Coping with complexity: Machine learning optimization of cell-free protein synthesis

OT-Mation: an open-source code for parsing CSV files into Python scripts for control of OT-2 liquid-handling robotics

OT-Mation is an open-source Python script designed to automate the programming of OT-2 liquid-handling robots, making combinatorial experiments more accessible to researchers. By parsing user-defined CSV files containing information on labware, reagents, pipettes, and experimental design, OT-Mation generates a bespoke Python script compatible with the OT-2 system. OT-Mation enhances reproducibility, reduces human error, and streamlines workflows, making it a valuable addition to any laboratory utilizing OT-2 robotics for liquid handling. While OT-Mation can be used for setting up any type of experiment on the OT-2, its real utility lies in making the connection between multifactorial experimental design software outputs (i.e. design of experiments arrays) and liquid-handling robot executable code. As such, OT-Mation helps bridge the gap between code-based flexibility and user-friendly operation, allowing researchers with limited programming skills to design and execute complex experiments efficiently. Graphical Abstract.

Cell-free protein synthesis platforms for accelerating drug discovery

Cell-free protein synthesis is a platform for streamlined production of macromolecules. Recently, several proteins with pharmaceutical relevance were synthesised and characterised. Off-the-shelf reagents and parallelised experimentation have enabled the exploration of many different conditions for in vitro protein synthesis and engineering. Herein is described how machine learning algorithms were applied for protein yield maximisation as well as for protein engineering and de novo design. Cell-free protein synthesis provides the biotechnological platform to unlock the power and benefit of AI/ML for drug discovery and improve human health.

Cell-Free Gene Expression: Methods and Applications

Cell-free gene expression (CFE) systems empower synthetic biologists to build biological molecules and processes outside of living intact cells. The foundational principle is that precise, complex biomolecular transformations can be conducted in purified enzyme or crude cell lysate systems. This concept circumvents mechanisms that have evolved to facilitate species survival, bypasses limitations on molecular transport across the cell wall, and provides a significant departure from traditional, cell-based processes that rely on microscopic cellular "reactors." In addition, cell-free systems are inherently distributable through freeze-drying, which allows simple distribution before rehydration at the point-of-use. Furthermore, as cell-free systems are nonliving, they provide built-in safeguards for biocontainment without the constraints attendant on genetically modified organisms. These features have led to a significant increase in the development and use of CFE systems over the past two decades. Here, we discuss recent advances in CFE systems and highlight how they are transforming efforts to build cells, control genetic networks, and manufacture biobased products.

Designing of an extract production protocol for industrial application of cell-free protein synthesis technology: Building from a current best practice to a quality by design approach

Cell-Free Protein Synthesis (CFPS) has, over the past decade, seen a substantial increase in interest from both academia and industry. Applications range from fundamental research, through high-throughput screening to niche manufacture of therapeutic products. This review/perspective focuses on Quality Control in CFPS. The importance and difficulty of measuring the Raw Material Attributes (RMAs) of whole cell extract, such as constituent protein and metabolite concentrations, and of understanding and controlling these complicated enzymatic reactions is explored, for both centralised and distributed industrial production of biotherapeutics. It is suggested that a robust cell-free extract production process should produce cell extract of consistent quality; however, demonstrating this is challenging without a full understanding of the RMAs and their interaction with reaction conditions and product. Lack of technology transfer and knowledge sharing is identified as a key limiting factor in the development of CFPS. The article draws upon the experiences of industrial process specialists, discussions within the Future Targeted Healthcare Manufacturing Hub Specialist Working Groups and evidence drawn from various sources to identify sources of process variation and to propose an initial guide towards systematisation of CFPS process development and reporting. These proposals include the development of small scale screening tools, consistent reporting of selected process parameters and analytics and application of industrial thinking and manufacturability to protocol development.

Key reaction components affect the kinetics and performance robustness of cell-free protein synthesis reactions

Cell-free protein synthesis (CFPS) reactions have grown in popularity with particular interest in applications such as gene construct prototyping, biosensor technologies and the production of proteins with novel chemistry. Work has frequently focussed on optimising CFPS protocols for improving protein yield, reducing cost, or developing streamlined production protocols. Here we describe a statistical Design of Experiments analysis of 20 components of a popular CFPS reaction buffer. We simultaneously identify factors and factor interactions that impact on protein yield, rate of reaction, lag time and reaction longevity. This systematic experimental approach enables the creation of a statistical model capturing multiple behaviours of CFPS reactions in response to components and their interactions. We show that a novel reaction buffer outperforms the reference reaction by 400% and importantly reduces failures in CFPS across batches of cell lysates, strains of E. coli, and in the synthesis of different proteins. Detailed and quantitative understanding of how reaction components affect kinetic responses and robustness is imperative for future deployment of cell-free technologies.

Biotechnology Applications of Cell-Free Expression Systems

Cell-free systems are a rapidly expanding platform technology with an important role in the engineering of biological systems. The key advantages that drive their broad adoption are increased efficiency, versatility, and low cost compared to in vivo systems. Traditionally, in vivo platforms have been used to synthesize novel and industrially relevant proteins and serve as a testbed for prototyping numerous biotechnologies such as genetic circuits and biosensors. Although in vivo platforms currently have many applications within biotechnology, they are hindered by time-constraining growth cycles, homeostatic considerations, and limited adaptability in production. Conversely, cell-free platforms are not hindered by constraints for supporting life and are therefore highly adaptable to a broad range of production and testing schemes. The advantages of cell-free platforms are being leveraged more commonly by the biotechnology community, and cell-free applications are expected to grow exponentially in the next decade. In this study, new and emerging applications of cell-free platforms, with a specific focus on cell-free protein synthesis (CFPS), will be examined. The current and near-future role of CFPS within metabolic engineering, prototyping, and biomanufacturing will be investigated as well as how the integration of machine learning is beneficial to these applications.

Tuning Cell-Free Composition Controls the Time Delay, Dynamics, and Productivity of TX-TL Expression

The composition of cell-free expression systems (TX-TL) is adjusted by adding macromolecular crowding agents and salts. However, the effects of these cosolutes on the dynamics of individual gene expression processes have not been quantified. Here, we carry out kinetic mRNA and protein level measurements on libraries of genetic constructs using the common cosolutes PEG-8000, Ficoll-400, and magnesium glutamate. By combining these measurements with biophysical modeling, we show that cosolutes have differing effects on transcription initiation, translation initiation, and translation elongation rates with trade-offs between time delays, expression tunability, and maximum expression productivity. We also confirm that biophysical models can predict translation initiation rates in TX-TL using Escherichia coli lysate. We discuss how cosolute composition can be tuned to maximize performance across different cell-free applications, including biosensing, diagnostics, and biomanufacturing.

Predicting cell behaviour parameters from glioblastoma on a chip images. A deep learning approach

The broad possibilities offered by microfluidic devices in relation to massive data monitoring and acquisition open the door to the use of deep learning technologies in a very promising field: cell culture monitoring. In this work, we develop a methodology for parameter identification in cell culture from fluorescence images using Convolutional Neural Networks (CNN). We apply this methodology to the in vitro study of glioblastoma (GBM), the most common, aggressive and lethal primary brain tumour. In particular, the aim is to predict the three parameters defining the go or grow GBM behaviour, which is determinant for the tumour prognosis and response to treatment. The data used to train the network are obtained from a mathematical model, previously validated with in vitro experimental results. The resulting CNN provides remarkably accurate predictions (Pearson's ρ > 0.99 for all the parameters). Besides, it proves to be sound, to filter noise and to generalise. After training and validation with synthetic data, we predict the parameters corresponding to a real image of a microfluidic experiment. The obtained results show good performance of the CNN. The proposed technique may set the first steps towards patient-specific tools, able to predict in real-time the tumour evolution for each particular patient, thanks to a combined in vitro-in silico approach.

Optimising protein synthesis in cell‐free systems, a review

Over the last decades, cell-free systems have been extensively used for in vitro protein expression. A vast range of protocols and cellular sources varying from prokaryotes and eukaryotes are now available for cell-free technology. However, exploiting the maximum capacity of cell free systems is not achieved by using traditional protocols. Here, what are the strategies and choices one can apply to optimise cell-free protein synthesis have been reviewed. These strategies provide robust and informative improvements regarding transcription, translation and protein folding which can later be used for the establishment of individual best cell-free reactions per lysate batch.

Statistical Design of Experiments for Synthetic Biology

The design and optimization of biological systems is an inherently complex undertaking that requires careful balancing of myriad synergistic and antagonistic variables. However, despite this complexity, much synthetic biology research is predicated on One Factor at A Time (OFAT) experimentation; the genetic and environmental variables affecting the activity of a system of interest are sequentially altered while all other variables are held constant. Beyond being time and resource intensive, OFAT experimentation crucially ignores the effect of interactions between factors. Given the ubiquity of interacting genetic and environmental factors in biology this failure to account for interaction effects in OFAT experimentation can result in the development of suboptimal systems. To address these limitations, an increasing number of studies have turned to Design of Experiments (DoE), a suite of methods that enable efficient, systematic exploration and exploitation of complex design spaces. This review provides an overview of DoE for synthetic biologists. Key concepts and commonly used experimental designs are introduced, and we discuss the advantages of DoE as compared to OFAT experimentation. We dissect the applicability of DoE in the context of synthetic biology and review studies which have successfully employed these methods, illustrating the potential of statistical experimental design to guide the design, characterization, and optimization of biological protocols, pathways, and processes.

Improving the reaction mix of a Pichia pastoris cell-free system using a design of experiments approach to minimise experimental effort

A renaissance in cell-free protein synthesis (CFPS) is underway, enabled by the acceleration and adoption of synthetic biology methods. CFPS has emerged as a powerful platform technology for synthetic gene network design, biosensing and on-demand biomanufacturing. Whilst primarily of bacterial origin, cell-free extracts derived from a variety of host organisms have been explored, aiming to capitalise on cellular diversity and the advantageous properties associated with those organisms. However, cell-free extracts produced from eukaryotes are often overlooked due to their relatively low yields, despite the potential for improved protein folding and posttranslational modifications. Here we describe further development of a Pichia pastoris cell-free platform, a widely used expression host in both academia and the biopharmaceutical industry. Using a minimised Design of Experiments (DOE) approach, we were able to increase the productivity of the system by improving the composition of the complex reaction mixture. This was achieved in a minimal number of experimental runs, within the constraints of the design and without the need for liquid-handling robots. In doing so, we were able to estimate the main effects impacting productivity in the system and increased the protein synthesis of firefly luciferase and the biopharmaceutical HSA by 4.8-fold and 3.5-fold, respectively. This study highlights the P. pastoris-based cell-free system as a highly productive eukaryotic platform and displays the value of minimised DOE designs.

Applying Statistical Design of Experiments To Understanding the Effect of Growth Medium Components on Cupriavidus necator H16 Growth

Chemically defined media (CDM) for cultivation of C. necator vary in components and compositions. This lack of consensus makes it difficult to optimize new processes for the bacterium. This study employed statistical design of experiments (DOE) to understand how basic components of defined media affect C. necator growth. Our growth model predicts that C. necator can be cultivated to high cell density with components held at low concentrations, arguing that CDM for large-scale cultivation of the bacterium for industrial purposes will be economically competitive. Although existing CDM for the bacterium are without amino acids, addition of a few amino acids to growth medium shortened lag phase of growth. The interactions highlighted by our growth model show how factors can interact with each other during a process to positively or negatively affect process output. This approach is efficient, relying on few well-structured experimental runs to gain maximum information on a biological process, growth.

Cupriavidus necator H16 is gaining significant attention as a microbial chassis for range of biotechnological applications. While the bacterium is a major producer of bioplastics, its lithoautotrophic and versatile metabolic capabilities make the bacterium a promising microbial chassis for biofuels and chemicals using renewable resources. It remains necessary to develop appropriate experimental resources to permit controlled bioengineering and system optimization of this microbe. In this study, we employed statistical design of experiments to gain understanding of the impact of components of defined media on C. necator growth and built a model that can predict the bacterium’s cell density based on medium components. This highlighted medium components, and interaction between components, having the most effect on growth: fructose, amino acids, trace elements, CaCl₂, and Na₂HPO₄ contributed significantly to growth (t values of <−1.65 or >1.65); copper and histidine were found to interact and must be balanced for robust growth. Our model was experimentally validated and found to correlate well (r² = 0.85). Model validation at large culture scales showed correlations between our model-predicted growth ranks and experimentally determined ranks at 100 ml in shake flasks (ρ = 0.87) and 1 liter in a bioreactor (ρ = 0.90). Our approach provides valuable and quantifiable insights on the impact of medium components on cell growth and can be applied to model other C. necator responses that are crucial for its deployment as a microbial chassis. This approach can be extended to other nonmodel microbes of medical and industrial biotechnological importance.

IMPORTANCE Chemically defined media (CDM) for cultivation of C. necator vary in components and compositions. This lack of consensus makes it difficult to optimize new processes for the bacterium. This study employed statistical design of experiments (DOE) to understand how basic components of defined media affect C. necator growth. Our growth model predicts that C. necator can be cultivated to high cell density with components held at low concentrations, arguing that CDM for large-scale cultivation of the bacterium for industrial purposes will be economically competitive. Although existing CDM for the bacterium are without amino acids, addition of a few amino acids to growth medium shortened lag phase of growth. The interactions highlighted by our growth model show how factors can interact with each other during a process to positively or negatively affect process output. This approach is efficient, relying on few well-structured experimental runs to gain maximum information on a biological process, growth.

Prediction of Human Induced Pluripotent Stem Cell Cardiac Differentiation Outcome by Multifactorial Process Modeling

Human cardiomyocytes (CMs) have potential for use in therapeutic cell therapy and high-throughput drug screening. Because of the inability to expand adult CMs, their large-scale production from human pluripotent stem cells (hPSC) has been suggested. Significant improvements have been made in understanding directed differentiation processes of CMs from hPSCs and their suspension culture-based production at chemically defined conditions. However, optimization experiments are costly, time-consuming, and highly variable, leading to challenges in developing reliable and consistent protocols for the generation of large CM numbers at high purity. This study examined the ability of data-driven modeling with machine learning for identifying key experimental conditions and predicting final CM content using data collected during hPSC-cardiac differentiation in advanced stirred tank bioreactors (STBRs). Through feature selection, we identified process conditions, features, and patterns that are the most influential on and predictive of the CM content at the process endpoint, on differentiation day 10 (dd10). Process-related features were extracted from experimental data collected from 58 differentiation experiments by feature engineering. These features included data continuously collected online by the bioreactor system, such as dissolved oxygen concentration and pH patterns, as well as offline determined data, including the cell density, cell aggregate size, and nutrient concentrations. The selected features were used as inputs to construct models to classify the resulting CM content as being "sufficient" or "insufficient" regarding pre-defined thresholds. The models built using random forests and Gaussian process modeling predicted insufficient CM content for a differentiation process with 90% accuracy and precision on dd7 of the protocol and with 85% accuracy and 82% precision at a substantially earlier stage: dd5. These models provide insight into potential key factors affecting hPSC cardiac differentiation to aid in selecting future experimental conditions and can predict the final CM content at earlier process timepoints, providing cost and time savings. This study suggests that data-driven models and machine learning techniques can be employed using existing data for understanding and improving production of a specific cell type, which is potentially applicable to other lineages and critical for realization of their therapeutic applications.

Engineering Life: A Review of Synthetic Biology

Synthetic biology is a field of scientific research that applies engineering principles to living organisms and living systems. It is a field that is increasing in scope with respect to organisms engineered, practical outcomes, and systems integration. There is a commercial dimension as well, where living organisms are engineered as green technologies that could offer alternatives to industrial standards in the pharmaceutical and petroleum-based chemical industries. This review attempts to provide an introduction to this field as well as a consideration of important contributions that exemplify how synthetic biology may be commensurate or even disproportionate with the complexity of living systems. The engineerability of living systems remains a difficult task, yet advancements are reported at an ever-increasing pace.

Cell-free protein synthesis: The transition from batch reactions to minimal cells and microfluidic devices

Thanks to the synthetic biology, the laborious and restrictive procedure for producing a target protein in living microorganisms by biotechnological approaches can now experience a robust, pliant yet efficient alternative. The new system combined with lab-on-chip microfluidic devices and nanotechnology offers a tremendous potential envisioning novel cell-free formats such as DNA brushes, hydrogels, vesicular particles, droplets, as well as solid surfaces. Acting as robust microreactors/microcompartments/minimal cells, the new platforms can be tuned to perform various tasks in a parallel and integrated manner encompassing gene expression, protein synthesis, purification, detection, and finally enabling cell-cell signaling to bring a collective cell behavior, such as directing differentiation process, characteristics of higher order entities, and beyond. In this review, we issue an update on recent cell-free protein synthesis (CFPS) formats. Furthermore, the latest advances and applications of CFPS for synthetic biology and biotechnology are highlighted. In the end, contemporary challenges and future opportunities of CFPS systems are discussed.

Two Modes of Evolution: Optimization and Expansion

We document and discuss two different modes of evolution across multiple systems, optimization and expansion. The former suffices in systems whose size and interactions do not change substantially over time, while the latter is a key property of open-ended evolution, where new players and interaction types enter the game. We first investigate systems from physics, biology, and engineering and argue that their evolutionary optimization dynamics is the cumulative effect of multiple independent events, or quakes, which are uniformly distributed on a logarithmic time scale and produce a decelerating fitness improvement when using the appropriate independent variable. The appropriate independent variable can be physical time for a disordered magnetic system, the number of generations for a bacterial system, or the number of produced units for a particular technological product. We then derive and discuss a simple microscopic theory that explains the nature of the involved optimization processes, and provide simulation results as illustration. Finally, we explore the evolution of human culture and technology, using empirical economic data as a proxy for human fitness. Assuming the overall dynamics is a combined optimization and expansion process, the two processes can be separated and quantified by superimposing the mathematical form of an optimization process on the empirical data and thereby transforming the independent variable. This variable turns out to increase faster than any exponential function of time, a property likely due to strong historical changes in the web of human interactions and to the associated increase in the amount of available knowledge. A microscopic theory for this time dependence remains, however, a challenging open problem.

High-Throughput Optimization Cycle of a Cell-Free Ribosome Assembly and Protein Synthesis System

Building variant ribosomes offers opportunities to reveal fundamental principles underlying ribosome biogenesis and to make ribosomes with altered properties. However, cell viability limits mutations that can be made to the ribosome. To address this limitation, the in vitro integrated synthesis, assembly and translation (iSAT) method for ribosome construction from the bottom up was recently developed. Unfortunately, iSAT is complex, costly, and laborious to researchers, partially due to the high cost of reaction buffer containing over 20 components. In this study, we develop iSAT in Escherichia coli BL21Rosetta2 cell lysates, a commonly used bacterial strain, with a cost-effective poly sugar and nucleotide monophosphate-based metabolic scheme. We achieved a 10-fold increase in protein yield over our base case with an evolutionary design of experiments approach, screening 490 reaction conditions to optimize the reaction buffer. The computationally guided, cell-free, high-throughput technology presented here augments the way we approach multicomponent synthetic biology projects and efforts to repurpose ribosomes.

Metabolic engineering of glycoprotein biosynthesis in bacteria

The demonstration more than a decade ago that glycoproteins could be produced in Escherichia coli cells equipped with the N-linked protein glycosylation machinery from Campylobacter jejuni opened the door to using simple bacteria for the expression and engineering of complex glycoproteins. Since that time, metabolic engineering has played an increasingly important role in developing and optimizing microbial cell glyco-factories for the production of diverse glycoproteins and other glycoconjugates. It is becoming clear that future progress in creating efficient glycoprotein expression platforms in bacteria will depend on the adoption of advanced strain engineering strategies such as rational design and assembly of orthogonal glycosylation pathways, genome-wide identification of metabolic engineering targets, and evolutionary engineering of pathway performance. Here, we highlight recent advances in the deployment of metabolic engineering tools and strategies to develop microbial cell glyco-factories for the production of high-value glycoprotein targets with applications in research and medicine.

Design of Experiments Methodology to Build a Multifactorial Statistical Model Describing the Metabolic Interactions of Alcohol Dehydrogenase Isozymes in the Ethanol Biosynthetic Pathway of the Yeast Saccharomyces cerevisiae

Multifactorial approaches can quickly and efficiently model complex, interacting natural or engineered biological systems in a way that traditional one-factor-at-a-time experimentation can fail to do. We applied a Design of Experiments (DOE) approach to model ethanol biosynthesis in yeast, which is well-understood and genetically tractable, yet complex. Six alcohol dehydrogenase (ADH) isozymes catalyze ethanol synthesis, differing in their transcriptional and post-translational regulation, subcellular localization, and enzyme kinetics. We generated a combinatorial library of all ADH gene deletions and measured the impact of gene deletion(s) and environmental context on ethanol production of a subset of this library. The data were used to build a statistical model that described known behaviors of ADH isozymes and identified novel interactions. Importantly, the model described features of ADH metabolic behavior without explicit a priori knowledge. The method is therefore highly suited to understanding and optimizing metabolic pathways in less well-understood systems.

Cell-free protein synthesis enabled rapid prototyping for metabolic engineering and synthetic biology

Advances in metabolic engineering and synthetic biology have facilitated the manufacturing of many valuable-added compounds and commodity chemicals using microbial cell factories in the past decade. However, due to complexity of cellular metabolism, the optimization of metabolic pathways for maximal production represents a grand challenge and an unavoidable barrier for metabolic engineering. Recently, cell-free protein synthesis system (CFPS) has been emerging as an enabling alternative to address challenges in biomanufacturing. This review summarizes the recent progresses of CFPS in rapid prototyping of biosynthetic pathways and genetic circuits (biosensors) to speed up design-build-test (DBT) cycles of metabolic engineering and synthetic biology.

Systems and synthetic biology approaches in understanding biological oscillators

Background

Self-sustained oscillations are a ubiquitous and vital phenomenon in living systems. From primitive single-cellular bacteria to the most sophisticated organisms, periodicities have been observed in a broad spectrum of biological processes such as neuron firing, heart beats, cell cycles, circadian rhythms, etc. Defects in these oscillators can cause diseases from insomnia to cancer. Elucidating their fundamental mechanisms is of great significance to diseases, and yet challenging, due to the complexity and diversity of these oscillators.

Results

Approaches in quantitative systems biology and synthetic biology have been most effective by simplifying the systems to contain only the most essential regulators. Here, we will review major progress that has been made in understanding biological oscillators using these approaches. The quantitative systems biology approach allows for identification of the essential components of an oscillator in an endogenous system. The synthetic biology approach makes use of the knowledge to design the simplest, de novo oscillators in both live cells and cell-free systems. These synthetic oscillators are tractable to further detailed analysis and manipulations.

Conclusion

With the recent development of biological and computational tools, both approaches have made significant achievements.

Bacterial cell-free expression technology to in vitro systems engineering and optimization

Cell-free expression system is a technology for the synthesis of proteins in vitro. The system is a platform for several bioengineering projects, e.g. cell-free metabolic engineering, evolutionary design of experiments, and synthetic minimal cell construction. Bacterial cell-free protein synthesis system (CFPS) is a robust tool for synthetic biology. The bacteria lysate, the DNA, and the energy module, which are the three optimized sub-systems for in vitro protein synthesis, compose the integrated system. Currently, an optimized E. coli cell-free expression system can produce up to ∼2.3 mg/mL of a fluorescent reporter protein. Herein, I will describe the features of ATP-regeneration systems for in vitro protein synthesis, and I will present a machine-learning experiment for optimizing the protein yield of E. coli cell-free protein synthesis systems. Moreover, I will introduce experiments on the synthesis of a minimal cell using liposomes as dynamic containers, and E. coli cell-free expression system as biochemical platform for metabolism and gene expression. CFPS can be further integrated with other technologies for novel applications in environmental, medical and material science.

Mini-review: In vitro Metabolic Engineering for Biomanufacturing of High-value Products

With the breakthroughs in biomolecular engineering and synthetic biology, many valuable biologically active compound and commodity chemicals have been successfully manufactured using cell-based approaches in the past decade. However, because of the high complexity of cell metabolism, the identification and optimization of rate-limiting metabolic pathways for improving the product yield is often difficult, which represents a significant and unavoidable barrier of traditional in vivo metabolic engineering. Recently, some in vitro engineering approaches were proposed as alternative strategies to solve this problem. In brief, by reconstituting a biosynthetic pathway in a cell-free environment with the supplement of cofactors and substrates, the performance of each biosynthetic pathway could be evaluated and optimized systematically. Several value-added products, including chemicals, nutraceuticals, and drug precursors, have been biosynthesized as proof-of-concept demonstrations of in vitro metabolic engineering. This mini-review summarizes the recent progresses on the emerging topic of in vitro metabolic engineering and comments on the potential application of cell-free technology to speed up the “design-build-test” cycles of biomanufacturing.

Cell-Free Synthetic Biology: Engineering Beyond the Cell

Cell-free protein synthesis (CFPS) technologies have enabled inexpensive and rapid recombinant protein expression. Numerous highly active CFPS platforms are now available and have recently been used for synthetic biology applications. In this review, we focus on the ability of CFPS to expand our understanding of biological systems and its applications in the synthetic biology field. First, we outline a variety of CFPS platforms that provide alternative and complementary methods for expressing proteins from different organisms, compared with in vivo approaches. Next, we review the types of proteins, protein complexes, and protein modifications that have been achieved using CFPS systems. Finally, we introduce recent work on genetic networks in cell-free systems and the use of cell-free systems for rapid prototyping of in vivo networks. Given the flexibility of cell-free systems, CFPS holds promise to be a powerful tool for synthetic biology as well as a protein production technology in years to come.

Compartmentalization of an all-E. coli Cell-Free Expression System for the Construction of a Minimal Cell

Cell-free expression is a technology used to synthesize minimal biological cells from natural molecular components. We have developed a versatile and powerful all-E. coli cell-free transcription-translation system energized by a robust metabolism, with the far objective of constructing a synthetic cell capable of self-reproduction. Inorganic phosphate (iP), a byproduct of protein synthesis, is recycled through polysugar catabolism to regenerate ATP (adenosine triphosphate) and thus supports long-lived and highly efficient protein synthesis in vitro. This cell-free TX-TL system is encapsulated into cell-sized unilamellar liposomes to express synthetic DNA programs. In this work, we study the compartmentalization of cell-free TX-TL reactions, one of the aspects of minimal cell module integration. We analyze the signals of various liposome populations by fluorescence microscopy for one and for two reporter genes, and for an inducible genetic circuit. We show that small nutrient molecules and proteins are encapsulated uniformly in the liposomes with small fluctuations. However, cell-free expression displays large fluctuations in signals among the same population, which are due to heterogeneous encapsulation of the DNA template. Consequently, the correlations of gene expression with the compartment dimension are difficult to predict accurately. Larger vesicles can have either low or high protein yields.

A versatile coupled cell-free transcription-translation system based on tobacco BY-2 cell lysates

Cell-free protein synthesis is a powerful method for the high-throughput production of recombinant proteins, especially proteins that are difficult to express in living cells. Here we describe a coupled cell-free transcription-translation system based on tobacco BY-2 cell lysates (BYLs). Using a combination of fractional factorial designs and response surface models, we developed a cap-independent system that produces more than 250 μg/mL of functional enhanced yellow fluorescent protein (eYFP) and about 270 μg/mL of firefly luciferase using plasmid templates, and up to 180 μg/mL eYFP using linear templates (PCR products) in 18 h batch reactions. The BYL contains actively-translocating microsomal vesicles derived from the endoplasmic reticulum, promoting the formation of disulfide bonds, glycosylation and the cotranslational integration of membrane proteins. This was demonstrated by expressing a functional full-size antibody (∼ 150 μg/mL), the model enzyme glucose oxidase (GOx) (∼ 7.3 U/mL), and a transmembrane growth factor (∼ 25 μg/mL). Subsequent in vitro treatment of GOx with peptide-N-glycosidase F confirmed the presence of N-glycans. Our results show that the BYL can be used as a high-throughput expression and screening platform that is particularly suitable for complex and cytotoxic proteins.

Preparation of amino acid mixtures for cell-free expression systems

Here we present a procedure for preparing amino acid mixtures–having both the desired composition and a physiological pH–at high concentrations for cell-free expression systems. Up to 2.1 mg/mL of active protein was synthesized in batch mode reactions with an all Escherichia coli cell-free expression system. Our method is fast, easy to execute, and economically advantageous compared to expensive commercial kits, making it useful for high-throughput experiments, incorporation of nonstandard amino acids, and cell-free metabolic engineering.

Synthetic biology outside the cell: linking computational tools to cell-free systems

As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems. In this review, we explore both in vivo and in vitro models of biochemical networks with a special focus on tools that could be applied to the construction of cell-free expression systems. We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems.

A cost-effective polyphosphate-based metabolism fuels an all E. coli cell-free expression system

A new cost-effective metabolism providing an ATP-regeneration system for cell-free protein synthesis is presented. Hexametaphosphate, a polyphosphate molecule, is used as phosphate donor together with maltodextrin, a polysaccharide used as carbon source to stimulate glycolysis. Remarkably, addition of enzymes is not required for this metabolism, which is carried out by endogenous catalysts present in the Escherichia coli crude extract. This new ATP regeneration system allows efficient recycling of inorganic phosphate, a strong inhibitor of protein synthesis. We show that up to 1.34-1.65mg/mL of active reporter protein is synthesized in batch-mode reaction after 5h of incubation. Unlike typical hybrid in vitro protein synthesis systems based on bacteriophage transcription, expression is carried out through E. coli promoters using only the endogenous transcription-translation molecular machineries provided by the extract. We demonstrate that traditional expensive energy regeneration systems, such as creatine phosphate, phosphoenolpyruvate or phosphoglycerate, can be replaced by a cost-effective metabolic scheme suitable for cell-free protein synthesis applications. Our work also shows that cell-free systems are useful platforms for metabolic engineering.

Synthesis of 2.3 mg/ml of protein with an all Escherichia coli cell-free transcription-translation system

Cell-free protein synthesis is becoming a useful technique for synthetic biology. As more applications are developed, the demand for novel and more powerful in vitro expression systems is increasing. In this work, an all Escherichia coli cell-free system, that uses the endogenous transcription and translation molecular machineries, is optimized to synthesize up to 2.3 mg/ml of a reporter protein in batch mode reactions. A new metabolism based on maltose allows recycling of inorganic phosphate through its incorporation into newly available glucose molecules, which are processed through the glycolytic pathway to produce more ATP. As a result, the ATP regeneration is more efficient and cell-free protein synthesis lasts up to 10 h. Using a commercial E. coli strain, we show for the first time that more than 2 mg/ml of protein can be synthesized in run-off cell-free transcription-translation reactions by optimizing the energy regeneration and waste products recycling. This work suggests that endogenous enzymes present in the cytoplasmic extract can be used to implement new metabolic pathways for increasing protein yields. This system is the new basis of a cell-free gene expression platform used to construct and to characterize complex biochemical processes in vitro such as gene circuits.

Fluorescent proteins and in vitro genetic organization for cell-free synthetic biology

To facilitate the construction of cell-free genetic devices, we evaluated the ability of 17 different fluorescent proteins to give easily detectable fluorescence signals in real-time from in vitro transcription-translation reactions with a minimal system consisting of T7 RNA polymerase and E. coli translation machinery, i.e., the PUREsystem. The data were used to construct a ratiometric fluorescence assay to quantify the effect of genetic organization on in vitro expression levels. Synthetic operons with varied spacing and sequence composition between two genes that coded for fluorescent proteins were then assembled. The resulting data indicated which restriction sites and where the restriction sites should be placed in order to build genetic devices in a manner that does not interfere with protein expression. Other simple design rules were identified, such as the spacing and sequence composition influences of regions upstream and downstream of ribosome binding sites and the ability of non-AUG start codons to function in vitro.

Protocols for implementing an Escherichia coli based TX-TL cell-free expression system for synthetic biology

Ideal cell-free expression systems can theoretically emulate an in vivo cellular environment in a controlled in vitro platform.¹ This is useful for expressing proteins and genetic circuits in a controlled manner as well as for providing a prototyping environment for synthetic biology.^2,3 To achieve the latter goal, cell-free expression systems that preserve endogenous Escherichia coli transcription-translation mechanisms are able to more accurately reflect in vivo cellular dynamics than those based on T7 RNA polymerase transcription. We describe the preparation and execution of an efficient endogenous E. coli based transcription-translation (TX-TL) cell-free expression system that can produce equivalent amounts of protein as T7-based systems at a 98% cost reduction to similar commercial systems.^4,5 The preparation of buffers and crude cell extract are described, as well as the execution of a three tube TX-TL reaction. The entire protocol takes five days to prepare and yields enough material for up to 3000 single reactions in one preparation. Once prepared, each reaction takes under 8 hr from setup to data collection and analysis. Mechanisms of regulation and transcription exogenous to E. coli, such as lac/tet repressors and T7 RNA polymerase, can be supplemented.⁶ Endogenous properties, such as mRNA and DNA degradation rates, can also be adjusted.⁷ The TX-TL cell-free expression system has been demonstrated for large-scale circuit assembly, exploring biological phenomena, and expression of proteins under both T7- and endogenous promoters.^6,8 Accompanying mathematical models are available.^9,10 The resulting system has unique applications in synthetic biology as a prototyping environment, or "TX-TL biomolecular breadboard."