Synthetic biology rests on the hope that biological “parts” like DNA and proteins can be engineered and assembled just like a machine or computer circuit, but the field still has some way to go before this is the case. As much as biologists know about the structure and function of biological molecules, their behavior when interacting with one another is still unpredictable.
A new approach detailed in this week’s issue of the journal Nature Biotechnology offers a more systematic approach to constructing biological “circuits”–one that makes it easier to predict how they will behave before they are synthesized. The researchers used the technique to engineer yeast for brewing different kinds of beer more precisely, but the approach could also be used in the production of biofuels and therapeutic drugs and for other applications.
Genomics has given scientists an inventory of genes and their associated proteins, but a significant portion of the genome is regulated by other genes in interacting networks. For example, genes can turn each other on or off, and genetic sequences called promoters can control whether certain genes get switched on or get repressed. Synthetic biologists use information about these interactions to construct synthetic gene networks (collections of genes that interact directly or indirectly through their RNA and protein products) that can be introduced into microbes or mammalian cells to perform a desired task, such as creating microbes that produce a chemical by-product that could be used as fuel.
James Collins, a synthetic biologist at Boston University and senior author of the new paper, notes that the engineers who construct computer circuits have hundreds of components to choose from and can map out a schema of the circuit ahead of time, knowing exactly what its components will do. When synthetic biologists construct a genetic circuit, however, it’s a process of trial and error that “can take months or years of tweaking,” he says. Furthermore, these biologists often rely on just a few parts that must be altered to achieve the desired effect.
To make the process more efficient and predictable, Collins’s group has developed an approach that relies on constructing libraries of component parts combined with mathematical modeling to predict the behavior of these parts in a gene network before it is built.
The scientists focused on gene promoters, which are important for fine-tuning the activation of genes in a network. They screened a large list of variants of two promoters to create a library of variants categorized according to the strength of their activity. The researchers then used this functional information to create a computer model of the promoters’ activity, which allowed the different variants to be incorporated into a gene network with a predictable and quantifiable result.
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