Synthetic biology industry: data-driven design is creating new opportunities in biotechnology

Deconstructing synthetic biology across scales: a conceptual approach for training synthetic biologists

Synthetic biology allows us to reuse, repurpose, and reconfigure biological systems to address society's most pressing challenges. Developing biotechnologies in this way requires integrating concepts across disciplines, posing challenges to educating students with diverse expertise. We created a framework for synthetic biology training that deconstructs biotechnologies across scales-molecular, circuit/network, cell/cell-free systems, biological communities, and societal-giving students a holistic toolkit to integrate cross-disciplinary concepts towards responsible innovation of successful biotechnologies. We present this framework, lessons learned, and inclusive teaching materials to allow its adaption to train the next generation of synthetic biologists.

Data hazards in synthetic biology

Data science is playing an increasingly important role in the design and analysis of engineered biology. This has been fueled by the development of high-throughput methods like massively parallel reporter assays, data-rich microscopy techniques, computational protein structure prediction and design, and the development of whole-cell models able to generate huge volumes of data. Although the ability to apply data-centric analyses in these contexts is appealing and increasingly simple to do, it comes with potential risks. For example, how might biases in the underlying data affect the validity of a result and what might the environmental impact of large-scale data analyses be? Here, we present a community-developed framework for assessing data hazards to help address these concerns and demonstrate its application to two synthetic biology case studies. We show the diversity of considerations that arise in common types of bioengineering projects and provide some guidelines and mitigating steps. Understanding potential issues and dangers when working with data and proactively addressing them will be essential for ensuring the appropriate use of emerging data-intensive AI methods and help increase the trustworthiness of their applications in synthetic biology.

Golden Standard: a complete standard, portable, and interoperative MoClo tool for model and non-model proteobacteria

Modular cloning has become a benchmark technology in synthetic biology. However, a notable disparity exists between its remarkable development and the need for standardization to facilitate seamless interoperability among systems. The field is thus impeded by an overwhelming proliferation of organism-specific systems that frequently lack compatibility. To overcome these issues, we present Golden Standard (GS), a Type IIS assembly method underpinned by the Standard European Vector Architecture. GS unlocks modular cloning applications for most bacteria, and delivers combinatorial multi-part assembly to create genetic circuits of up to twenty transcription units (TUs). Reliance on MoClo syntax renders GS fully compatible with many existing tools and it sets the path towards efficient reusability of available part libraries and assembled TUs. GS was validated in terms of DNA assembly, portability, interoperability and phenotype engineering in α-, β-, γ- and δ-proteobacteria. Furthermore, we provide a computational pipeline for parts characterization that was used to assess the performance of GS parts. To promote community-driven development of GS, we provide a dedicated web-portal including a repository of parts, vectors, and Wizard and Setup tools that guide users in designing constructs. Overall, GS establishes an open, standardized framework propelling the progress of synthetic biology as a whole.

Unigems: plasmids and parts to facilitate teaching on assembly, gene expression control and logic in E. coli

Synthetic biology enables the creative combination of engineering and molecular biology for exploration of fundamental aspects of biological phenomena. However, there are limited resources available for such applications in the educational context, where straightforward setup, easily measurable phenotypes and extensibility are of particular importance. We developed unigems, a set of ten plasmids that enable classroom-based investigation of gene-expression control and biological logic gates to facilitate teaching synthetic biology and genetic engineering. It is built on a high-copy plasmid backbone and is easily extensible thanks to a common primer set that facilitates Gibson assembly of PCR-generated or synthesized DNA parts into the target vector. It includes two reporter genes with either two constitutive (high- or low-level) or two inducible (lactose- or arabinose-) promoters, as well as a single-plasmid implementation of an AND logic gate. The set can readily be employed in undergraduate teaching settings, during outreach events and for training of iGEM teams. All plasmids have been deposited in Addgene.

Graph Visualization: Alternative Models Inspired by Bioinformatics

Currently, the methods and means of human-machine interaction and visualization as its integral part are being increasingly developed. In various fields of scientific knowledge and technology, there is a need to find and select the most effective visualization models for various types of data, as well as to develop automation tools for the process of choosing the best visualization model for a specific case. There are many data visualization tools in various application fields, but at the same time, the main difficulty lies in presenting data of an interconnected (node-link) structure, i.e., networks. Typically, a lot of software means use graphs as the most straightforward and versatile models. To facilitate visual analysis, researchers are developing ways to arrange graph elements to make comparing, searching, and navigating data easier. However, in addition to graphs, there are many other visualization models that are less versatile but have the potential to expand the capabilities of the analyst and provide alternative solutions. In this work, we collected a variety of visualization models, which we call alternative models, to demonstrate how different concepts of information representation can be realized. We believe that adapting these models to improve the means of human-machine interaction will help analysts make significant progress in solving the problems researchers face when working with graphs.

Molecular membrane separation: plants inspire new technologies

Plants draw up their surrounding soil solution to gain water and nutrients required for growth, development and reproduction. Obtaining adequate water and nutrients involves taking up both desired and undesired elements from the soil solution and separating resources from waste. Desirable and undesirable elements in the soil solution can share similar chemical properties, such as size and charge. Plants use membrane separation mechanisms to distinguish between different molecules that have similar chemical properties. Membrane separation enables distribution or retention of resources and efflux or compartmentation of waste. Plants use specialised membrane separation mechanisms to adapt to challenging soil solution compositions and distinguish between resources and waste. Coordination and regulation of these mechanisms between different tissues, cell types and subcellular membranes supports plant nutrition, environmental stress tolerance and energy management. This review considers membrane separation mechanisms in plants that contribute to specialised separation processes and highlights mechanisms of interest for engineering plants with enhanced performance in challenging conditions and for inspiring the development of novel industrial membrane separation technologies. Knowledge gained from studying plant membrane separation mechanisms can be applied to developing precision separation technologies. Separation technologies are needed for harvesting resources from industrial wastes and transitioning to a circular green economy.

Synthetic Biology Design as a Paradigm Shift toward Manufacturing Affordable Adeno-Associated Virus Gene Therapies

Gene therapy has demonstrated enormous potential for changing how we combat disease. By directly engineering the genetic composition of cells, it provides a broad range of options for improving human health. Adeno-associated viruses (AAVs) represent a leading gene therapy vector and are expected to address a wide range of conditions in the coming decade. Three AAV therapies have already been approved by the FDA to treat Leber's congenital amaurosis, spinal muscular atrophy, and hemophilia B. Yet these therapies cost around $850,000, $2,100,000, and $3,500,000, respectively. Such prices limit the broad applicability of AAV gene therapy and make it inaccessible to most patients. Much of this problem arises from the high manufacturing costs of AAVs. At the same time, the field of synthetic biology has grown rapidly and has displayed a special aptitude for addressing biomanufacturing problems. Here, we discuss emerging efforts to apply synthetic biology design to decrease the price of AAV production, and we propose that such efforts could play a major role in making gene therapy much more widely accessible.

Prediction of strain engineerings that amplify recombinant protein secretion through the machine learning approach MaLPHAS

This article presents a discussion of the process of precision fermentation (PF), describing the history of the space, the expected 70% growth over the next 5 years, various applications of precision fermented products, and the markets available to be disrupted by the technology. A range of prokaryotic and eukaryotic host organisms used for PF are described, with the advantages, disadvantages and applications of each. The process of setting up PF and strain engineering is described, as well as various ways that computational analysis and design techniques can be employed to assist PF engineering. The article then describes the design and implementation of a machine learning method, machine learning predictions having amplified secretion (MaLPHAS) to predict strain engineerings, which optimise the secretion of a recombinant protein. This approach showed an in silico cross-validated R 2 accuracy on the training data of up to 46.6% and in an in vitro test on a Komagataella phaffii strain, identified one gene engineering out of five predicted, which was shown to double the secretion of a heterologous protein and outperform three of the best-known edits from the literature for improving secretion in K. phaffii.

basicsynbio and the BASIC SEVA collection: software and vectors for an established DNA assembly method

Standardized deoxyribonucleic acid (DNA) assembly methods utilizing modular components provide a powerful framework to explore designs and iterate through Design-Build-Test-Learn cycles. Biopart Assembly Standard for Idempotent Cloning (BASIC) DNA assembly uses modular parts and linkers, is highly accurate, easy to automate, free for academic and commercial use and enables hierarchical assemblies through an idempotent format. These features enable applications including pathway engineering, ribosome binding site (RBS) tuning, fusion protein engineering and multiplexed guide ribonucleic acid (RNA) expression. In this work, we present basicsynbio, open-source software encompassing a Web App (https://basicsynbio.web.app/) and Python Package (https://github.com/LondonBiofoundry/basicsynbio), enabling BASIC construct design via simple drag-and-drop operations or programmatically. With basicsynbio, users can access commonly used BASIC parts and linkers while designing new parts and assemblies with exception handling for common errors. Users can export sequence data and create instructions for manual or acoustic liquid-handling platforms. Instruction generation relies on the BasicBuild Open Standard, which is parsed for bespoke workflows and is serializable in JavaScript Object Notation for transfer and storage. We demonstrate basicsynbio, assembling 30 vectors using sequences including modules from the Standard European Vector Architecture (SEVA). The BASIC SEVA vector collection is compatible with BASIC and Golden Gate using BsaI. Vectors contain one of six antibiotic resistance markers and five origins of replication from different compatibility groups. The collection is available via Addgene under an OpenMTA agreement. Furthermore, vector sequences are available from within the basicsynbio application programming interface with other collections of parts and linkers, providing a powerful environment for designing assemblies for bioengineering applications. Graphical Abstract.

Schrödinger and the Possible Existence of Different Types of Life

Eighty years ago, Nobel Prize-winner physicist Erwin Schrödinger gave three lectures in Dublin’s Trinity College, titled What is Life? The physical aspect of the living cell to explain life in terms of the chemistry and physics laws. Life definitions rely on the cellular theory, which poses in the first place that life is made up of cells. The recent discovery of giant viruses, along with the development of synthetic cells at the beginning of century 21st, has challenged the current idea of what life is. Thus, rather than having arrived at a close answer to Schrödinger’s question, modern biology has touched down at a novel scenario in which several types of life—as opposed to only one—actually might exist on Earth and possibly the Universe. Eighty years after the Dublin lectures, the Schrödinger question could be: “What are lives”?

Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing

Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a wide range of scientific disciplines, including the development of artificial cell factories for biomanufacturing. In this paper, we review the latest studies on the application of data-driven methods for the design of new proteins, pathways, and strains. We first briefly introduce the various types of data and databases relevant to industrial biomanufacturing, which are the basis for data-driven research. Different types of algorithms, including traditional ML and more recent deep learning methods, are also presented. We then demonstrate how these data-based approaches can be applied to address various issues in cell factory development using examples from recent studies, including the prediction of protein function, improvement of metabolic models, and estimation of missing kinetic parameters, design of non-natural biosynthesis pathways, and pathway optimization. In the last section, we discuss the current limitations of these data-driven approaches and propose that data-driven methods should be integrated with mechanistic models to complement each other and facilitate the development of synthetic strains for industrial biomanufacturing.

Machine Learning Guided Batched Design of a Bacterial Ribosome Binding Site

Optimization of gene expression levels is an essential part of the organism design process. Fine control of this process can be achieved by engineering transcription and translation control elements, including the ribosome binding site (RBS). Unfortunately, the design of specific genetic parts remains challenging because of the lack of reliable design methods. To address this problem, we have created a machine learning guided Design-Build-Test-Learn (DBTL) cycle for the experimental design of bacterial RBSs to demonstrate how small genetic parts can be reliably designed using relatively small, high-quality data sets. We used Gaussian Process Regression for the Learn phase of the cycle and the Upper Confidence Bound multiarmed bandit algorithm for the Design of genetic variants to be tested in vivo. We have integrated these machine learning algorithms with laboratory automation and high-throughput processes for reliable data generation. Notably, by Testing a total of 450 RBS variants in four DBTL cycles, we have experimentally validated RBSs with high translation initiation rates equaling or exceeding our benchmark RBS by up to 34%. Overall, our results show that machine learning is a powerful tool for designing RBSs, and they pave the way toward more complicated genetic devices.

Virtual Parts Repository 2: Model-Driven Design of Genetic Regulatory Circuits

Engineering genetic regulatory circuits is key to the creation of biological applications that are responsive to environmental changes. Computational models can assist in understanding especially large and complex circuits for which manual analysis is infeasible, permitting a model-driven design process. However, there are still few tools that offer the ability to simulate the system under design. One of the reasons for this is the lack of accessible model repositories or libraries that cater to the modular composition of models of synthetic systems. Here, we present the second version of the Virtual Parts Repository, a framework to facilitate the model-driven design of genetic regulatory circuits, which provides reusable, modular, and composable models. The new framework is service-oriented, easier to use in computational workflows, and provides several new features and access methods. New features include supporting hierarchical designs via a graph-based repository or compatible remote repositories, enriching existing designs, and using designs provided in Synthetic Biology Open Language documents to derive system-scale and hierarchical Systems Biology Markup Language models. We also present a reaction-based modeling abstraction inspired by rule-based modeling techniques to facilitate scalable and modular modeling of complex and large designs. This modeling abstraction enhances the modeling capability of the framework, for example, to incorporate design patterns such as roadblocking, distributed deployment of genetic circuits using plasmids, and cellular resource dependency. The framework and the modeling abstraction presented in this paper allow computational design tools to take advantage of computational simulations and ultimately help facilitate more predictable applications.

Principles of synthetic biology

In synthetic biology, biological cells and processes are dismantled and reassembled to make novel systems that do useful things. Designs are encoded by deoxyribonucleic acid (DNA); DNA makes biological (bio-)parts; bioparts are combined to make devices; devices are built into biological systems. Computers are used at all stages of the Design-Build-Test-Learn cycle, from mathematical modelling through to the use of robots for the automation of assembly and experimentation. Synthetic biology applies engineering principles of standardisation, modularity, and abstraction, enabling fast prototyping and the ready exchange of designs between synthetic biologists working around the world. Like toy building blocks, compatible modular designs enable bioparts to be combined and optimised easily; biopart specifications are shared in open registries. Synthetic biology is made possible due to major advances in DNA sequencing and synthesis technologies, and through knowledge gleaned in the field of systems biology. Systems biology aims to understand biology across scales, from the molecular and cellular, up to tissues and organisms, and describes cells as complex information-processing systems. By contrast, synthetic biology seeks to design and build its own systems. Applications of synthetic biology are wide-ranging but include impacting healthcare to improve diagnosis and make better treatments for disease; it seeks to improve the environment by finding novel ways to clean up pollution, make industrial processes for chemical synthesis sustainable, and remove the need for damaging farming practices by making better fertilisers. Synthetic biology has the potential to change the way we live and help us to protect the future of our planet.

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.

Exploring presentations of sustainability by US synthetic biology companies

The field of synthetic biology is increasingly being positioned as a key driver of a more sustainable, bio-based economy, and has seen rapid industry growth over the past 15 years. In this paper we undertake an exploratory investigation of the relationship between sustainability and synthetic biology, identifying and analyzing sustainability-related language on the public websites of 24, US-based synthetic biology companies. We observe that sustainability is a visible part of the self-presentation of the nascent synthetic biology industry, explicitly mentioned by 18 of the 24 companies. The dominant framing of sustainability on these company websites emphasizes environmental gains and "free-market" approaches to sustainability, with little explicit mention of social dimensions of sustainability such as access, justice or intergenerational equity. Furthermore, the model of sustainability presented focuses on incremental transition towards environmental sustainability through direct substitution of products and processes using bioengineered alternatives (n = 16 companies), with no change in societal consumption or policy frameworks required in order to see sustainability gains. One-third of the companies analyzed (n = 8) mention "nature" on their websites, variously framing it as a resource to be managed or as a source of inspiration; whether the latter signals a potentially more complex relationship with nature than advanced free-market models of sustainability remains to be seen. As the synthetic biology industry begins to grow in size and visibility, we suggest this is an opportune time for the community to engage in explicit deliberation about its approach to sustainability.

Post-Transcriptional Control in the Regulation of Polyhydroxyalkanoates Synthesis

The large production of non-degradable petrol-based plastics has become a major global issue due to its environmental pollution. Biopolymers produced by microorganisms such as polyhydroxyalkanoates (PHAs) are gaining potential as a sustainable alternative, but the high cost associated with their industrial production has been a limiting factor. Post-transcriptional regulation is a key step to control gene expression in changing environments and has been reported to play a major role in numerous cellular processes. However, limited reports are available concerning the regulation of PHA accumulation in bacteria, and many essential regulatory factors still need to be identified. Here, we review studies where the synthesis of PHA has been reported to be regulated at the post-transcriptional level, and we analyze the RNA-mediated networks involved. Finally, we discuss the forthcoming research on riboregulation, synthetic, and metabolic engineering which could lead to improved strategies for PHAs synthesis in industrial production, thereby reducing the costs currently associated with this procedure.

Biofoundries are a nucleating hub for industrial translation

Contemporary synthetic biology embraces the entire innovation pipeline; it is a transformative technology platform impacting new applications and improving existing industrial products and processes. However, challenges still emerge at the interface of upstream and downstream processes, integral to the value chain. It is now clear that biofoundries have a key role to play in addressing this; they provide unique and accessible infrastructure to drive the standardization necessary to deliver systematic design and engineering of biological systems and workflows. As for other biofoundries, the success of the London Biofoundry has been in part due to its expertise in establishing channels for industrial translation through its extensive strategic collaborations. It has also become cemented as a key component of various consortia and partnerships that serve the broader bioeconomy and industrial strategies. Adopting a networked approach enables links to be made between infrastructure, researchers, industrialists and policy makers to de-risk the economic challenges of scale-up, as well as contribute to the growing bioeconomy.

Prototyping of microbial chassis for the biomanufacturing of high-value chemical targets

Metabolic engineering technologies have been employed with increasing success over the last three decades for the engineering and optimization of industrial host strains to competitively produce high-value chemical targets. To this end, continued reductions in the time taken from concept, to development, to scale-up are essential. Design-Build-Test-Learn pipelines that are able to rapidly deliver diverse chemical targets through iterative optimization of microbial production strains have been established. Biofoundries are employing in silico tools for the design of genetic parts, alongside combinatorial design of experiments approaches to optimize selection from within the potential design space of biological circuits based on multi-criteria objectives. These genetic constructs can then be built and tested through automated laboratory workflows, with performance data analysed in the learn phase to inform further design. Successful examples of rapid prototyping processes for microbially produced compounds reveal the potential role of biofoundries in leading the sustainable production of next-generation bio-based chemicals.

A Perspective on Synthetic Biology in Drug Discovery and Development-Current Impact and Future Opportunities

The global impact of synthetic biology has been accelerating, because of the plummeting cost of DNA synthesis, advances in genetic engineering, growing understanding of genome organization, and explosion in data science. However, much of the discipline's application in the pharmaceutical industry remains enigmatic. In this review, we highlight recent examples of the impact of synthetic biology on target validation, assay development, hit finding, lead optimization, and chemical synthesis, through to the development of cellular therapeutics. We also highlight the availability of tools and technologies driving the discipline. Synthetic biology is certainly impacting all stages of drug discovery and development, and the recognition of the discipline's contribution can further enhance the opportunities for the drug discovery and development value chain.

Future trends in synthetic biology in Asia

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.

Enabling high-throughput biology with flexible open-source automation

Our understanding of complex living systems is limited by our capacity to perform experiments in high throughput. While robotic systems have automated many traditional hand-pipetting protocols, software limitations have precluded more advanced maneuvers required to manipulate, maintain, and monitor hundreds of experiments in parallel. Here, we present Pyhamilton, an open-source Python platform that can execute complex pipetting patterns required for custom high-throughput experiments such as the simulation of metapopulation dynamics. With an integrated plate reader, we maintain nearly 500 remotely monitored bacterial cultures in log-phase growth for days without user intervention by taking regular density measurements to adjust the robotic method in real-time. Using these capabilities, we systematically optimize bioreactor protein production by monitoring the fluorescent protein expression and growth rates of a hundred different continuous culture conditions in triplicate to comprehensively sample the carbon, nitrogen, and phosphorus fitness landscape. Our results demonstrate that flexible software can empower existing hardware to enable new types and scales of experiments, empowering areas from biomanufacturing to fundamental biology.

Responsible innovation in synthetic biology in response to COVID-19: the role of data positionality

Synthetic biology, as an engineering approach to biological systems, has the potential to disruptively innovate the development of vaccines, therapeutics, and diagnostics. Data accessibility and differences in data-usage capabilities are important factors in shaping this innovation landscape. In this paper, the data that underpin synthetic biology responses to the COVID-19 pandemic are analyzed as positional information goods-goods whose value depends on exclusivity. The positionality of biological data impacts the ability to guide innovations toward societally preferred goals. From both an ethical and economic point of view, positionality can lead to suboptimal as well as beneficial situations. When aiming for responsible innovation (i.e. embedding societal deliberation in the innovation process), it is important to consider hurdles and facilitators in data access and use. Central governance and knowledge commons provide routes to mitigate the negative effects of data positionality.

A role for Biofoundries in rapid development and validation of automated SARS-CoV-2 clinical diagnostics

The SARS-CoV-2 pandemic has shown how a rapid rise in demand for patient and community sample testing can quickly overwhelm testing capability globally. With most diagnostic infrastructure dependent on specialized instruments, their exclusive reagent supplies quickly become bottlenecks, creating an urgent need for approaches to boost testing capacity. We address this challenge by refocusing the London Biofoundry onto the development of alternative testing pipelines. Here, we present a reagent-agnostic automated SARS-CoV-2 testing platform that can be quickly deployed and scaled. Using an in-house-generated, open-source, MS2-virus-like particle (VLP) SARS-CoV-2 standard, we validate RNA extraction and RT-qPCR workflows as well as two detection assays based on CRISPR-Cas13a and RT-loop-mediated isothermal amplification (RT-LAMP). In collaboration with an NHS diagnostic testing lab, we report the performance of the overall workflow and detection of SARS-CoV-2 in patient samples using RT-qPCR, CRISPR-Cas13a, and RT-LAMP. The validated RNA extraction and RT-qPCR platform has been installed in NHS diagnostic labs, increasing testing capacity by 1000 samples per day.

Biological Materials: The Next Frontier for Cell-Free Synthetic Biology

Advancements in cell-free synthetic biology are enabling innovations in sustainable biomanufacturing, that may ultimately shift the global manufacturing paradigm toward localized and ecologically harmonized production processes. Cell-free synthetic biology strategies have been developed for the bioproduction of fine chemicals, biofuels and biological materials. Cell-free workflows typically utilize combinations of purified enzymes, cell extracts for biotransformation or cell-free protein synthesis reactions, to assemble and characterize biosynthetic pathways. Importantly, cell-free reactions can combine the advantages of chemical engineering with metabolic engineering, through the direct addition of co-factors, substrates and chemicals -including those that are cytotoxic. Cell-free synthetic biology is also amenable to automatable design cycles through which an array of biological materials and their underpinning biosynthetic pathways can be tested and optimized in parallel. Whilst challenges still remain, recent convergences between the materials sciences and these advancements in cell-free synthetic biology enable new frontiers for materials research.