Multivariate Networks in the Life Sciences

Bioinformatics can be defined as the development and use of computational methods to solve problems from the life sciences. With the advent of omics technologies, the flood of biological data has been growing exponentially, and the traditional manual analysis and exploration of biological data is less and less an option. Networks are a powerful abstraction that can be utilized to structure, explore, and analyze biological data on different levels: from the atomic details to cellular processes to evolutionary relationships. In this chapter, we will introduce the basic characteristics of the different types of biological networks, give examples of actual visualizations, and discuss current challenges.

[1]  Adam J. Smith,et al.  The Database of Interacting Proteins: 2004 update , 2004, Nucleic Acids Res..

[2]  Mario Albrecht,et al.  On Open Problems in Biological Network Visualization , 2009, GD.

[3]  Astrid Junker,et al.  Visual Analysis of Transcriptome Data in the Context of Anatomical Structures and Biological Networks , 2012, Front. Plant Sci..

[4]  Astrid Junker,et al.  VANTED v2: a framework for systems biology , 2012 .

[5]  Astrid Junker,et al.  FluxMap: A VANTED add-on for the visual exploration of flux distributions in biological networks , 2012, BMC Systems Biology.

[6]  Andreas Kerren,et al.  Network Visualization for Integrative Bioinformatics , 2014, Approaches in Integrative Bioinformatics.

[7]  Hao Chen,et al.  Content-rich biological network constructed by mining PubMed abstracts , 2004, BMC Bioinformatics.

[8]  Purvesh Khatri,et al.  Onto-Tools: an ensemble of web-accessible, ontology-based tools for the functional design and interpretation of high-throughput gene expression experiments , 2004, Nucleic Acids Res..

[9]  Sarala M. Wimalaratne,et al.  The Systems Biology Graphical Notation , 2009, Nature Biotechnology.

[10]  Andreas Kerren,et al.  Toward the role of interaction in Visual Analytics , 2012, Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC).

[11]  Falk Schreiber,et al.  Creating views on integrated multidomain data , 2011, Bioinform..

[12]  Lincoln Stein,et al.  Reactome knowledgebase of human biological pathways and processes , 2008, Nucleic Acids Res..

[13]  M. Tomita,et al.  Pathway Projector: Web-Based Zoomable Pathway Browser Using KEGG Atlas and Google Maps API , 2009, PloS one.

[14]  H. V. van Leeuwen,et al.  An Ultra-High-Density, Transcript-Based, Genetic Map of Lettuce , 2013, G3: Genes, Genomes, Genetics.

[15]  Kiyoko F. Aoki-Kinoshita,et al.  From genomics to chemical genomics: new developments in KEGG , 2005, Nucleic Acids Res..

[16]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Matthias Klapperstück,et al.  VANTED v2: a framework for systems biology applications , 2012, BMC Systems Biology.

[18]  Matthew A. Hibbs,et al.  Visualization of omics data for systems biology , 2010, Nature Methods.

[19]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

[20]  Gary D Bader,et al.  BIND--The Biomolecular Interaction Network Database. , 2001, Nucleic acids research.

[21]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.