  It is tempting to believe that we now own the genome. The ability to read and
re-write it at will has ushered in a stunning period in the history of science.
Nonetheless, there is an Achilles heel exposed by all of the genomic data that
has accrued: we still don't know how to interpret it. Many genes are subject to
sophisticated programs of transcriptional regulation, mediated by DNA sequences
that harbor binding sites for transcription factors which can up- or
down-regulate gene expression depending upon environmental conditions. This
gives rise to an input-output function describing how the level of expression
depends upon the parameters of the regulated gene { for instance, on the number
and type of binding sites in its regulatory sequence. In recent years, the
ability to make precision measurements of expression, coupled with the ability
to make increasingly sophisticated theoretical predictions, have enabled an
explicit dialogue between theory and experiment that holds the promise of
covering this genomic Achilles heel. The goal is to reach a predictive
understanding of transcriptional regulation that makes it possible to calculate
gene expression levels from DNA regulatory sequence. This review focuses on the
canonical simple repression motif to ask how well the models that have been
used to characterize it actually work. We consider a hierarchy of increasingly
sophisticated experiments in which the minimal parameter set learned at one
level is applied to make quantitative predictions at the next. We show that
these careful quantitative dissections provide a template for a predictive
understanding of the many more complex regulatory arrangements found across all
domains of life.
