Dataset Information

Predicting the Lifetime of Dynamic Networks Experiencing Persistent Random Attacks.

ABSTRACT: Estimating the critical points at which complex systems abruptly flip from one state to another is one of the remaining challenges in network science. Due to lack of knowledge about the underlying stochastic processes controlling critical transitions, it is widely considered difficult to determine the location of critical points for real-world networks, and it is even more difficult to predict the time at which these potentially catastrophic failures occur. We analyse a class of decaying dynamic networks experiencing persistent failures in which the magnitude of the overall failure is quantified by the probability that a potentially permanent internal failure will occur. When the fraction of active neighbours is reduced to a critical threshold, cascading failures can trigger a total network failure. For this class of network we find that the time to network failure, which is equivalent to network lifetime, is inversely dependent upon the magnitude of the failure and logarithmically dependent on the threshold. We analyse how permanent failures affect network robustness using network lifetime as a measure. These findings provide new methodological insight into system dynamics and, in particular, of the dynamic processes of networks. We illustrate the network model by selected examples from biology, and social science.

SUBMITTER: Podobnik B

PROVIDER: S-EPMC4585692 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting the Lifetime of Dynamic Networks Experiencing Persistent Random Attacks.

Podobnik Boris B Lipic Tomislav T Horvatic Davor D Majdandzic Antonio A Bishop Steven R SR Eugene Stanley H H

Scientific reports 20150921

Estimating the critical points at which complex systems abruptly flip from one state to another is one of the remaining challenges in network science. Due to lack of knowledge about the underlying stochastic processes controlling critical transitions, it is widely considered difficult to determine the location of critical points for real-world networks, and it is even more difficult to predict the time at which these potentially catastrophic failures occur. We analyse a class of decaying dynamic ...[more]

PMID: 26387609

Similar Datasets

Project description:BackgroundSeveral studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree classifier, integrate diverse biological networks and show that our method outperforms established methods.ResultsBy applying random walks on biological networks, we were able to predict synthetic lethal interactions at a true positive rate of 95 percent against a false positive rate of 10 percent in S. cerevisiae. Similarly, in C. elegans, we achieved a true positive rate of 95 against a false positive rate of 7 percent. Furthermore, we demonstrate that the inclusion of non-interacting gene pairs results in a considerable performance improvement.ConclusionWe presented a method based on random walks that accurately captures aspects of network topology towards the goal of classifying potential genetic interactions as either synthetic lethal or non-interacting. Our method, which is generalizable to all types of biological networks, is likely to perform well with limited information, as estimated by holding out large portions of the synthetic lethal interactions and non-interactions.

Project description:BackgroundThe ability to predict which pairs of amino acid residues in a protein are in contact with each other offers many advantages for various areas of research that focus on proteins. For example, contact prediction can be used to reduce the computational complexity of predicting the structure of proteins and even to help identify functionally important regions of proteins. These predictions are becoming especially important given the relatively low number of experimentally determined protein structures compared to the amount of available protein sequence data.ResultsHere we have developed and benchmarked a set of machine learning methods for performing residue-residue contact prediction, including random forests, direct-coupling analysis, support vector machines, and deep networks (stacked denoising autoencoders). These methods are able to predict contacting residue pairs given only the amino acid sequence of a protein. According to our own evaluations performed at a resolution of +/- two residues, the predictors we trained with the random forest algorithm were our top performing methods with average top 10 prediction accuracy scores of 85.13% (short range), 74.49% (medium range), and 54.49% (long range). Our ensemble models (stacked denoising autoencoders combined with support vector machines) were our best performing deep network predictors and achieved top 10 prediction accuracy scores of 75.51% (short range), 60.26% (medium range), and 43.85% (long range) using the same evaluation. These tests were blindly performed on targets from the CASP11 dataset; and the results suggested that our models achieved comparable performance to contact predictors developed by groups that participated in CASP11.ConclusionsDue to the challenging nature of contact prediction, it is beneficial to develop and benchmark a variety of different prediction methods. Our work has produced useful tools with a simple interface that can provide contact predictions to users without requiring a lengthy installation process. In addition to this, we have released our C++ implementation of the direct-coupling analysis method as a standalone software package. Both this tool and our RFcon web server are freely available to the public at http://dna.cs.miami.edu/RFcon /.

Project description:Acoustics is the primary means of sensing and communication in the ocean for humans and many marine animals. Natural fluctuations in the ocean, however, degrade these abilities in ways that have been previously difficult to forecast. Here, we address this issue by predicting sensing and communication degradation in terms of acoustic attenuation, dispersion and temporal decorrelation at typical operational ranges and frequencies in continental-shelf environments. This is done with analytic expressions derived from first physical principles. The analytic expressions provide the statistics of the acoustic field after forward propagating through an ocean waveguide containing 3-D random inhomogeneities from the independent or combined effects of rough sea-surfaces, near-sea-surface air bubbles and internal waves. The formulation also includes Doppler effects caused by the inhomogeneities' random horizontal motion, enabling modeling and prediction over a wide range of environments and frequencies. Theoretical predictions are confirmed with available acoustic measurements in several continental-shelf environments using standard oceanographic measurements for environmental support. We quantify how the acoustic signals decorrelate over timescales determined by the underlying temporal coherence of ocean dynamic processes. Surface gravity waves and near-sea-surface air bubbles decorrelate acoustic signals over seconds or less, whereas internal waves affect acoustic coherence at timescales of several to tens of minutes. Doppler spread caused by the inhomogeneities' motion further reduces acoustic temporal coherence, and becomes important at the high frequencies necessary for communication and fine-scale sensing. We also show that surface gravity waves and bubbles in high sea states can cause increasingly significant attenuation as frequency increases. The typical durations of marine mammal vocalizations that carry over great distances are found to be consistent with the coherence timescales quantified here and so avoid random distortion of signal information even by incoherent reception.

Dataset Information

Predicting the Lifetime of Dynamic Networks Experiencing Persistent Random Attacks.

Publications

Predicting the Lifetime of Dynamic Networks Experiencing Persistent Random Attacks.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets