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Boston U Researchers Develop 'Feed-Forward' Synthetic Network Model

NEW YORK (GenomeWeb News) – A Boston University research team has devised a new computer modeling technique for building predicable gene networks from a set of libraries containing diversified components — an approach that they say will accelerate the pace of synthetic biology.

Using this "feed-forward" approach, the researchers demonstrated that they could create libraries of regulatory promoters for the yeast Saccharomyces cerevisiae and use these in networks with predictable inputs and outputs. Then they went a step further, producing a synthetic gene network that accurately predicted when yeast cells in liquid media would settle — an output that could benefit those brewing beer, other libations, and even bioethanol.

"If synthetic biology is going to advance, it is essential to identify techniques that increase the predictability of gene network engineering and decrease the amount of hands-on molecular biology required to get a functional network up and running," senior author James Collins, a biomedical engineering researcher at Boston University, and his colleagues wrote.

In an effort to achieve this goal, Collins and his colleagues came up with a feed-forward method for informing how synthetic networks are designed and assembled rather than using computer modeling to interpret synthetic biology experiments. This involves characterizing the parts used in the network — in this case regulated promoters — before assembling the network.

The researchers first tested this in S. cerevisiae, constructing a library of 20 promoters designed to be repressed by the antibiotic tetracycline. After gauging each promoter's minimum and maximum output and characterizing all of the components in their network, the researchers created an in silico model to predict how outputs would vary depending on input and promoter level in the system.

Indeed, they demonstrated that they could accurately forecast different outputs depending on the promoter being modeled, illustrating the impact of promoter strength on networks involving these promoters.

"Using regulated promoters as our example, we describe here how a simple synthesis technique can be used rapidly create and characterize component libraries for synthetic biology," they explained. "Working in S. cerevisiae, we demonstrate how such libraries can be teamed with predictive modeling to rationally guide the construction of gene networks that have diverse outputs."

Next, the trio tackled a more complex circuit: a genetic timer containing not one but two promoter libraries. After validating the approach, the researchers tested whether they could control the timing with which yeast cells sedimented — a step involved in fermenting beer, wine, and bioethanol — using three different networks. The researchers reported that all three networks predicted sedimentation times that closely resembled experimental results.

"Although screening of mutated parts is not a new technique, our approach represents an advance over previous methods by coupling qualitative and quantitative modeling with library diversity to guide the construction of synthetic gene networks with predictable functions," the authors wrote. "In robust networks such as our feed-forward loop, models built entirely from component property sets are sufficient to guide the choice of parts required to elicit specific network phenotypes."

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