We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Knowing the set of physical protein-protein interactions (PPIs) that occur in a particular context-a tissue, disease, or other condition-can provide valuable insights into key research questions. However, while the number of identified human PPIs is expanding rapidly, context information remains limited, and for most non-human species context-specific networks are completely unavailable. The Integrated Interactions Database (IID) provides one of the most comprehensive sets of context-specific human PPI networks, including networks for 133 tissues, 91 disease conditions, and many other contexts. Importantly, it also provides context-specific networks for 17 non-human species including model organisms and domesticated animals. These species are vitally important for drug discovery and agriculture. IID integrates interactions from multiple databases and datasets. It comprises over 4.8 million PPIs annotated with several types of context: tissues, subcellular localizations, diseases, and druggability information (the latter three are new annotations not available in the previous version). This update increases the number of species from 6 to 18, the number of PPIs from ∼1.5 million to ∼4.8 million, and the number of tissues from 30 to 133. IID also now supports topology and enrichment analyses of returned networks. IID is available at http://ophid.utoronto.ca/iid.

/pdf/iid-2018-update-context-specific-physical-protein-protein-6hxxikiznv.pdf

IID 2018 update: context-specific physical protein-protein interactions in human, model organisms and domesticated species.

“Α picture is worth a thousand words.” This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion of capturing complex ideas using images is very appealing, would 1000 words be enough to describe the unknown in a research field such as the life sciences? Life sciences is one of the biggest generators of enormous datasets, mainly as a result of recent and rapid technological advances; their complexity can make these datasets incomprehensible without effective visualization methods. Here we discuss the past, present and future of genomic and systems biology visualization. We briefly comment on many visualization and analysis tools and the purposes that they serve. We focus on the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts, and also comment on the future human-computer interaction trends that would enable for enhancing visualization further.

https://link.springer.com/content/pdf/10.1186%2Fs13742-015-0077-2.pdf

Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future

Network biology has the capability to integrate, represent, interpret, and model complex biological systems by collectively accommodating biological omics data, biological interactions and associations, graph theory, statistical measures, and visualizations. Biological networks have recently been shown to be very useful for studies that decipher biological mechanisms and disease etiologies and for studies that predict therapeutic responses, at both the molecular and system levels. In this review, we briefly summarize the general framework of biological network studies, including data resources, network construction methods, statistical measures, network topological properties, and visualization tools. We also introduce several recent biological network applications and methods for the studies of rare diseases.

https://www.mdpi.com/2073-4425/10/10/797/pdf

Biological Network Approaches and Applications in Rare Disease Studies.

The genetic, proteomic, disease and pharmacological studies have generated rich data in protein interaction, disease regulation and drug activities useful for systems-level study of the biological, disease and drug therapeutic processes. These studies are facilitated by the established and the emerging computational methods. More recently, the network descriptors developed in other disciplines have become more increasingly used for studying the protein-protein, gene regulation, metabolic, disease networks. There is an inadequate coverage of these useful network features in the public web servers. We therefore introduced upto 313 literature-reported network descriptors in PROFEAT web server, for describing the topological, connectivity and complexity characteristics of undirected unweighted (uniform binding constants and molecular levels), undirected edge-weighted (varying binding constants), undirected node-weighted (varying molecular levels), undirected edge-node-weighted (varying binding constants and molecular levels) and directed unweighted (oriented process) networks. The usefulness of the PROFEAT computed network descriptors is illustrated by their literature-reported applications in studying the protein-protein, gene regulatory, gene co-expression, protein-drug and metabolic networks. PROFEAT is accessible free of charge at http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi.

/pdf/a-protein-network-descriptor-server-and-its-use-in-studying-4voi2nl11e.pdf

A protein network descriptor server and its use in studying protein, disease, metabolic and drug targeted networks

Network visualization tools offer features enabling a variety of analyses to satisfy diverse requirements. Considering complexity and diversity of data and tasks, there is no single best layout, no single best file format or visualization tool: one size does not fit all. One way to cope with these dynamics is to support multiple scenarios and workflows. NAViGaTOR (Network Analysis, Visualization & Graphing TORonto) offers a complete system to manage diverse workflows from one application. It allows users to manipulate large graphs interactively using an innovative graphical user interface (GUI) and through fast layout algorithms with a small memory footprint. NAViGaTOR facilitates integrative network analysis by supporting not only visualization but also visual data mining.

/pdf/navigator-large-scalable-and-interactive-navigation-and-3ijlw9jay7.pdf

NAViGaTOR: Large Scalable and Interactive Navigation and Analysis of Large Graphs

Proceedings: AACR 103rd Annual Meeting 2012‐‐ Mar 31‐Apr 4, 2012; Chicago, IL

BACKGROUND: Most cancers lack any effective early disease markers, prognostic and predictive signatures. We fail treating cancer due to multiple ways cancer initiates and develops treatment resistance. While drug modes of action are complex and poorly understood, using Comparative Toxicogenomics Database is an effective way to identify drug combinations. Integrating signatures with deregulated network information may lead to identifying novel treatment option for individual patients. APPROACH: A systematic graph analysis was used to extract network structure differences between normal and tumor patient samples in non-small cell lung cancer. Three gene expression datasets with 27 squamous cell carcinoma, 129 adenocarcinoma, 20 large cell carcinoma and 141 normal samples, and 18 prognostic non-small cell lung cancer gene signatures were used to construct normal and tumor co-expression graphs. RESULTS: We enumerated all 5-node graphlets in normal and tumor graphs, and separated them into 3 categories: unique for normal graph, unique for tumor graph, or present in both. We further focused on subgraphs with the same membership across all 3 datasets and unique to tumor graph. Using gene enrichment analysis with a hypergeometric test we identified 9 subgraphs significantly enriched in the term “regulation of lymphocyte activation” (p 0.9 (11 interactions) and 0.8 (2 interactions). CONCLUSIONS: Systematic integration and network analysis of non-small cell lung signatures identifies potential treatment options and insights to the difference in the underlying wiring related to immune system, an emerging hallmark of cancer.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4912. doi:1538-7445.AM2012-4912

Abstract 4912: Systematic, comparative network analysis on non-small cell lung cancer

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NAViGaTOR: Large Scalable and Interactive Navigation and Analysis of Large Graphs

Abstract 4912: Systematic, comparative network analysis on non-small cell lung cancer