Live Cell Compartment Tracking: Object Tracking in Oscillating Intensity Images

Mathematical modeling has made great strides since the Lotka-Volterra predator-prey models. Newer models attempt to describe sub-cellular signal transduction pathways, such as the JAK-STAT and NF-κB pathways. However, the tools to accurately determine reaction and translocation rates in these pathways still have a number of drawbacks, including the effects of concentration scale on determining reaction rates and the effects of bulky additions to translocation rates. One method of overcoming these problems in signal transduction rate determination is to sample and stain cells from a full population at specific time points. However, fixed cell methods can only generate an average population rate. This could become an issue if the rate depends on the genotype of one of the proteins in the pathway. Another method of overcoming these problems in signal transduction rates is to use unmarked nuclei in live-cell imaging techniques. However, live cell imaging methods poses different problems, primarily how to find and track nuclei and cytoplasm when cells are actively moving and the nuclear and cytoplasmic intensities are by necessity fluctuating. To date, there is only one software package designed for tracking cells under these conditions - Cell Tracker (Shen et al., 2006). Cell Tracker is designed to handle the tracking of live cell images for protein translocation studies. They recommend using a separate color channel to mark the nucleus, although results can be obtained using unmarked nuclei. The results from Cell Tracker with unmarked nuclei are often less than optimal. We have developed a novel segmentation scheme and variation of the particle filter algorithm to allow more accurate tracking in time series with unmarked nuclei. The proposed segmentation scheme uses a non-parametric level set algorithm to refine a fast initial thresholding step. The tracking scheme uses a dense optical flow calculation to assist the particle filter algorithm in continuing to follow the true positions of the nuclei. To test the proposed algorithm, a novel mimicry of cell movement has been developed using random perturbations of a triangular mesh structure through the use of the finite element method

Seeking gene relationships in gene expression data using support vector machine regression

Several genetic determinants responsible for individual variation in gene expression have been located using linkage and association analyses. These analyses have revealed regulatory relationships between genes. The heritability of expression variation as a quantitative phenotype reflects its underlying genetic architecture. Using support vector machine regression (SVMR) and gene ontological information, we proposed an approach to identify gene relationships in expression data provided by Genetic Analysis Workshop 15 that would facilitate subsequent genetic analyses. A group of related genes were selected for a shared biological theme, and SVMR was trained to form a regression model using the training gene expressions. The model was subsequently used to search for and capture similarly related genes. SVMR shows promising capability in modeling and seeking gene relationships through expression data

Seeking gene relationships in gene expression data using support vector machine regression-0

Morley et al. [2]. The Affymetrix probeset IDs are listed in parentheses.<p><b>Copyright information:</b></p><p>Taken from "Seeking gene relationships in gene expression data using support vector machine regression"</p><p>http://www.biomedcentral.com/1753-6561/1/S1/S51</p><p>BMC Proceedings 2007;1(Suppl 1):S51-S51.</p><p>Published online 18 Dec 2007</p><p>PMCID:PMC2367560.</p><p></p

Seeking gene relationships in gene expression data using support vector machine regression-2

Protein genes. Group A is the genes selected for the SVMR training set. Groups B and C are the genes that were targeted and captured in two separate SVMR searches.<p><b>Copyright information:</b></p><p>Taken from "Seeking gene relationships in gene expression data using support vector machine regression"</p><p>http://www.biomedcentral.com/1753-6561/1/S1/S51</p><p>BMC Proceedings 2007;1(Suppl 1):S51-S51.</p><p>Published online 18 Dec 2007</p><p>PMCID:PMC2367560.</p><p></p

Seeking gene relationships in gene expression data using support vector machine regression-1

Iological group, ribosomal proteins. A1 and A2 are from the same gene, . Correlation coefficients for expression of genes and are > 0.987, but they appear to share no direct biological relationship, even though their NPL LOD score distributions show high similarity as well.<p><b>Copyright information:</b></p><p>Taken from "Seeking gene relationships in gene expression data using support vector machine regression"</p><p>http://www.biomedcentral.com/1753-6561/1/S1/S51</p><p>BMC Proceedings 2007;1(Suppl 1):S51-S51.</p><p>Published online 18 Dec 2007</p><p>PMCID:PMC2367560.</p><p></p

Seeking gene relationships in gene expression data using support vector machine regression-4

The gene pool hit a group of genes that formed set A. The pathway reconstruction was done using 4.0 (Ariadne Genomics, Inc.).<p><b>Copyright information:</b></p><p>Taken from "Seeking gene relationships in gene expression data using support vector machine regression"</p><p>http://www.biomedcentral.com/1753-6561/1/S1/S51</p><p>BMC Proceedings 2007;1(Suppl 1):S51-S51.</p><p>Published online 18 Dec 2007</p><p>PMCID:PMC2367560.</p><p></p