Visual exploration of large metabolic models

Michael Aichem, Tobias Czauderna, Yan Zhu, Jinxin Zhao, Matthias Klapperstück, Karsten Klein, Jian Li, Falk Schreiber

Research output: Contribution to journalArticleResearchpeer-review

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.

Original languageEnglish
Pages (from-to)4460-4468
Number of pages9
JournalBioinformatics
Volume37
Issue number23
DOIs
Publication statusPublished - 10 May 2021

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