Modeling the metabolism of arabidopsis thaliana : application of network decomposition and network reduction in the context of petri nets

  • Motivation: Arabidopsis thaliana is a well-established model system for the analysis of the basic physiological and metabolic pathways of plants. Nevertheless, the system is not yet fully understood, although many mechanisms are described, and information for many processes exists. However, the combination and interpretation of the large amount of biological data remain a big challenge, not only because data sets for metabolic paths are still incomplete. Moreover, they are often inconsistent, because they are coming from different experiments of various scales, regarding, for example, accuracy and/or significance. Here, theoretical modeling is powerful to formulate hypotheses for pathways and the dynamics of the metabolism, even if the biological data are incomplete. To develop reliable mathematical models they have to be proven for consistency. This is still a challenging task because many verification techniques fail already for middle-sized models. Consequently, new methods, like decomposition methods or reduction approaches, are developed to circumvent this problem. Methods: We present a new semi-quantitative mathematical model of the metabolism of Arabidopsis thaliana. We used the Petri net formalism to express the complex reaction system in a mathematically unique manner. To verify the model for correctness and consistency we applied concepts of network decomposition and network reduction such as transition invariants, common transition pairs, and invariant transition pairs. Results: We formulated the core metabolism of Arabidopsis thaliana based on recent knowledge from literature, including the Calvin cycle, glycolysis and citric acid cycle, glyoxylate cycle, urea cycle, sucrose synthesis, and the starch metabolism. By applying network decomposition and reduction techniques at steady-state conditions, we suggest a straightforward mathematical modeling process. We demonstrate that potential steady-state pathways exist, which provide the fixed carbon to nearly all parts of the network, especially to the citric acid cycle. There is a close cooperation of important metabolic pathways, e.g., the de novo synthesis of uridine-5-monophosphate, the γ-aminobutyric acid shunt, and the urea cycle. The presented approach extends the established methods for a feasible interpretation of biological network models, in particular of large and complex models.

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Author:Ina KochORCiD, Joachim Nöthen, Enrico SchleiffORCiDGND
URN:urn:nbn:de:hebis:30:3-463519
DOI:https://doi.org/10.3389/fgene.2017.00085
ISSN:1664-8021
Pubmed Id:https://pubmed.ncbi.nlm.nih.gov/28713420
Parent Title (English):Frontiers in genetics
Publisher:Frontiers Media
Place of publication:Lausanne
Contributor(s):Julio Vera
Document Type:Article
Language:English
Year of Completion:2017
Date of first Publication:2017/06/30
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2018/04/26
Tag:Arabidopsis thaliana metabolism; Petri net; common transition pairs; invariant transition pairs; model verification; network reduction; systems biology; transition invariant
Volume:8
Issue:Art. 85
Page Number:22
First Page:1
Last Page:22
Note:
Copyright: © 2017 Koch, Nöthen and Schleiff. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
HeBIS-PPN:431865248
Institutes:Biowissenschaften / Biowissenschaften
Informatik und Mathematik / Informatik
Wissenschaftliche Zentren und koordinierte Programme / Center for Membrane Proteomics (CMP)
Exzellenzcluster / Exzellenzcluster Makromolekulare Komplexe
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Sammlungen:Universitätspublikationen
Open-Access-Publikationsfonds:Biowissenschaften
Licence (German):License LogoCreative Commons - Namensnennung 4.0