In one demonstration, the researchers used a genetic network to precisely control a process in yeast that is crucial for brewing beer, called flocculation. The timing and strength of flocculation determines whether a beer is cloudy and tannic or clear and smooth, and initiating the process often relies on chemical additives. To genetically engineer flocculation control into yeast, the researchers constructed a toggle switch, a type of synthetic network that makes use of two mutually opposing promoters that can toggle between one state and another. If one promoter is weak and the other is strong, the stronger one will eventually overwhelm the weak one, flipping the switch. The length of time that it takes to flip the switch can be tweaked by changing the relative strengths of the two promoters, essentially creating a genetic timer. The researchers engineered this network into yeast so that when the toggle switches on, the yeast is made to flocculate.
Collins says that simply knowing the properties of components like promoters is not always enough to predict their behavior in complex networks like a genetic timer. His team had to assemble and characterize a single network from a set of parts in order to create a model that could be generalized for other parts in the library. Although this approach does require more work up front to build and characterize a library of components and experimentally test a network, Collins says that it is much more efficient than trying to retrofit a network by trial and error after it is built.
Jeff Hasty, a synthetic biologist at the University of California, San Diego, says that the study addresses a critical “component problem” in synthetic biology: there are only a limited number of components available and limited ways to regulate their activity. Although other groups have begun to construct libraries of genetic parts, Hasty says that the computer modeling shown in this study is also important for predicting how the parts will behave. While electrical engineers can make use of fundamental laws to predict the behavior of circuits, biologists are far from having those kinds of axioms. They must instead develop models based on empirical evidence from experimentation, Hasty says, in order for synthetic networks to “have some hope of behaving the way we predict they’ll behave.”