KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data

Kinase inhibitors as targeted therapies have played an important role in improving cancer outcomes. However, there are still considerable challenges, such as resistance, non-response, patient stratification, polypharmacology, and identifying combination therapy where understanding a tumor kinase activity profile could be transformative. Here, we develop a graph- and statistics-based algorithm, called KSTAR, to convert phosphoproteomic measurements of cells and tissues into a kinase activity score that is generalizable and useful for clinical pipelines, requiring no quantification of the phosphorylation sites. In this work, we demonstrate that KSTAR reliably captures expected kinase activity differences across different tissues and stimulation contexts, allows for the direct comparison of samples from independent experiments, and is robust across a wide range of dataset sizes. Finally, we apply KSTAR to clinical breast cancer phosphoproteomic data and find that there is potential for kinase activity inference from KSTAR to complement the current clinical diagnosis of HER2 status in breast cancer patients

Jogo educativo sobre drogas para cegos: construção e avaliação

Estudo realizado com o objetivo de construir e avaliar um jogo educativo sobre drogas psicoativas acessível a pessoas cegas, desenvolvido em três etapas: construção do jogo educativo, avaliação por três especialistas em educação especial e doze cegos. Foi construído um jogo de tabuleiro denominado Drogas: jogando limpo . Na Versão Alfa os especialistas fizeram sugestões em relação às e instruções e ao tabuleiro: textura das casas, peças do jogo e escrita em Braille. Na Versão Beta, procedeu-se à avaliação pelos cegos, os quais sugeriram alterações na textura das casas e colocação de velcro para fixação do pino no tabuleiro. Passou-se, então, à Versão Gama, jogada pelas últimas três duplas de cegos e considerada adequada. Na avaliação dos juízes, o jogo revelou-se adequado, pois permite o acesso à informação sobre drogas psicoativas de maneira lúdica

MCAM: Multiple Clustering Analysis Methodology for Deriving Hypotheses and Insights from High-Throughput Proteomic Datasets

Advances in proteomic technologies continue to substantially accelerate capability for generating experimental data on protein levels, states, and activities in biological samples. For example, studies on receptor tyrosine kinase signaling networks can now capture the phosphorylation state of hundreds to thousands of proteins across multiple conditions. However, little is known about the function of many of these protein modifications, or the enzymes responsible for modifying them. To address this challenge, we have developed an approach that enhances the power of clustering techniques to infer functional and regulatory meaning of protein states in cell signaling networks. We have created a new computational framework for applying clustering to biological data in order to overcome the typical dependence on specific a priori assumptions and expert knowledge concerning the technical aspects of clustering. Multiple clustering analysis methodology (‘MCAM’) employs an array of diverse data transformations, distance metrics, set sizes, and clustering algorithms, in a combinatorial fashion, to create a suite of clustering sets. These sets are then evaluated based on their ability to produce biological insights through statistical enrichment of metadata relating to knowledge concerning protein functions, kinase substrates, and sequence motifs. We applied MCAM to a set of dynamic phosphorylation measurements of the ERRB network to explore the relationships between algorithmic parameters and the biological meaning that could be inferred and report on interesting biological predictions. Further, we applied MCAM to multiple phosphoproteomic datasets for the ERBB network, which allowed us to compare independent and incomplete overlapping measurements of phosphorylation sites in the network. We report specific and global differences of the ERBB network stimulated with different ligands and with changes in HER2 expression. Overall, we offer MCAM as a broadly-applicable approach for analysis of proteomic data which may help increase the current understanding of molecular networks in a variety of biological problems.National Institutes of Health (U.S.) (NIH-U54-CA112967 )National Institutes of Health (U.S.) (NIH-R01-CA096504

Le Village suisse comme modèle d'urbanisme

This chapter introduces systems biology, its context, aims, concepts and strategies. It then describes approaches and methods used for collection of high-dimensional structural and functional genomics data, including epigenomics, transcriptomics, proteomics, metabolomics and lipidomics, and how recent technological advances in these fields have moved the bottleneck from data production to data analysis and bioinformatics. Finally, the most advanced mathematical and computational methods used for clustering, feature selection, prediction analysis, text mining and pathway analysis in functional genomics and systems biology are reviewed and discussed in the context of use cases