Synthetic Biological Systems Research Articles

Synthetic Biology Tools for Engineering Microbial Cells to Fight Superbugs.

With the increase in clinical cases of bacterial infections with multiple antibiotic resistance, the world has entered a health crisis. Overuse, inappropriate prescribing, and lack of innovation of antibiotics have contributed to the surge of microorganisms that can overcome traditional antimicrobial treatments. In 2017, the World Health Organization published a list of pathogenic bacteria, including Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Escherichia coli (ESKAPE). These bacteria can adapt to multiple antibiotics and transfer their resistance to other organisms; therefore, studies to find new therapeutic strategies are needed. One of these strategies is synthetic biology geared toward developing new antimicrobial therapies. Synthetic biology is founded on a solid and well-established theoretical framework that provides tools for conceptualizing, designing, and constructing synthetic biological systems. Recent developments in synthetic biology provide tools for engineering synthetic control systems in microbial cells. Applying protein engineering, DNA synthesis, and in silico design allows building metabolic pathways and biological circuits to control cellular behavior. Thus, synthetic biology advances have permitted the construction of communication systems between microorganisms where exogenous molecules can control specific population behaviors, induce intracellular signaling, and establish co-dependent networks of microorganisms.

Integration of electrochemical interface and cell-free synthetic biology for biosensing

• Review about advanced electrochemical biosensors built on cell-free synthetic biology systems. • Summary of the strategies and methods for efficiently integrating electrochemical interface with cell-free synthetic biology. • Critical discussion on current progress and prospect of future development directions. With increasing concerns about health, safety, and environmental issues, the development of sensitive, reliable and efficient biosensing technologies for the early detection of disease biomarkers and toxic chemicals is highly desirable. Electrochemical techniques have the advantages of low cost, high sensitivity and easy miniaturization. Cell-free synthetic biology has emerged as a powerful solution to utilize and extend the capacities of biological systems for specific purposes, and its unprecedented disagree of control and design flexibility has brought new inspiration to the field of electrochemical biosensing. This mini-review summarized the research progress on integrating cell-free synthetic biology, including genetic circuits and CRISPR-Cas systems, with nucleic acid- and enzyme-based electrochemical interfaces to develop high-performance biosensors. Finally, we also discussed the challenges and prospects of their in-depth and comprehensive integration.

Spacer2PAM: A computational framework to guide experimental determination of functional CRISPR-Cas system PAM sequences.

RNA-guided nucleases from CRISPR-Cas systems expand opportunities for precise, targeted genome modification. Endogenous CRISPR-Cas systems in many prokaryotes are attractive to circumvent expression, functionality, and unintended activity hurdles posed by heterologous CRISPR-Cas effectors. However, each CRISPR-Cas system recognizes a unique set of protospacer adjacent motifs (PAMs), which requires identification by extensive screening of randomized DNA libraries. This challenge hinders development of endogenous CRISPR-Cas systems, especially those based on multi-protein effectors and in organisms that are slow-growing or have transformation idiosyncrasies. To address this challenge, we present Spacer2PAM, an easy-to-use, easy-to-interpret R package built to predict and guide experimental determination of functional PAM sequences for any CRISPR-Cas system given its corresponding CRISPR array as input. Spacer2PAM can be used in a ‘Quick’ method to generate a single PAM prediction or in a ‘Comprehensive’ method to inform targeted PAM libraries small enough to screen in difficult to transform organisms. We demonstrate Spacer2PAM by predicting PAM sequences for industrially relevant organisms and experimentally identifying seven PAM sequences that mediate interference from the Spacer2PAM-informed PAM library for the type I-B CRISPR-Cas system from Clostridium autoethanogenum. We anticipate that Spacer2PAM will facilitate the use of endogenous CRISPR-Cas systems for industrial biotechnology and synthetic biology.

Compartmentalized Aqueous-in-Aqueous Droplets for Flow Biocatalysis.

Compartmentalized bioreactions are vital for living cells to regulate biological events since they facilitate isolated yet orchestrated reactions and releases of biological molecules. Engineering bioreactions in compartmentalized droplet bioreactors not only promotes understanding of biological cells but also enhances control in synthetic biology systems. A typical droplet bioreactor is enclosed by impermeable water-in-oil interfaces, which inhibit the reaction rate with the accumulation of aqueous products. This work constructs aqueous two-phase system (ATPS) droplet bioreactors featuring selectively permeable interfaces, which are capable of sequestering reagents in aqueous droplets while constantly releasing products into the aqueous surroundings. Benefiting from this selective permeability, the proposed droplet bioreactor achieves a conversion rate up to 63.2% compared to the 17.9% from the impermeable aqueous-in-oil droplet reactor via coupled reaction-separation. More importantly, it is revealed that uniform aqueous-in-aqueous droplet clusters by microfluidics exhibit an up to 6-fold reaction rate enhancement compared to non-microfluidic ATPS reactors, indicating a unique flow interface effect in droplet clusters. This work offers a new route to allow enzymatic reactions to benefit from efficient flow chemistry via optimized aqueous-aqueous interfaces.

GAMES: A Dynamic Model Development Workflow for Rigorous Characterization of Synthetic Genetic Systems.

Mathematical modeling is invaluable for advancing understanding and design of synthetic biological systems. However, the model development process is complicated and often unintuitive, requiring iteration on various computational tasks and comparisons with experimental data. Ad hoc model development can pose a barrier to reproduction and critical analysis of the development process itself, reducing the potential impact and inhibiting further model development and collaboration. To help practitioners manage these challenges, we introduce the Generation and Analysis of Models for Exploring Synthetic Systems (GAMES) workflow, which includes both automated and human-in-the-loop processes. We systematically consider the process of developing dynamic models, including model formulation, parameter estimation, parameter identifiability, experimental design, model reduction, model refinement, and model selection. We demonstrate the workflow with a case study on a chemically responsive transcription factor. The generalizable workflow presented in this tutorial can enable biologists to more readily build and analyze models for various applications.

Primer on Mathematical Modeling in C. elegans.

Recently, applications of mathematical and computational models to biological processes have helped investigators to systematically interpret data, test hypotheses built on experimental data, generate new hypotheses, and guide the design of new experiments, protocols, and synthetic biological systems. Availability of diverse quantitative data is a prerequisite for successful mathematical modeling. The ability to acquire high-quality quantitative data for a broad range of biological processes and perform precise perturbation makes C. elegans an ideal model system for such studies. In this primer, we examine the general procedure of modeling biological systems and demonstrate this process using the heat-shock response in C. elegans as a case study. Our goal is to facilitate the initial discussion between worm biologists and their potential collaborators from quantitative disciplines.

Cell-Free Biosensors and AI Integration.

Cell-free biosensors hold a great potential as alternatives for traditional analytical chemistry methods providing low-cost low-resource measurement of specific chemicals. However, their large-scale use is limited by the complexity of their development.In this chapter, we present a standard methodology based on computer-aided design (CAD ) tools that enables fast development of new cell-free biosensors based on target molecule information transduction and reporting through metabolic and genetic layers, respectively. Such systems can then be repurposed to represent complex computational problems, allowing defined multiplex sensing of various inputs and integration of artificial intelligence in synthetic biological systems.

Application of Phage Recombinase Systems in Synthetic Biology

Application of Phage Recombinase Systems in Synthetic Biology

Design and Evaluation of Synthetic RNA-Based Incoherent Feed-Forward Loop Circuits.

RNA-based regulators are promising tools for building synthetic biological systems that provide a powerful platform for achieving a complex regulation of transcription and translation. Recently, de novo-designed synthetic RNA regulators, such as the small transcriptional activating RNA (STAR), toehold switch (THS), and three-way junction (3WJ) repressor, have been utilized to construct RNA-based synthetic gene circuits in living cells. In this work, we utilized these regulators to construct type 1 incoherent feed-forward loop (IFFL) circuits in vivo and explored their dynamic behaviors. A combination of a STAR and 3WJ repressor was used to construct an RNA-only IFFL circuit. However, due to the fast kinetics of RNA–RNA interactions, there was no significant timescale difference between the direct activation and the indirect inhibition, that no pulse was observed in the experiments. These findings were confirmed with mechanistic modeling and simulation results for a wider range of conditions. To increase delay in the inhibition pathway, we introduced a protein synthesis process to the circuit and designed an RNA–protein hybrid IFFL circuit using THS and TetR protein. Simulation results indicated that pulse generation could be achieved with this RNA–protein hybrid model, and this was further verified with experimental realization in E. coli. Our findings demonstrate that while RNA-based regulators excel in speed as compared to protein-based regulators, the fast reaction kinetics of RNA-based regulators could also undermine the functionality of a circuit (e.g., lack of significant timescale difference). The agreement between experiments and simulations suggests that the mechanistic modeling can help debug issues and validate the hypothesis in designing a new circuit. Moreover, the applicability of the kinetic parameters extracted from the RNA-only circuit to the RNA–protein hybrid circuit also indicates the modularity of RNA-based regulators when used in a different context. We anticipate the findings of this work to guide the future design of gene circuits that rely heavily on the dynamics of RNA-based regulators, in terms of both modeling and experimental realization.

An engineered IL-2 reprogrammed for anti-tumor therapy using a semi-synthetic organism

The implementation of applied engineering principles to create synthetic biological systems promises to revolutionize medicine, but application of fundamentally redesigned organisms has thus far not impacted practical drug development. Here we utilize an engineered microbial organism with a six-letter semi-synthetic DNA code to generate a library of site-specific, click chemistry compatible amino acid substitutions in the human cytokine IL-2. Targeted covalent modification of IL-2 variants with PEG polymers and screening identifies compounds with distinct IL-2 receptor specificities and improved pharmacological properties. One variant, termed THOR-707, selectively engages the IL-2 receptor beta/gamma complex without engagement of the IL-2 receptor alpha. In mice, administration of THOR-707 results in large-scale activation and amplification of CD8+ T cells and NK cells, without Treg expansion characteristic of IL-2. In syngeneic B16-F10 tumor-bearing mice, THOR-707 enhances drug accumulation in the tumor tissue, stimulates tumor-infiltrating CD8+ T and NK cells, and leads to a dose-dependent reduction of tumor growth. These results support further characterization of the immune modulatory, anti-tumor properties of THOR-707 and represent a fundamental advance in the application of synthetic biology to medicine, leveraging engineered semi-synthetic organisms as cellular factories to facilitate discovery and production of differentiated classes of chemically modified biologics.

Rate dependent adhesion of nanoscale polymer contacts

The adhesive interactions between polymer nanofibers strongly influence the mechanical behavior of their networks in synthetic materials and biological systems. A treatment of the adhesive interactions at polymer contacts must take into account the viscoelastic behavior of the material in the contact region and the associated energy dissipation. This study focuses on the rate-dependent adhesion of polyacrylonitrile (PAN) nanofibers which interact with strong van der Waals forces. Experimental measurements of the detachment force between PAN fibers with diameters 400 and 4000 nm showed that the apparent work of adhesion increases with the rate of unloading by a factor of two within a three orders of magnitude increase in the unloading rate. In order to obtain further insights into this rate-dependent adhesion, the Maugis-Dugdale (M-D) elastic contact model for normal detachment was extended to include a linear viscoelastic behavior of the PAN nanofibers. The extended model predicted the normal pull-off force instabilities in good agreement with the experiments, capturing well the effect of the unloading rate. The calculated viscoelastic time constants were of the order of milliseconds, suggesting fast relaxations in the contact region, which explain the instantaneous full-strength reattachment of nanoscale polymer fibers during slip-stick experiments.

Toward Full-Stack In Silico Synthetic Biology: Integrating Model Specification, Simulation, Verification, and Biological Compilation.

We present the Infobiotics Workbench (IBW), a user-friendly, scalable, and integrated computational environment for the computer-aided design of synthetic biological systems. It supports an iterative workflow that begins with specification of the desired synthetic system, followed by simulation and verification of the system in high-performance environments and ending with the eventual compilation of the system specification into suitable genetic constructs. IBW integrates modeling, simulation, verification, and biocompilation features into a single software suite. This integration is achieved through a new domain-specific biological programming language, the Infobiotics Language (IBL), which tightly combines these different aspects of in silico synthetic biology into a full-stack integrated development environment. Unlike existing synthetic biology modeling or specification languages, IBL uniquely blends modeling, verification, and biocompilation statements into a single file. This allows biologists to incorporate design constraints within the specification file rather than using decoupled and independent formalisms for different in silico analyses. This novel approach offers seamless interoperability across different tools as well as compatibility with SBOL and SBML frameworks and removes the burden of doing manual translations for standalone applications. We demonstrate the features, usability, and effectiveness of IBW and IBL using well-established synthetic biological circuits.

Recent Advances in the Synthetic Biology of Natural Drugs.

Natural drugs have been transformed and optimized during the long process of evolution. These compounds play a very important role in the protection of human health and treatment of human diseases. Sustainable approaches to the generation of raw materials for pharmaceutical products have been extensively investigated in drug research and development because chemical synthesis is costly and generates pollution. The present review provides an overview of the recent advances in the synthetic biology of natural drugs. Particular attention is paid to the investigations of drugs that may be mass-produced by the pharmaceutical industry after optimization of the corresponding synthetic systems. The present review describes the reconstruction and optimization of biosynthetic pathways for nine drugs, including seven drugs from plant sources and two drugs from microbial sources, suggesting a new strategy for the large-scale preparation of some rare natural plant metabolites and highly bioactive microbial compounds. Some of the suggested synthetic methods remain in a preliminary exploration stage; however, a number of these methods demonstrated considerable application potential. The authors also discuss the advantages and disadvantages of the application of synthetic biology and various expression systems for heterologous expression of natural drugs. Thus, the present review provides a useful perspective for researchers attempting to use synthetic biology to produce natural drugs.

Synthetic neural-like computing in microbial consortia for pattern recognition

Complex biological systems in nature comprise cells that act collectively to solve sophisticated tasks. Synthetic biological systems, in contrast, are designed for specific tasks, following computational principles including logic gates and analog design. Yet such approaches cannot be easily adapted for multiple tasks in biological contexts. Alternatively, artificial neural networks, comprised of flexible interactions for computation, support adaptive designs and are adopted for diverse applications. Here, motivated by the structural similarity between artificial neural networks and cellular networks, we implement neural-like computing in bacteria consortia for recognizing patterns. Specifically, receiver bacteria collectively interact with sender bacteria for decision-making through quorum sensing. Input patterns formed by chemical inducers activate senders to produce signaling molecules at varying levels. These levels, which act as weights, are programmed by tuning the sender promoter strength Furthermore, a gradient descent based algorithm that enables weights optimization was developed. Weights were experimentally examined for recognizing 3 × 3-bit pattern.

Control of synthetic microbial consortia in time, space, and composition.

While synthetic microbial systems are becoming increasingly complicated, single-strain systems cannot match the complexity of their multicellular counterparts. Such complexity, however, is much more difficult to control. Recent advances have increased our ability to control temporal, spatial, and community compositional organization, including modular adhesive systems, strain growth relationships, and asymmetric cell division. While these systems generally work independently, combining them into unified systems has proven difficult. Once such unification is proven successful we will unlock a new frontier of synthetic biology and open the door to the creation of synthetic biological systems with true multicellularity.

Engineering AraC to make it responsive to light instead of arabinose.

The L-arabinose-responsive AraC and its cognate PBAD promoter underlie one of the most often used chemically inducible prokaryotic gene expression systems in microbiology and synthetic biology. Here, we change the sensing capability of AraC from L-arabinose to blue light, making its dimerization and the resulting PBAD activation light-inducible. We engineer an entire family of blue light-inducible AraC dimers in Escherichia coli (BLADE) to control gene expression in space and time. We show that BLADE can be used with pre-existing L-arabinose-responsive plasmids and strains, enabling optogenetic experiments without the need to clone. Furthermore, we apply BLADE to control, with light, the catabolism of L-arabinose, thus externally steering bacterial growth with a simple transformation step. Our work establishes BLADE as a highly practical and effective optogenetic tool with plug-and-play functionality-features that we hope will accelerate the broader adoption of optogenetics and the realization of its vast potential in microbiology, synthetic biology and biotechnology.

Bespoke Turing Systems

Reaction–diffusion systems are an intensively studied form of partial differential equation, frequently used to produce spatially heterogeneous patterned states from homogeneous symmetry breaking via the Turing instability. Although there are many prototypical “Turing systems” available, determining their parameters, functional forms, and general appropriateness for a given application is often difficult. Here, we consider the reverse problem. Namely, suppose we know the parameter region associated with the reaction kinetics in which patterning is required—we present a constructive framework for identifying systems that will exhibit the Turing instability within this region, whilst in addition often allowing selection of desired patterning features, such as spots, or stripes. In particular, we show how to build a system of two populations governed by polynomial morphogen kinetics such that the: patterning parameter domain (in any spatial dimension), morphogen phases (in any spatial dimension), and even type of resulting pattern (in up to two spatial dimensions) can all be determined. Finally, by employing spatial and temporal heterogeneity, we demonstrate that mixed mode patterns (spots, stripes, and complex prepatterns) are also possible, allowing one to build arbitrarily complicated patterning landscapes. Such a framework can be employed pedagogically, or in a variety of contemporary applications in designing synthetic chemical and biological patterning systems. We also discuss the implications that this freedom of design has on using reaction–diffusion systems in biological modelling and suggest that stronger constraints are needed when linking theory and experiment, as many simple patterns can be easily generated given freedom to choose reaction kinetics.

Analysis of metabolic network disruption in engineered microbial hosts due to enzyme promiscuity

Increasing understanding of metabolic and regulatory networks underlying microbial physiology has enabled creation of progressively more complex synthetic biological systems for biochemical, biomedical, agricultural, and environmental applications. However, despite best efforts, confounding phenotypes still emerge from unforeseen interplay between biological parts, and the design of robust and modular biological systems remains elusive. Such interactions are difficult to predict when designing synthetic systems and may manifest during experimental testing as inefficiencies that need to be overcome. Transforming organisms such as Escherichia coli into microbial factories is achieved via several engineering strategies, used individually or in combination, with the goal of maximizing the production of chosen target compounds. One technique relies on suppressing or overexpressing selected genes; another involves introducing heterologous enzymes into a microbial host. These modifications steer mass flux towards the set of desired metabolites but may create unexpected interactions. In this work, we develop a computational method, termed Metabolic Disruption Workflow (MDFlow), for discovering interactions and network disruptions arising from enzyme promiscuity – the ability of enzymes to act on a wide range of molecules that are structurally similar to their native substrates. We apply MDFlow to two experimentally verified cases where strains with essential genes knocked out are rescued by interactions resulting from overexpression of one or more other genes. We demonstrate how enzyme promiscuity may aid cells in adapting to disruptions of essential metabolic functions. We then apply MDFlow to predict and evaluate a number of putative promiscuous reactions that can interfere with two heterologous pathways designed for 3-hydroxypropionic acid (3-HP) production. Using MDFlow, we can identify putative enzyme promiscuity and the subsequent formation of unintended and undesirable byproducts that are not only disruptive to the host metabolism but also to the intended end-objective of high biosynthetic productivity and yield. As we demonstrate, MDFlow provides an innovative workflow to systematically identify incompatibilities between the native metabolism of the host and its engineered modifications due to enzyme promiscuity.

Host‐Orthogonal Genetic Systems: Development of Host‐Orthogonal Genetic Systems for Synthetic Biology (Adv. Biology 3/2021)

The orthogonal genetic system is an artificially engineered system that runs in parallel with the native genetic system. In article number 2000252, Huimin Zhao, Shuobo Shi, and co-workers describe the advances and future challenges in developing such systems. The orthogonal genetic system can be controlled at will and is widely applicable in biosynthesis and thus represents a powerful approach for synthetic biology.

Access and benefit‐sharing DNA Componentry for plant synthetic biology: Bioparts expressed in plant chassis

Societal Impact StatementThe “Parts Agenda” is an approach to synthetic biology that fragments genetic resources into functional bioparts to help design and build biological devices and systems. Access and benefit‐sharing (ABS), and the issue of how to regulate digital sequence information (DSI) within the current ABS regime, poses a problem for synthetic biology because it assumes fragmented and abstracted bioparts can be traced to their country of origin for the purposes of benefit‐sharing, and that contributions to information and knowledge can be quantified and appropriately valued. Any DSI regulatory solutions should account for genetic resource fragmentation and other complexities of modern scientific practice.Summary The inclusion of digital sequence information (DSI, including genetic sequence data) in the existing access and benefit‐sharing (ABS) regime will alter the practice of synthetic biology. The potential impediments could be magnified for the “Parts Agenda”: the approach to synthetic biology that fragments genetic resources into their smallest functional units to create standardized, interchangeable “bioparts”, the building blocks for assembling synthetic biological devices. These biological devices are themselves interchangeable and can be used to engineer higher order synthetic biological systems. This article examines how the extension of ABS laws to include DSI could foreseeably apply to the creation and use of plant‐derived and other bioparts in engineered biological devices expressed in plant chassis. The article demonstrates that ABS issues will be similar for all approaches to synthetic biology, but that the Parts Agenda is uniquely exposed to the potential regulatory burden of bilateral ABS transactions between users and providers of genetic resources. The original vision for bioparts was one of openness and sharing, with access and use unencumbered by intellectual property. This article shows that open access to bioparts will not last long if DSI is enclosed within the current ABS regime, destabilizing the values of openness and sharing in synthetic biology that are ostensibly foundational to this still developing field.