Genes2Networks: Connecting Lists of Proteins by Using Background Literature-based Mammalian Networks

In recent years, in-silico literature-based mammalian protein-protein interaction network datasets have been developed. These datasets contain binary interactions extracted manually from legacy experimental biomedical research literature. Placing lists of genes or proteins identified as significantly changing in multivariate experiments, in the context of background knowledge about binary interactions, can be used to place these genes or proteins in the context of pathways and protein complexes.
Genes2Networks is a software system that integrates the content of ten mammalian literature-based interaction network datasets. Filtering to prune low-confidence interactions was implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from “seed” lists of human Entrez gene names. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Genes2Networks is available at http://actin.pharm.mssm.edu/genes2networks.
Genes2Network is a powerful web-based software application tool that can help experimental biologists to interpret high-throughput experimental results used in genomics and proteomics studies where the output of these experiments is a list of significantly changing genes or proteins. The system can be used to find relationships between nodes from the seed list, and predict novel nodes that play a key role in a common function

Visualization of the Phosphoproteomic Data from AfCS with the Google Motion Chart Gadget

Results from multivariate molecular biological experiments become increasingly complex. Hence, the challenge of projecting high-dimensional data onto few dimensions for effective data visualization is becoming increasingly important in Systems Biology. Effective data visualization can summarize the activity of many variables over time as well as display relationships between variables. Dynamic interactive visualization tools can provide scientists with ways of visually identifying relationship and patterns, and improve communication of results on the web and in presentations. For this, interactive systems with animation have great potential since they add dimensions to static images limited to two dimensions. Interactivity and animation is particularly useful for showing time-series trends in multi-dimensional data. The Flash-based Motion Chart Google Gadget available through GoogleDocs is a recent advance in multi-dimensional data visualization. The Motion Chart Gadget is a component of the Trendalyzer software, which was developed for web-based animation of statistical results. Here we demonstrate the use of this Gadget to visualize molecular biological data, the phosphoproteomics results published on the Data Center of the Signaling Gateway web-site

Connecting Seed Lists of Mammalian Proteins Using Steiner Trees

Multivariate experiments and genomics studies applied to mammalian cells often produce lists of genes or proteins altered under treatment/disease vs. control/normal conditions. Such lists can be identified in known protein-protein interaction networks to produce subnetworks that “connect” the genes or proteins from the lists. Such subnetworks are valuable for biologists since they can suggest regulatory mechanisms that are altered under different conditions. Often such subnetworks are overloaded with links and nodes resulting in connectivity diagrams that are illegible due to edge overlap. In this study, we attempt to address this problem by implementing an approximation to the Steiner Tree problem to connect seed lists of mammalian proteins/genes using literature-based protein-protein interaction networks. To avoid over-representation of hubs in the resultant Steiner Trees we assign a cost to Steiner Vertices based on their connectivity degree. We applied the algorithm to lists of genes commonly mutated in colorectal cancer to demonstrate the usefulness of this approach

Intracellular Regulatory Networks are close to Monotone Systems

Several meso-scale biological intracellular regulatory networks that have specified directionality of interactions have been recently assembled from experimental literature. Directed networks where links are characterized as positive or negative can be converted to systems of differential equations and analyzed as dynamical systems. Such analyses have shown that networks containing only sign-consistent loops, such as positive feed-forward and feedback loops function as monotone systems that display well-ordered behavior. Perturbations to monotone systems have unambiguous global effects and a predictability characteristic that confers advantages for robustness and adaptability. We find that three intracellular regulatory networks: bacterial and yeast transcriptional networks and a mammalian signaling network contain far more sign-consistent feedback and feed-forward loops than expected for shuffled networks. Inconsistent loops with negative links can be more easily removed from real regulatory networks as compared to shuffled networks. This topological feature in real networks emerges from the presence of hubs that are enriched for either negative or positive links, and is not due to a preference for double negative links in paths. These observations indicate that intracellular regulatory networks may be close to monotone systems and that this network topology contributes to the dynamic stability

Genes2Networks: Connecting Lists of Proteins by Using Background Literature-based Mammalian Networks

In recent years, in-silico literature-based mammalian protein-protein interaction network datasets have been developed. These datasets contain binary interactions extracted manually from legacy experimental biomedical research literature. Placing lists of genes or proteins identified as significantly changing in multivariate experiments, in the context of background knowledge about binary interactions, can be used to place these genes or proteins in the context of pathways and protein complexes.
Genes2Networks is a software system that integrates the content of ten mammalian literature-based interaction network datasets. Filtering to prune low-confidence interactions was implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from “seed” lists of human Entrez gene names. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Genes2Networks is available at http://actin.pharm.mssm.edu/genes2networks.
Genes2Network is a powerful web-based software application tool that can help experimental biologists to interpret high-throughput experimental results used in genomics and proteomics studies where the output of these experiments is a list of significantly changing genes or proteins. The system can be used to find relationships between nodes from the seed list, and predict novel nodes that play a key role in a common function

O Xondaro: aspectos da formação física, comportamental e espiritual do povo Guarani

TCC (graduação) - Universidade Federal de Santa Catarina. Centro de Filosofia e Ciências Humanas. Intercultural Indígena do Sul da Mata AtlânticaNeste trabalho busco iniciar a discussão sobre o ensinamento da arte do xondaro e a sua importância para o povo Guarani. O xondaro também é um ritual de iniciação e transição da fase de adolescência para a vida adulta. O xondaro está muito presente no dia a dia de cada indivíduo guarani, nele são ensinados três aspectos fundamentais, que na minha concepção singular, classifico através de movimentos corporais, comportamentais e espirituais. A partir desses três aspectos que incorporam outros temas também, não menos importantes, tento trazer informações da memória como, por exemplo, o que é xondaro? Para que serve o xondaro? Como se pratica o xondaro? As crianças, por exemplo, desde que nascem vão se familiarizando com o som e a melodia, e assim sucessivamente ao crescer vão integrando o universo xondaro. E aqui abrindo um leque da tradição guarani, também de acordo da minha vivência, posso afirmar que a casa de reza (Opy) é o centro de aprendizagem Guarani. As famílias, as casas, as aldeias pertencem a esse imenso universo e neste caso o Xondaro seria extensão da casa de reza. Comparado com ensino acadêmico vigente no país, me arrisco a dizer que o Xondaro também pode ser sinônimo de atividade de campo, prática e pesquisa para o povo Guarani.Kova’e xerembiapo py ma anhepynru xeayu xondaro reguare ha’egui mba’erã pa jareko nhandevy Guarani kuery pe. Xondaro ma ombo’e avi tujakue reko py inhe´enguxu hapy ha’egui kunhã gue oguapyapy. Xondaro ma panve jaiporu opamba’epy jaiko arupi, há’evy ma mboapy henda rupi nhombo’e, xee aenxa’ã arupi ta’u, há’evy ma xee aexaarami ramomã ojerojya, na’inhante’yin-a há’egui opy’i re oikoa vi. Kova’e mboapy regua gui ma oguerovy avi omboae regua nhandereko avi, imporã va’e meme avi, apy ma xee aru xeanka guiae xema’endu’a nha’ã, mba’e va’e pa xondaro? Mba’erã tu xondaro? Mba’ere tu nhanhembo’e xondaro? Kyringue’i rei nha’ã, oiko’i guive xondaro oendu ovy, oexa ovy mba’epu’i, há’evy ma tuja ovy xondaro rupiae. Há’evy ma apy xeayu’i tavi nhande reko reguare avi, xee aenxa’ã arami ramomã tuja kueve’igui aendu va’e kue gui ramomã, opy’i mã nhanhembo’e hetea opamba’e imporã va’e. omenda’iva’e kuery, oo kuery, tekoa ikuai va’e maguive mã xondaro oiporu meme’in há’egui xondaro mã opy’i gui aevi ma onhembo’e. Jaikuaa potá ramomã koo jurua kuery onhembo’e arami aevi mã jareko xonado, xee akua arami ramomã xondaro py nhanhembo’e avi tembiapo mba’emo jaekaa, jajapoa há’egui jaikuaa potaa nhande kuery reko rupi vy

A Novel BiLevel Paradigm for Image-to-Image Translation

Image-to-image (I2I) translation is a pixel-level mapping that requires a large number of paired training data and often suffers from the problems of high diversity and strong category bias in image scenes. In order to tackle these problems, we propose a novel BiLevel (BiL) learning paradigm that alternates the learning of two models, respectively at an instance-specific (IS) and a general-purpose (GP) level. In each scene, the IS model learns to maintain the specific scene attributes. It is initialized by the GP model that learns from all the scenes to obtain the generalizable translation knowledge. This GP initialization gives the IS model an efficient starting point, thus enabling its fast adaptation to the new scene with scarce training data. We conduct extensive I2I translation experiments on human face and street view datasets. Quantitative results validate that our approach can significantly boost the performance of classical I2I translation models, such as PG2 and Pix2Pix. Our visualization results show both higher image quality and more appropriate instance-specific details, e.g., the translated image of a person looks more like that person in terms of identity

A New Chamber for Studying the Behavior of Drosophila

Methods available for quickly and objectively quantifying the behavioral phenotypes of the fruit fly, Drosophila melanogaster, lag behind in sophistication the tools developed for manipulating their genotypes. We have developed a simple, easy-to-replicate, general-purpose experimental chamber for studying the ground-based behaviors of fruit flies. The major innovative feature of our design is that it restricts flies to a shallow volume of space, forcing all behavioral interactions to take place within a monolayer of individuals. The design lessens the frequency that flies occlude or obscure each other, limits the variability in their appearance, and promotes a greater number of flies to move throughout the center of the chamber, thereby increasing the frequency of their interactions. The new chamber design improves the quality of data collected by digital video and was conceived and designed to complement automated machine vision methodologies for studying behavior. Novel and improved methodologies for better quantifying the complex behavioral phenotypes of Drosophila will facilitate studies related to human disease and fundamental questions of behavioral neuroscience