scholarly journals LifeWebs: A (global) database of bipartite ecological interaction networks

2021 ◽  
Vol 5 ◽  
Author(s):  
Philip Butterill ◽  
Leonardo Jorge ◽  
Shuang Xing ◽  
Tom Fayle
Keyword(s):  
Interaction Network ◽  
Community Level ◽  
Interaction Networks ◽  
Sampling Effort ◽  
Ecological Communities ◽  
Bipartite Networks ◽  
Data Types ◽  
User Friendliness ◽  
Global Database ◽  
Machine Readable

The structure and dynamics of ecological interactions are nowadays recognized as a crucial challenge to comprehend the assembly, functioning and maintenance of ecological communities, their processes and the services they provide. Nevertheless, while standards and databases for information on species occurrences, traits and phylogenies have been established, interaction networks have lagged behind on the development of these standards. Here, we discuss the challenges and our experiences in developing a global database of bipartite interaction networks. LifeWebs*1 is an effort to compile community-level interaction networks from both published and unpublished sources. We focus on bipartite networks that comprise one specific type of interaction between two groups of species (e.g., plants and herbivores, hosts and parasites, mammals and their microbiota), which are usually presented in a co-occurrence matrix format. However, with LifeWebs, we attempt to go beyond simple matrices by integrating relevant metadata from the studies, especially sampling effort, explicit species information (traits and taxonomy/phylogeny), and environmental/geographic information on the communities. Specifically, we explore 1) the unique aspects of community-level interaction networks when compared to data on single inter-specific interactions, occurrence data, and other biodiversity data and how to integrate these different data types. 2) The trade-off between user friendliness in data input/output vs. machine-readable formats, especially important when data contributors need to provide large amounts of data usually compiled in a non-machine-readable format. 3) How to have a single framework that is general enough to include disparate interaction types while retaining all the meaningful information. We envision LifeWebs to be in a good position to test a general standard for interaction network data, with a large variety of already compiled networks that encompass different types of interactions. We provide a framework for integration with other types of data, and formalization of the data necessary to represent networks into established biodiversity standards.

scholarly journals Effect of Localization on the Stability of Mutualistic Ecological Networks

2015 ◽  
Author(s):  
Samir Suweis ◽  
Jacopo Grilli ◽  
Jayanth Banavar ◽  
Stefano Allesina ◽  
Amos Maritan
Keyword(s):  
Species Abundance ◽  
Interaction Network ◽  
Species Interaction ◽  
Interaction Networks ◽  
Ecological Communities ◽  
Weighted Degree ◽  
Perturbation Propagation ◽  
The Stability ◽  
The Impact ◽  
The Relationship

The relationships between the core-periphery architecture of the species interaction network and the mechanisms ensuring the stability in mutualistic ecological communities are still unclear. In particular, most studies have focused their attention on asymptotic resilience or persistence, neglecting how perturbations propagate through the system. Here we develop a theoretical framework to evaluate the relationship between architecture of the interaction networks and the impact of perturbations by studying localization, a measure describing the ability of the perturbation to propagate through the network. We show that mutualistic ecological communities are localized, and localization reduces perturbation propagation and attenuates its impact on species abundance. Localization depends on the topology of the interaction networks, and it positively correlates with the variance of the weighted degree distribution, a signature of the network topological hetereogenity. Our results provide a different perspective on the interplay between the architecture of interaction networks in mutualistic communities and their stability.

scholarly journals Mutual Information as a General Measure of Structure in Interaction Networks

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 528 ◽  
Author(s):  
Gilberto Corso ◽  
Gabriel M. F. Ferreira ◽  
Thomas M. Lewinsohn
Keyword(s):  
Mutual Information ◽  
Biological Diversity ◽  
Spatial Scales ◽  
Interaction Networks ◽  
Ecological Communities ◽  
Bipartite Networks ◽  
General Measure ◽  
Interaction Structure ◽  
Interactive Networks ◽  
Modes Of Interaction

Entropy-based indices are long-established measures of biological diversity, nowadays used to gauge partitioning of diversity at different spatial scales. Here, we tackle the measurement of diversity of interactions among two sets of organisms, such as plants and their pollinators. Actual interactions in ecological communities are depicted as bipartite networks or interaction matrices. Recent studies concentrate on distinctive structural patterns, such as nestedness or modularity, found in different modes of interaction. By contrast, we investigate mutual information as a general measure of structure in interactive networks. Mutual information (MI) measures the degree of reciprocal matching or specialization between interacting organisms. To ascertain its usefulness as a general measure, we explore (a) analytical solutions for different models; (b) the response of MI to network parameters, especially size and occupancy; (c) MI in nested, modular, and compound topologies. MI varies with fundamental matrix parameters: dimension and occupancy, for which it can be adjusted or normalized. Apparent differences among topologies are contingent on dimensions and occupancy, rather than on topological patterns themselves. As a general measure of interaction structure, MI is applicable to conceptually and empirically fruitful analyses, such as comparing similar ecological networks along geographical gradients or among interaction modalities in mutualistic or antagonistic networks.

scholarly journals Real-world Interaction Networks Buffer Impact of Small Evolutionary Shifts On Biodiversity

2014 ◽  
Author(s):  
Gabriel E Leventhal ◽  
Liyu Wang ◽  
Roger D Kouyos
Keyword(s):  
Real World ◽  
Environmental Changes ◽  
Interaction Network ◽  
Species Interaction ◽  
Interaction Networks ◽  
Community Diversity ◽  
Ecological Communities ◽  
Interaction Structure ◽  
Evolutionary Changes ◽  
Biodiversity Maintenance

Biodiversity maintenance and community evolution depend on the species interaction network. The "diversity-stability debate" has revealed that the complex interaction structure within real-world ecosystems determines how ecological communities respond to environmental changes, but can have opposite effects depending on the community type. Here we quantify the influence of shifts on community diversity and stability at both the species level and the community level. We use interaction networks from 19 real-world mutualistic communities and simulate shifts to antagonism. We demonstrate that both the placement of the shifting species in the community, as well as the structure of the interaction network as a whole contribute to stability and diversity maintenance under shifts. Our results suggest that the interaction structure of natural communities generally enhances community robustness against small ecological and evolutionary changes, but exacerbates the consequences of large changes.

scholarly journals ProtFinder: finding subcellular locations of proteins using protein interaction networks

2022 ◽  
Author(s):  
Aayush Grover ◽  
Laurent Gatto
Keyword(s):  
Protein Interaction ◽  
Intelligent System ◽  
Interaction Network ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Subcellular Localization ◽  
Human Protein Atlas ◽  
Protein Subcellular Localization Prediction ◽  
Localization Prediction ◽  
Roc Score

Protein subcellular localization prediction plays a crucial role in improving our understandings of different diseases and consequently assists in building drug targeting and drug development pipelines. Proteins are known to co-exist at multiple subcellular locations which make the task of prediction extremely challenging. A protein interaction network is a graph that captures interactions between different proteins. It is safe to assume that if two proteins are interacting, they must share some subcellular locations. With this regard, we propose ProtFinder - the first deep learning-based model that exclusively relies on protein interaction networks to predict the multiple subcellular locations of proteins. We also integrate biological priors like the cellular component of Gene Ontology to make ProtFinder a more biology-aware intelligent system. ProtFinder is trained and tested using the STRING and BioPlex databases whereas the annotations of proteins are obtained from the Human Protein Atlas. Our model gives an AUC-ROC score of 90.00% and an MCC score of 83.42% on a held-out set of proteins. We also apply ProtFinder to annotate proteins that currently do not have confident location annotations. We observe that ProtFinder is able to confirm some of these unreliable location annotations, while in some cases complementing the existing databases with novel location annotations.

scholarly journals ReactomeFIViz: the Reactome FI Cytoscape app for pathway and network-based data analysis

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 146 ◽  
Author(s):  
Guanming Wu ◽  
Eric Dawson ◽  
Adrian Duong ◽  
Robin Haw ◽  
Lincoln Stein
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Graphical Models ◽  
High Throughput ◽  
Interaction Network ◽  
Large Data ◽  
Relevant Information ◽  
Data Sets ◽  
Data Types ◽  
Biological Studies

High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network and human curated pathways from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.

scholarly journals Dynamic population stage structure due to juvenile–adult asymmetry stabilizes complex ecological communities

2021 ◽  
Vol 118 (21) ◽  
pp. e2023709118
Author(s):  
André M. de Roos
Keyword(s):  
Self Regulation ◽  
Interaction Network ◽  
Population Level ◽  
Stage Structure ◽  
Current Theory ◽  
Species Interaction ◽  
Interaction Matrix ◽  
Foraging Efficiency ◽  
Ecological Communities ◽  
The Stability

Natural ecological communities are diverse, complex, and often surprisingly stable, but the mechanisms underlying their stability remain a theoretical enigma. Interactions such as competition and predation presumably structure communities, yet theory predicts that complex communities are stable only when species growth rates are mostly limited by intraspecific self-regulation rather than by interactions with resources, competitors, and predators. Current theory, however, considers only the network topology of population-level interactions between species and ignores within-population differences, such as between juvenile and adult individuals. Here, using model simulations and analysis, I show that including commonly observed differences in vulnerability to predation and foraging efficiency between juvenile and adult individuals results in up to 10 times larger, more complex communities than observed in simulations without population stage structure. These diverse communities are stable or fluctuate with limited amplitude, although in the model only a single basal species is self-regulated, and the population-level interaction network is highly connected. Analysis of the species interaction matrix predicts the simulated communities to be unstable but for the interaction with the population-structure subsystem, which completely cancels out these instabilities through dynamic changes in population stage structure. Common differences between juveniles and adults and fluctuations in their relative abundance may hence have a decisive influence on the stability of complex natural communities and their vulnerability when environmental conditions change. To explain community persistence, it may not be sufficient to consider only the network of interactions between the constituting species.

scholarly journals Comment on “Information arms race explains plant-herbivore chemical communication in ecological communities”

2021 ◽  
Author(s):  
Ethan Bass ◽  
André Kessler
Keyword(s):  
Chemical Communication ◽  
Evolutionary Theory ◽  
Null Model ◽  
Arms Race ◽  
Community Level ◽  
Ecological Communities ◽  
Proposed Model ◽  
Plant Herbivore

Zu et al (Science, 19 Jun 2020, p. 1377) propose that an ‘information arms-race’ between plants and herbivores explains plant-herbivore communication at the community level. However, our analysis shows that key assumptions of the proposed model either a) conflict with standard evolutionary theory or b) are not supported by the available evidence. We also show that the presented statistical patterns can be explained more parsimoniously (e.g. through a null model) without invoking an unlikely process of community selection.

scholarly journals XlinkCyNET: a Cytoscape application for visualization of protein interaction networks based on cross-linking mass-spectrometry identifications

2020 ◽  
Author(s):  
Diogo Borges Lima ◽  
Ying Zhu ◽  
Fan Liu
Keyword(s):  
Mass Spectrometry ◽  
Protein Interaction ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Cross Linking ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Link Type

ABSTRACTSoftware tools that allow visualization and analysis of protein interaction networks are essential for studies in systems biology. One of the most popular network visualization tools in biology is Cytoscape, which offers a large selection of plugins for interpretation of protein interaction data. Chemical cross-linking coupled to mass spectrometry (XL-MS) is an increasingly important source for such interaction data, but there are currently no Cytoscape tools to analyze XL-MS results. In light of the suitability of Cytoscape platform but also to expand its toolbox, here we introduce XlinkCyNET, an open-source Cytoscape Java plugin for exploring large-scale XL-MS-based protein interaction networks. XlinkCyNET offers rapid and easy visualization of intra and intermolecular cross-links and the locations of protein domains in a rectangular bar style, allowing subdomain-level interrogation of the interaction network. XlinkCyNET is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/xlinkcynet and at https://www.theliulab.com/software/xlinkcynet.

Restoration of pollination interactions in communities invaded by non-native plants.

2020 ◽  
pp. 377-390
Author(s):  
Christopher N. Kaiser-Bunbury ◽  
◽  
Benno I. Simmons ◽  
◽  
Keyword(s):  
Plant Species ◽  
Restoration Ecology ◽  
Invasive Plant ◽  
Ecosystem Processes ◽  
Community Level ◽  
Ecological Communities ◽  
Management Interventions ◽  
And Function ◽  
Network Metrics ◽  
The Impact

Invasive plant species degrade and homogenize ecosystems worldwide, thereby altering ecosystem processes and function. To mitigate and reverse the impact of invasive plants on pollination, a key ecosystem function, conservation scientists and practitioners restore ecological communities and study the impact of such management interventions on plant-pollinator communities. Here, we describe opportunities and challenges associated with restoring pollination interactions as part of a holistic ecosystem-based restoration approach. We introduce a few general concepts in restoration ecology, and outline best planning and evaluation practices of restoring pollination interactions on the community level. Planning involves the selection of suitable plant species to support diverse pollinator communities, which includes considerations of the benefits and disadvantages of using native vs exotic, and bridge and framework plant species for restoration. We emphasize the central role of scientific- and community-level approaches for the planning phase of pollination restoration. For evaluation purposes, we argue that appropriate network indicators have the advantage of detecting changes in species behaviour with consequences for ecosystem processes and functions before these changes show up in altered species communities. Suitable network metrics may include interaction diversity and evenness, and network measures that describe the distribution of species, such as network and species-level specialization, modularity and motifs. Finally, we discuss the usefulness of the network approach in evaluating the benefits of restoration interventions for pollination interactions, and propose that applied network ecologists take a central role in transferring theory into practice.

scholarly journals Identification of microbial interaction network: zero-inflated latent Ising model based approach

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Jie Zhou ◽  
Weston D. Viles ◽  
Boran Lu ◽  
Zhigang Li ◽  
Juliette C. Madan ◽  
...  
Keyword(s):  
Ising Model ◽  
Gut Microbiota ◽  
Relative Abundance ◽  
Interaction Network ◽  
Interaction Networks ◽  
Endocrine Regulation ◽  
Microbial Interaction ◽  
Conditional Correlation ◽  
Zero Inflation ◽  
Step Algorithm

Abstract Background Throughout their lifespans, humans continually interact with the microbial world, including those organisms which live in and on the human body. Research in this domain has revealed the extensive links between the human-associated microbiota and health. In particular, the microbiota of the human gut plays essential roles in digestion, nutrient metabolism, immune maturation and homeostasis, neurological signaling, and endocrine regulation. Microbial interaction networks are frequently estimated from data and are an indispensable tool for representing and understanding the conditional correlation between the microbes. In this high-dimensional setting, zero-inflation and unit-sum constraint for relative abundance data pose challenges to the reliable estimation of microbial interaction networks. Methods and Results To identify the microbial interaction network, the zero-inflated latent Ising (ZILI) model is proposed which assumes the distribution of relative abundance relies only on finite latent states and provides a novel way to solve issues induced by the unit-sum and zero-inflation constrains. A two-step algorithm is proposed for the model selection of ZILI. ZILI is evaluated through simulated data and subsequently applied to an infant gut microbiota dataset from New Hampshire Birth Cohort Study. The results are compared with results from Gaussian graphical model (GGM) and dichotomous Ising model (DIS). Providing ZILI is the true data-generating model, the simulation studies show that the two-step algorithm can identify the graphical structure effectively and is robust to a range of parameter settings. For the infant gut microbiota dataset, the final estimated networks from GGM and ZILI turn out to have significant overlap in which the ZILI tends to select the sparser network than those from GGM. From the shared subnetwork, a hub taxon Lachnospiraceae is identified whose involvement in human disease development has been discovered recently in literature. Conclusions Constrains induced by relative abundance of microbiota such as zero inflation and unit sum render the conditional correlation analysis unreliable for conventional methods such as GGM. The proposed optimal categoricalization based ZILI model provides an alternative yet elegant way to deal with these difficulties. The results from ZILI have reasonable biological interpretation. This model can also be used to study the microbial interaction in other body parts.

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