Neural encoding: the brain's representation of space.

Recent studies have shown that a part of the brain makes use of a grid of equilateral triangles to encode the location of the animal within the local environment.

Better estimation of protein-DNA interaction parameters improve prediction of functional sites.

BACKGROUND: Characterizing transcription factor binding motifs is a common bioinformatics task. For transcription factors with variable binding sites, we need to get many suboptimal binding sites in our training dataset to get accurate estimates of free energy penalties for deviating from the consensus DNA sequence. One procedure to do that involves a modified SELEX (Systematic Evolution of Ligands by Exponential Enrichment) method designed to produce many such sequences. RESULTS: We analyzed low stringency SELEX data for E. coli Catabolic Activator Protein (CAP), and we show here that appropriate quantitative analysis improves our ability to predict in vitro affinity. To obtain large number of sequences required for this analysis we used a SELEX SAGE protocol developed by Roulet et al. The sequences obtained from here were subjected to bioinformatic analysis. The resulting bioinformatic model characterizes the sequence specificity of the protein more accurately than those sequence specificities predicted from previous analysis just by using a few known binding sites available in the literature. The consequences of this increase in accuracy for prediction of in vivo binding sites (and especially functional ones) in the E. coli genome are also discussed. We measured the dissociation constants of several putative CAP binding sites by EMSA (Electrophoretic Mobility Shift Assay) and compared the affinities to the bioinformatics scores provided by methods like the weight matrix method and QPMEME (Quadratic Programming Method of Energy Matrix Estimation) trained on known binding sites as well as on the new sites from SELEX SAGE data. We also checked predicted genome sites for conservation in the related species S. typhimurium. We found that bioinformatics scores based on SELEX SAGE data does better in terms of prediction of physical binding energies as well as in detecting functional sites. CONCLUSION: We think that training binding site detection algorithms on datasets from binding assays lead to better prediction. The improvements in accuracy came from the unbiased nature of the SELEX dataset rather than from the number of sites available. We believe that with progress in short-read sequencing technology, one could use SELEX methods to characterize binding affinities of many low specificity transcription factors.

Repression of the yeast HO gene by the MATalpha2 and MATa1 homeodomain proteins.

The HO gene in Saccharomyces cerevisiae is regulated by a large and complex promoter that is similar to promoters in higher order eukaryotes. Within this promoter are 10 potential binding sites for the a1-alpha2 heterodimer, which represses HO and other haploid-specific genes in diploid yeast cells. We have determined that a1-alpha2 binds to these sites with differing affinity, and that while certain strong-affinity sites are crucial for repression of HO, some of the weak-affinity sites are dispensable. However, these weak-affinity a1-alpha2-binding sites are strongly conserved in related yeast species and have a role in maintaining repression upon the loss of strong-affinity sites. We found that these weak sites are sufficient for a1-alpha2 to partially repress HO and recruit the Tup1-Cyc8 (Tup1-Ssn6) co-repressor complex to the HO promoter. We demonstrate that the Swi5 activator protein is not bound to URS1 in diploid cells, suggesting that recruitment of the Tup1-Cyc8 complex by a1-alpha2 prevents DNA binding by activator proteins resulting in repression of HO.

Optimal path to epigenetic switching.

We use large deviation methods to calculate rates of noise-induced transitions between states in multistable genetic networks. We analyze a synthetic biochemical circuit, the toggle switch, and compare the results to those obtained from a numerical solution of the master equation.

Combined analysis of expression data and transcription factor binding sites in the yeast genome.

BACKGROUND: The analysis of gene expression using DNA microarrays provides genome wide profiles of the genes controlled by the presence or absence of a specific transcription factor. However, the question arises of whether a change in the level of transcription of a specific gene is caused by the transcription factor acting directly at the promoter of the gene or through regulation of other transcription factors working at the promoter. RESULTS: To address this problem we have devised a computational method that combines microarray expression and site preference data. We have tested this approach by identifying functional targets of the a1-alpha2 complex, which represses haploid-specific genes in the yeast Saccharomyces cerevisiae. Our analysis identified many known or suspected haploid-specific genes that are direct targets of the a1-alpha2 complex, as well as a number of previously uncharacterized targets. We were also able to identify a number of haploid-specific genes which do not appear to be direct targets of the a1-alpha2 complex, as well as a1-alpha2 target sites that do not repress transcription of nearby genes. Our method has a much lower false positive rate when compared to some of the conventional bioinformatic approaches. CONCLUSIONS: These findings show advantages of combining these two forms of data to investigate the mechanism of co-regulation of specific sets of genes.