Synthetic biology promises to revolutionize biotechnology by applying engineering principles to biological systems1
. In less than a decade this field has already yielded technological applications, providing new avenues for drug manufacture2, 3
and therapeutics5, 6
, while also offering promises in alternative energy7
. A major focus of the field is the synthesis of gene networks with predictable behavior8-10
, either to endow cells with novel functions11-15
or provide study for analogous natural systems8, 16-19
. Despite a booming community and notable successes, the basic task of assembling a predictable gene network from biomolecular parts remains a significant challenge and often takes many months before a desired network is realized20
. To advance synthetic biology, 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.
Current approaches of gene network construction typically use a small set of components plundered from different natural systems, which are then assembled and tested in vivo
, often without guidance from a priori
mathematical modeling13, 21
. Networks rarely behave as intended the first time, usually because chosen parts have the correct function but lack the specific quantitative properties required. Even for those few synthetic biology studies which do utilize computational assistance22-25
, in silico
results have been mainly used for data interpretation, not for guiding design and assembly. Instead, in most projects an initial failed network is usually resolved over months of iterative retrofitting20
, often by fine-tuning imperfect parts by mutation, by identifying alternative parts, or by adding on extra features to counterbalance the problems. Directed evolution has been shown to provide a short-cut through this phase21
, but is complicated by the additional work needed to couple networks to selective pressures.
This time-consuming post-hoc tweaking phase stems, in part, from having to work with a limited set of imperfect components. Although this lack of reliable parts is being addressed by community efforts26
, it remains an acute problem due to there being a limited number of components and the fact that the majority are inadequately characterized, e.g., many promoters are simply characterized as being ‘weak’ or ‘strong’. What is needed to resolve this problem and fast-track synthetic biology is a new approach that creates libraries of components ahead
of any assembly; and then, by starting with a finer granular range of choices for each component, modeling can be used to quickly pick out the correct part needed to generate the intended network function. This approach offers the added attraction of allowing substantially different network outcomes to be chosen in advance, simply by selecting functionally-equivalent components with slightly different properties. This exploits a feature common to many types of finely-balanced networks, where small changes to one component can dramatically impact the behavior of the entire system.
Using regulated promoters as our example, we describe here how a simple synthesis technique can be used to rapidly create and parallel-characterize component libraries for synthetic biology. 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. We also illustrate a plug-and-play application for one of our network designs by using it to control the timing of yeast sedimentation.