Abstract
BackgroundTo study complex biochemical reaction networks in living cells researchers more and more rely on databases and computational methods. In order to facilitate computational approaches, visualisation techniques are highly important. Biochemical reaction networks, e.g. metabolic pathways are often depicted as graphs and these graphs should be drawn dynamically to provide flexibility in the context of different data. Conventional layout algorithms are not sufficient for every kind of pathway in biochemical research. This is mainly due to certain conventions to which biochemists/biologists are used to and which are not in accordance to conventional layout algorithms. A number of approaches has been developed to improve this situation. Some of these are used in the context of biochemical databases and make more or less use of the information in these databases to aid the layout process. However, visualisation becomes also more and more important in modelling and simulation tools which mostly do not offer additional connections to databases. Therefore, layout algorithms used in these tools have to work independently of any databases. In addition, all of the existing algorithms face some limitations with respect to the number of edge crossings when it comes to larger biochemical systems due to the interconnectivity of these. Last but not least, in some cases, biochemical conventions are not met properly.ResultsOut of these reasons we have developed a new algorithm which tackles these problems by reducing the number of edge crossings in complex systems, taking further biological conventions into account to identify and visualise cycles. Furthermore the algorithm is independent from database information in order to be easily adopted in any application. It can also be tested as part of the SimWiz package (free to download for academic users at [1]).ConclusionThe new algorithm reduces the complexity of pathways, as well as edge crossings and edge length in the resulting graphical representation. It also considers existing and further biological conventions to create a drawing most biochemists are familiar with. A lot of examples can be found on [2].
Highlights
To study complex biochemical reaction networks in living cells researchers more and more rely on databases and computational methods
Computational approaches include the usage of modelling and
In order to facilitate the understanding of the results, sophisticated visualisation techniques are required
Summary
As the starting point for our algorithm we used the implementation of the Becker et al algorithm that is based on the Java graph library YFiles [19]. The two cycles represent the two main recycling processes of enzyme intermediates and are crucial for the reaction mechanism Complex graph → draw the found cycle with a circular layout algorithm and separate the remaining nodes of the pathway into further cyclic and hierarchical subgraphs (Figure 3, line 6). In contrast to this picture our new algorithm finds two cycles joined at argininosuccinate This result is achieved by splitting nodes in found cycles which could be part of another cycle (Figure 3, line 11). Split nodes are joined and the subgraph is inspected to find connected components before the improved hierarchical layout algorithm places the nodes of this subgraph (Figure 3, line 18). The placement of labels is automatically done by the used layout algorithms of the YFiles package
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
