Katharina Anna Zweig

Global microRNA level regulation of EGFR-driven cell-cycle protein network in breast cancer

The EGFR‐driven cell‐cycle pathway has been extensively studied due to its pivotal role in breast cancer proliferation and pathogenesis. Although several studies reported regulation of individual pathway components by microRNAs (miRNAs), little is known about how miRNAs coordinate the EGFR protein network on a global miRNA (miRNome) level. Here, we combined a large‐scale miRNA screening approach with a high‐throughput proteomic readout and network‐based data analysis to identify which miRNAs are involved, and to uncover potential regulatory patterns. Our results indicated that the regulation of proteins by miRNAs is dominated by the nucleotide matching mechanism between seed sequences of the miRNAs and 3′‐UTR of target genes. Furthermore, the novel network‐analysis methodology we developed implied the existence of consistent intrinsic regulatory patterns where miRNAs simultaneously co‐regulate several proteins acting in the same functional module. Finally, our approach led us to identify and validate three miRNAs (miR‐124, miR‐147 and miR‐193a‐3p) as novel tumor suppressors that co‐target EGFR‐driven cell‐cycle network proteins and inhibit cell‐cycle progression and proliferation in breast cancer.

Algorithmics of Large and Complex Networks

Network Algorithms.- Design and Engineering of External Memory Traversal Algorithms for General Graphs.- Minimum Cycle Bases and Their Applications.- A Survey on Approximation Algorithms for Scheduling with Machine Unavailability.- Iterative Compression for Exactly Solving NP-Hard Minimization Problems.- Approaches to the Steiner Problem in Networks.- A Survey on Multiple Objective Minimum Spanning Tree Problems.- Traffic Networks.- Engineering Route Planning Algorithms.- From State-of-the-Art Static Fleet Assignment to Flexible Stochastic Planning of the Future.- Traffic Networks and Flows over Time.- Communication Networks.- Interactive Communication, Diagnosis and Error Control in Networks.- Resource Management in Large Networks.- Multicast Routing and Design of Sparse Connectors.- Management of Variable Data Streams in Networks.- Models of Non-atomic Congestion Games - From Unicast to Multicast Routing.- New Data Structures for IP Lookup and Conflict Detection.- Network Analysis and Simulation.- Group-Level Analysis and Visualization of Social Networks.- Modeling and Designing Real-World Networks.- Algorithms and Simulation Methods for Topology-Aware Sensor Networks.

Survey: Cycle bases in graphs characterization, algorithms, complexity, and applications

Cycles in graphs play an important role in many applications, e.g., analysis of electrical networks, analysis of chemical and biological pathways, periodic scheduling, and graph drawing. From a mathematical point of view, cycles in graphs have a rich structure. Cycle bases are a compact description of the set of all cycles of a graph. In this paper, we survey the state of knowledge on cycle bases and also derive some new results. We introduce different kinds of cycle bases, characterize them in terms of their cycle matrix, and prove structural results and a priori length bounds. We provide polynomial algorithms for the minimum cycle basis problem for some of the classes and prove APX-hardness for others. We also discuss three applications and show that they require different kinds of cycle bases.

One plus one makes three (for social networks).

Members of social network platforms often choose to reveal private information, and thus sacrifice some of their privacy, in exchange for the manifold opportunities and amenities offered by such platforms. In this article, we show that the seemingly innocuous combination of knowledge of confirmed contacts between members on the one hand and their email contacts to non-members on the other hand provides enough information to deduce a substantial proportion of relationships between non-members. Using machine learning we achieve an area under the (receiver operating characteristic) curve () of at least for predicting whether two non-members known by the same member are connected or not, even for conservative estimates of the overall proportion of members, and the proportion of members disclosing their contacts.

A network-based method to assess the statistical significance of mild co-regulation effects.

Recent development of high-throughput, multiplexing technology has initiated projects that systematically investigate interactions between two types of components in biological networks, for instance transcription factors and promoter sequences, or microRNAs (miRNAs) and mRNAs. In terms of network biology, such screening approaches primarily attempt to elucidate relations between biological components of two distinct types, which can be represented as edges between nodes in a bipartite graph. However, it is often desirable not only to determine regulatory relationships between nodes of different types, but also to understand the connection patterns of nodes of the same type. Especially interesting is the co-occurrence of two nodes of the same type, i.e., the number of their common neighbours, which current high-throughput screening analysis fails to address. The co-occurrence gives the number of circumstances under which both of the biological components are influenced in the same way. Here we present SICORE, a novel network-based method to detect pairs of nodes with a statistically significant co-occurrence. We first show the stability of the proposed method on artificial data sets: when randomly adding and deleting observations we obtain reliable results even with noise exceeding the expected level in large-scale experiments. Subsequently, we illustrate the viability of the method based on the analysis of a proteomic screening data set to reveal regulatory patterns of human microRNAs targeting proteins in the EGFR-driven cell cycle signalling system. Since statistically significant co-occurrence may indicate functional synergy and the mechanisms underlying canalization, and thus hold promise in drug target identification and therapeutic development, we provide a platform-independent implementation of SICORE with a graphical user interface as a novel tool in the arsenal of high-throughput screening analysis.

Understanding Human Navigation Using Network Analysis

We have considered a simple word game called the word-morph. After making our participants play a stipulated number of word-morph games, we have analyzed the experimental data. We have given a detailed analysis of the learning involved in solving this word game. We propose that people are inclined to learn landmarks when they are asked to navigate from a source to a destination. We note that these landmarks are nodes that have high closeness-centrality ranking.

Drawing trees in a streaming model

We pose a new visualization challenge, asking Graph Drawing algorithms to cope with the requirements of Streaming applications. In this model a source produces a graph one edge at a time. When an edge is produced, it is immediately drawn and its placement cannot be altered. The drawing has an image persistence, that controls the lifetime of edges. If the persistence is k, an edge remains in the drawing for the time spent by the source to generate k edges, and then it fades away. In this model we study the area requirement of planar straight-line grid drawings of trees and we assess the output quality of the presented algorithms by computing the competitive ratio with respect to the best known offline algorithms.

How to Forget the Second Side of the Story: A New Method for the One-Mode Projection of Bipartite Graphs

Many relationships naturally come in a bipartite setting: authors that write articles, proteins that interact with genes, or customers that buy, rent or rate products. Often we are interested in the clustering behavior of one side of the graph, i.e., in finding groups of similar articles or products. To find these clusters, a one-mode projection is classically applied, which results in a normal graph that can then be clustered by various methods. For data with strongly skewed degree distributions, a classical one-mode projection leads to very dense graphs with little information. In this article we propose a new method for a meaningful one-mode projection of any kind of bipartite graph

Wanderer between the Worlds - Self-Organized Network Stability in Attack and Random Failure Scenarios

B

A fixed degree sequence model for the one-mode projection of multiplex bipartite graphs

to a sparse general graph