Biomolecular Networks: Methods and Applications in Systems Biology (Wiley Series in Bioinformatics)

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In its simplest form, each node is placed on the circumference of a circle and links are drawn as straight-line segments between them.


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More complicated versions attempt to order the nodes to uncover network symmetries and other versions place nodes on multiple concentric circles. The purpose of this layout is to highlight the the highly connected parts of the network and show how they relate to the remainder of the network e.

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S1a—c in the Supplementary Material. Directed edges are particularly important when visualizing regulatory networks. One approach to visualizing draws nodes on a series of horizontal lines so that edges are directed from nodes on lower horizontal lines to nodes on higher horizontal lines. This approach, invented by Sugiyama et al.

An example from CellDesigner is shown in Figure S2b. The drawings are also known as spring embeddings Eades, ; Frick et al. Force-directed algorithms attempt to place nodes so that all forces are in equillibrium. This approach is quite popular because the algorithms are simple to implement, produce relatively good drawings, and are easy tweak for specific applications. In fact, nearly every network visualization tool implements the version described by Frick, Sander and Wang Frick et al.

First International Symposium on Symbolic Systems Biology (ISSSB'11)

The main drawback of force-directed algorithms is that they can require a significant amount of time before converging to equilibrium. Fortunately, these algorithms are often easy to visually animate so the user can watch the network model incrementally approach equillibrium and perhaps terminate the algorithm when a good drawing it obtained. As the name suggests, simulated annealing methods model problem space as a set of states each with an associated energy so that low energy states correspond to potential solutions.

GeneWays Rzhetsky et al. Grid Layout Li and Kurata, uses a related method and shows how, for a yeast network, their method seems to spacially cluster functionally related nodes. The main drawback of simulated annealing algorithms is that they tend to be slow even in comparison with force-directed methods. Consequently, there are practical limits on the size of networks to which they can be applied. All of the above mentioned layouts assume a graphical network model where interactions are between exactly two interactors. Although these simple models have been shown to yield biological insight, they are incapable of modeling more complicated biological relationships that involve more than two interactors like protein complexes, relationships that depend on external factors like cell state, or regulatory circuits.

Unfortunately, extending current visualizations to handle this added complexity is not straight-forward. Consequently, successful visualization depends on exploiting domain-specific knowledge to reduce difficult general problems to something more manageable. There have been a few successful attempts in this direction:.


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  8. Both Pathway Studio Nikitin et al. In particular, both systems partition the drawing space into regions corresponding to the subcellular localizations and then search for layouts where nodes are forcibly constrained to their respective locations. Both systems make use of modified force-directed algorithms to achieve this. Pathway Studio uses representative cartoons as backgrounds for each region in order to improve readability.

    Two other tools, Cell Illustrator Nagasaki et al. For an example, see Figure 1. Some improvement to this functionality is warranted. VisANT, e. Unfortunately, these windows cover nearby neighbors making it difficult to see how the composite node members relate to the greater network. When the user attempts to expand a node in Patika e. S4 , there must be space for the node to be expanded in the drawing, otherwise Patika will not expand the node.

    Hierarchical clusters of the nodes or edges can be very useful for obtaining simplified visualizations of large, complex networks. Schwikowski et al. Similar to quotient graphs e. Bourqui et al. More general hierarchical clusters of proteins exist, including, e. The SCOP hierarchy contains several levels of protein domain similarity differentiated by increasing specificity: class, fold, superfamily, family, protein and species. Lappe et al. Edge thickness between superfamilies indicates the number of links between proteins in each superfamily.

    A recent Cytoscape plugin, GenePro Vlasblom et al. Given a protein interaction network and a predefined clustering on its nodes, GenePro initially presents the user with the most abstract view and then allows the user to expand clusters in a new window to see cluster members. GenePro can also render nodes as pie charts showing the fractions of proteins sharing a common feature. Most tools do not attempt to visualize time series data. Those that do, e. BioTapestry Longabaugh et al.

    Mechanistic modeling of genome scale molecular interaction networks

    Unfortunately, this approach fails to take advantage of the reduced network size at each time point and the small number of changes from one time point to the next. Later, the user may click the node again to hide those neighbors. The best tools provide animations from one network view to another so that the user can easily maintain a mental mapping from the previous to the current view. Currently, only a few tools including GeneWays Rzhetsky et al. Making use of higher dimensions is difficult because users are generally viewing the network on a two-dimensional screen.

    Consequently, dynamic navigation is a necessity and is often awkward without specialized equipment. In addition, graphical complexity increases because network entities must not only represent data but also simulate distance from the user. In Bioinformatics, these are more commonly called clustergrams, heatmaps with dendrograms, or cluster maps. Here, each node corresponds to exactly one row and one column in the matrix and the intersection of a row and column is colored to represent the existence or strength of the link between the corresponding nodes. Unfortunately, general-purpose orderings are rarely sufficient for specific biological applications so there has been some recent investigation of support for computing user-assisted orderings e.

    Henry and Fekete, Unfortunately, the lack of standards describing typical biological objects results in the need for each visualization to come equipped with a legend describing symbols and colors. In some cases, the use of such node and edge attributes actually detracts from readability. This has motivated the development of informal standards that have evolved via imitation and the popularity of some tools.

    For example, elements of the Pathway Studio layout style has migrated into the BiologicalNetworks package. However, concern about a lack of formal standards has been increasing Klipp et al. Cook et al. Unfortunately, according to Kohn et al. For example, in the interest of visual compactness, MIM diagrams do not specify an order for steps in a reaction.

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    Biomolecular Networks: Methods and Applications in Systems Biology

    Kohn et al. Others however Kurata et al. Kitano defines an alternative graphical notation called Systems Biology Graphical Notation SBGN that uses state-transition diagrams to model regulatory networks. In constrast to MIM diagrams, SBGN diagrams do enforce an event sequence and are consequently less compact because they may contain multiple nodes for a single molecule.

    Figure S2 shows an example from CellDesigner. There are currently few network tools designed for both the visualization of biological networks and the analysis of these networks. Clearly, a more optimal choice would be a network tool that supports both visualization and analysis, with a seamless integration between these two procedures.

    References

    BiologicalNetworks is an early leader here allowing pathway visualizations from queries that combine gene expression data analysis with simple topological patterns. Several specialized tools exist for the integration of very specific types of visualization and data. Wilmascope Dwyer et al. GridLayout Li and Kurata, highlights functionally related nodes by placing them in roughly the same regions of the drawing. A large number of tools Dahlquist et al. However, in many cases the functionality offered by the biological network tool is insufficient or cumbersome for the task at hand.

    Several tools offer various means for third parties to develop new functionality and integrate it directly within the tool. Plugins are an important way for advanced users to customize and extend an application. In fact, all three of these tools are based on a somewhat generic software design in order to stimulate a community of third party develops capable of expanding their system to address a greater range of biological applications.

    Plugins require however, a significant amount of effort and expertise to create, and thus do not represent a feasible avenue for tailoring systems to meet the needs of a particular scientific application for most labs. Scripting and query languages can provide a convenient trade-off between the power and flexibility of plugins whilst conserving the convenience of features available through graphic user interfaces.

    Currently, Pathway Studio offers a wizard interface for creating very simple network and data queries and only BiologicalNetworks provides a language interface for expressing such queries. The GUESS user interface consists of a component for network visualization and a component for entering commands in the scripting language. The language allows for the analysis and modification of network layouts, however, it is sufficiently simple that users do not require previous programming experience.

    In addition, it is relatively easy to extend its functionality by creating interfaces to other libraries such as the R statistical library. Currently, GUESS is more widely used in the social network community but has been used for biological networks. To the best of our knowledge, only Pathway Studio supports a multi-user environment that allows users to set sharing permissions on data and computed results. Most of the biological network tools are distributed with pre-formated versions of popular interaction databases [e.

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