Hyh mouse harboring a point mutation in the Napa gen coding for aSNAP is the first example of a fertility problem directly related to a factor necessary for the general mechanism of regulated secretion that is specifically affecting the acrosome reaction. Mutant mice with a mild clinical and neuropathological phenotype are able to copulate and the amount of live and motile sperm that they produce is similar to that of wild type mice; however, they have a much reduced fertility. Even when fertilization was assessed in vitro, sperm from hyh mice behave poorly as compared with sperm from wild type mice. Notwithstanding, the difference between genotypes was less dramatic in the in vitro assays than in the mating studies. Thus, other defects not assessed by in vitro fertilization might contribute to this phenotype. In this context, although SP mice present a preserved general motor activity and are able to copulate, they do have gait and equilibrium impairments that could affect the frequency and efficiency of copulation. Development of such a labelling method may ultimately permit for the evaluation of the dynamic metabolism of inositol pyrophosphates in intact cells. Finally, the availability of a rapid method for analyzing the IP6Ks/Vip1s reactions allows for the identification of small molecule inhibitors or enhancers using a small chemical compound library. Conversion of IP6 to higher inositol pyrophosphates can be easily analysed on small 1066 cm gels that can be prepared, run, and stained in less than two hours, allowing for the simultaneous analysis of 100 s of reactions. Using traditional HPLC-based assays such analyses would be entirely impractical. The potential therapeutic potential of such compounds is supported by the recent identification of the critical role inositol pyrophosphates play in insulin secretion and oncongenic processes. Though the inositol pyrophosphate field appears to be more complex than previously described, our identification of a PAGE-based analytic method will serve to increase both access to and the ease with which we can study these highly energetic cell signalling molecules.Even if comprehensive data were already available for malaria parasites, a robust method for analyzing all of the data would also be needed. Researchers generally use gene expression data to find new genes involved in processes using a “guilt-by-association” approach and to understand transcriptional regulation. In both cases genes first need to be clustered by their expression similarity. However, cluster boundary Reversine 656820-32-5 determinations often are subjective and non-optimal for the purpose of function prediction. This challenge is addressed by an algorithm termed Ontology-based Pattern Identification, which has been shown to identify gene clusters of better quality than unsupervised clustering algorithms such as the robust k-means clustering we used previously. The OPI analysis begins with a piece of knowledge, such as a group of genes believed to share.