A new dynamical layout algorithm for complex biochemical reaction networks

<p>Abstract</p> <p>Background</p> <p>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. Bio...

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Main Authors: Kummer Ursula, Wegner Katja
Format: Article
Language:English
Published: BMC 2005-08-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/6/212
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spelling doaj-ab2ce8f846054068a8fa84d4570ab9c52020-11-24T22:59:02ZengBMCBMC Bioinformatics1471-21052005-08-016121210.1186/1471-2105-6-212A new dynamical layout algorithm for complex biochemical reaction networksKummer UrsulaWegner Katja<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>).</p> <p>Conclusion</p> <p>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 <abbrgrp><abbr bid="B2">2</abbr></abbrgrp>.</p> http://www.biomedcentral.com/1471-2105/6/212
collection DOAJ
language English
format Article
sources DOAJ
author Kummer Ursula
Wegner Katja
spellingShingle Kummer Ursula
Wegner Katja
A new dynamical layout algorithm for complex biochemical reaction networks
BMC Bioinformatics
author_facet Kummer Ursula
Wegner Katja
author_sort Kummer Ursula
title A new dynamical layout algorithm for complex biochemical reaction networks
title_short A new dynamical layout algorithm for complex biochemical reaction networks
title_full A new dynamical layout algorithm for complex biochemical reaction networks
title_fullStr A new dynamical layout algorithm for complex biochemical reaction networks
title_full_unstemmed A new dynamical layout algorithm for complex biochemical reaction networks
title_sort new dynamical layout algorithm for complex biochemical reaction networks
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2005-08-01
description <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>).</p> <p>Conclusion</p> <p>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 <abbrgrp><abbr bid="B2">2</abbr></abbrgrp>.</p>
url http://www.biomedcentral.com/1471-2105/6/212
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