Visual exploration of large metabolic models

Author:

Aichem Michael1ORCID,Czauderna Tobias2,Zhu Yan3,Zhao Jinxin3,Klapperstück Matthias2,Klein Karsten1,Li Jian3,Schreiber Falk12

Affiliation:

1. Department of Computer and Information Science, University of Konstanz, 78464 Konstanz, Germany

2. Faculty of Information Technology, Department of Human Centred-Computing, Monash University, Melbourne, VIC 3800, Australia

3. Biomedicine Discovery Institute, Infection & Immunity Program and Department of Microbiology, Monash University, Melbourne, VIC 3800, Australia

Abstract

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.

Funder

Deutsche Forschungsgemeinschaft

German Research Foundation

National Health and Medical Research Council

Australian NHMRC Principal Research Fellow

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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