The BioGRID Interaction Database: 2011 update

The Biological General Repository for Interaction Datasets (BioGRID) is a public database that archives and disseminates genetic and protein interaction data from model organisms and humans (http://www.thebiogrid.org). BioGRID currently holds 347 966 interactions (170 162 genetic, 177 804 protein) curated from both high-throughput data sets and individual focused studies, as derived from over 23 000 publications in the primary literature. Complete coverage of the entire literature is maintained for budding yeast (Saccharomyces cerevisiae), fission yeast (Schizosaccharomyces pombe) and thale cress (Arabidopsis thaliana), and efforts to expand curation across multiple metazoan species are underway. The BioGRID houses 48 831 human protein interactions that have been curated from 10 247 publications. Current curation drives are focused on particular areas of biology to enable insights into conserved networks and pathways that are relevant to human health. The BioGRID 3.0 web interface contains new search and display features that enable rapid queries across multiple data types and sources. An automated Interaction Management System (IMS) is used to prioritize, coordinate and track curation across international sites and projects. BioGRID provides interaction data to several model organism databases, resources such as Entrez-Gene and other interaction meta-databases. The entire BioGRID 3.0 data collection may be downloaded in multiple file formats, including PSI MI XML. Source code for BioGRID 3.0 is freely available without any restrictions

Dynamics of conflicts in Wikipedia

In this work we study the dynamical features of editorial wars in Wikipedia (WP). Based on our previously established algorithm, we build up samples of controversial and peaceful articles and analyze the temporal characteristics of the activity in these samples. On short time scales, we show that there is a clear correspondence between conflict and burstiness of activity patterns, and that memory effects play an important role in controversies. On long time scales, we identify three distinct developmental patterns for the overall behavior of the articles. We are able to distinguish cases eventually leading to consensus from those cases where a compromise is far from achievable. Finally, we analyze discussion networks and conclude that edit wars are mainly fought by few editors only.Comment: Supporting information adde

Origin of Irreversibility of Cell Cycle Start in Budding Yeast

In budding yeast, the commitment to entry into a new cell division cycle is made irreversible by positive feedback-driven expression of the G1 cyclins Cln1,2

Cdc48 and Cofactors Npl4-Ufd1 Are Important for G1 Progression during Heat Stress by Maintaining Cell Wall Integrity in Saccharomyces cerevisiae

The ubiquitin-selective chaperone Cdc48, a member of the AAA (ATPase Associated with various cellular Activities) ATPase superfamily, is involved in many processes, including endoplasmic reticulum-associated degradation (ERAD), ubiquitin- and proteasome-mediated protein degradation, and mitosis. Although Cdc48 was originally isolated as a cell cycle mutant in the budding yeast Saccharomyces cerevisiae, its cell cycle functions have not been well appreciated. We found that temperature-sensitive cdc48-3 mutant is largely arrested at mitosis at 37°C, whereas the mutant is also delayed in G1 progression at 38.5°C. Reporter assays show that the promoter activity of G1 cyclin CLN1, but not CLN2, is reduced in cdc48-3 at 38.5°C. The cofactor npl4-1 and ufd1-2 mutants also exhibit G1 delay and reduced CLN1 promoter activity at 38.5°C, suggesting that Npl4-Ufd1 complex mediates the function of Cdc48 at G1. The G1 delay of cdc48-3 at 38.5°C is a consequence of cell wall defect that over-activates Mpk1, a MAPK family member important for cell wall integrity in response to stress conditions including heat shock. cdc48-3 is hypersensitive to cell wall perturbing agents and is synthetic-sick with mutations in the cell wall integrity signaling pathway. Our results suggest that the cell wall defect in cdc48-3 is exacerbated by heat shock, which sustains Mpk1 activity to block G1 progression. Thus, Cdc48-Npl4-Ufd1 is important for the maintenance of cell wall integrity in order for normal cell growth and division

Bayesian Orthogonal Least Squares (BOLS) algorithm for reverse engineering of gene regulatory networks

<p>Abstract</p> <p>Background</p> <p>A reverse engineering of gene regulatory network with large number of genes and limited number of experimental data points is a computationally challenging task. In particular, reverse engineering using linear systems is an underdetermined and ill conditioned problem, i.e. the amount of microarray data is limited and the solution is very sensitive to noise in the data. Therefore, the reverse engineering of gene regulatory networks with large number of genes and limited number of data points requires rigorous optimization algorithm.</p> <p>Results</p> <p>This study presents a novel algorithm for reverse engineering with linear systems. The proposed algorithm is a combination of the orthogonal least squares, second order derivative for network pruning, and Bayesian model comparison. In this study, the entire network is decomposed into a set of small networks that are defined as unit networks. The algorithm provides each unit network with P(D|H_i), which is used as confidence level. The unit network with higher P(D|H_i) has a higher confidence such that the unit network is correctly elucidated. Thus, the proposed algorithm is able to locate true positive interactions using P(D|H_i), which is a unique property of the proposed algorithm.</p> <p>The algorithm is evaluated with synthetic and <it>Saccharomyces cerevisiae </it>expression data using the dynamic Bayesian network. With synthetic data, it is shown that the performance of the algorithm depends on the number of genes, noise level, and the number of data points. With Yeast expression data, it is shown that there is remarkable number of known physical or genetic events among all interactions elucidated by the proposed algorithm.</p> <p>The performance of the algorithm is compared with Sparse Bayesian Learning algorithm using both synthetic and <it>Saccharomyces cerevisiae </it>expression data sets. The comparison experiments show that the algorithm produces sparser solutions with less false positives than Sparse Bayesian Learning algorithm.</p> <p>Conclusion</p> <p>From our evaluation experiments, we draw the conclusion as follows: 1) Simulation results show that the algorithm can be used to elucidate gene regulatory networks using limited number of experimental data points. 2) Simulation results also show that the algorithm is able to handle the problem with noisy data. 3) The experiment with Yeast expression data shows that the proposed algorithm reliably elucidates known physical or genetic events. 4) The comparison experiments show that the algorithm more efficiently performs than Sparse Bayesian Learning algorithm with noisy and limited number of data.</p

Ghosts of Yellowstone: Multi-Decadal Histories of Wildlife Populations Captured by Bones on a Modern Landscape

Natural accumulations of skeletal material (death assemblages) have the potential to provide historical data on species diversity and population structure for regions lacking decades of wildlife monitoring, thereby contributing valuable baseline data for conservation and management strategies. Previous studies of the ecological and temporal resolutions of death assemblages from terrestrial large-mammal communities, however, have largely focused on broad patterns of community composition in tropical settings. Here, I expand the environmental sampling of large-mammal death assemblages into a temperate biome and explore more demanding assessments of ecological fidelity by testing their capacity to record past population fluctuations of individual species in the well-studied ungulate community of Yellowstone National Park (Yellowstone). Despite dramatic ecological changes following the 1988 wildfires and 1995 wolf re-introduction, the Yellowstone death assemblage is highly faithful to the living community in species richness and community structure. These results agree with studies of tropical death assemblages and establish the broad capability of vertebrate remains to provide high-quality ecological data from disparate ecosystems and biomes. Importantly, the Yellowstone death assemblage also correctly identifies species that changed significantly in abundance over the last 20 to ∼80 years and the directions of those shifts (including local invasions and extinctions). The relative frequency of fresh versus weathered bones for individual species is also consistent with documented trends in living population sizes. Radiocarbon dating verifies the historical source of bones from Equus caballus (horse): a functionally extinct species. Bone surveys are a broadly valuable tool for obtaining population trends and baseline shifts over decadal-to-centennial timescales

Recipes and mechanisms of cellular reprogramming: a case study on budding yeast Saccharomyces cerevisiae

<p>Abstract</p> <p>Background</p> <p>Generation of induced pluripotent stem cells (iPSCs) and converting one cell type to another (transdifferentiation) by manipulating the expression of a small number of genes highlight the progress of cellular reprogramming, which holds great promise for regenerative medicine. A key challenge is to find the recipes of perturbing genes to achieve successful reprogramming such that the reprogrammed cells function in the same way as the natural cells.</p> <p>Results</p> <p>We present here a systems biology approach that allows systematic search for effective reprogramming recipes and monitoring the reprogramming progress to uncover the underlying mechanisms. Using budding yeast as a model system, we have curated a genetic network regulating cell cycle and sporulation. Phenotypic consequences of perturbations can be predicted from the network without any prior knowledge, which makes it possible to computationally reprogram cell fate. As the heterogeneity of natural cells is important in many biological processes, we find that the extent of this heterogeneity restored by the reprogrammed cells varies significantly upon reprogramming recipes. The heterogeneity difference between the reprogrammed and natural cells may have functional consequences.</p> <p>Conclusions</p> <p>Our study reveals that cellular reprogramming can be achieved by many different perturbations and the reprogrammability of a cell depends on the heterogeneity of the original cell state. We provide a general framework that can help discover new recipes for cellular reprogramming in human.</p

Daughter-Specific Transcription Factors Regulate Cell Size Control in Budding Yeast

The asymmetric localization of cell fate determinants results in asymmetric cell cycle control in budding yeast