Visual exploration of large metabolic models

Abstract Motivation Large metabolic models, including genome-scale metabolic models, are nowadays common in systems biology, biotechnology and pharmacology. They typically contain thousands of metabolites and reactions and therefore methods for their automatic visualization and interactive explorati...

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Bibliographic Details
Published in:Bioinformatics 2021-12, Vol.37 (23), p.4460-4468
Main Authors: Aichem, Michael, Czauderna, Tobias, Zhu, Yan, Zhao, Jinxin, Klapperstück, Matthias, Klein, Karsten, Li, Jian, Schreiber, Falk
Format: Article
Language:English
Subjects:
Algorithms
Genome
Software
Systems Biology
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Summary:Abstract Motivation Large metabolic models, including genome-scale metabolic models, are nowadays common in systems biology, biotechnology and pharmacology. They typically contain thousands of metabolites and reactions and therefore methods for their automatic visualization and interactive exploration can facilitate a better understanding of these models. Results 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 analyze a large model as follows. Starting from a decomposition into several subsystems, relationships between these subsystems are identified and an overview is computed and visualized. From this overview, detailed subviews may be constructed and visualized in order to explore subsystems and relationships in greater detail. Decompositions may either be predefined or computed, using built-in or self-implemented methods. Realized 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. Availability and implementation 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.
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btab335