Dynamic control of gene expression can have far-reaching implications for biotechnological applications and biological discovery. Thanks to the advantages of light, optogenetics has emerged as an ideal technology for this task. Current state-of-the-art methods for optical expression control fail to combine precision with repeatability and cannot withstand changing operating culture conditions. Here, we present a novel fully automatic experimental platform for the robust and precise long-term optogenetic regulation of protein production in liquid Escherichia coli cultures. Using a computer-controlled light-responsive two-component system, we accurately track prescribed dynamic green fluorescent protein expression profiles through the application of feedback control, and show that the system adapts to global perturbations such as nutrient and temperature changes. We demonstrate the efficacy and potential utility of our approach by placing a key metabolic enzyme under optogenetic control, thus enabling dynamic regulation of the culture growth rate with potential applications in bacterial physiology studies and biotechnology.
The predictability and robustness of engineered bacteria depend on the many interactions between synthetic constructs and their host cells. Expression from synthetic constructs is an unnatural load for the host that typically reduces growth, triggers stresses and leads to decrease in performance or failure of engineered cells. Work in systems and synthetic biology has now begun to address this through new tools, methods and strategies that characterise and exploit host-construct interactions in bacteria. Focusing on work in E. coli, we review here a selection of the recent developments in this area, highlighting the emerging issues and describing the new solutions that are now making the synthetic biology community consider the cell just as much as they consider the construct.
Harvard University researchers have developed a new method of using CRISPR to alter single letters in the DNA code. This opens up the possibility of reversing mutations caused by the changing of only one letter, which represents nearly two thirds of all genetic mutations.
Students at TU Delft are using the latest technology to develop bacteria cells that can independently produce a bio-lens. The DNA in the bacteria cells is processed to enable the cells to form a micro-lens independently. The lenses, that can be used for microscopy, for example, also have the potential for making solar cells of the future more efficient. With their bio-lenses, the team of students are hoping for victory at the international student 'iGEM' (International Genetically Engineered Machine) competition held in Boston from 27 to 31 October. In the competition, students attempt to solve problems in society with the aid of synthetic biology. Low-cost lenses To make a bio-lens, the DNA in the bacteria cells is processed, enabling it to produce new proteins. The protein, similar to that produced by a sea sponge, is used to build a thin layer of glass. The bacteria cell creates the biological lens with this piece of glass. It is the first time that bacteria cells have produced a lens independently. The lenses are significantly more environmentally-friendly than existing micro-lenses used in microscopes, for example. Conventional lenses are produced at high temperature, low-pressure and in a high acid environment. The process also releases chemicals. The iGEM student team's bio-lenses are created at lower temperatures and standard air pressure. This makes them better for the environment and cheaper to make, since much less energy is required to achieve the necessary conditions. A potential use for micro-lenses is on solar panels. A research group from France has already shown that existing industrial micro-lenses can improve the efficiency of solar cells by up to 50%. This is because the lenses enable more light to be absorbed by the solar cells. However, these micro-lenses are far too expensive to make, which is why they are not yet used on solar cells. This could be achievable using the bio-lenses developed by the Delft iGEM team. They are attempting to use a layer of their cells to improve the efficiency of solar panels.
Chemical circuits can coordinate elaborate sequences of events in cells and tissues, from the self-assembly of biological complexes to the sequence of embryonic development. However, autonomously directing the timing of events in synthetic systems using chemical signals remains challenging. Here we demonstrate that a simple synthetic DNA strand-displacement circuit can release target sequences of DNA into solution at a constant rate after a tunable delay that can range from hours to days. The rates of DNA release can be tuned to the order of 1-100 nM per day. Multiple timer cir-cuits can release different DNA strands at different rates and times in the same solution. This circuit can thus facilitate precise coordination of chemical events in vitro without external stimulation.
A group of Singularity University alum have created the first patented, real-time biosensing device. The tech lets you hack your health with biometrics by reading antioxidant level changes on the skin from the palm of your hand.
One goal of metabolic engineering and synthetic biology for cyanobacteria and microalgae is to engineer strains that can optimally produce biofuels and commodity chemicals. However, the current workflow is slow and labor intensive with respect to assembly of genetic parts and characterization of production yields because of the slow growth rates of these organisms. Here, we review recent progress in the microfluidic photobioreactors and identify opportunities and unmet needs in metabolic engineering and synthetic biology. Because of the unprecedented experimental resolution down to the single cell level, long-term real-time monitoring capability, and high throughput with low cost, microfluidic photobioreactor technology will be an indispensible tool to speed up the development process, advance fundamental knowledge, and realize the full potential of metabolic engineering and synthetic biology for cyanobacteria and microalgae.
Cholera is a potentially mortal, infectious disease caused by Vibrio cholerae bacterium. Current treatment methods of cholera still have limitations. Beneficial microbes that could sense and kill the V. cholerae could offer potential alternative to preventing and treating cholera. However, such V. cholerae targeting microbe is still not available. This microbe requires a sensing system to be able to detect the presence of V. cholera bacterium. To this end, we designed and created a synthetic genetic sensing system using nonpathogenic Escherichia coli as the host. To achieve the system, we have moved proteins used by V. cholerae for quorum sensing into E. coli. These sensor proteins have been further layered with a genetic inverter based on CRISPRi technology. Our design process was aided by computer models simulating in vivo behavior of the system. Our sensor shows high sensitivity to presence of V. cholerae supernatant with tight control of expression of output GFP protein.
Throughout the decades of its history, the advances in bacteria-based bio-industries have coincided with great leaps in strain engineering technologies. Recently unveiled clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated proteins (Cas) systems are now revolutionizing biotechnology as well as biology. Diverse technologies have been derived from CRISPR/Cas systems in bacteria, yet the applications unfortunately have not been actively employed in bacteria as extensively as in eukaryotic organisms. A recent trend of engineering less explored strains in industrial microbiology-metabolic engineering, synthetic biology, and other related disciplines-is demanding facile yet robust tools, and various CRISPR technologies have potential to cater to the demands. Here, we briefly review the science in CRISPR/Cas systems and the milestone inventions that enabled numerous CRISPR technologies. Next, we describe CRISPR/Cas-derived technologies for bacterial strain development, including genome editing and gene expression regulation applications. Then, other CRISPR technologies possessing great potential for industrial applications are described, including typing and tracking of bacterial strains, virome identification, vaccination of bacteria, and advanced antimicrobial approaches. For each application, we note our suggestions for additional improvements as well. In the same context, replication of CRISPR/Cas-based chromosome imaging technologies developed originally in eukaryotic systems is introduced with its potential impact on studying bacterial chromosomal dynamics. Also, the current patent status of CRISPR technologies is reviewed. Finally, we provide some insights to the future of CRISPR technologies for bacterial systems by proposing complementary techniques to be developed for the use of CRISPR technologies in even wider range of applications.
A central aim of synthetic biology is to build organisms that can perform useful activities in response to specified conditions. The digital computing paradigm which has proved so successful in electrical engineering is being mapped to synthetic biological systems to allow them to make such decisions. However, stochastic molecular processes have graded input-output functions, thus, bioengineers must select those with desirable characteristics and refine their transfer functions to build logic gates with digital-like switching behaviour. Recent efforts in genome mining and the development of programmable RNA-based switches, especially CRISPRi, have greatly increased the number of parts available to synthetic biologists. Improvements to the digital characteristics of these parts are required to enable robust predictable design of deeply layered logic circuits.
A few years ago, Michael Levin faced a conundrum. He and his colleagues at the Tufts Center for Regenerative and Developmental Biology just outside Boston wanted to find a model that would explain why the flatworm—a model organism used throughout biology—looks the way it does. At a fundamental level, they wanted to be able to describe the cascade of events that leads to the growth of a head in one place and a tail in the other. In fact, it was almost the same problem that Thomas Hunt Morgan, a Nobel Prize-winning evolutionary biologist and one of the founders of the modern study of genetics, faced more than a century ago. Back then, he was busying himself making careful cuts into flatworms. $$!ad_code_content_spilt_video_ad!$$ He sliced them lengthwise. He dissected them in half. From each segment, the worm grew a complete body. Eventually, Morgan carved off 1/279th of the worm, a bit selected from its mid-section, and demonstrated that it could regenerate an entirely new animal. He was trying, unsuccessfully, to understand why and how certain body parts developed where they did: Why did a head appear at one end of the worm after a cut? Over the next 100 years, using new tools and insights, researchers replicated Morgan’s efforts in increasing detail: Make a cut here, a fully formed trunk and head will reappear. Tinker with this particular chemical or knock down this gene, and create a worm with a tail at either end. There are more than a thousand such papers. Yet, still, nobody has been able to fully explain why a head forms where it does. Levin and his colleagues at the center, where he’s the director, have been tackling this problem for years. In that time, they have helped explain some basic questions about development and regeneration. By tweaking certain signals within flatworms, for example, he has been able to grow a worm with four heads or one with no head but a tail on either end. He and his team have even grown an eye on a tadpole’s belly. But those experiments didn’t help them understand how it all fit together. “We have a massive literature of results saying ‘I did this to the worm and this happened,’ ” Levin says. “And we’re increasingly drowning in ever higher resolution genetic data sets. And yet, since Morgan cut his first worm, we still don’t have any model that explains more than a couple of different cuts.” And so, “after 120 years of really smart people going at it, I started to wonder, maybe this is beyond the ability of us to come up with off the top of our heads, to create a model that fits all the data.” ->Developmental biologists use the flatworm Schmidtea mediterranea to study regeneration.<- Computers, however, now offer enough power that Levin thought it might be possible to create such a model for a flatworm, one that would detail, in silico, the cascade of events that lead to the growth of a head at one end and a tail in the other. Still, even with a supercomputer’s worth of computational power, it wouldn’t be easy. Yet Levin’s ambitions didn’t stop there. Full, in-depth models beguile nearly every aspect of biology, and Levin hoped to create a tool that could be employed beyond flatworm development, one that could eventually model the vastly more complex world of human diseases. If he did it right, it might even help develop cures for those diseases. All he needed first was the right computer program. :break: Enter Evolution It’s fitting that Levin’s computer models of biology are inspired by one of the fundamental tenets of biology itself, evolution. He builds his models around the idea that computer algorithms can meet, mate, and select the most fit version to then mate with other fit algorithms. These so-called genetic algorithms were first proposed by computer scientist John Holland in the 1960s. But even earlier, in the 1940s and ’50s, “people were already thinking about using inspiration from biology to build life-like computer programs,” says Melanie Mitchell, a professor of computer science at Portland State University and author of the book An Introduction to Genetic Algorithms. For instance, John Von Neumann, one of the earliest computer scientists in the 1940s, envisioned computers that could replicate themselves, with code serving as what we now call DNA. Mitchell says that Holland saw the field of genetic algorithms as a mathematical tool that could help explain how adaptation occurs in evolution. What Holland saw as theory, others took as a practical tool. For instance, one of his students, David Goldberg, used these new genetic algorithms to optimize plans for gas pipelines by mating different models until the algorithm came up with the best design. But while early computer scientists were limited by memory and speed, today’s more powerful computers can run increasingly complex models to process millions of possible combinations and save the best chunks of code before passing them on to the next model, just as in natural evolution. Mitchell says these models have applications in engineering, big data, drug design, banking, and ever more realistic computer graphics and animations. Unlike biological evolution, where individuals meet, mate, and pass on useful traits that best fit the environment, computer-based evolution starts with a goal or a set of rules. Then the computer generates millions, even billions, of models to try to meet those goals. The ones that solve part of the problem or meet some aspect of the goal have a higher likelihood of passing on that relevant code to the next generation. Robotics researchers have employed this approach as well. Josh Bonguard at the University of Vermont modeled robots that learned to evolve walking. Columbia University’s Hod Lipson used this approach to simulate machines that learned how to crawl on a table. He spun off a company called Nutonian that allows scientists to input their data, and then the program evolves equations until one explains the data. That equation could allow researchers to optimize designs, model what might happen in the future, or show how changes in one part of a system might affect the final result. “It can be used anywhere,” Lipson says, “from finance to rainfall in the Amazon.” “This approach—reverse engineering—it’s like a Russian spy movie,” says Johannes Jaeger, a developmental geneticist and scientific director of the Konrad Lorenz Institute for Evolution and Cognition Research in Austria. “You have some kind of gadget, like in the Cold War, some kind of Russian technology. You don’t know what it does. Here, you have an organism, you don’t know how it works, and you’re trying to infer the networks that make the pattern that you see in animals.” Jaeger began working in this field more than a decade ago and has used such algorithms to model the genetic network that created a segmented body pattern in fruit flies. But none of the models are as complex as the development of an entire creature’s shape. :break: Billions of Experiments When Levin first proposed his modeling project a few years ago, his colleagues in biology found the proposal absurd. “Pretty much nobody I talked to thought it was going to work,” Levin says. His critics had two overarching reactions. Some thought it would be impossible to find any model that worked; biologists would say, he said: “ ‘You’re telling me this program is going to take random models and by recombining random changes to random models you’re going to find the right model? That’s ridiculously impossible.’ ” Levin disregarded that criticism. That was how evolution had worked, he thought, and computers finally seemed powerful enough to try. The second criticism he heard was that they’d find many models that explained the data, maybe 10, maybe 1,000. How would they know which one was the correct one? “In theory,” Levin says, “that was a possible outcome. But we didn’t have any. It’s actually very difficult to find a model that does what it needs to. I wasn’t worried. If we found more than one—fabulous.” Levin hired post-doc Daniel Lobo to lead the flatworm modeling project, a computer science PhD who had worked with Hod Lipson, and whose research was also inspired by Johannes Jaeger. (Lobo now heads his own lab at the University of Maryland, Baltimore County.) Lobo had the right combination of computer expertise and interest in biology, and he’d written a paper about applying genetic algorithms to the evolution of shape that caught Levin’s attention. He’d used such algorithms to automatically design structures optimized for unmanned landings, such as those of the rovers sent to Mars, and Levin was impressed with the way Lobo combined a deep knowledge of the field with an interest in making it practical. The first challenge was to take the more than 1,000 experiments that had been done on flatworm shape and create one language to describe those results. It’s a not insignificant challenge. Natural language, as opposed to computer code, is ambiguous, even in scientific papers. At the same time, the team had to decide what to encode. They didn’t need exact dimensions of a flatworm head, but they did need to include relative scale, for instance. Eventually, Lobo created a standardized language and a standardized mathematical approach that represents the shapes of the worm’s regions, its organs, and how they’re interconnected. The end result was a searchable database of results, which is now available to all flatworm biologists. Next, Lobo designed the simulation itself, a virtual worm on which candidate models would test their results. The computer compares the results of the simulated experiments to the real-world results expected from the database. The models receive scores based on how well they predict the outcomes seen in flesh-and-blood flatworms. Those with high scores reproduce; those with bad scores are discarded. After four years of work, they’d come up with a common language for scientific papers, distilled the most crucial aspects of the worm to put in a model, developed a simulator, and created the algorithm to find the shape model. Levin felt fairly sure that the project would work—but they needed one more piece of equipment. Common lab computers, no matter how powerful, can’t yet quickly process the massive computations needed to evolve an entire biological model, in effect replicating millions of years of evolution. They needed a supercomputer. So the team rented time on Stampede, the University of Texas supercomputer that can perform up to 10 quadrillion mathematical operations per second. Levin says the first models performed terribly; they didn’t get anything right. But by the 100th generation, some of the models started to predict some of the correct results. By the 1,000th, the models were increasingly matching the real-world experimental results. In the end, it took 6 billion simulated experiments, 26,727 generations of models, and about 42 hours of processing by the Stampede computer before the computer came up with one result. This was what they’d been waiting for: one model that could explain the 1,000 existing experiments that generate the head-trunk-tail pattern in a flatworm. To test the model, the team introduced data from two papers on shape formation that had purposefully not been included in the dataset when developing the models. The model accurately explained the results of both papers. So Levin and Lobo took it one step further. The model predicted experiments that had never been done before. The team tested those experiments in the real world, and they worked. The new results were published in May, 2016, describing the activity of a previously unknown gene that played a role in shape formation. Intriguingly, the model suggests the existence of a second node that hasn’t yet been explained by current scientific knowledge; it could be a protein, it could be a particular chemical. “The computer knows there’s a product that should be there, that seems to be important. In a way, it’s predicting a product that we don’t yet know,” Lobo says. :break: From Silicon Models to Hard Data The shape investigation a success, Levin and Lobo turned their attention to modeling disease. They started with melanoma—skin cancer—and did so by focusing on pigmentation cells in tadpoles. They conducted experiments in which tadpoles were exposed to particular chemicals during their development. The tadpoles had no obvious chromosomal damage or genetic trigger, but for some of them, the chemicals would spark a change in all of their pigmentation cells. Those cells then turned metastatic and invaded tissues throughout their bodies. But for other tadpoles under exactly the same conditions, nothing changed. Could the algorithm determine why? Graduate student Maria Lobikin conducted dozens of experiments, knocking out genes or applying drugs and determining what percentage of the tadpoles became hyper-pigmented. Then she combined those results with other research published in the last decade. The team followed the same approach, creating a standardized language to describe the experiments and using a supercomputer to evolve a model to understand how, in some tadpoles, under certain biological circumstances, the cells flipped to a hyper-pigmented, cancerous state. The computer-generated model came quite close to the results of the existing experiments. The model predicted the results of all papers but one; perhaps the algorithm had even caught inaccurate data, a finding that was published in the journal Science Signaling in October 2015. “I said, you know what, go back and redo this experiment just to make sure, and sure enough, the data were slightly off,” Levin says. “It’s almost like a verification step. If it’s having trouble matching the results of one experiment, maybe the problem’s not the model, maybe the problem’s your data.” More recently, they asked the model a question. Is there any way to create a scenario where only some of the pigment cells become cancerous, where it’s not an all-or-none response? The computer generated an unusual three-step combination of drugs. When the team tried the experiment suggested by the computer model, they were able to create the first partially-pigmented animals. While the tadpole model is far from an ideal surrogate for human disease, Levin points out that this research supports what other scientists have claimed, that cancer is not always a result of specific DNA damage. Rather it may also be a systems disorder, where the exact right set of circumstances in the system generate conditions for cancer to grow. The simulated experiments demonstrate how artificial intelligence can augment human abilities, both Lobo and Levin say. “I see it definitely not as a way to replace biologists,” Jaeger says with a laugh. He says it’s nearly impossible for humans to process all the relevant parameters to generate a model, but computer power can do what our brains simply can’t. $$!ad_code_content_spilt_video_ad2!$$ The duo see many more such experiments in the future. At his Baltimore lab, Lobo is now focusing on bacteria, as modeling the ways in which the microbes create different compounds could be useful for the field of synthetic biology. He’s also trying to reverse-engineer cancer tumors to attempt to discover the best possible treatments to cause them to collapse. Levin sees applications in many fields: drug development, regenerative medicine, and understanding metabolism and disease. (Levin recently was awarded one of the first two Paul Allen Frontiers Group grants, a $30 million grant over eight years, to support risky, unconventional research; these computer-generated models are only a portion of his lab’s research.) In any case, employing computer algorithms to wrestle with yet unanswered questions in biology will, researchers say, only become more mainstream. Lipson says this approach is crucial: “We’re in a stage where biology is producing lots and lots of data,” he says. “But magically it’s not going to make sense out of nowhere. You need these types of systems to make sense of the data we have.” In other words, systems that mimic evolution—and might help us evolve solutions as well.
Efforts to engineer microbial factories have benefitted from mining biological diversity and high throughput synthesis of novel enzymatic ensembles, yet screening and optimizing metabolic pathways remain rate-limiting steps. Metabolite-responsive biosensors may help to address these persistent challenges by enabling the monitoring of metabolite levels in individual cells and metabolite-responsive feedback control. We are currently limited to naturally-evolved biosensors, which are insufficient for monitoring many metabolites of interest. Thus, a method for engineering novel biosensors would be powerful, yet we lack a generalizable approach that enables the construction of a wide range of biosensors. As a step towards this goal, we here explore several strategies for converting a metabolite-binding protein into a metabolite-responsive transcriptional regulator. By pairing a modular protein design approach with a library of synthetic promoters and applying robust statistical analyses, we identified quantitative design principles for engineering biosensor-regulated bacterial promoters and for achieving design-driven improvements of biosensor performance. We demonstrated the feasibility of this strategy by fusing a programmable DNA binding motif (zinc finger module) with a model ligand binding protein (maltose binding protein), to generate a novel biosensor conferring maltose-regulated gene expression. This systematic investigation provides insights that may guide the development of additional novel biosensors for diverse synthetic biology applications.
The team of researchers at Saarland University, led by Professor of Condensed Matter Physics Karin Jacobs, initially had something quite different in mind. Originally, the team set out to research and describe the characteristics of hydrophobins – a group of naturally occurring proteins. ‘We noticed that the hydrophobins form colonies when they are placed in water. They immediately arrange themselves into tightly packed structures at the interface between water and glass or between water and air,’ explains Karin Jacobs. ‘There must therefore be an attractive force acting between the individual hydrophobin molecules, otherwise they would not organize themselves into colonies.’ But Professor Jacobs, research scientist Dr Hendrik Hähl and their team did not know how strong this force was.
This is where the neighbouring research group led by Professor Ralf Seemann got involved. One of Seemann’s research teams, which is headed by Dr Jean-Baptiste Fleury, studies processes that occur at the interfaces between two liquids. The research team set up a minute experimental arrangement with four tiny intersecting flow channels, like a crossroads, and allowed a stream of oil to flow continuously from one side of the crossing to the other. From the other two side channels they injected ‘fingers’ of water which protruded into the crossing zone. As the hydrophobins tended to gather at the interface of the carrier medium, they were in this case arranged at the water-oil interface at the front of the fingers. The physicists then ‘pushed’ the two fingers closer and closer together in order to see when the attractive force took effect. ‘At some point the two aqueous fingers suddenly coalesced to form a single stable interface consisting of two layers,’ says Ralph Seemann. ‘The weird thing is that it also functions the other way around, that is, when we use oil fingers to interrupt a continuous flow of water,’ he explains. This finding is quite new, as up until now other molecules have only exhibited this sort of behaviour in the one or the other scenario. Normally proteins will orient themselves so that either their hydrophilic (‘water loving’) sides are in contact with the aqueous medium, or their hydrophobic (‘water fearing’) side is in contact with an oily medium. That a type of molecule can form stable bilayers in both environments is something wholly new.
Encouraged by these findings, the researchers decided to undertake a third phase of experiments to find out whether the stable bilayer could be reconfigured to form a small membrane-bound transport sac — a vesicle. They attempted to inflate the stable membrane bilayer in a manner similar to creating a soap bubble, but using water rather than air. The experiment worked. The cell-like sphere with the outer bilayer of natural proteins was stable. ‘That’s something no one else has achieved,’ says Jean-Baptiste Fleury, who carried out the successful experiments. Up until now it had only been possible to create monolayer membranes or vesicles from specially synthesized macromolecules. Vesicles made from a bilayer of naturally occurring proteins that can also be tailored for use in an aqueous or an oil-based environment are something quite new.
In subsequent work, the research scientists have also demonstrated that ion channels can be incorporated into these vesicles, allowing charged particles (ions) to be transported through the bilayer of hydrophobins in a manner identical to the way ions pass through the lipid bilayers of natural cells.
As a result, the physicists now have a basis for further research work, such as examining the means of achieving more precisely targeted drug delivery. In one potential scenario, the vesicles could be used to transport water-soluble molecules through an aqueous milieu or fat-soluble molecules through an oily environment. Dr Hendrik Hähl describes the method as follows: ‘Essentially we are throwing a vesicle “cape” over the drug molecule. And because the “cape” is composed of naturally occurring molecules, vesicles such as these have the potential to be used in the human body.’
The results of this research work were a surprise. Originally, the goal was simply to measure the energy associated with the agglomeration of the hydrophobin molecules when they form colonies. But the discovery that hydrophobin bilayers could be formed in both orientations, opened the door to experiments designed to see whether vesicles could be formed. That one thing would lead to another in this way, offers an excellent example of the benefits of this type of basic, curiosity-driven research. ‘The “discovery” of these vesicles is archetypal of this kind of fundamental research. Or to put it another way, if someone had said to us at the beginning: “Create these structures from a natural bilayer,” we very probably wouldn’t have succeeded,’ says Professor Karin Jacobs in summary.
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.
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