Email updates

Keep up to date with the latest news and content from BMC Systems Biology and BioMed Central.

This article is part of the supplement: BioSysBio 2007: Systems Biology, Bioinformatics, Synthetic Biology

Open Access Poster presentation

A systems biology approach to modelling tea (Camellia sinensis)

Alex Marshall1*, Sirisha Gollapudi1, Jacquie de Silva2 and Charlie Hodgman1

Author Affiliations

1 Multidisciplinary Centre for Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD, UK

2 Unilever Research & Development, Colworth Science Park, Sharnbrook, Bedford, MK44 1LQ, UK

For all author emails, please log on.

BMC Systems Biology 2007, 1(Suppl 1):P13  doi:10.1186/1752-0509-1-S1-P13


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1752-0509/1/S1/P13


Published:8 May 2007

© 2007 Marshall et al; licensee BioMed Central Ltd.

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.

thumbnailFigure 1. 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].

Acknowledgements

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

References

  1. Hammond JP, Broadley MR, Craigon DJ, Higgins J, Emmerson ZF, Townsend HJ, White PJ, May ST: Using genomic DNA-based probe-selection to improve the sensitivity of high-density oligonucleotide arrays when applied to heterologous species.

    Plant Methods 2005., 1 PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  2. Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, et al.: The Systems Biology Markup Language (SBML): A medium for the representation and exchange of biochemical network models.

    Bioinformatics 2003, 19:524-531. PubMed Abstract | Publisher Full Text OpenURL

  3. Mueller LA, Zhang P, Rhee SY: AraCyc: A Biochemical Pathway Database for Arabidopsis.

    Plant Physiology 2003, 132:453-460. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  4. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks.

    Genome Research 2003, 13:2498-2504. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL