Revisiting the Yeast Cell Cycle Problem with the Improved 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. On a previous work we presented the TriGen algorithm, a genetic algorithm that finds triclusters of gene expression that take into account the experimental conditions and the time points simultaneously, and was applied to the yeast (Saccharomyces Cerevisiae) cell cycle problem. In this article we present some improvements on the genetic algorithm and we also present the results of applying the improved TriGen algorithm to the yeast cell cycle problem, where the goal is to identify all genes whose expression levels are regulated by the cell cycle

TriGen: A genetic algorithm to mine triclusters in temporal gene expression data

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 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 longitudinal experiments in which the genes are evaluated under certain conditions at several time points. We present the TriGen algorithm, a genetic algorithm that finds triclusters of gene expression that take into account the experimental conditions and the time points simultaneously. We have used TriGen to mine datasets related to synthetic data, yeast (Saccharomyces cerevisiae) cell cycle and human inflammation and host response to injury experiments. TriGen has proved to be capable of extracting groups of genes with similar patterns in subsets of conditions and times, and these groups have shown to be related in terms of their functional annotations extracted from the Gene Ontology.Ministerio de Ciencia y Tecnología TIN2011-28956-C00Ministerio de Ciencia y Tecnología TIN2009-13950Junta de Andalucía TIC-752

TrLab: Una metodología para la extracción y evaluación de patrones de comportamiento de grandes volúmenes de datos biológicos dependientes del tiempo

La tecnología de microarray ha revolucionado la investigación biotecnológica gracias a la posibilidad de monitorizar los niveles de concentración de ARN. El análisis de dichos datos representa un reto computacional debido a sus características. Las técnicas de Clustering han sido ampliamente aplicadas para crear grupos de genes que exhiben comportamientos similares. El Biclustering emerge como una valiosa herramienta para el análisis de microarrays ya que relaja la restricción de agrupamiento permitiendo que los genes sean evaluados sólo bajo un subconjunto de condiciones experimentales. Sin embargo, ante la consideración de una tercera dimensión, el tiempo, el Triclustering se presenta como la herramienta apropiada para el análisis de experimentos longitudinales en los que los genes son evaluados bajo un cierto subconjunto de condiciones en un subconjunto de puntos temporales. Estos triclusters proporcionan información oculta en forma de patrón de comportamiento para experimentos temporales con microarrays. En esta investigación se presenta TrLab, una metodología para la extracción de patrones de comportamiento de grandes volúmenes de datos biológicos dependientes del tiempo. Esta metodología incluye el algoritmo TriGen, un algoritmo genético para la búsqueda de triclusters, teniendo en cuenta de forma simultánea, los genes, condiciones experimentales y puntos temporales que lo componen, además de tres medidas de evaluación que conforman el núcleo de dicho algoritmo así como una medida de calidad para los triclusters encontrados. Todas estas aportaciones estarán integradas en una aplicación con interfaz gráfica que permita su fácil utilización por parte de expertos en el campo de la biología. Las tres medidas de evaluación desarrolladas son: MSR3D basada en la adaptación a las tres dimensiones del Residuo Cuadrático Medio, LSL basada en el cálculo de la recta de mínimos cuadrados que mejor ajusta la representación gráfica del tricluster y MSL basada en el cálculo de los ángulos que forman el patrón de comportamiento del tricluster. La medida de calidad se denomina TRIQ y aglutina todos los aspectos que determinan el valor de un tricluster: calidad de correlación, gráfica y biológica

Green Glycol: A Novel 2-Step Process

Ethylene glycol demand is growing rapidly, particularly in the global polyethylene terephthalate markets.¹ Traditional production of non-renewable ethylene glycol involves steam cracking of ethane or the methanol-to-olefin process to obtain ethylene.6 In response to environmental movements, Coca-Cola® began creating ethylene glycol from renewably sourced ethanol, by producing the ethylene oxide intermediate in a two-step reaction process.² Novel research at Leiden University, entitled Direct conversion of ethanol into ethylene oxide on gold based catalysts, explores a catalyst which produces ethylene oxide in one step, showing potential for a more efficient renewable process.³ This project explores the scaling of the Leiden research to an industrial level. The makeup raw material flows accounting for the recycle streams in the process are 237,000 MT fuel-grade ethanol per year, 81,000 MT oxygen per year, and 26,000 MT carbon dioxide diluent per year. The design first reacts ethanol and low concentration oxygen feeds to form an ethylene oxide intermediate, as well as undesired byproducts. A series of separations isolate ethylene oxide for further reaction, while recycling unconverted feeds and diluents. EO is then hydrolyzed to form mono-, di-, tri-, and higher order glycols. The following separation series removes water for recycle, then isolates fiber grade (99.9 wt%) monoethylene glycol as the main product. The bottoms of this separation results in an ethylene glycol mixture that is sold as a slurry for additional revenue. A financial analysis of the process over a 15 year period shows that the process does not directly compete with the existing monoethylene glycol market. However, a 14.5% green premium on the selling price of monoethylene glycol would reach a 15% IRR and achieve profitability. Future work should be focused on investigating catalyst performance and reproducing similar reaction behavior in industrial-scale conditions