VANESA - A Software Application for the Visualization and Analysis of Networks in Systems Biology Applications

Author:

Brinkrolf Christoph1,Janowski Sebastian Jan2,Kormeier Benjamin1,Lewinski Martin1,Hippe Klaus1,Borck Daniela1,Hofestädt Ralf1

Affiliation:

1. 1Bielefeld University, Faculty of Technology, Bioinformatics Department, D-33501Bielefeld, Germany

2. 2Bielefeld University, Faculty of Technology, Bioinformatics Department, D-33501Bielefeld, Germany

Abstract

Summary VANESA is a modeling software for the automatic reconstruction and analysis of biological networks based on life-science database information. Using VANESA, scientists are able to model any kind of biological processes and systems as biological networks. It is now possible for scientists to automatically reconstruct important molecular systems with information from the databases KEGG, MINT, IntAct, HPRD, and BRENDA. Additionally, experimental results can be expanded with database information to better analyze the investigated elements and processes in an overall context. Users also have the possibility to use graph theoretical approaches in VANESA to identify regulatory structures and significant actors within the modeled systems. These structures can then be further investigated in the Petri net environment of VANESA. It is platform-independent, free-of-charge, and available at http://vanesa.sf.net.

Publisher

Walter de Gruyter GmbH

Subject

General Medicine

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Editorial: Petri nets for cellular process modelling;Biosystems;2022-02

2. Data Warehousing of Life Science Data;Integrative Bioinformatics;2022

3. VANESA: An open-source hybrid functional Petri net modeling and simulation environment in systems biology;Biosystems;2021-12

4. OMPetri;Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology - ICBBT '18;2018

5. PROFEAT Update: A Protein Features Web Server with Added Facility to Compute Network Descriptors for Studying Omics-Derived Networks;Journal of Molecular Biology;2017-02

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