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  Integration of large-scale data for extraction of integrated Arabidopsis root cell-type specific models

Scheunemann, M., Brady, S. M., & Nikoloski, Z. (2018). Integration of large-scale data for extraction of integrated Arabidopsis root cell-type specific models. Scientific Reports, 8(1): 7919. doi:10.1038/s41598-018-26232-8.

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 Creators:
Scheunemann, M.1, Author           
Brady, Siobhan M.2, Author
Nikoloski, Z.3, Author           
Affiliations:
1Mathematical Modelling and Systems Biology - Nikoloski, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753310              
2external, ou_persistent22              
3Mathematical Modelling and Systems Biology, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753341              

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 Abstract: Plant organs consist of multiple cell types that do not operate in isolation, but communicate with each other to maintain proper functions. Here, we extract models specific to three developmental stages of eight root cell types or tissue layers in Arabidopsis thaliana based on a state-of-the-art constraint-based modeling approach with all publicly available transcriptomics and metabolomics data from this system to date. We integrate these models into a multi-cell root model which we investigate with respect to network structure, distribution of fluxes, and concordance to transcriptomics and proteomics data. From a methodological point, we show that the coupling of tissue-specific models in a multi-tissue model yields a higher specificity of the interconnected models with respect to network structure and flux distributions. We use the extracted models to predict and investigate the flux of the growth hormone indole-3-actetate and its antagonist, trans-Zeatin, through the root. While some of predictions are in line with experimental evidence, constraints other than those coming from the metabolic level may be necessary to replicate the flow of indole-3-actetate from other simulation studies. Therefore, our work provides the means for data-driven multi-tissue metabolic model extraction of other Arabidopsis organs in the constraint-based modeling framework.

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Language(s): eng - English
 Dates: 2018
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: URI: https://doi.org/10.1038/s41598-018-26232-8
Other: Scheunemann2018
DOI: 10.1038/s41598-018-26232-8
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Title: Scientific Reports
  Abbreviation : Sci. Rep.
Source Genre: Journal
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Publ. Info: London, UK : Nature Publishing Group
Pages: 7919 Volume / Issue: 8 (1) Sequence Number: 7919 Start / End Page: - Identifier: ISSN: 2045-2322
CoNE: https://pure.mpg.de/cone/journals/resource/2045-2322