Wegner Katja, Kummer Ursula
Bioinformatics and Computational Biochemistry, EML Research, Schloss-Wolfsbrunnenweg 33, D-69118 Heidelberg, Germany.
BMC Bioinformatics. 2005 Aug 26;6:212. doi: 10.1186/1471-2105-6-212.
To 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.
Out 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).
The 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.
为了研究活细胞中的复杂生化反应网络，研究人员越来越依赖数据库和计算方法。为了便于采用计算方法，可视化技术非常重要。生化反应网络，例如代谢途径，通常被描绘为图形，并且这些图形应该动态绘制，以便在不同数据的背景下提供灵活性。传统的布局算法对于生化研究中的每种途径都不够充分。这主要是由于生物化学家/生物学家习惯的某些惯例，而这些惯例与传统布局算法不一致。已经开发了许多方法来改善这种情况。其中一些方法在生化数据库的背景下使用，并或多或少地利用这些数据库中的信息来辅助布局过程。然而，可视化在建模和模拟工具中也变得越来越重要，而这些工具大多不提供与数据库的额外连接。因此，这些工具中使用的布局算法必须独立于任何数据库工作。此外，由于较大生化系统的互连性，所有现有算法在处理较大生化系统时，在边交叉数量方面都面临一些限制。最后但同样重要的是，在某些情况下，生化惯例没有得到妥善遵循。
出于这些原因，我们开发了一种新算法，该算法通过减少复杂系统中的边交叉数量来解决这些问题，同时考虑进一步的生物学惯例以识别和可视化循环。此外，该算法独立于数据库信息，以便可以轻松地在任何应用中采用。它也可以作为SimWiz软件包的一部分进行测试（学术用户可在1免费下载）。
新算法降低了途径的复杂性，以及所得图形表示中的边交叉和边长。它还考虑了现有的和进一步的生物学惯例，以创建大多数生物化学家都熟悉的图形。在2上可以找到很多示例。
