VisANT: an integrative framework for networks in systems biology - PubMed (original) (raw)

VisANT: an integrative framework for networks in systems biology

Zhenjun Hu et al. Brief Bioinform. 2008 Jul.

Abstract

The essence of a living cell is adaptation to a changing environment, and a central goal of modern cell biology is to understand adaptive change under normal and pathological conditions. Because the number of components is large, and processes and conditions are many, visual tools are useful in providing an overview of relations that would otherwise be far more difficult to assimilate. Historically, representations were static pictures, with genes and proteins represented as nodes, and known or inferred correlations between them (links) represented by various kinds of lines. The modern challenge is to capture functional hierarchies and adaptation to environmental change, and to discover pathways and processes embedded in known data, but not currently recognizable. Among the tools being developed to meet this challenge is VisANT (freely available at http://visant.bu.edu) which integrates, mines and displays hierarchical information. Challenges to integrating modeling (discrete or continuous) and simulation capabilities into such visual mining software are briefly discussed.

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Figures

Fig. 1

Fig. 1

Method-based loading & filtering of large-scale interaction data set. (I) Directly load interactions associated with method M0046 into VisANT through interaction statistics page. (II) Directly load interactions associated with method M0034 through methods table in VisANT. (III) The combined interaction network of M0034 and M0046 shown in VisANT. (IV) The interaction network with the overlap of the two data sets created using built-in filter.

Fig. 2

Fig. 2

Network of cancers rebuilt in VisANT using metagraph with subset data of cancer extracted from the work of Goh etc[47]. Each metanode (gray box) represents one type of cancer. The correlations between cancers are evaluated based on the number of shared genes. Mouse clicking a metanode will reveal its substructure: genes involved in the cancer, and their correlations to one another if any. An example of an expanded node for ovarian and endometrial cancers is shown. The original data shows that ovarian cancer of endometrial type involves three different genes (MSH6, GTBP, HNPCC5) (I) which are actually all the same gene with official name MSH6 as discovered by the Name Normalization function (II).

Fig. 3

Fig. 3

Improved readability and performance with multi-scale information integrated in pathway visualization using metagraph. Dark boxes represent the KEGG pathways; light boxes with dark border are contracted metanodes representing a group of proteins; gray boxes with light border representing the protein complex, filled dark circles represent protein and open circles represent compounds. (I) Five signaling pathways of Homo sapiens visualized using metagraph, dashed lines indicate that there are shared nodes. (II) Same number of pathways visualized as an interaction network. The size of the node is reduced to improve the readability.

Fig. 4

Fig. 4

Node duplication preserves information. (I) Segment of MAPK signal transduction pathway in yeast visualized using static image in KEGG. There are only one path from SHO1 to PBS2: SHO1→STE20→STE11→PBS2. (II) Corresponding pathway visualized in VisANT retains the same path through node duplication. (III) Visualization of pathway as interaction network lost the information of condition-dependency, while results in two different paths from SHO1 to PBS2: SHO1→STE20→STE11→PBS2 and SHO1→CDC42→ STE20→STE11→PBS2.

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