Modeling Translation in Protein Synthesis with TASEP: A Tutorial and Recent Developments

The phenomenon of protein synthesis has been modeled in terms of totally asymmetric simple exclusion processes (TASEP) since 1968. In this article, we provide a tutorial of the biological and mathematical aspects of this approach. We also summarize several new results, concerned with limited resources in the cell and simple estimates for the current (protein production rate) of a TASEP with inhomogeneous hopping rates, reflecting the characteristics of real genes.Comment: 25 pages, 7 figure

Nitrous oxide fluxes from a commercial beef cattle feedlot in Kansas

Emission of greenhouse gases, including nitrous oxide (N₂O), from open beef cattle feedlots is becoming an environmental concern; however, research measuring emission rates of N₂O from open beef cattle feedlots has been limited. This study was conducted to quantify N₂O emission fluxes as affected by pen surface conditions, in a commercial beef cattle feedlot in the state of Kansas, USA, from July 2010 through September 2011. The measurement period represented typical feedlot conditions, with air temperatures ranging from −24 to 39°C. Static flux chambers were used to collect gas samples from pen surfaces at 0, 15, and 30 minutes. Gas samples were analyzed with a gas chromatograph and from the measured concentrations, N₂O fluxes were calculated. Median emission flux from the moist/muddy surface condition was 2.03 mg m⁻² hour⁻¹, which was about 20 times larger than the N₂O fluxes from the other pen surface conditions. In addition, N₂O peaks from the moist/muddy pen surface condition were six times larger than emission peaks previously reported for agricultural soils

An Integrated Statistical Approach to Compare Transcriptomics Data Across Experiments:A Case Study on the Identification of Candidate Target Genes of the Transcription Factor PPARα

An effective strategy to elucidate the signal transduction cascades activated by a transcription factor is to compare the transcriptional profiles of wild type and transcription factor knockout models. Many statistical tests have been proposed for analyzing gene expression data, but most tests are based on pair-wise comparisons. Since the analysis of microarrays involves the testing of multiple hypotheses within one study, it is generally accepted that one should control for false positives by the false discovery rate (FDR). However, it has been reported that this may be an inappropriate metric for comparing data across different experiments. Here we propose an approach that addresses the above mentioned problem by the simultaneous testing and integration of the three hypotheses (contrasts) using the cell means ANOVA model. These three contrasts test for the effect of a treatment in wild type, gene knockout, and globally over all experimental groups. We illustrate our approach on microarray experiments that focused on the identification of candidate target genes and biological processes governed by the fatty acid sensing transcription factor PPARα in liver. Compared to the often applied FDR based across experiment comparison, our approach identified a conservative but less noisy set of candidate genes with same sensitivity and specificity. However, our method had the advantage of properly adjusting for multiple testing while integrating data from two experiments, and was driven by biological inference. Taken together, in this study we present a simple, yet efficient strategy to compare differential expression of genes across experiments while controlling for multiple hypothesis testing

Statistical Analysis

In this Appendix, we provide an outline of methods used in analyzing molecular biology data. We give a summary of types of data encountered and the appropriate methods to apply for the questions of interest. Statistical techniques described include the t test, the Wilcoxon rank sum test, the Mann-Whitney-Wilcoxon test, ANOVA, regression, and the chi-square test. For each method, we give the appropriate assumptions, the details of the test, and a complete concrete example to follow. We also discuss related ideas such as multiple comparisons and why correlation does not imply causation