Cities represent humanity’s most intense impact on our Planet, with more than half of all humans now residing in urban areas. Indeed, urbanization has well understood impacts on both individual species and general patterns of biodiversity. However, species do not exist in isolation, but are instead members of complex interaction networks that shape patterns of diversity and influence ecosystem services. Despite the importance of species interaction for creating patterns of diversity, we do not understand how urbanization alters these interactions. Here, we investigate how an interaction network (food web) is reshaped by urbanization. We show that, consistent with theory, cities tend to support less diverse ecological communities, and rare species that interact with few species are particularly sensitive to urbanization. As a result, remnant urban food webs tend to have more interactions per species and greater connectance, creating more integrated interaction networks. We discuss t...

A compilation of body sizes, abundance estimates, and trophic and non-trophic interactions for species in the subtidal kelp forest communities of the Alaskan Aleutian Islands.

We have developed to quantitatively assay protein-protein interactions (PPIs) by barcode sequencing (Schlecht et al., Nature Comm. 2017, 8: 1-9). Here, we scale up this technology to quantify changes in relative in vivo PPI abundance of 1.6 million protein pairs across 9 growth conditions, with replication, for a total of 44 million measurements. To the best of our knowledge, this is the first large-scale study to move beyond a “static” view of the protein interactome by examining how PPI networks change across environments. This new dynamic view of a cell’s protein interactome yields several headline findings: (1) Screening multiple conditions discovers ~3-4 times as many PPIs as a single-condition screen, indicating that previous work has underestimated the size of the protein interactome emergent from a cell genome. (2) Most PPIs are found in only a handful of conditions -- we call these PPIs “mutable”-- and these have been underrepresented in single-condition screens. (3) Mutable PPIs populate a newly-discovered and distinct “accessory” module of the protein interactome that is loosely connected, highly dynamic, and enriched for proteins involved in transcription, RNA processing, and translation (4) Mutable PPIs have several features that distinguish them from immutable PPIs: they are less likely to co-express, co-localize, and be explained by simple mass action kinetics, and more likely to contain intrinsically disordered regions, evolve quickly, and be of low abundance in standard conditions. These results settle a long-standing debate concerning the statistical robustness of a “date hub/party hub” dichotomy and its implications for modularity of PPI networks (Han et al., Nature, 2004, 430: 88-93). Taken together, our results suggest that protein interactomes contain previously uncharacterized and highly dynamic regions that reorganize in response to cellular changes, and that this reorganization is due, to a larger extent, by post translational modifications.

This is a cooperative database containing published data on species interaction networks. The database currently contains datasets on species interactions from several communities in different parts of the world. Data currently available are for a variety of interaction types, includying plant-pollinator, plant-frugivore, plant-herbivore, plant-ant mutualist and predator-prey interactions. Our goal is to expand the database to make it a repository of data on any kind of interactions.

Curated protein-protein and genetic interaction repository of raw protein and genetic interactions from major model organism species, with data compiled through comprehensive curation efforts.

Food webs typically quantify interactions between species, whereas evolution operates through the success of alleles within populations of a single species. To bridge this gap, we quantify genotypic interaction networks among individuals of a single specialized parasitoid species and its obligately to cyclically parthenogenetic aphid host along a climatic gradient. As a case study for the kinds of questions genotype food webs could be used to answer, we show that genetically-similar parasitoids became more likely to attack genetically-similar hosts in warmer sites (i.e. there was network-wide congruence between the within-species shared allelic distance of the parasitoid and that of its host). Narrowing of host-genotype-niche breadth by parasitoids could reduce resilience of the network to changes in host genetic structure or invasion by novel host genotypes, and inhibit biological control. Thus, our approach can be easily used to detect changes to sub-species-level food webs, which may have important ecological and evolutionary implications, such as promoting host-race specialisation or the accelerated loss of functional diversity following extinctions of closely-related genotypes.

In recent years a number of calculative models based on protein-protein interaction (PPI) networks have been proposed successively. However, due to false positives, false negatives, and the incompleteness of PPI networks, there are still many challenges affecting the design of computational models with satisfactory predictive accuracy when inferring key proteins. This study proposes a prediction model called WPDINM for detecting key proteins based on a novel weighted protein-domain interaction (PDI) network. In WPDINM, a weighted PPI network is constructed first by combining the gene expression data of proteins with topological information extracted from the original PPI network. Simultaneously, a weighted domain-domain interaction (DDI) network is constructed based on the original PDI network. Next, through integrating the newly obtained weighted PPI network and weighted DDI network with the original PDI network, a weighted PDI network is further constructed. Then, based on topological features and biological information, including the subcellular localization and orthologous information of proteins, a novel PageRank-based iterative algorithm is designed and implemented on the newly constructed weighted PDI network to estimate the criticality of proteins. Finally, to assess the prediction performance of WPDINM, we compared it with 12 kinds of competitive measures. Experimental results show that WPDINM can achieve a predictive accuracy rate of 90.19, 81.96, 70.72, 62.04, 55.83, and 51.13% in the top 1%, top 5%, top 10%, top 15%, top 20%, and top 25% separately, which exceeds the prediction accuracy achieved by traditional state-of-the-art competing measures. Owing to the satisfactory identification effect, the WPDINM measure may contribute to the further development of key protein identification.

The PPI network was constructed using the genes that are regulated by the SNPs associated with 18 AiDs. STRING PPI data was used for building the network. The list of proteins present in the Ai-PPIN and the edgelist of the network is provided here.

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 27, 2016. Curated database of information about known biomolecular interactions and key cellular processes assembled into signaling pathways. All interactions are assembled into pathways, and can be accessed by performing searches for biomolecules, or processes, or by viewing predefined pathways. This was a collaborative project between the NCI and Nature Publishing Group (NPG) from 2006 until September 22nd, 2012, and is no longer being updated. PID is aimed at the cancer research community and others interested in cellular pathways, such as neuroscientists, developmental biologists, and immunologists. The database focuses on the biomolecular interactions that are known or believed to take place in human cells. It can be browsed as an online encyclopedia, used to run computational analyses, or employed in ways that combine these two approaches. In addition to PID''''s predefined pathways, search results are displayed as dynamically constructed interaction networks. These features of PID render it a useful tool for both biologists and bioinformaticians. PID offers a range of search features to facilitate pathway exploration. Users can browse the predefined set of pathways or create interaction network maps centered on a single molecule or cellular process of interest. In addition, the batch query tool allows users to upload long list(s) of molecules, such as those derived from microarray experiments, and either overlay these molecules onto predefined pathways or visualize the complete molecular connectivity map. Users can also download molecule lists, citation lists and complete database content in extensible markup language (XML) and Biological Pathways Exchange (BioPAX) Level 2 format. The database is supplemented by a concise editorial section that includes specially written synopses of recent important research articles in areas related to cancer research, and specially commissioned Bioinformatics Primers that provide practical advice on how to make the most of other relevant online resources. The database and editorial content are updated monthly, and users can opt to receive a monthly email alert to stay informed about new content. Note: as of September 23, 2012 the PID is no longer being actively curated. NCI will maintain the PID website and data for twelve months beyond September 2012 to allow interested parties to obtain the previously curated data before the site is retired in September 2013.

Biological Magnetic Resonance Bank Entry 52326: An extended interaction site determines binding between AP180 and AP2 in clathrin mediated endocytosis

In recent years, evidence has been found that plant-pollinator interactions are altered by land-use and that genetic diversity also plays a role. However, how land-use and genetic diversity influence plant-pollinator interactions, particularly in the Neotropics, where many endemic plants exist is still an open question. Cucurbita pepo is a monoecious plant and traditional crop wide distributed, with high rates of molecular evolution, landraces associated with human cultural management and a history of coevolution with bees, which makes this species a promising model for studying the effect of landscape and genetic diversity on plant-pollinator interactions. Here, we assess 1) whether female and male flowers differences have an effect on the interaction network, 2) how C. pepo genetic diversity affects flower-bee visitation network structure, and 3) what is the effect that land-use, accounting for C. pepo genetic variability, has on pumpkin-bee interaction network variables. Our results indicate that female and male flowers presented the same community composition and network structure suggesting that the differences do not have a significant effect on network evolution. Genetic diversity has a positive effect on modularity, nestedness, and number of interactions, which, when considered, allows to observe how land-use variables can have an enhancing or buffering effect on nestedness. Our results suggest that considering genetic diversity is relevant for a better understanding of the effect of landscape on interaction networks. Additionally, this understanding has great value in conserving biodiversity and enhancing the stability of interaction networks in a world facing great challenges of habitat and diversity loss.

Dataset

This dataset was created by sendraa

Contents

A database of human molecular interaction networks that integrates human protein-protein and transcriptional regulatory interactions from 15 distinct resources and aims to give direct and easy access to the integrated data set and to enable users to perform network-based investigations. The database includes tools (i) to search for molecular interaction partners of query genes or proteins in the integrated dataset, (ii) to inspect the origin, evidence and functional annotation of retrieved proteins and interactions, (iii) to visualize and adjust the resulting interaction network, (iv) to filter interactions based on method of derivation, evidence and type of experiment as well as based on gene expression data or gene lists and (v) to analyze the functional composition of interaction networks.

Community ecologists have made great advances in understanding how natural communities can be both diverse and stable by studying communities as interaction networks. However, focus has been on interaction networks aggregated over time, neglecting the consequences of the seasonal organization of interactions, henceforth seasonal structure, for community stability. Here, we extended previous theoretical findings on the topic in two ways: (i) by integrating empirical seasonal structure of 11 plant-hummingbird communities into dynamic models, and (ii) by tackling multiple facets of network stability together. We show that, in a competition context, seasonal structure enhances community stability by allowing diverse and resilient communities while preserving their robustness to species extinctions. The positive effects of empirical seasonal structure on network stability vanished when using randomized seasonal structures, suggesting that eco-evolutionary dynamics produce stabilizing seasonal structures. We also show that the effects of seasonal structure on community stability are mainly mediated by changes in network structure and productivity, suggesting that the seasonal structure of a community is an important and yet neglected aspect in the diversity-stability and diversity-productivity debates. Methods We used data from 11 independent sites in the tropical forests of Ecuador (Table 1, Fig. S1) in which interactions among flowering plants and hummingbirds were recorded along transects by using camera traps, as described in Graham & Weinstein (2018). Graham CH, Weinstein BG. Towards a predictive model of species interaction beta diversity. Ecol Lett. 2018;21(9):1299–310.

FunCoup network information for gene F8WAR5 in Homo sapiens. ARI4A_HUMAN AT-rich interactive domain-containing protein 4A

In Spring 2020, many U.S. colleges and universities rapidly shifted to online instruction and implemented social distancing policies to respond to the Covid-19 pandemic. Students experienced an unprecedented disruption of their interpersonal academic and social networks due to the loss of physical proximity. We used egocentric network analysis and latent profile analysis with survey data from April 2020 and conducted follow-up interviews in September 2020 to examine some of the pandemic’s immediate effects on student interpersonal network change. We found the disappearance of interpersonal network patterns featuring coworkers and academic ties, as well as reductions in students’ overall number of connections and the role diversity of their networks. Results suggest potential ongoing reduction of peer academic relationships, implying that institutional personnel may need to pay particular attention to academic connections in online spaces and to re-generating students’ academic networks when on-campus physical spaces may again be used to support learning.

FunCoup network information for gene F8WCU9 in Homo sapiens. ARID2_HUMAN AT-rich interactive domain-containing protein 2

FunCoup network information for gene F8WEW4 in Homo sapiens. PGLT1_HUMAN Protein O-glucosyltransferase 1

FunCoup network information for gene F8VW65 in Homo sapiens. BLT3B_HUMAN Bridge-like lipid transfer protein family member 3B

Human gene interaction network data used to reproduce gene classification experiments https://github.com/krishnanlab/node2vecplus_benchmarks