Synthetic biology involves the creation or redesign of biological systems so that they possess novel abilities. This multidisciplinary field of science brings together engineers and biologists, who are developing these systems for useful purposes, across numerous areas including diagnostics, therapeutics, manufacturing and agriculture.
Here, we take a closer look at some of the recent developments and applications of synthetic biology.
Gene-OFF switch expands synthetic biology toolbox
In efforts to advance diagnostics and biologically produced therapeutics and fine chemicals, synthetic biologists are constructing artificial networks of genes and modular regulatory elements. These networks are then introduced into cells and can “sense” biological signals and/or chemical substances within the cell. They can also act in response to these stimuli.
Researchers from Harvard’s Wyss Institute and Arizona State University have developed a “ribocomputing device” that is capable of simultaneously sensing multiple biological RNA signals – acting as a “molecular logic board”. Upon receiving a specific combination of signals, the device is designed to “activate”, producing a desired protein. The team has also created a device that effectively does the complete opposite task – it shuts down the expression of a protein in response to a specific stimulus.
“Our Repressor Switch devices add a new capability to the synthetic biology toolbox for researchers designing synthetic biological circuits,” said co-corresponding author Prof. Peng Yin. “They have the potential to usher in the possibility of more sophisticated and accurate applications in different areas of next-generation diagnostics, environmental reporting, as well as biomanufacturing.”
Publication: Kim, J, Zhou, Y, Carlson, PD et al. De novo-designed translation-repressing riboregulators for multi-input cellular logic. Nat Chem Biol. 2019;15:1173–1182. doi:10.1038/s41589-019-0388-1
Exploring the synthetic biology revolution
Michael Jewett, director of Northwestern’s Center for Synthetic Biology, and colleagues recently reported on how cell-free engineering has evolved from a research tool to a central pillar of numerous applications in synthetic biology, with potential implications for many areas.
Jewett et al. have successfully developed a high-yielding “one-pot” cell-free protein synthesis approach base on a genetically recoded strain of Escherichia coli. The approach has been designed to produce the highest batch reaction expression yield of a protein to date and is capable of synthesizing proteins with non-canonical amino acids, paving the way for the development of novel enzymes and therapeutics.
“By having a platform that enables high-level gene expression in a one-pot use, the process becomes a lot more democratized,” Jewett said. “That’s exciting, because it will hopefully make it easier for other labs to use cell-free gene expression systems.”
Publication: Silverman, AD, Karim, AS & Jewett, MC Cell-free gene expression: an expanded repertoire of applications. Nat Rev Genet. 2020;21:151–170. doi:10.1038/s41576-019-0186-3
Deep learning gets a “toehold” on synthetic biology
Data scientists from the Wyss Institute at Harvard University and synthetic biologists from the Massachusetts Institute of Technology have collaborated in efforts to design a system that can navigate the complex set of instructions governing biological organisms, to enable them to devise innovative solutions to biological problems. The system exploits the combined computational power of machine learning, neural networks and other algorithmic architectures.
The team honed in on a specific class of engineered RNA molecules known as “toehold switches”. In their “off” state, the switch is configured into a hair-pin structure, however when a complementary RNA strand associates with a “trigger” sequence attached the hairpin, the structure unfolds and “activates”. The unfolded RNA exposes previously hidden regions which can be translated, as ribosomes can now successfully associate. Toehold switches are a powerful means of controlling gene expression.
“Computer vision algorithms have become very good at analyzing images, so we created a picture-like representation of all the possible folding states of each toehold switch, and trained a machine learning algorithm on those pictures so it could recognize the subtle patterns indicating whether a given picture would be a good or a bad toehold,” said Nicolaas Angenent-Mari, from the Wyss Institute.
Publication: Angenent-Mari, NM., Garruss, AS., Soenksen, LR. et al. A deep learning approach to programmable RNA switches. Nat Commun. 2020;11:5057. doi:10.1038/s41467-020-18677-1
Hazardous fluoride detected in drinking water using synthetic biology
Synthetic biologists from Northwestern University have designed a testing system that can detect hazardous levels of fluoride in drinking water. Designed to be both simple and inexpensive, the system works by adding just one drop of the water sample to a test tube and mixing the solution – a color change (to yellow) indicates excessive fluoride. The test tube houses a complex synthetic biology reaction, which is based on RNA folding mechanisms.
“RNA folds into a little pocket and waits for a fluoride ion,” explained Northwestern’s Julius Lucks. “The ion can fit perfectly into that pocket. If the ion shows up, then RNA expresses a gene that turns the water yellow. If the ion doesn’t show up, then RNA changes shape and stops the process. It’s literally a switch.”
The RNA reaction is free-dried before being added to the test tube, making it safe and shelf-stable. To rehydrate the reaction, 20 mL of water is added, using a small accompanying pipette, results are achieved in ~ 2 hours.
Publication: Thavarajah W, Silverman AD, Verosloff MS, Kelley-Loughnane N, Jewett MC, Lucks JB. Point-of-use detection of environmental fluoride via a cell-free riboswitch-based biosensor. ACS Synth Biol. 2020;9(1):10–18. doi:10.1021/acssynbio.9b00347
Adapting machine learning algorithms to the needs of synthetic biology
Scientists from the Department of Energy’s Lawrence Berkeley National Laboratory have developed a novel tool that is able manipulate machine learning algorithms to the needs of synthetic biology. Using a limited set of training data, the tool can predict how alterations in cell DNA or biochemistry will impact its behavior. This insight can then be harnessed by synthetic biologist to engineer systems with desired behaviors and novel functions.
“The possibilities are revolutionary,” said Hector Garcia Martin, a researcher in Berkeley Lab’s Biological Systems and Engineering Division. “Right now, bioengineering is a very slow process. It took 150 person years to create the anti-malarial drug, artemisinin. If you’re able to create new cells to specification in a couple weeks or months instead of years, you could really revolutionize what you can do with bioengineering.”
Publication: Radivojević, T, Costello, Z, Workman, K et al. A machine learning Automated Recommendation Tool for synthetic biology. Nat Commun. 2020;11:4879. doi:10.1038/s41467-020-18008-4
