Using Combinatorial Libraries to Engineer Genetic Circuits Advances Synthetic Biology
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(Boston) — Streamlining the construction of synthetic gene networks has led a team of Boston University researchers to develop a technique that couples libraries of diversified components with computer modeling to guide predictable gene network construction without the back and forth tweaking.
By applying engineering principles to biological systems where a set of components can evolve into networks that display desired behaviors – known as synthetic biology –has led to new opportunities for biofabrication, drug manufacturing, and even potential biofuels.
And while there have been notable successes, the basic process of building and assembling a predictable gene network from bio-molecular parts remains a major challenge that is often frustrating. The time-consuming tweaking phase often requires many months of swapping out different chemical inputs, RNA regulators and promotors before the sought-after network is realized.
In a paper published online this week in Nature Biotechnology, the research team, led by James J. Collins, BU professor of biomedical engineering, focused on ways to speed up the construction process by assembling a library of 20 versions of two gene promotors and a simple synthesis technique to create component libraries for synthetic biology. Each version covered a wide range gene expression. With the activity levels calculated from the component libraries, the scientists turned to a computer model and designed and built a basic gene circuit to predict how fluorescent protein expression varied with levels of promoter-inhibiting chemicals.
Using the same simulation, for the simple gene circuit the researchers went the next step with a genetic timer, a more complicated circuit. However, computer simulation, on its own, was unable to predict the behavior of this timing circuit. They then built a representative genetic timer using a promoter from each of their libraries and, over time, tracked its behavior. Based on information from one network, the research team was able to calibrate their model and achieve accurate predictions from all the other possible network combinations. These timers, the study notes, are effectively genetic toggle switches.
One last test of these genetic timers was to assemble and test one in yeast, which could accurately time yeast sedimentation — a process that can be applied to biotechnology and some popular brewed beverages.
“The phenotype is crucial in industrial beer, wine and bioethanol fermentation, as it allows for easy removal of yeast sediments after all the sugars have been converted to ethanol,” the paper noted.
The researchers concluded that their method using combinatorial libraries to engineer genetic circuits moves the “tweaking” from the back-end of gene network engineering to the front-end.
“Projects undertaken with this approach will help accelerate synthetic biology by yielding many more components for the community,” the paper concludes, noting the need for extensive characterization of each component is eliminated or substantially reduced.
“Our work also provides an accessible method for introducing predictable, controlled variability to networks, a feature that is increasingly becoming desirable as synthetic biology enters its second decade.”
The research paper, “Diversity-based, Model-Guided Construction of Synthetic Gene Networks with Predicted Functions, “was authored by Tom Ellis and Xiao Wang, both post-doctoral students at Boston University’s Center for BioDynamics and Center for Advanced Biotechnology and Collins.
Founded in 1839, Boston University is an internationally recognized institution of higher education and research. With more than 30,000 students, it is the fourth largest independent university in the United States. BU consists of 17 colleges and schools along with a number of multi-disciplinary centers and institutes which are central to the school’s research and teaching mission.