Unravelling the Yeast Cell Cycle Using the TriGen Algorithm

Analyzing microarray data represents a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior under the conditions tested. Biclustering emerges as an improvement of classical clustering since it relaxes the constraints for grouping allowing genes to be evaluated only under a subset of the conditions and not under all of them. However, this technique is not appropriate for the analysis of temporal microarray data in which the genes are evaluated under certain conditions at several time points. In this paper, we present the results of applying the TriGen algorithm, a genetic algorithm that finds triclusters that take into account the experimental conditions and the time points, to the yeast cell cycle problem, where the goal is to identify all genes whose expression levels are regulated by the cell cycle

Root hair-endophyte stacking (RHESt) in an ancient Afro-Indian crop creates an unusual physico-chemical barrier to trap pathogen(s)

WKM was supported by generous scholarships from the Government of Egypt and the University of Guelph (International Graduate Student Scholarships, 2012, 2014). This research was supported by grants to MNR by the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA), Grain Farmers of Ontario (GFO), Natural Sciences and Engineering Research Council of Canada (NSERC) and the CIFSRF program funded by the International Development Research Centre (IDRC, Ottawa) and Canadian Department of Global Affairs.The objectives of this study were to isolate bacterial endophytes from finger millet, assay for anti-Fusarium activity and characterize the underlying cellular, molecular and biochemical mechanisms. We report an unusual symbiosis between the host and a root-inhabiting bacterial endophyte

The influence of T cell development on pathogen specificity and autoreactivity

T cells orchestrate adaptive immune responses upon activation. T cell activation requires sufficiently strong binding of T cell receptors on their surface to short peptides derived from foreign proteins bound to protein products of the major histocompatibility (MHC) gene products, which are displayed on the surface of antigen presenting cells. T cells can also interact with peptide-MHC complexes, where the peptide is derived from host (self) proteins. A diverse repertoire of relatively self-tolerant T cell receptors is selected in the thymus. We study a model, computationally and analytically, to describe how thymic selection shapes the repertoire of T cell receptors, such that T cell receptor recognition of pathogenic peptides is both specific and degenerate. We also discuss the escape probability of autoimmune T cells from the thymus.Comment: 12 pages, 7 figure

Statistical Mechanics of Horizontal Gene Transfer in Evolutionary Ecology

The biological world, especially its majority microbial component, is strongly interacting and may be dominated by collective effects. In this review, we provide a brief introduction for statistical physicists of the way in which living cells communicate genetically through transferred genes, as well as the ways in which they can reorganize their genomes in response to environmental pressure. We discuss how genome evolution can be thought of as related to the physical phenomenon of annealing, and describe the sense in which genomes can be said to exhibit an analogue of information entropy. As a direct application of these ideas, we analyze the variation with ocean depth of transposons in marine microbial genomes, predicting trends that are consistent with recent observations using metagenomic surveys.Comment: Accepted by Journal of Statistical Physic

Expert Failure: Re-evaluating Research Assessment

EDITORIAL © 2013 Eisen et al. Funding organisations, scientists, and the general public need robust and reliable ways to evaluate the output of scientific research. In this issue of PLOS Biology, Adam Eyre-Walker and Nina Stoletzki analyse the subjective assessment and citations of more than 6,000 published papers [1]. They show that expert assessors are biased by the impact factor (IF) of the journal in which the paper has been published and cannot consistently and independently judge the “merit” of a paper or predict its future impact, as measured by citations. They also show that citations themselves are not a reliable way to assess merit as they are inherently highly stochastic. In a final twist, the authors argue that the IF is probably the least-bad metric amongst the small set that they analyse, concluding that it is the best surrogate of the merit of individual papers currently available