Topological evolution of networks : case studies in the US airlines and language Wikipedias

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 195-198).This thesis examines the topology of engineering systems and how that topology changes over time. Topology refers to the relative arrangement and connectivity of the elements of a system. We review network theory relevant to topological evolution and use graph-theoretical methods to analyze real systems, represented as networks. Using existing graph generative models, we develop a profile of canonical graphs and tools to compare a real network to that profile. The developed metrics are used to track topology changes over the history of real networks. This theoretical work is applied to two case studies. The first discusses the US airline industry in terms of routes. We study various airlines and segments of the industry statistically and find commonly occurring patterns. We show that there are topology transitions in the history of airlines in the period 1990-2007. Most airline networks have similar topology and historical patterns, with the exception of Southwest Airlines. We show mathematically that Southwest's topology is different. We propose two heuristic growth models, one featuring hub-seeding derived from the underlying patterns of evolution of JetBlue Airways and one featuring local interconnectedness, derived from the patterns of growth of Southwest. The two models match the topologies of these airlines better than canonical models over time. Results suggest that Southwest is becoming more centralized, closer to the hub-spoke topologies of other airlines. Our second case study discusses the growth of language Wikipedia networks, where nodes are articles and hyperlinks are the connections between them. These knowledge networks are subject to different constraints than air transportation systems. The topology of these networks and their growth principles are completely different. Most Wikipedias studied grow by coalescence, with multiple disconnected thematic clusters of pages growing separately and over time, converging to a giant connected component via weak links. These topologies start out as simple trees, and coalesce into sparse hierarchical structures with random interlinking. One striking exception is the history of the Chinese Wikipedia, which grows fully connected from its inception. We discuss these patterns of growth comparatively across Wikipedias, and in general, compared to airline networks. Our work suggests that complex engineering systems are hybrids of pure canonical forms and that they undergo distinct phase transitions during their evolution. We find commonality among systems and uncover important differences by learning from the exceptions.by Gergana Assenova Bounova.Ph.D

Graph-theoretical consideration in the design of complex engineering systems for robustness and scalability

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005.Includes bibliographical references (p. 113-114).(cont.) and (2) a forward approach which achieves optimality at the start and grows the system using an optimization technique. We systematically compare the two staging techniques in the context of telescope arrays and evaluate the hypothesis that the backward approach is superior for telescope arrays because it incorporates knowledge of the desired future end state of the system. The modelling framework introduced is applicable to various engineering problems susceptible to network representation, including biological systems, telecommunication networks, transportation routes, and space exploration systems.System optimization for extensibility and robustness is a fundamental challenge of engineering disciplines. Traditional approaches have aimed to optimize cost and performance of a system at a given point in its lifespan. However, as systems evolve with increasing resources and load, system extensibility has to be included in the earliest stages of planning and deployment. In this thesis, we study the staged deployment of large telescope array configurations as an optimization problem subject to cost, performance and network robustness. The LOFAR (LOw Frequency ARray) is the world's largest telescope array, deploying in its full design 25000 antennas over 350kin in diameter in Northern Europe. These are deployed in clusters, and planned to be built in stages, with current funding allowing for 15000 arrays over 100km. This new generation of telescope arrays requires new system design principles and modelling techniques. We develop a staged optimization framework for modelling network behavior, robustness, and extensibility. We represent large telescope arrays as generalized networks, with nodes as the telescope stations and edges as the cable links between them. We model network design and growth with both graph-theoretical and physical metrics pertaining to imaging properties of each array configuration. Additionally, we model the probability of failure of each topology, both from environmental conditions and random events along the baseline. We make recommendations about the best cost-performance and robustness trade-off configurations. We introduce two staging principles for system deployment and configuration: (1) a backward approach, in which the design is optimized in the future and scaled down for the initial stages,by Gergana Assenova Bounova.S.M

Large variety in a panel of human colon cancer organoids in response to EZH2 inhibition

EZH2 inhibitors have gained great interest for their use as anti-cancer therapeutics. However, most research has focused on EZH2 mutant cancers and recently adverse effects of EZH2 inactivation have come to light. To determine whether colorectal cancer cells respond to EZH2 inhibition and to explore which factors influence the degree of response, we treated a panel of 20 organoid lines derived from human colon tumors with different concentrations of the EZH2 inhibitor GSK126. The resulting responses were associated with mutation status, gene expression and responses to other drugs. We found that the response to GSK126 treatment greatly varied between organoid lines. Response associated with the mutation status of ATRX and PAX2, and correlated with BIK expression. It also correlated well with response to Nutlin-3a which inhibits MDM2-p53 interaction thereby activating p53 signaling. Sensitivity to EZH2 ablation depended on the presence of wild type p53, as tumor organoids became resistant when p53 was mutated or knocked down. Our exploratory study provides insight into which genetic factors predict sensitivity to EZH2 inhibition. In addition, we show that the response to EZH2 inhibition requires wild type p53. We conclude that a subset of colorectal cancer patients may benefit from EZH2-targeting therapies

Quantifying ChIP-seq data:A spiking method providing an internal reference for sample-to-sample normalization

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) experiments are widely used to determine, within entire genomes, the occupancy sites of any protein of interest, including, for example, transcription factors, RNA polymerases, or histones with or without various modifications. In addition to allowing the determination of occupancy sites within one cell type and under one condition, this method allows, in principle, the establishment and comparison of occupancy maps in various cell types, tissues, and conditions. Such comparisons require, however, that samples be normalized. Widely used normalization methods that include a quantile normalization step perform well when factor occupancy varies at a subset of sites, but may miss uniform genome-wide increases or decreases in site occupancy. We describe a spike adjustment procedure (SAP) that, unlike commonly used normalization methods intervening at the analysis stage, entails an experimental step prior to immunoprecipitation. A constant, low amount from a single batch of chromatin of a foreign genome is added to the experimental chromatin. This "spike" chromatin then serves as an internal control to which the experimental signals can be adjusted. We show that the method improves similarity between replicates and reveals biological differences including global and largely uniform changes

Consensus molecular subtype classification of colorectal adenomas

Consensus molecular subtyping is an RNA expression-based classification system for colorectal cancer (CRC). Genomic alterations accumulate during CRC pathogenesis, including the premalignant adenoma stage, leading to changes in RNA expression. Only a minority of adenomas progress to malignancies, a transition that is associated with specific DNA copy number aberrations or microsatellite instability (MSI). We aimed to investigate whether colorectal adenomas can already be stratified into consensus molecular subtype (CMS) classes, and whether specific CMS classes are related to the presence of specific DNA copy number aberrations associated with progression to malignancy. RNA sequencing was performed on 62 adenomas and 59 CRCs. MSI status was determined with polymerase chain reaction-based methodology. DNA copy number was assessed by low-coverage DNA sequencing (n = 30) or array-comparative genomic hybridisation (n = 32). Adenomas were classified into CMS classes together with CRCs from the study cohort and from The Cancer Genome Atlas (n = 556), by use of the established CMS classifier. As a result, 54 of 62 (87%) adenomas were classified according to the CMS. The CMS3 ‘metabolic subtype’, which was least common among CRCs, was most prevalent among adenomas (n = 45; 73%). One of the two adenomas showing MSI was classified as CMS1 (2%), the ‘MSI immune’ subtype. Eight adenomas (13%) were classified as the ‘canonical’ CMS2. No adenomas were classified as the ‘mesenchymal’ CMS4, consistent with the fact that adenomas lack invasion-associated stroma. The distribution of the CMS classes among adenomas was confirmed in an independent series. CMS3 was enriched with adenomas at low risk of progressing to CRC, whereas relatively more high-risk adenomas were observed in CMS2. We conclude that adenomas can be stratified into the CMS classes. Considering that CMS1 and CMS2 expression signatures may mark adenomas at increased risk of progression, the distribution of the CMS classes among adenomas is consistent with the proportion of adenomas expected to progress to CRC

Overview of metrics and their correlation patterns for multiple-metric topology analysis on heterogeneous graph ensembles

This study is an overview of network topology metrics and a computational approach to analyzing graph topology via multiple-metric analysis on graph ensembles. The paper cautions against studying single metrics or combining disparate graph ensembles from different domains to extract global patterns. This is because there often exists considerable diversity among graphs that share any given topology metric, patterns vary depending on the underlying graph construction model, and many real data sets are not actual statistical ensembles. As real data examples, we present five airline ensembles, comprising temporal snapshots of networks of similar topology. Wikipedia language networks are shown as an example of a nontemporal ensemble. General patterns in metric correlations, as well as exceptions, are discussed by representing the data sets via hierarchically clustered correlation heat maps. Most topology metrics are not independent and their correlation patterns vary across ensembles. In general, density-related metrics and graph distance-based metrics cluster and the two groups are orthogonal to each other. Metrics based on degree-degree correlations have the highest variance across ensembles and cluster the different data sets on par with principal component analysis. Namely, the degree correlation, the s metric, their elasticities, and the rich club moments appear to be most useful in distinguishing topologies

Afreen Siddiqi

A Posteriori Design Change Analysis for Complex Engineering Projects Engineering changes are an inherent part of the design and development process and can play an important role in driving the overall success of the system. This work seeks to create a multidimensional understanding of change activity in large systems that can help in improving future design and development efforts. This is achieved by a posteriori analysis of design changes. It is proposed that by constructing a temporal, spatial, and financial view of change activity within and across these dimensions, it becomes possible to gain useful insights regarding the system of study. Engineering change data from the design and development of a multiyear, multibillion dollar development project of an offshore oil and gas production system is used as a case study in this work. It is shown that the results from such an analysis can be used for identifying better design and management strategies (in similar systems and projects) and for targeting design improvement in identified subsystems. The isolation and identification of change hotspots can be helpful in uncovering potential systemic design issues that may be prevalent. Similarly, strategic engineering and management decisions can be made if the major cost drivers are known