Princeton researchers report in PLoS Computational Biology that they developed a model of cellular growth in Saccharomyces cerevisiae that's based on the expression levels of a set of small genes. The model, they add, can predict growth under any conditions for which there is gene expression data and in Saccharomyces bayanus and in Schizosaccharomyces pombe, which the authors say suggests that the model "describes fundamental characteristics of the unicellular eukaryotic growth regulatory program." In PLoS Pathogens, British researchers show that mathematical models complement basic research efforts and, in their case, the study of bacteriophage–host interactions and of using phages to halt pathogenic bacteria. "The excellent fit of our model to the data confirms the value of such combined approaches," the authors write. Researchers led by Duccio Cavalieri say in PLoS One that pathway signatures can be used to find similarities among microarray experiments. They developed a method that first generates pathway signatures by using Eugene and then uses those signatures to search through databases. They add that their method looks to be more reliable than gene-based approaches.
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