Dominating Biological Networks

Proteins are essential macromolecules of life that carry out most cellular processes. Since proteins aggregate to perform function, and since protein-protein interaction (PPI) networks model these aggregations, one would expect to uncover new biology from PPI network topology. Hence, using PPI networks to predict protein function and role of protein pathways in disease has received attention. A debate remains open about whether network properties of “biologically central (BC)” genes (i.e., their protein products), such as those involved in aging, cancer, infectious diseases, or signaling and drug-targeted pathways, exhibit some topological centrality compared to the rest of the proteins in the human PPI network. To help resolve this debate, we design new network-based approaches and apply them to get new insight into biological function and disease. We hypothesize that BC genes have a topologically central (TC) role in the human PPI network. We propose two different concepts of topological centrality. We design a new centrality measure to capture complex wirings of proteins in the network that identifies as TC those proteins that reside in dense extended network neighborhoods. Also, we use the notion of domination and find dominating sets (DSs) in the PPI network, i.e., sets of proteins such that every protein is either in the DS or is a neighbor of the DS. Clearly, a DS has a TC role, as it enables efficient communication between different network parts. We find statistically significant enrichment in BC genes of TC nodes and outperform the existing methods indicating that genes involved in key biological processes occupy topologically complex and dense regions of the network and correspond to its “spine” that connects all other network parts and can thus pass cellular signals efficiently throughout the network. To our knowledge, this is the first study that explores domination in the context of PPI networks.

Dominating Biological Networks

Proteins are essential macromolecules of life that carry out most cellular processes. Since proteins aggregate to perform function, and since protein-protein interaction (PPI) networks model these aggregations, one would expect to uncover new biology from PPI network topology. Hence, using PPI networks to predict protein function and role of protein pathways in disease has received attention. A debate remains open about whether network properties of “biologically central (BC)” genes (i.e., their protein products), such as those involved in aging, cancer, infectious diseases, or signaling and drug-targeted pathways, exhibit some topological centrality compared to the rest of the proteins in the human PPI network. To help resolve this debate, we design new network-based approaches and apply them to get new insight into biological function and disease. We hypothesize that BC genes have a topologically central (TC) role in the human PPI network. We propose two different concepts of topological centrality. We design a new centrality measure to capture complex wirings of proteins in the network that identifies as TC those proteins that reside in dense extended network neighborhoods. Also, we use the notion of domination and find dominating sets (DSs) in the PPI network, i.e., sets of proteins such that every protein is either in the DS or is a neighbor of the DS. Clearly, a DS has a TC role, as it enables efficient communication between different network parts. We find statistically significant enrichment in BC genes of TC nodes and outperform the existing methods indicating that genes involved in key biological processes occupy topologically complex and dense regions of the network and correspond to its “spine” that connects all other network parts and can thus pass cellular signals efficiently throughout the network. To our knowledge, this is the first study that explores domination in the context of PPI networks.

Simulated poaching affects connectedness and efficiency in social networks of African savanna elephants

Poaching of mature and socially influential African savanna elephants for their prominent tusks alters the structure of their social networks. To learn if targeted poaching affects the functioning of elephant associations, we simulated network formation and disturbance via ‘poaching’ experiments in one wild and 100 virtual populations. To simulate virtual networks, we built an individual-based model guided by empirical association trends. After poaching of 1) the most mature or socially central individuals or 2) individuals selected at random, we evaluated network connectedness and efficiency. The networks never broke down, suggesting structural robustness. Unlike in age-specific deletions, eliminating individuals with the highest topological centrality decreased network connectedness and efficiency. The simulated networks, although structurally stable, became less functionally resilient when subject to poaching-like stress. Our work may offer new insights into elephant behavior vis-à-vis anthropogenic pressure, and inform conservation efforts focused on translocation of social species or trophy hunting practices.

Vulnerability analysis of critical infrastructure under disruptions: An application to China Railway High-speed

Considerable attention has been paid to the vulnerability of critical infrastructures because of the increasing occurrence of disruptive events, such as man-made or natural disasters. Even small disruptions could eventually affect the normal function of infrastructure systems. Enhancing the reliability of these systems and their robustness to disruptions is necessary and urgent. High-speed rail is a critical infrastructure that is subject to various disruptions, including component aging, malicious attacks, natural disasters, and demand surges. In this study, we analyze the topological centrality indicators of China Railway High-speed network using network theory and take real train flow information for assessing the importance of network components in terms of vulnerability to disruption. By Monte Carlo simulation, we analyze the risk of the China Railway High-speed network under random attacks and spatially localized failures. The significance of taking pre-actions for protecting critical infrastructures by mitigating its vulnerability to disruptions is emphasized.

A novel topological centrality measure capturing biologically important proteins

In the present study, a novel metric of centrality—weighted sum of loads eigenvector centrality (WSL-EC)—based on graph spectra is defined and its performance in identifying topologically and biologically important nodes is comparatively investigated with common metrics of centrality in a human protein–protein interaction network.