Miranda van Uitert

Large-Scale Mutagenesis in p19ARF- and p53-Deficient Mice Identifies Cancer Genes and Their Collaborative Networks

Summary p53 and p19ARF are tumor suppressors frequently mutated in human tumors. In a high-throughput screen in mice for mutations collaborating with either p53 or p19ARF deficiency, we identified 10,806 retroviral insertion sites, implicating over 300 loci in tumorigenesis. This dataset reveals 20 genes that are specifically mutated in either p19ARF-deficient, p53-deficient or wild-type mice (including Flt3, mmu-mir-106a-363, Smg6, and Ccnd3), as well as networks of significant collaborative and mutually exclusive interactions between cancer genes. Furthermore, we found candidate tumor suppressor genes, as well as distinct clusters of insertions within genes like Flt3 and Notch1 that induce mutants with different spectra of genetic interactions. Cross species comparative analysis with aCGH data of human cancer cell lines revealed known and candidate oncogenes (Mmp13, Slamf6, and Rreb1) and tumor suppressors (Wwox and Arfrp2). This dataset should prove to be a rich resource for the study of genetic interactions that underlie tumorigenesis.

Biclustering Sparse Binary Genomic Data

Genomic datasets often consist of large, binary, sparse data matrices. In such a dataset, one is often interested in finding contiguous blocks that (mostly) contain ones. This is a biclustering problem, and while many algorithms have been proposed to deal with gene expression data, only two algorithms have been proposed that specifically deal with binary matrices. None of the gene expression biclustering algorithms can handle the large number of zeros in sparse binary matrices. The two proposed binary algorithms failed to produce meaningful results. In this article, we present a new algorithm that is able to extract biclusters from sparse, binary datasets. A powerful feature is that biclusters with different numbers of rows and columns can be detected, varying from many rows to few columns and few rows to many columns. It allows the user to guide the search towards biclusters of specific dimensions. When applying our algorithm to an input matrix derived from TRANSFAC, we find transcription factors with distinctly dissimilar binding motifs, but a clear set of common targets that are significantly enriched for GO categories.

Sample-path large deviations for tandem and priority queues with Gaussian inputs

This paper considers Gaussian flows multiplexed in a queueing network. A single node being a useful but often incomplete setting, we examine more advanced models. We focus on a (two-node) tandem queue, fed by a large number of Gaussian inputs. With service rates and buffer sizes at both nodes scaled appropriately, Schilder’s sample-path large-deviations theorem can be applied to calculate the asymptotics of the overflow probability of the second queue. More specifically, we derive a lower bound on the exponential decay rate of this overflow probability and present an explicit condition for the lower bound to match the exact decay rate. Examples show that this condition holds for a broad range of frequently used Gaussian inputs. The last part of the paper concentrates on a model for a single node, equipped with a priority scheduling policy. We show that the analysis of the tandem queue directly carries over to this priority queueing system.

Insertional Mutagenesis in Mice Deficient for p15Ink4b, p16Ink4a, p21Cip1, and p27Kip1 Reveals Cancer Gene Interactions and Correlations with Tumor Phenotypes

The cyclin dependent kinase (CDK) inhibitors p15, p16, p21, and p27 are frequently deleted, silenced, or downregulated in many malignancies. Inactivation of CDK inhibitors predisposes mice to tumor development, showing that these genes function as tumor suppressors. Here, we describe high-throughput murine leukemia virus insertional mutagenesis screens in mice that are deficient for one or two CDK inhibitors. We retrieved 9,117 retroviral insertions from 476 lymphomas to define hundreds of loci that are mutated more frequently than expected by chance. Many of these loci are skewed toward a specific genetic context of predisposing germline and somatic mutations. We also found associations between these loci with gender, age of tumor onset, and lymphocyte lineage (B or T cell). Comparison of retroviral insertion sites with single nucleotide polymorphisms associated with chronic lymphocytic leukemia revealed a significant overlap between the datasets. Together, our findings highlight the importance of genetic context within large-scale mutation detection studies, and they show a novel use for insertional mutagenesis data in prioritizing disease-associated genes that emerge from genome-wide association studies.

The Asymptotic Workload Behavior of Two Coupled Queues

We consider a system of two coupled queues Q1 and Q2. When both queues are backlogged, they are each served at unit rate. However, when one queue empties, the service rate at the other queue increases. Thus, the two queues are coupled through the mechanism for dynamically sharing surplus service capacity. We derive the asymptotic workload behavior at Q1 for various scenarios where at least one of the two queues has a heavy-tailed service time distribution. First of all, we consider a situation where the traffic load at Q1 is below the nominal unit service rate. We show that if the service time distribution at Q1 is heavy-tailed, then the workload behaves exactly as if Q1 is served in isolation at a constant rate, which only depends on the service time distribution at Q2 through its mean. In addition, we establish that if the service time distribution at Q1 is exponential, then the workload distribution is either exponential or semi-exponential, depending on whether the traffic load at Q2 exceeds the nominal service rate or not. Next, we focus on a regime where the traffic load at Q1exceeds the nominal service rate, so that Q1 relies on the surplus capacity from Q2 to maintain stability. In that case, the workload distribution at Q1 is determined by the heaviest of the two service time distributions, so that Q1 may inherit potentially heavier-tailed characteristics from Q2.

A Reduced-Load Equivalence for Generalised Processor Sharing Networks with Long-Tailed Input Flows

We consider networks where traffic is served according to the Generalised Processor Sharing (GPS) principle. GPS-based scheduling algorithms are considered important for providing differentiated quality of service in integrated-services networks. We are interested in the workload of a particular flow i at the bottleneck node on its path. Flow i is assumed to have long-tailed traffic characteristics. We distinguish between two traffic scenarios, (i) flow i generates instantaneous traffic bursts and (ii) flow i generates traffic according to an on/off process. In addition, we consider two configurations of feed-forward networks. First we focus on the situation where other flows join the path of flow i. Then we extend the model by adding flows which can branch off at any node, with cross traffic as a special case. We prove that under certain conditions the tail behaviour of the workload distribution of flow i is equivalent to that in a two-node tandem network where flow i is served in isolation at constant rates. These rates only depend on the traffic characteristics of the other flows through their average rates. This means that the results do not rely on any specific assumptions regarding the traffic processes of the other flows. In particular, flow i is not affected by excessive activity of flows with ‘heavier-tailed’ traffic characteristics. This confirms that GPS has the potential to protect individual flows against extreme behaviour of other flows, while obtaining substantial multiplexing gains.

Sample-path large deviations for generalized processor sharing queues with Gaussian inputs

In this paper we consider the generalized processor sharing (GPS) mechanism serving two traffic classes. These classes consist of a large number of independent identically distributed Gaussian flows with stationary increments. We are interested in the logarithmic asymptotics or exponential decay rates of the overflow probabilities. We first derive both an upper and a lower bound on the overflow probability. Scaling both the buffer sizes of the queues and the service rate with the number of sources, we apply Schilders sample-path large deviations theorem to calculate the logarithmic asymptotics of the upper and lower bound. We discuss in detail the conditions under which the upper and lower bound match. Finally we show that our results can be used to choose the values of the GPS weights. The results are illustrated by numerical examples.

A Tandem Queue With LÉVY Input: A New Representation Of The Downstream Queue Length

textabstractIn this paper we present a new representation for the steady state distribution of the workload of the second queue in a two-node tandem network. It involves the difference of two suprema over two adjacent intervals. In case of spectrally-positive L

Large buffer asymptotics for generalized processor sharing queues with Gaussian inputs

In this paper we derive large-buffer asymptotics for a two-class Generalized Processor Sharing (GPS) model. We assume both classes to have Gaussian characteristics. We distinguish three cases depending on whether the GPS weights are above or below the average rate at which traffic is sent. First, we calculate exact asymptotic upper and lower bounds, then we calculate the logarithmic asymptotics, and finally we show that the decay rates of the upper and lower bound match. We apply our results to two special Gaussian models: the integrated Gaussian process and the fractional Brownian motion. Finally we derive the logarithmic large-buffer asymptotics for the case where a Gaussian flow interacts with an on-off flow.

Transient analysis of traffic generated by bursty sources, and its application to measurement-based admission control

The first part of the paper is devoted to a transient analysis of traffic generated by bursty sources. These sources are governed by a modulating process, whose state determines the traffic rate at which the source transmits. The class of modulating processes contains, e.g., on/off traffic sources with general on and off times (but is considerably broader). We focus on the probability of extreme fluctuations of the resulting traffic rate, or more precisely, we determine the probability of the number of sources being in the on state reaching a certain threshold, given a measurement of the number of sources in the on state t units of time ago. In particular, we derive large deviations asymptotics of this probability when the number of sources is large. These asymptotics are numerically manageable, and it is empirically verified that they lead to an overestimation of the probability of our interest. The analysis is extended to alternative measurement procedures. These procedures allow to take into account, for instance, more historic measurements than just one, possibly combined with an exponential weighting of these measurements. In the second part of the paper, we apply the asymptotic calculation methods to gain insight into the feasibility of measurement‐based admission control (MBAC) algorithms for ATM or IP networks. These algorithms attempt to regulate the networks load (to provide the customers with a sufficient Quality of Service), and at the same time achieve an acceptable utilization of the resources. An MBAC algorithm may base acceptance or rejection of a new request on the measured momentary load imposed on the switch or router; if this load is below a given threshold, the source can be admitted. We investigate whether such a scheme is robust under the possible stochastic properties of the traffic offered. Both the burst level (i.e., the distribution of the on and off times of the sources) and the call level (particularly the distribution of the call duration) are taken into account. Special attention is paid to the influence of the bursts, silences, or call durations having a distribution with a “heavy tail”.