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Modeling in synthetic biology constitutes a powerful means in our continuous search for improved performance with a rational Design-Build-Test-Learn approach. Particularly, kinetic models unravel system dynamics and enable system analysis for guiding experimental design. However, a systematic yet modular pipeline that allows one to identify the appropriate model and guide the experimental designs while tracing the entire model development and analysis is still lacking. Here, we develop BMSS2, a unified tool that streamlines and automates model selection by combining information criterion ranking with upstream and parallel analysis algorithms. These include Bayesian parameter inference, a priori and a posteriori identifiability analysis, and global sensitivity analysis. In addition, the database-driven design supports interactive model storage/retrieval to encourage reusability and facilitate automated model selection. This allows ease of model manipulation and deposition for the selection and analysis, thus enabling better utilization of models in guiding experimental design.

DNA assembly is a vital process in biotechnology and synthetic biology research, during which DNA plasmids are designed and constructed using bioparts to engineer microorganisms for a wide range of applications. Here, we present an enzymatic homology-based DNA assembly method, SENAX (Stellar ExoNuclease Assembly miX), that can efficiently assemble multiple DNA fragments at ambient temperature from 30 to 37 °C and requires homology overlap as short as 12-18 base pairs. SENAX relies only on a 3'-5' exonuclease, XthA (ExoIII), followed by Escherichia coli transformation, enabling easy scaling up and optimization. Importantly, SENAX can efficiently assemble short fragments down to 70 bp into a vector, overcoming a key shortcoming of existing commonly used homology-based technologies. To the best of our knowledge, this has not been reported elsewhere using homology-based methods. This advantage leads us to develop a framework to perform DNA assembly in a more modular manner using reusable promoter-RBS short fragments, simplifying the construction process and reducing the cost of DNA synthesis. This approach enables commonly used short bioparts (e.g., promoter, RBS, insulator, terminator) to be reused by the direct assembly of these parts into intermediate constructs. SENAX represents a novel accurate, highly efficient, and automation-friendly DNA assembly method., (© 2022. The Author(s).)

Temperature is a ubiquitous physical cue that is non-invasive, penetrative and easy to apply. In the growing field of thermogenetics, through beneficial repurposing of natural thermosensing mechanisms, synthetic biology is bringing new opportunities to design and build robust temperature-sensitive (TS) sensors which forms a thermogenetic toolbox of well characterised biological parts. Recent advancements in technological platforms available have expedited the discovery of novel or de novo thermosensors which are increasingly deployed in many practical temperature-dependent biomedical, industrial and biosafety applications. In all, the review aims to convey both the exhilarating recent technological developments underlying the advancement of thermosensors and the exciting opportunities the nascent thermogenetic field holds for biomedical and biotechnology applications., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Synthetic biology aims to develop novel biological systems and increase their reproducibility using engineering principles such as standardization and modularization. It is important that these systems can be represented and shared in a standard way to ensure they can be easily understood, reproduced, and utilized by other researchers. The Synthetic Biology Open Language (SBOL) is a data standard for sharing biological designs and information about their implementation and characterization. Previously, this standard has only been used to represent designs in systems where the same design is implemented in every cell; however, there is also much interest in multicellular systems, in which designs involve a mixture of different types of cells with differing genotype and phenotype. Here, we show how the SBOL standard can be used to represent multicellular systems, and, hence, how researchers can better share designs with the community and reliably document intended system functionality.

Constructing a complex functional gene circuit composed of different modular biological parts to achieve the desired performance remains challenging without a proper understanding of how the individual module behaves. To address this, mathematical models serve as an important tool toward better interpretation by quantifying the performance of the overall gene circuit, providing insights, and guiding the experimental designs. As different gene circuits might require exclusively different mathematical representations in the form of ordinary differential equations to capture their transient dynamic behaviors, a recurring challenge in model development is the selection of the appropriate model. Here, we developed an automated biomodel selection system (BMSS) which includes a library of pre-established models with intuitive or unintuitive features derived from a vast array of expression profiles. Selection of models is built upon the Akaike information criteria (AIC). We tested the automated platform using characterization data of routinely used inducible systems, constitutive expression systems, and several different logic gate systems (NOT, AND, and OR gates). The BMSS achieved a good agreement for all the different characterization data sets and managed to select the most appropriate model accordingly. To enable exchange and reproducibility of gene circuit design models, the BMSS platform also generates Synthetic Biology Open Language (SBOL)-compliant gene circuit diagrams and Systems Biology Markup Language (SBML) output files. All aspects of the algorithm were programmed in a modular manner to ease the efforts on model extensions or customizations by users. Taken together, the BMSS which is implemented in Python supports users in deriving the best mathematical model candidate in a fast, efficient, and automated way using part/circuit characterization data.

Background: Gene regulation in biological systems is impacted by the cellular and genetic context-dependent effects of the biological parts which comprise the circuit. Here, we have sought to elucidate the limitations of engineering biology from an architectural point of view, with the aim of compiling a set of engineering solutions for overcoming failure modes during the development of complex, synthetic genetic circuits., Results: Using a synthetic biology approach that is supported by computational modelling and rigorous characterisation, AND, OR and NOT biological logic gates were layered in both parallel and serial arrangements to generate a repertoire of Boolean operations that include NIMPLY, XOR, half adder and half subtractor logics in a single cell. Subsequent evaluation of these near-digital biological systems revealed critical design pitfalls that triggered genetic context-dependent effects, including 5' UTR interferences and uncontrolled switch-on behaviour of the supercoiled σ54 promoter. In particular, the presence of seven consecutive hairpins immediately downstream of the promoter transcription start site severely impeded gene expression., Conclusions: As synthetic biology moves forward with greater focus on scaling the complexity of engineered genetic circuits, studies which thoroughly evaluate failure modes and engineering solutions will serve as important references for future design and development of synthetic biological systems. This work describes a representative case study for the debugging of genetic context-dependent effects through principles elucidated herein, thereby providing a rational design framework to integrate multiple genetic circuits in a single prokaryotic cell.

Synthetic biology aims to systematically design and construct novel biological systems that address energy, environment, and health issues. Herein, we describe the development of a synthetic genetic system, which comprises quorum sensing, killing, and lysing devices, that enables Escherichia coli to sense and kill a pathogenic Pseudomonas aeruginosa strain through the production and release of pyocin. The sensing, killing, and lysing devices were characterized to elucidate their detection, antimicrobial and pyocin release functionalities, which subsequently aided in the construction of the final system and the verification of its designed behavior. We demonstrated that our engineered E. coli sensed and killed planktonic P. aeruginosa, evidenced by 99% reduction in the viable cells. Moreover, we showed that our engineered E. coli inhibited the formation of P. aeruginosa biofilm by close to 90%, leading to much sparser and thinner biofilm matrices. These results suggest that E. coli carrying our synthetic genetic system may provide a novel synthetic biology-driven antimicrobial strategy that could potentially be applied to fighting P. aeruginosa and other infectious pathogens.

Synthetic biology harnesses the power of natural microbes by re-engineering metabolic pathways to manufacture desired compounds. Droplet technology has emerged as a high-throughput tool to screen single cells for synthetic biology, while the challenges in sensitive flexible single-cell secretion assay for bioproduction of high-value chemicals remained. Here, a novel droplet modifiable graphene oxide (GO) aptasensor was developed, enabling sensitive flexible detection of different target compounds secreted from single cells. Fluorophore-labeled aptamers were stably anchored on GO through π–π stacking interactions to minimize the non-specific interactions for low-background detection of target compounds with high signal-to-noise ratios. The assay's versatility was exhibited by adapting aptamer sequences to measure metabolic secretions like ATP and naringenin. To show the case, engineered E. coli were constructed for the bioproduction of naringenin. The high signal-to-noise ratio assay (∼2.72) was approached to precisely measure the naringenins secreted from single E. coli in the droplets. Consequently, secretory cells (Gib) were clearly distinguished from wild-type (WT) cells, with a low overlap in cell populations (∼0%) for bioproduction. [ABSTRACT FROM AUTHOR]

Advances in hardware automation in synthetic biology laboratories are not yet fully matched by those of their software counterparts. Such automated laboratories, now commonly called biofoundries, require software solutions that would help with many specialized tasks such as batch DNA design, sample and data tracking, and data analysis, among others. Typically, many of the challenges facing biofoundries are shared, yet there is frequent wheel-reinvention where many labs develop similar software solutions in parallel. In this article, we present the first attempt at creating a standardized, open-source Python package. A number of tools will be integrated and developed that we envisage will become the obvious starting point for software development projects within biofoundries globally. Specifically, we describe the current state of available software, present usage scenarios and case studies for common problems, and finally describe plans for future development. SynBiopython is publicly available at the following address: http://synbiopython.org. [ABSTRACT FROM AUTHOR]

Synthetic biology research and technology translation has garnered increasing interest from the governments and private investors in Asia, where the technology has great potential in driving a sustainable bio‐based economy. This Perspective reviews the latest developments in the key enabling technologies of synthetic biology and its application in bio‐manufacturing, medicine, food and agriculture in Asia. Asia‐centric strengths in synthetic biology to grow the bio‐based economy, such as advances in genome editing and the presence of biofoundries combined with the availability of natural resources and vast markets, are also highlighted. The potential barriers to the sustainable development of the field, including inadequate infrastructure and policies, with suggestions to overcome these by building public‐private partnerships, more effective multi‐lateral collaborations and well‐developed governance framework, are presented. Finally, the roles of technology, education and regulation in mitigating potential biosecurity risks are examined. Through these discussions, stakeholders from different groups, including academia, industry and government, are expectantly better positioned to contribute towards the establishment of innovation and bio‐economy hubs in Asia. [ABSTRACT FROM AUTHOR]

Abstract: Modeling of biological parts is of crucial importance as it enables the in silico study of synthetic biological systems prior to the actual construction of genetic circuits, which can be time consuming and costly. Because standard biological parts are utilized to build the synthetic systems, it is important that each of these standard parts is well characterized and has a corresponding mathematical model that could simulate the characteristics of the part. These models could be used in computer aided design (CAD) tools during the design stage to facilitate the building of the model of biological systems. This paper describes the development of a mathematical model that is able to simulate both the dynamic and static performance of a biological device created using standard parts. We modeled an example quorum sensing device that produces green fluorescent protein (GFP) as reporter in the presence of Acyl Homoserine Lactone (AHL). The parameters of the model were estimated using experimental results. The simulation results show that the model was able to simulate behavior similar to experimental results. Since it is important that these models and the content in the models can be searchable and readable by machines, standard SBML (system biology markup language) format was used to store the models. All parts and reactions are fully annotated to enable easy searching, and the models follow the Minimum Information Requested In the Annotation of Models (MIRIAM) compliance as well as the Minimum Information About a Simulation Experiment (MIASE). [Copyright &y& Elsevier]

Biosynthetic production of vanillin via microbial biotransformation offers a low-cost alternative for manufacturers to meet the consumer demands for natural vanillin. However, identifying the high vanillin-producing microbial strains among multitudinous samples using the current analytical techniques such as liquid chromatography (LC) and mass spectroscopy (MS) is time consuming, cumbersome and costly. Here, we propose an alternative low-cost and rapid aptamer-based capacitive assay for sensitive and selective vanillin recognition based on the preferential binding affinity and stability of the aptamer-vanillin complex. This redox-free approach measures the changes in non-faradaic capacitance of transducing graphene-oxide (GO) activated gold electrodes immobilized with vanillin-specific aptamer. Aptamer-vanillin interaction on the GO surface alters the aptamer conformation causing surface charge redistribution detected via capacitance-frequency characteristics. The aptasensor showed a wide dynamic detection range of 10 pM-10 nM and a low limit of detection (LOD) of ~9.91 pM, which is the best reported so far. Finally, validation of the capacitive aptasensing against high-performance LC (HPLC) reference containing vanillin dissolved in a complex M9 medium with interfering compounds (ferulic acid and glycerol), to mimic typical microbial biotransformation, demonstrated good sensitivity and selectivity. The successful proof-of-concept can be translated to high-throughput electronic multi-analyte screening kits supplementary to existing analytical tools. Unlabelled Image • Label-free non-faradaic capacitive detection of vanillin by graphene oxide aptasensor. • Redox-free augmentation of bare GO vanillin response (6-fold) achieved using hybridized aptamer receptor. • The graphene oxide aptasensor showed a wide dynamic detection range (10 pM-10 nM). • The sensor showed a very low detection limit (9.91 pM) for vanillin detection. • The excellent sensor selectivity in a real complex biosynthesis HPLC matrix. [ABSTRACT FROM AUTHOR]