Food web topology and nested keystone species complexes

Important species may be in critically central network positions in ecological interaction networks. Beyond quantifying which one is the most central species in a food web, a multi-node approach can identify the key sets of the most central n species as well. However, for sets of different size n, these structural keystone species complexes may differ in their composition. If larger sets contain smaller sets, higher nestedness may be a proxy for predictive ecology and efficient management of ecosystems. On the contrary, lower nestedness makes the identification of keystones more complicated. Our question here is how the topology of a network can influence nestedness as an architectural constraint. Here, we study the role of keystone species complexes in 27 real food webs and quantify their nestedness. After quantifying their topology properties, we determine their keystones species complexes, calculate their nestedness and statistically analyze the relationship between topological indices and nestedness. A better understanding of the cores of ecosystems is crucial for efficient conservation efforts and to know which networks will have more nested keystone species complexes would be a great help for prioritizing species that could preserve the ecosystem’s structural integrity

ProcessPageRank - A Network-based Approach to Process Prioritization Decisions

Deciding which business processes to improve first is a challenge most corporate decision-makers face. The literature offers many approaches, techniques, and tools that support such process prioritization decisions. Despite the broad knowledge about measuring the performance of individual processes and determining related need for improvement, the interconnectedness of processes has not been considered in process prioritization decisions yet. So far, the interconnectedness of business processes is captured for descriptive purposes only, for example in business process architectures. This drawback systematically biases process prioritization decisions. As a first step to address this gap, we propose the ProcessPageRank (PPR), an algorithm based on the Google PageRank that ranks processes according to their network-adjusted need for improvement. The PPR is grounded in the literature related to process improvement, process performance measurement, and network analysis. For demonstration purposes, we created a software prototype and applied the PPR to five process network archetypes to illustrate how the interconnectedness of business processes affects process prioritization decisions

Network science based quantification of resilience demonstrated on the Indian Railways Network

The structure, interdependence, and fragility of systems ranging from power grids and transportation to ecology, climate, biology and even human communities and the Internet, have been examined through network science. While the response to perturbations has been quantified, recovery strategies for perturbed networks have usually been either discussed conceptually or through anecdotal case studies. Here we develop a network science-based quantitative methods framework for measuring, comparing and interpreting hazard responses and as well as recovery strategies. The framework, motivated by the recently proposed temporal resilience paradigm, is demonstrated with the Indian Railways Network. The methods are demonstrated through the resilience of the network to natural or human-induced hazards and electric grid failure. Simulations inspired by the 2004 Indian Ocean Tsunami and the 2012 North Indian blackout as well as a cyber-physical attack scenario. Multiple metrics are used to generate various recovery strategies, which are simply sequences in which system components should be recovered after a disruption. Quantitative evaluation of recovery strategies suggests that faster and more resource-effective recovery is possible through network centrality measures. Case studies based on two historical events, specifically the 2004 Indian Ocean tsunami and the 2012 North Indian blackout, and a simulated cyber-physical attack scenario, provides means for interpreting the relative performance of various recovery strategies. Quantitative evaluation of recovery strategies suggests that faster and more resource-effective restoration is possible through network centrality measures, even though the specific strategy may be different for sub-networks or for the partial recovery

Developing restoration schemes for a road transportation network in the event of a disaster

Transportation systems such as rail, road, and waterways are key component of critical infrastructure systems, providing connectivity between other components to enable the production and distribution of goods and services. During large scale disasters such as earth quakes and floods, this connectivity is disrupted, restricting or completely halting the flow of goods and services. To ensure that the connectivity between the different modes of transportation are restored in an aftermath of these disruptions, the interdependence between them and the importance of individual elements to the overall connectivity have to be studied and formulated to develop a system-level restoration plan. This paper presents a framework to develop efficient restoration schemes for a road transportation network in an aftermath of a disruption. The road transportation network is modelled using graph theory analytics. Using a system oriented parameter such as the Eigen-vector centrality measure associated with the road transportation, it is possible to understand the importance of different network components. This model captures the interdependence between the different elements in the road transportation network critical in understanding failure effects by identifying the important nodes in the network using the Eigen-vector centrality measure. The model is constructed from publically available data for Saint-Louis, Missouri. By performing a sensitivity analysis, we have found that the node with the highest Eigen-vector centrality measures are shown to provide a higher value within a ninety-five percent confidence level, indicating low sensitivity to changes in input parameters. This provides a measure to determine the most important nodes to place back into service to assist in restoring an urban center\u27s supply chain in the wake of an extreme event. --Abstract, page iii

Nine Quick Tips for Analyzing Network Data

These tips provide a quick and concentrated guide for beginners in the analysis of network data

Compact Integration of Multi-Network Topology for Functional Analysis of Genes

The topological landscape of molecular or functional interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, a pressing yet-unsolved challenge is how to combine multiple heterogeneous networks, each having different connectivity patterns, to achieve more accurate inference. Here, we describe the Mashup framework for scalable and robust network integration. In Mashup, the diffusion in each network is first analyzed to characterize the topological context of each node. Next, the high-dimensional topological patterns in individual networks are canonically represented using low-dimensional vectors, one per gene or protein. These vectors can then be plugged into off-the-shelf machine learning methods to derive functional insights about genes or proteins. We present tools based on Mashup that achieve state-of-the-art performance in three diverse functional inference tasks: protein function prediction, gene ontology reconstruction, and genetic interaction prediction. Mashup enables deeper insights into the struct ure of rapidly accumulating and diverse biological network data and can be broadly applied to other network science domains. Keywords: interactome analysis; network integration; heterogeneous networks; dimensionality reduction; network diffusion; gene function prediction; genetic interaction prediction; gene ontology reconstruction; drug response predictionNational Institutes of Health (U.S.) (Grant R01GM081871

Connectivity modelling for a species-driven nature recovery network in Oxfordshire

The development of England’s new Nature Recovery Network has been piloted in several counties in the country, but few have systematically mapped connectivity based on species dispersal. This study proposes and evaluates a novel modelling framework that integrates various layers of species information into a spatial conservation prioritization analysis. It aims to strategically identify optimal zones for nature recovery that can maximize species connectivity in Oxfordshire, using bats as a focal species. The framework was able to not only identify key landscape corridors but also stepping stone habitats for bats and emphasized how well-placed, small-scale green and blue infrastructure, such as hedgerows and ponds, can be just as effective as larger reserves. It also found that the current coverage of protected areas may not adequately be protecting woodland habitat needed for connectivity. Next steps for Oxfordshire’s NRN should scale up the application of this connectivity framework to address these areas of priority in the landscape

PhosphoEffect: Prioritizing Variants On or Adjacent to Phosphorylation Sites through Their Effect on Kinase Recognition Motifs.

Phosphorylation sites often have key regulatory functions and are central to many cellular signaling pathways, so mutations that modify them have the potential to contribute to pathological states such as cancer. Although many classifiers exist for prioritization of coding genomic variants, to our knowledge none of them explicitly account for the alteration or creation of kinase recognition motifs that alter protein structure, function, regulation of activity, and interaction networks through modifying the pattern of phosphorylation. We present a novel computational pipeline that uses a random forest classifier to predict the pathogenicity of a variant, according to its direct or indirect effect on local phosphorylation sites and the predicted functional impact of perturbing a phosphorylation event. We call this classifier PhosphoEffect and find that it compares favorably and with increased accuracy to the existing classifier PolyPhen 2.2.2 when tested on a dataset of known variants enriched for phosphorylation sites and their neighbors