In PLoS Computational Biology this week, scientists at the University of Leeds used a computational approach to better understand how to predict protein folding. Using a model of a specific fast folding protein, they were able to show that "obtaining an accurate description of the mechanisms of folding and unfolding is far from trivial." Their model, they say, can be used to explain the fast folding of many proteins.
Also in PLoS Computational Biology, Yale's Mark Gerstein and Mike Snyder have created a framework by which combining different techniques can optimize the process of resequencing. They applied their framework specifically toward resequencing structural variants, which, they write, "is considered in many respects the most challenging step in genome resequencing."
Researchers in Barcelona have used a large-scale approach to study biomass changes in a microbial community in response to environmental toxins. Their work was published in PLoS One. Applying a recently published new method based on confocal laser scanning microscopy and image-program analysis, they were able to show that in situ, lead and copper drastically reduced total biomass in cyanobacterial populations over the course of a week.
Finally, a paper in PLoS Neglected Tropical Diseases from scientists at the University of York used microarrays to perform comparative gene expression analysis of three species of Leishmania: L. major, L. infantum, and L. braziliensis. Leishmania is a single-celled parasite transmitted by sand flies in more than 88 tropical and sub-tropical countries. They found that there are only a small number of differences between species "with host genetics playing only a minor role in influencing the parasites' response to their intracellular habitat."