Visual Exploration of Large Metabolic Models

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

Visual Exploration of Large Metabolic Models

Dateien

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Datum

2021

Autor:innen

Aichem, Michael

Czauderna, Tobias

Zhu, Yan

Zhao, Jinxin

Klapperstück, Matthias

Klein, Karsten

Li, Jian

Schreiber, Falk

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Zeitschriftenartikel

Publikationsstatus

Published

Erschienen in

Bioinformatics. Oxford University Press. 2021, 37(23), pp. 4460-4468. ISSN 0266-7061. eISSN 1367-4811. Available under: doi: 10.1093/bioinformatics/btab335

Zusammenfassung

Motivation:
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.

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 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.

Availability:
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.

Fachgebiet (DDC)

004 Informatik

Zitieren

ISO 690

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. 2021, 37(23), pp. 4460-4468. ISSN 0266-7061. eISSN 1367-4811. Available under: doi: 10.1093/bioinformatics/btab335

BibTex

@article{Aichem2021-05-10Visua-53691,
  year={2021},
  doi={10.1093/bioinformatics/btab335},
  title={Visual Exploration of Large Metabolic Models},
  number={23},
  volume={37},
  issn={0266-7061},
  journal={Bioinformatics},
  pages={4460--4468},
  author={Aichem, Michael and Czauderna, Tobias and Zhu, Yan and Zhao, Jinxin and Klapperstück, Matthias and Klein, Karsten and Li, Jian and Schreiber, Falk}
}

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Universitätsbibliographie

Ja

Begutachtet

Ja