Reconstructing Spatiotemporal Gene Expression Data from Partial Observations

Developmental transcriptional networks in plants and animals operate in both space and time. To understand these transcriptional networks it is essential to obtain whole-genome expression data at high spatiotemporal resolution. Substantial amounts of spatial and temporal microarray expression data previously have been obtained for the Arabidopsis root; however, these two dimensions of data have not been integrated thoroughly. Complicating this integration is the fact that these data are heterogeneous and incomplete, with observed expression levels representing complex spatial or temporal mixtures. Given these partial observations, we present a novel method for reconstructing integrated high resolution spatiotemporal data. Our method is based on a new iterative algorithm for finding approximate roots to systems of bilinear equations.Comment: 19 pages, 4 figure

Transcellular communication at the immunological synapse: A vesicular traffic-mediated mutual exchange

The cell's ability to communicate with the extracellular environment, with other cells, and with itself is a crucial feature of eukaryotic organisms. In the immune system, T lymphocytes assemble a specialized structure upon contact with antigen-presenting cells bearing a peptide-major histocompatibility complex ligand, known as the immunological synapse (IS). The IS has been extensively characterized as a signaling platform essential for T-cell activation. Moreover, emerging evidence identifies the IS as a device for vesicular traffic-mediated cell-to-cell communication as well as an active release site of soluble molecules. Here, we will review recent advances in the role of vesicular trafficking in IS assembly and focused secretion of microvesicles at the synaptic area in naïve T cells and discuss the role of the IS in transcellular communication

Genetic Mapping in the Presence of Genotyping Errors

Genetic maps are built using the genotypes of many related individuals. Genotyping errors in these data sets can distort genetic maps, especially by inflating the distances. We have extended the traditional likelihood model used for genetic mapping to include the possibility of genotyping errors. Each individual marker is assigned an error rate, which is inferred from the data, just as the genetic distances are. We have developed a software package, called TMAP, which uses this model to find maximum-likelihood maps for phase-known pedigrees. We have tested our methods using a data set in Vitis and on simulated data and confirmed that our method dramatically reduces the inflationary effect caused by increasing the number of markers and leads to more accurate orders