Visual Exploration of Large Metabolic Models

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AICHEM, Michael, Tobias CZAUDERNA, Yan ZHU, Jinxin ZHAO, Matthias KLAPPERSTÜCK, Karsten KLEIN, Jian LI, Falk SCHREIBER, 2021. Visual Exploration of Large Metabolic Models. In: Bioinformatics. Oxford University Press, btab335. ISSN 0266-7061. eISSN 1367-4811. Available under: doi: 10.1093/bioinformatics/btab335

@article{Aichem2021-05-10Visua-53691, title={Visual Exploration of Large Metabolic Models}, year={2021}, doi={10.1093/bioinformatics/btab335}, issn={0266-7061}, journal={Bioinformatics}, author={Aichem, Michael and Czauderna, Tobias and Zhu, Yan and Zhao, Jinxin and Klapperstück, Matthias and Klein, Karsten and Li, Jian and Schreiber, Falk}, note={Article Number: btab335} }

Zhu, Yan Visual Exploration of Large Metabolic Models Aichem, Michael Li, Jian Klein, Karsten Zhao, Jinxin Schreiber, Falk Klein, Karsten Li, Jian Czauderna, Tobias Aichem, Michael Schreiber, Falk Motivation:<br />Large metabolic models, including genome-scale metabolic models (GSMMs), are nowadays common in systems biology, biotechnology and pharmacology. They typically contain thousands of metabolites and reactions and therefore methods for their automatic visualisation and interactive exploration can facilitate a better understanding of these models.<br /><br />Results:<br />We developed a novel method for the visual exploration of large metabolic models and implemented it in LMME (Large Metabolic Model Explorer), an add-on for the biological network analysis tool VANTED. The underlying idea of our method is to analyse a large model as follows. Starting from a decomposition into several subsystems, relationships between these subsystems are identified and an overview is computed and visualised. From this overview, detailed subviews may be constructed and visualised in order to explore subsystems and relationships in greater detail. Decompositions may either be predefined or computed, using built-in or self-implemented methods. Realised as add-on for VANTED, LMME is embedded in a domain-specific environment, allowing for further related analysis at any stage during the exploration. We describe the method, provide a use case, and discuss the strengths and weaknesses of different decomposition methods.<br /><br />Availability:<br />The methods and algorithms presented here are implemented in LMME, an open-source add-on for VANTED. LMME can be downloaded from www.cls.uni-konstanz.de/software/lmme and VANTED can be downloaded from www.vanted.org. The source code of LMME is available from GitHub, at https://github.com/LSI-UniKonstanz/lmme. 2021-05-17T08:42:06Z Czauderna, Tobias Klapperstück, Matthias Zhu, Yan eng Klapperstück, Matthias terms-of-use 2021-05-10 2021-05-17T08:42:06Z Zhao, Jinxin

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