We have developed a new DNA microarray-based technology, termed protein binding microarrays (PBMs), that allows rapid, high-throughput characterization of the in vitro DNA binding site sequence specificities of transcription factors in a single day. Using PBMs, we identified the DNA binding site sequence specificities of the yeast transcription factors Abf1, Rap1, and Mig1. Comparison of these proteins' in vitro binding sites versus their in vivo binding sites indicates that PBM-derived sequence specificities can accurately reflect in vivo DNA sequence specificities. In addition to previously identified targets, Abf1, Rap1, and Mig1 bound to 107, 90, and 75 putative new target intergenic regions, respectively, many of which were upstream of previously uncharacterized open reading frames (ORFs). Comparative sequence analysis indicates that many of these newly identified sites are highly conserved across five sequenced sensu stricto yeast species and thus are likely to be functional in vivo binding sites that potentially are utilized in a conditionspecific manner. Similar PBM experiments will likely be useful in identifying novel cis regulatory elements and transcriptional regulatory networks in various genomes.The interactions between transcription factors (TFs) and their DNA binding sites are an integral part of transcriptional regulatory networks. They control the coordinated expression of thousands of genes during normal growth and in response to external stimuli. Significant progress has been made recently in the accumulation and analysis of mRNA transcript Correspondence should be addressed to M.L.B.

The propagation of information through signaling cascades spans a wide range of time-scales, including the rapid ligand-receptor interaction and the much slower response of downstream gene expression. To determine which dynamic range dominates a response, we used periodic stimuli to measure the frequency dependence of signal transduction in the osmo-adaptation pathway of Saccharomyces cerevisiae. We applied system identification methods to infer a concise predictive model. We found that the dynamics of the osmo-adaptation response are dominated by a fast-acting negative feedback through the kinase Hog1 that does not require protein synthesis. After large osmotic shocks, an additional, much slower, negative feedback through gene expression allows cells to respond faster to future stimuli.The mechanisms cells use to sense and respond to environmental changes include complicated systems of biochemical reactions that occur with rates spanning a wide dynamic range. Reactions can be fast, such as association and dissociation between a ligand and its receptor (< 1 s), or slow, such as protein synthesis (> 10 3 s). Though a system may comprise hundreds of reactions, often only a few of them dictate the system dynamics. Unfortunately, identification of the dominant processes is often difficult, and many models instead incorporate knowledge of all reactions in the system. Although occasionally successful (1-4), this exhaustive approach often suffers from missing information, such as unknown interactions or parameters.Here we used systems engineering tools to study how oscillatory signals propagate through a signal transduction cascade, allowing us to identify and concisely model the interactions that dominate system dynamics. The cornerstone of this approach is to measure the cascade output in response to input signals oscillating at a range of frequencies (5,6). By comparing the frequency response of the wild-type network to that of mutants, the molecular underpinnings of network dynamics can be determined. Studies of neural and other physiological systems have used systems theory (6), while control-theory has also been applied to cellular networks (7-14).We focused on the high osmolarity glycerol (HOG) Mitogen-activated protein kinase (MAPK) cascade in the budding yeast Saccharomyces cerevisiae. This cascade forms a core module of the hyperosmotic-shock-response system and is particularly well suited to frequency-response * To whom correspondence should be addressed: avano@mit.edu. NIH Public Access Author ManuscriptScience. Author manuscript; available in PMC 2010 August 5. analysis for several reasons. First, both the input (extracellular osmolyte concentration) and output (activity of the MAPK Hog1) of the network are easily measured and manipulated. Second, the molecular components of the network have been well studied, facilitating connection of dynamic models with molecular events. Finally, the system contains multiple negative feedback loops that operate on different time-scales (4,15,16). It is still unc...