of smaller non-polymeric molecules to preserve, process, and transmit information. In this paper, we present a general framework for quantifying chemical memory, which is not limited to polymers and extends to mixtures of molecules of all types. We show that the theoretical limit for molecular information is two orders of magnitude denser by mass than DNA, although this comes with different practical constraints on total capacity. We experimentally demonstrate kilobyte-scale information storage in mixtures of small synthetic molecules, and we consider some of the new perspectives that will be necessary to harness the information capacity available from the vast non-genomic chemical space.

Controlling cell-to-cell variability with synthetic gene circuits

Cell-to-cell variability originating, for example, from the intrinsic stochasticity of gene expression, presents challenges for designing synthetic gene circuits that perform robustly. Conversely, synthetic biology approaches are instrumental in uncovering mechanisms underlying variability in natural systems. With a focus on reducing noise in individual genes, the field has established a broad synthetic toolset. This includes noise control by engineering of transcription and translation mechanisms either individually, or in combination to achieve independent regulation of mean expression and its variability. Synthetic feedback circuits use these components to establish more robust operation in closed-loop, either by drawing on, but also by extending traditional engineering concepts. In this perspective, we argue that major conceptual advances will require new theory of control adapted to biology, extensions from single genes to networks, more systematic considerations of origins of variability other than intrinsic noise, and an exploration of how noise shaping, instead of noise reduction, could establish new synthetic functions or help understanding natural functions.

Revolutionizing agriculture with synthetic biology

Synthetic biology is here to stay and will transform agriculture if given the chance. The huge challenges facing food, fuel and chemical production make it vital to give synthetic biology that chance-notwithstanding the shifts in mindset, training and infrastructure investment this demands. Here, we assess opportunities for agricultural synthetic biology and ways to remove barriers to their realization.

Synthetic biology for improving cell fate decisions and tissue engineering outcomes

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

Robust Characterization of Two Distinct Glutarate Sensing Transcription Factors of Pseudomonas putida l-Lysine Metabolism

A significant bottleneck in synthetic biology involves screening large genetically encoded libraries for desirable phenotypes such as chemical production. However, transcription factor-based biosensors can be leveraged to screen thousands of genetic designs for optimal chemical production in engineered microbes. In this study we characterize two glutarate sensing transcription factors (CsiR and GcdR) from Pseudomonas putida. The genomic contexts of csiR homologues were analyzed, and their DNA binding sites were bioinformatically predicted. Both CsiR and GcdR were purified and shown to bind upstream of their coding sequencing in vitro. CsiR was shown to dissociate from DNA in vitro when exogenous glutarate was added, confirming that it acts as a genetic repressor. Both transcription factors and cognate promoters were then cloned into broad host range vectors to create two glutarate biosensors. Their respective sensing performance features were characterized, and more sensitive derivatives of the GcdR biosensor were created by manipulating the expression of the transcription factor. Sensor vectors were then reintroduced into P. putida and evaluated for their ability to respond to glutarate and various lysine metabolites. Additionally, we developed a novel mathematical approach to describe the usable range of detection for genetically encoded biosensors, which may be broadly useful in future efforts to better characterize biosensor performance.

Characterization of Gene Circuit Parts Based on Multiobjective Optimization by Using Standard Calibrated Measurements

Standardization and characterization of biological parts is necessary for the further development of bottom-up synthetic biology. Herein, an easy-to-use methodology that embodies both a calibration procedure and a multiobjective optimization approach is proposed to characterize biological parts. The calibration procedure generates values for specific fluorescence per cell expressed as standard units of molecules of equivalent fluorescein per particle. The use of absolute standard units enhances the characterization of model parameters for biological parts by bringing measurements and estimations results from different sources into a common domain, so they can be integrated and compared faithfully. The multiobjective optimization procedure exploits these concepts by estimating the values of the model parameters, which represent biological parts of interest, while considering a varied range of experimental and circuit contexts. Thus, multiobjective optimization provides a robust characterization of them. The proposed calibration and characterization methodology can be used as a guide for good practices in dry and wet laboratories; thus allowing not only portability between models, but is also useful for generating libraries of tested and well-characterized biological parts.

Optogenetic Medicine: Synthetic Therapeutic Solutions Precision-Guided by Light

Gene- and cell-based therapies are well recognized as central pillars of next-generation medicine, but controllability remains a critical issue for clinical applications. In this context, optogenetics is opening up exciting new opportunities for precision-guided medicine by using illumination with light of appropriate intensity and wavelength as a trigger signal to achieve pinpoint spatiotemporal control of cellular activities, such as transgene expression. In this review, we highlight recent advances in optogenetics, focusing on devices for biomedical applications. We introduce the construction and applications of optogenetic-based biomedical tools to treat neurological diseases, diabetes, heart diseases, and cancer, as well as bioelectronic implants that combine light-interfaced electronic devices and optogenetic systems into portable personalized precision bioelectronic medical tools. Optogenetics-based technology promises the capability to achieve traceless, remotely controlled precision dosing of an enormous range of therapeutic outputs. Finally, we discuss the prospects for optogenetic medicine, as well as some emerging challenges.

Enhanced growth-driven stepwise inducible expression system development in haloalkaliphilic desulfurizing Thioalkalivibrio versutus

Highly toxic and flammable H₂S gas has become an environmental threat. Because of its ability to efficiently remove H₂S by oxidation, Thioalkalivibrio versutus is gaining more attention. Haloalkaliphilic autotrophs, like the bio-desulfurizing T. versutus, grow weakly. Weak growth makes any trial for developing potent genetic tools required for genetic engineering far from achieved. In this study, the fed-batch strategy improved T. versutus growth by 1.6 fold in maximal growth rate, 9-fold in O.D₆₀₀ values and about 3-fold in biomass and protein productions. The strategy also increased the favorable desulfurization product, sulfur, by 2.7 fold in percent yield and 1.5-fold in diameter. A tight iron-inducible expression system for T. versutus was successfully developed. The system was derived from fed-batch cultivation coupled with new design, build, test and validate (DPTV) approach. The inducible system was validated by toxin expression. Fed-batch cultivation coupled with DPTV approach could be applied to other autotrophs.

Building Dynamic Cellular Machineries in Droplet-Based Artificial Cells with Single-Droplet Tracking and Analysis

Although the application of droplet microfluidics has grown exponentially in chemistry and biology over the past decades, robust universal platforms for the routine generation and comprehensive analysis of droplet-based artificial cells are still rare. Here we report using microfluidic droplets to reproduce a variety of types of cellular machinery in in vitro artificial cells. In combination with a unique image-based analysis method, the system enables full automation in tracking single droplets with high accuracy, high throughput, and high sensitivity. These powerful performances allow broad applicability evident in three representative droplet-based analytical prototypes that we develop for (i) droplet digital detection, (ii) in vitro transcription and translation reactions, and (iii) spatiotemporal dynamics of cell-cycle oscillations. The capacities of this platform to generate, incubate, track, and analyze individual microdroplets via real-time, long-term imaging unleash its great potential in accelerating cell-free synthetic biology. Moreover, the wide scope covering from digital to analog to morphological detections makes this droplet analysis technique adaptable for many other divergent types of droplet-based chemical and biological assays.

Identification of five long noncoding RNAs signature and risk score for prognosis of bladder urothelial carcinoma

Nowadays, an increasing number of studies illustrated that bladder urothelial cancer (BLCA) may act as the most common subtype of urological malignancies with a high rate of recurrence and metastasis. In this study, we attempted to establish a prognostic model and identify the possible pathway crosstalk. Long noncoding RNAs (lncRNAs) and mRNA expression and corresponding clinical information of patients with BLCA were downloaded from The Cancer Genome Atlas (TCGA). The differentially expressed genes analysis, univariate Cox analysis, the least absolute shrinkage, and selection operator Cox (LASSO Cox) regression model were then applied to identify five crucial lncRNAs (AC092725.1, AC104071.1, AL023584.1, AL132642.1, and AL137804.1). The multivariate cox analysis was utilized to calculate the regression coefficients (β_i ). The risk-score model was subsequently constructed as follows: (0.13541AC092725.1) + (0.20968AC104071.1) + (0.1525AL023584.1) - (0.14768AL132642.1) + (0.14387AL137804.1). Nomogram and assessment of overall survival (OS) prediction were verificated by the receiver operating characteristic curve in the testing group. As to 3-, 5-year OS prediction, the area under curve (AUC) for the nomogram of training data set was 0.83 and 0.86. Besides, the AUC (0.883 and 0.879) presented excellent predictive power in the testing group. In addition, the calibration plots validated the predictive performance of the nomogram. Weighted correlation network analysis (WGCNA) coupled with functional enrichment analysis contributed to explore the potential pathways, including PI3K-Akt, HIF-1, and Jak-STAT signaling pathways. Construction of the risk-score model and data analysis were both derived from multiple packages on the basis of the R platform chiefly.

Stem cell bioengineering: building from stem cell biology

New fundamental discoveries in stem cell biology have yielded potentially transformative regenerative therapeutics. However, widespread implementation of stem-cell-derived therapeutics remains sporadic. Barriers that impede the development of these therapeutics can be linked to our incomplete understanding of how the regulatory networks that encode stem cell fate govern the development of the complex tissues and organs that are ultimately required for restorative function. Bioengineering tools, strategies and design principles represent core components of the stem cell bioengineering toolbox. Applied to the different layers of complexity present in stem-cell-derived systems - from gene regulatory networks in single stem cells to the systemic interactions of stem-cell-derived organs and tissues - stem cell bioengineering can address existing challenges and advance regenerative medicine and cellular therapies.

Dichotomous Nature of Bistability Generated by Negative Cooperativity in Receptor-Ligand Binding

Positive cooperativity in receptor-ligand binding plays an important role in cell signaling as it generates an ultrasensitive response, a requirement for nonlinear phenomena such as bistability and oscillations in feedback regulated reaction networks. On the other hand, negative cooperativity typically produces a hyperbolic response and is thus less explored. However, recently negative cooperativity was shown to generate an ultrasensitive response under the condition of strong ligand affinity. In this work, we have used mathematical modeling to investigate the effect of negative cooperativity in receptor-ligand interaction on the bistability in a positive feedback regulatory motif. We systematically investigated the effect of negative cooperativity, modifying the two equilibrium constants of the receptor-ligand binding, on the robustness and tunability of bistability. We show that in the regime where negative cooperativity exhibits robust bistability, positive cooperativity results in poor bistability and vice versa. Further we find that the robustness and tunability of bistability depend crucially on the stability of singly and doubly engaged receptors. Our modeling highlights the ability of negative cooperativity to produce complex phenomena with potential applications in designing synthetic devices or in explaining experimental observations in cell biology.

Spy&Go purification of SpyTag-proteins using pseudo-SpyCatcher to access an oligomerization toolbox

Peptide tags are a key resource, introducing minimal change while enabling a consistent process to purify diverse proteins. However, peptide tags often provide minimal benefit post-purification. We previously designed SpyTag, forming an irreversible bond with its protein partner SpyCatcher. SpyTag provides an easy route to anchor, bridge or multimerize proteins. Here we establish Spy&Go, enabling protein purification using SpyTag. Through rational engineering we generated SpyDock, which captures SpyTag-fusions and allows efficient elution. Spy&Go enabled sensitive purification of SpyTag-fusions from Escherichia coli, giving superior purity than His-tag/nickel-nitrilotriacetic acid. Spy&Go allowed purification of mammalian-expressed, N-terminal, C-terminal or internal SpyTag. As an oligomerization toolbox, we established a panel of SpyCatcher-linked coiled coils, so SpyTag-fusions can be dimerized, trimerized, tetramerized, pentamerized, hexamerized or heptamerized. Assembling oligomers for Death Receptor 5 stimulation, we probed multivalency effects on cancer cell death. Spy&Go, combined with simple oligomerization, should have broad application for exploring multivalency in signaling.

Mechanistic Models of Inducible Synthetic Circuits for Joint Description of DNA Copy Number, Regulatory Protein Level, and Cell Load

Accurate predictive mathematical models are urgently needed in synthetic biology to support the bottom-up design of complex biological systems, minimizing trial-and-error approaches. The majority of models used so far adopt empirical Hill functions to describe activation and repression in exogenously-controlled inducible promoter systems. However, such equations may be poorly predictive in practical situations that are typical in bottom-up design, including changes in promoter copy number, regulatory protein level, and cell load. In this work, we derived novel mechanistic steady-state models of the lux inducible system, used as case study, relying on different assumptions on regulatory protein (LuxR) and cognate promoter (Plux) concentrations, inducer-protein complex formation, and resource usage limitation. We demonstrated that a change in the considered model assumptions can significantly affect circuit output, and preliminary experimental data are in accordance with the simulated activation curves. We finally showed that the models are identifiable a priori (in the analytically tractable cases) and a posteriori, and we determined the specific experiments needed to parametrize them. Although a larger-scale experimental validation is required, in the future the reported models may support synthetic circuits output prediction in practical situations with unprecedented details.

Real-World Synthetic Biology: Is It Founded on an Engineering Approach, and Should It Be?

Authors often assert that a key feature of 21st-century synthetic biology is its use of an 'engineering approach'; design using predictive models, modular architecture, construction using well-characterized standard parts, and rigorous testing using standard metrics. This article examines whether this is, or even should be, the case. A brief survey of synthetic biology projects that have reached, or are near to, commercial application outside laboratories shows that they showed very few of these attributes. Instead, they featured much trial and error, and the use of specialized, custom components and assays. What is more, consideration of the special features of living systems suggest that a conventional engineering approach will often not be helpful. The article concludes that the engineering approach may be useful in some projects, but it should not be used to define or constrain synthetic biological endeavour, and that in fact the conventional engineering has more to gain by expanding and embracing more biological ways of working.