In PNAS this week, a pair of researchers from the University of Washington presents a way to tune gene networks in Escherichia coli. The method combines "the straightforward tunability of
translation initiation rates via the [ribosome binding site] spacer region with the high mutation rate and strong bias for insertion/deletion mutations inherent to simple sequence repeats (SSR)," the duo says. SSRs are then placed between the Shine-Dalgarno sequence and the start codon of the target gene, sequences the pair dubs rbSSR sequences. "We believe the same approach we have used to engineer highly tunable elements with simple sequence repeats can be extended to other network parameters in bacteria and to higher organisms by tuning the spacing between known regulatory motifs such as those responsible for transcription initiation or intron splicing efficiency," the duo adds.
Also in PNAS, California Institute of Technology researchers examine whether gene regulatory networks control developmental gene expression patterns. To do so, they developed a Boolean computational model of endomesoderm speciﬁcation in the sea urchin embryo. "The Boolean computational model we present here provides a direct test of whether the observed dynamic sequence of spatial and temporal gene expression can be computed by using the information included in the GRN model," they write. "Indeed we ﬁnd that, with a few exceptions, the Boolean computation sufﬁces to reconstruct the observed spatial and temporal gene expression patterns, and this supports the idea that GRN models may contain the necessary information to operate large-scale developmental spatial speciﬁcation systems."
Finally, researchers led by Shu-Bing Qian at Cornell University introduce their global translational initiation sequencing, or GTI-seq, method that allows for the global mapping of translation initiation sites. The method uses both lactimidomycin and cycloheximide, ribosome E-site translation inhibitors, to detect initiation and elongation events. "A systematic, high-throughput method like GTI-seq offers a top-down approach, in which one can identify a set of candidate genes for intensive study," the authors write. "GTI-seq is readily applicable to broad fields of fundamental biology."