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A systems biology approach to modelling tea (Camellia sinensis)

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Abstract

Tea manufacture induces a variety of stresses that affect tea quality. We are using microarray data to track transcriptional changes occurring during wounding and withering of the leaves to identify metabolic pathways that could influence tea aroma and flavour. Current transcriptomic approaches include the use of a partial, tea-specific array. In order to monitor a larger number of genes we have performed cross-species analyses using Affymetrix Arabidopsis genome arrays [1]. Arabidopsis metabolic SBML [2] network data from AraCyc [3], KEGG and Reactome were collated and merged, then subsequently overlaid with the tea expression data. Subnetworks were constructed by connecting the shortest paths between the differentially expressed genes and the downstream aroma-related compounds, therefore identifying the pathways involved in aroma.

Conclusion

We present the initial output of this project and address how cross-species expression data can be used to colour a network and analysed using a variety of subgraph analyses.

Figure 1
figure1

This figure shows Cytoscape [4] layouts of (a) the merged AraCyc, KEGG and Reactome network, (b) the AraCyc metabolic network with gene identifiers, (c) the subgraph extracted based on the tea wounding and withering expression data [identified by green nodes] connected to tea aroma related compounds [identified by red nodes].

References

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    Mueller LA, Zhang P, Rhee SY: AraCyc: A Biochemical Pathway Database for Arabidopsis. Plant Physiology. 2003, 132: 453-460. 10.1104/pp.102.017236.

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Acknowledgements

Many thanks to the NASC Arrays X-species Service, Peter Clarke, Shao Chih Kuo and Thomas Spriggs for their help throughout the project.

Author information

Correspondence to Alex Marshall.

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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Keywords

  • Short Path
  • Expression Data
  • Microarray Data
  • System Biology
  • Network Data