Research Article Correlation-Based Network Generation, Visualization, and Analysis as a Powerful Tool in Biological Studies: A Case Study in Cancer Cell Metabolism Albert Batushansky, 1 David Toubiana, 2 and Aaron Fait 1 1 Te Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, 84990 Midreshet Ben-Gurion, Israel 2 Telekom Innovation Laboratories, Department of Information Systems Engineering, Ben-Gurion University of the Negev, 84105 Beer Sheva, Israel Correspondence should be addressed to Albert Batushansky; batushanskya@missouri.edu Received 11 May 2016; Revised 3 August 2016; Accepted 18 August 2016 Academic Editor: Shigehiko Kanaya Copyright © 2016 Albert Batushansky et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In the last decade vast data sets are being generated in biological and medical studies. Te challenge lies in their summary, complexity reduction, and interpretation. Correlation-based networks and graph-theory based properties of this type of networks can be successfully used during this process. However, the procedure has its pitfalls and requires specifc knowledge that ofen lays beyond classical biology and includes many computational tools and sofware. Here we introduce one of a series of methods for correlation-based network generation and analysis using freely available sofware. Te pipeline allows the user to control each step of the network generation and provides fexibility in selection of correlation methods and thresholds. Te pipeline was implemented on published metabolomics data of a population of human breast carcinoma cell lines MDA-MB-231 under two conditions: normal and hypoxia. Te analysis revealed signifcant diferences between the metabolic networks in response to the tested conditions. Te network under hypoxia had 1.7 times more signifcant correlations between metabolites, compared to normal conditions. Unique metabolic interactions were identifed which could lead to the identifcation of improved markers or aid in elucidating the mechanism of regulation between distantly related metabolites induced by the cancer growth. 1. Introduction Advanced technology methods for high-throughput bio- logical studies, such as metabolomics and transcriptomics developed during the last decades, are successfully applied in biomedical research [1], plant studies [2], and micro- biology [3]. Te wide use of these technologies led to the accumulation of data on biological processes at their multiple levels (metabolic, genetic, enzymatic, physiological, phenotypical, etc.) and called for the development of tools to ease the visualization, analysis, and interpretation of an ofen complex and multidimensional matrix. Furthermore, the readily available “omics” technologies in biological lab- oratories prompted biologists to enter a feld ofen needing extensive computational knowhow and led to the increased interest in biological interaction networks [4]. Tus, in the recent decades networks describing cellular processes were generated for human [5], yeast [6], and plants [7]. Networks can be presented as graphs, that is, a set of vertices (V) connected by edges (E), and consequently can be analyzed using graph theory, an approach that has been increasingly implemented in biological studies during the last decade. It is commonly accepted that graph theory as a scientifc discipline was frst used by the Swiss mathematician Leonhard Euler in 1735-1736, tackling the K¨ onigsberg bridge problem. Later, in the 19th and 20th centuries, graph theory was formulated and eventually introduced for applied felds, such as physics, computer science, and biology [8]. Today, graph theory consists of many tens of basic defnitions and properties [9]. Te understanding of the biological networks lies in the nature of the vertices and edges between them; that is, the vertices may represent one of the components of the Hindawi Publishing Corporation BioMed Research International Volume 2016, Article ID 8313272, 9 pages http://dx.doi.org/10.1155/2016/8313272