Path finding methods accounting for stoichiometry in metabolic networks

Graph-based methods have been widely used for the analysis of biological networks. Their application to metabolic networks has been much discussed, in particular noting that an important weakness in such methods is that reaction stoichiometry is neglected. In this study, we show that reaction stoichiometry can be incorporated into path-finding approaches via mixed-integer linear programming. This major advance at the modeling level results in improved prediction of topological and functional properties in metabolic networks

In-silico gene essentiality analysis of polyamine biosynthesis reveals APRT as a potential target in cancer

Constraint-based modeling for genome-scale metabolic networks has emerged in the last years as a promising approach to elucidate drug targets in cancer. Beyond the canonical biosynthetic routes to produce biomass, it is of key importance to focus on metabolic routes that sustain the proliferative capacity through the regulation of other biological means in order to improve in-silico gene essentiality analyses. Polyamines are polycations with central roles in cancer cell proliferation, through the regulation of transcription and translation among other things, but are typically neglected in in silico cancer metabolic models. In this study, we analysed essential genes for the biosynthesis of polyamines. Our analysis corroborates the importance of previously known regulators of the pathway, such as Adenosylmethionine Decarboxylase 1 (AMD1) and uncovers novel enzymes predicted to be relevant for polyamine homeostasis. We focused on Adenine phosphoribosyltransferase (APRT) and demonstrated the detrimental consequence of APRT gene silencing on diferent leukaemia cell lines. Our results highlight the importance of revisiting the metabolic models used for in-silico gene essentiality analyses in order to maximize the potential for drug target identifcation in cance

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

Análisis de técnicas de Deep learning para la generación automática de Keywords

Diferentes técnicas de inteligencia artificial han sido utilizadas como herramientas para automatizar la generación y gestión de campañas de marketing en la plataforma Google Ads. En este contexto, la minería de textos puede servir para la generación automática de palabras clave utilizando técnicas de aprendizaje profundo. El objetivo de este trabajo ha sido intentar desarrollar una herramienta sistemática, basada esencialmente en redes neuronales, para la generación de Keywords. Con este fin, se ha realizado un estudio previo sobre el estado del arte con el fin de identificar las posibles alternativas que existen, y tratar de implementar algunas de ellas, para después analizar su funcionamiento. Se ha analizado cómo la codificación de la información, carácter a carácter o como palabras, puede ser muy importante a la hora de conseguir unos modelos con mejor capacidad predictiva, y como su utilidad depende de la complejidad del modelo utilizado y del tamaño muestral. La modelización se realizó mediante redes recurrentes y redes de convolución, asociando la mejora en los resultados a la técnica de modelado más adecuada dependiendo de los textos originales.<br /

Integrating tracer-based metabolomics data and metabolic fluxes in a linear fashion via Elementary Carbon Modes

Constraints-based modeling is an emergent area in Systems Biology that includes an increasing set of methods for the analysis of metabolic networks. In order to refine its predictions, the development of novel methods integrating high-throughput experimental data is currently a key challenge in the field. In this paper, we present a novel set of constraints that integrate tracer-based metabolomics data from Isotope Labeling Experiments and metabolic fluxes in a linear fashion. These constraints are based on Elementary Carbon Modes (ECMs), a recently developed concept that generalizes Elementary Flux Modes at the carbon level. To illustrate the effect of our ECMs-based constraints, a Flux Variability Analysis approach was applied to a previously published metabolic network involving the main pathways in the metabolism of glucose. The addition of our ECMs-based constraints substantially reduced the under-determination resulting from a standard application of Flux Variability Analysis, which shows a clear progress over the state of the art. In addition, our approach is adjusted to deal with combinatorial explosion of ECMs in genome-scale metabolic networks. This extension was applied to infer the maximum biosynthetic capacity of non-essential amino acids in human metabolism. Finally, as linearity is the hallmark of our approach, its importance is discussed at a methodological, computational and theoretical level and illustrated with a practical application in the field of Isotope Labeling Experiments. © 2012 Elsevier Inc.This work was supported by the Ministerio de Ciencia e Innovación of Spain (Grant no. SAF2011-25726) and FEDER Funds, the Instituto de Salud Carlos III and European Regional Development Fund ISCIII-RTICC (RD06/0020/0046), the Generalitat de Catalunya (2009SGR1308), 2009 CTP 00026 and Icrea Academia Award 2010 granted to M. Cascante. In addition, the work of Jon Pey was supported by the Basque Government.Peer Reviewe

Assessment of FBA Based Gene Essentiality Analysis in Cancer with a Fast Context-Specific Network Reconstruction Method

<div><p>Motivation</p><p>Gene Essentiality Analysis based on Flux Balance Analysis (FBA-based GEA) is a promising tool for the identification of novel metabolic therapeutic targets in cancer. The reconstruction of cancer-specific metabolic networks, typically based on gene expression data, constitutes a sensible step in this approach. However, to our knowledge, no extensive assessment on the influence of the reconstruction process on the obtained results has been carried out to date.</p><p>Results</p><p>In this article, we aim to study context-specific networks and their FBA-based GEA results for the identification of cancer-specific metabolic essential genes. To that end, we used gene expression datasets from the Cancer Cell Line Encyclopedia (CCLE), evaluating the results obtained in 174 cancer cell lines. In order to more clearly observe the effect of cancer-specific expression data, we did the same analysis using randomly generated expression patterns. Our computational analysis showed some essential genes that are fairly common in the reconstructions derived from both gene expression and randomly generated data. However, though of limited size, we also found a subset of essential genes that are very rare in the randomly generated networks, while recurrent in the sample derived networks, and, thus, would presumably constitute relevant drug targets for further analysis. In addition, we compare the <i>in-silico</i> results to high-throughput gene silencing experiments from Project Achilles with conflicting results, which leads us to raise several questions, particularly the strong influence of the selected biomass reaction on the obtained results. Notwithstanding, using previous literature in cancer research, we evaluated the most relevant of our targets in three different cancer cell lines, two derived from Gliobastoma Multiforme and one from Non-Small Cell Lung Cancer, finding that some of the predictions are in the right track.</p></div