Visualizing regulatory interactions in metabolic networks

<p>Abstract</p> <p>Background</p> <p>Direct visualization of data sets in the context of biochemical network drawings is one of the most appealing approaches in the field of data evaluation within systems biology. One important type of information that is very helpful in interpreting and understanding metabolic networks has been overlooked so far. Here we focus on the representation of this type of information given by the strength of regulatory interactions between metabolite pools and reaction steps.</p> <p>Results</p> <p>The visualization of such interactions in a given metabolic network is based on a novel concept defining the regulatory strength (RS) of effectors regulating certain reaction steps. It is applicable to any mechanistic reaction kinetic formula. The RS values are measures for the strength of an up- or down-regulation of a reaction step compared with the completely non-inhibited or non-activated state, respectively. One numerical RS value is associated to any effector edge contained in the network. The RS is approximately interpretable on a percentage scale where 100% means the maximal possible inhibition or activation, respectively, and 0% means the absence of a regulatory interaction. If many effectors influence a certain reaction step, the respective percentages indicate the proportion in which the different effectors contribute to the total regulation of the reaction step. The benefits of the proposed method are demonstrated with a complex example system of a dynamic <it>E. coli </it>network.</p> <p>Conclusion</p> <p>The presented visualization approach is suitable for an intuitive interpretation of simulation data of metabolic networks under dynamic as well as steady-state conditions. Huge amounts of simulation data can be analyzed in a quick and comprehensive way. An extended time-resolved graphical network presentation provides a series of information about regulatory interaction within the biological system under investigation.</p

Interactive visualization of metabolic networks using virtual reality

A combination of graph layouts in 3D space, interactive computer graphics, and virtual reality (VR) can increase the size of understandable networks for metabolic network visualization. Two models, the directed graph and the compound graph, were used to represent a metabolic network. The directed graph, or nonhierarchical visualization, considers the adjacency relationships. For the nonhierarchical visualization, the weighted GEM-3D layout was adopted to emphasize the reactions among metabolite nodes. The compound graph, or hierarchical visualization, explicitly takes the hierarchical relationships like the pathway-molecule hierarchy or the compartment-molecule hierarchy into consideration to improve the performance and perception. An algorithm was designed, which combines the hierarchical force model with the simulated annealing method, to efficiently generate an effective layout for the compound graph. A detail-on-demand method improved the rendering performance and perception of the hierarchical visualization. The directed graph was also used to represent a sub-network composed of reactions of interest (ROIs), which reveal reactions involving a specific node. The fan layout was proposed for ROIs focusing on a metabolite node. The radial layout was adopted for ROIs focusing on a gene node. Graphics scenes were constructed for the network. The shapes and material properties of geometric objects, such as colors, transparencies, and textures, can encode biological properties, such as node names, reaction edge types, etc. Graphics animations like color morph, shape morph, and edge vibration were used to superimpose gene expression profiling data to the network. Interactions for an effective visualization were defined and implemented using VR interfaces. A pilot usability study and some qualitative comparisons were conducted to show potential advantages of stereoscopic VR for metabolic network visualization

Representing experimental biological data in metabolic networks

This paper describes a novel approach to representing experimental biological data in metabolic networks. The aim is to allow biologists to visualise and analyse the data in the context of the underlying processes. Biological networks can be modelled as graphs and visualised using graph drawing methods. We present a general method for mapping experimental data onto nodes and edges of a graph and to visualise the data-enriched networks in 2 dimensions such that the data is easy to understand. Our focus is on time series data occurring during developmental analysis. We demonstrate the utility of our approach by a real world example from the seed development of barley (Hordeum vulgare)