Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles

Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing this reconstruction problem. However, such data can be limited in size and/or are expensive to acquire. On the other hand, observational data of the organism in steady state (e.g. wild-type) are more readily available, but their informational content is inadequate for the task at hand. We develop a computational approach to appropriately utilize both data sources for estimating a regulatory network. The proposed approach is based on a three-step algorithm to estimate the underlying directed but cyclic network, that uses as input both perturbation screens and steady state gene expression data. In the first step, the algorithm determines causal orderings of the genes that are consistent with the perturbation data, by combining an exhaustive search method with a fast heuristic that in turn couples a Monte Carlo technique with a fast search algorithm. In the second step, for each obtained causal ordering, a regulatory network is estimated using a penalized likelihood based method, while in the third step a consensus network is constructed from the highest scored ones. Extensive computational experiments show that the algorithm performs well in reconstructing the underlying network and clearly outperforms competing approaches that rely only on a single data source. Further, it is established that the algorithm produces a consistent estimate of the regulatory network.Comment: 24 pages, 4 figures, 6 table

Taming the 800 pound gorilla: The rise and decline of NTP DDoS attacks

Distributed Denial of Service (DDoS) attacks based on Network Time Protocol (NTP) amplification, which became prominent in December 2013, have received significant global attention. We chronicle how this attack rapidly rose from obscurity to become the dominant large DDoS vector. Via the lens of five distinct datasets, we characterize the advent and evolution of these attacks. Through a dataset that measures a large fraction of global Internet traffic, we show a three order of magnitude rise in NTP. Using a large darknet, we observe a similar rise in global scanning activity, both malicious and research. We then dissect an active probing dataset, which reveals that the pool of amplifiers totaled 2.2M unique IPs and includes a small number of mega amplifiers, servers that replied to a single tiny probe packet with gigabytes of data. This dataset also allows us, for the first time, to analyze global DDoS attack victims (including ports attacked) and incidents, where we show 437K unique IPs targeted with at least 3 trillion packets, totaling more than a petabyte. Finally, ISP datasets shed light on the local impact of these attacks. In aggregate, we show the magnitude of this major Internet threat, the community\u27s response, and the effect of that response

1 Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles

# These authors contributed equally to this work

Understanding IPv6 Internet Background Radiation

We report the results of a study to collect and analyze IPv6 Internet background radiation. This study, the largest of its kind, collects unclaimed traffic on the IPv6 Internet by announcing five large /12 covering prefixes; these cover the majority of allocated IPv6 space on today’s Internet. Our analysis characterizes the nature of this traffic across regions, over time, and by the allocation and routing status of the intended destinations, which we show help to identify the causes of this traffic. We compare results to unclaimed traffic in IPv4, and highlight case studies that explain a large fraction of the data or highlight notable properties. We describe how announced covering prefixes differ from traditional network telescopes, and show how this technique can help both network operators and the research community identify additional potential issues and misconfigurations in this critical Internet transition period

Impact of increasing number of orderings used in the RIPE algorithm.

<p>Average performance measures, in percentages, for RIPE in the synthetic network .</p