A Systems Approach for Determining Gene Expression from Experimental Observation of Compound Presence and Absence


Different genes are expressed in different tissues, depending on functional objectives and selection pressures. Based on complete knowledge of the structure of the metabolic network and all the reactions taking place in the cell, elementary modes (EMs) and minimal cut sets (MCSs) can relate compounds observed in a tissue, to the genes being expressed by respectively providing the full set of non-decomposable routes of reactions and compounds that lead to the synthesis of external products, and the full set of possible target genes for blocking the synthesis of external products. So, for a particular tissue, only the EMs containing the reactions that are related to the genes being expressed in those tissues, are active for the production of the corresponding compounds. This concept is used to develop an algorithm for determining a matrix of reactions (which can be related to corresponding genes) taking place in a tissue, using experimental observations of compounds in a tissue. The program is applied to the Arabidopsis flower and identified 20 core reactions occurring in all the viable EMs. They originate from the trans-cinnamate compound and lead to the formation of kaempferol and quercetin compounds and their derivatives, as well as anthocyanin compounds. Analyses of the patterns in the matrix identify reaction sets related to certain functions such as the formation of derivatives of the two anthocyanin compounds present, as well as the reactions leading from the network’s external substrate erythrose-4P to L-Phenylalanine, cinnamyl-alc to trans-cinnamate and so on. The program can be used to successfully determine genes taking place in a tissue, and the patterns in the resulting matrix can be analysed to determine gene sets and the state of the tissue.

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Clark, S. and Verwoerd, W. (2014) A Systems Approach for Determining Gene Expression from Experimental Observation of Compound Presence and Absence. Advances in Bioscience and Biotechnology, 5, 478-491. doi: 10.4236/abb.2014.55058.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Klamt, S. and Stelling, J. (2003) Two Approaches for Metabolic Pathway Analysis? Trends in Biotechnology, 21, 64-69. http://dx.doi.org/10.1016/S0167-7799(02)00034-3
[2] Papin, J.A. (2004) Comparison of Network-Based Pathway Analysis Methods. Trends in Biotechnology, 22, 400-405.
[3] Clark, S.T. and Verwoerd, W.S. (2012) Minimal Cut Sets and the Use of Failure Modes in Metabolic Networks. Metabolites, 2, 567-595.
[4] Gagneur, J. and Klamt, S. (2004) Computation of Elementary Modes: A Unifying Framework and the New Binary Approach. BMC Bioinformatics, 5, 175.
[5] Klamt, S. (2006) Generalized Concept of Minimal Cut Sets in Biochemical Networks. Biosystems, 83, 233-247.
[6] Schuster, S., Dandekar, T. and Fell, D.A. (1999) Detection of Elementary Flux Modes in Biochemical Networks: A Promising Tool for Pathway Analysis and Metabolic Engineering. Trends in Biotechnology, 17, 53-60.
[7] Trinh, C.T., Wlaschin, A. and Srienc, F. (2009) Elementary Mode Analysis: A Useful Metabolic Pathway Analysis Tool for Characterizing Cellular Metabolism. Applied Microbiology and Biotechnology, 81, 813-826.
[8] Klamt, S. and Gilles, E. (2004) Minimal Cut Sets in Biochemical Reaction Networks. Bioinformatics, 20, 226-234.
[9] (2011) The Arabidopsis Information Resource (TAIR).
[10] Roberts, P.C. (2008) Gene Expression Microarray Data Analysis Demystified. Biotechnology annual Review, 14, 29-61. http://dx.doi.org/10.1016/S1387-2656(08)00002-1
[11] Casati, P. and Walbot, V. (2003) Gene Expression Profiling in Response to Ultraviolet Radiation in Maize Genotypes with Varying Flavonoid Content. Plant Physiology, 132, 1739-1754. http://dx.doi.org/10.1104/pp.103.022871
[12] DeRisi, J.L., Iyer, V.R. and Brown, P.O. (1997) Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale. Science, 278, 680-686.
[13] Díaz, H., Andrews, B.A., Hayes, A., Castrillo, J., Oliver, S.G. and Asenjo, J.A. (2009) Global Gene Expression in Recombinant and Non-Recombinant Yeast Saccharomyces cerevisiae in Three Different Metabolic States. Biotechnology Advances, 27, 1092-1117.
[14] Massa, M.S., Chiogna, M. and Romualdi, C. (2010) Gene Set Analysis Exploiting the Topology of a Pathway. BMC Systems Biology, 4, 121.
[15] Ackermann, M. and Strimmer, K. (2009) A General Modular Framework for Gene Set Enrichment Analysis. BMC Bioinformatics, 10, 47. http://dx.doi.org/10.1186/1471-2105-10-47
[16] Nam, D. and Kim, S.-Y. (2008) Gene-Set Approach for Expression Pattern Analysis. Briefings in Bioinformatics, 9, 189-197. http://dx.doi.org/10.1093/bib/bbn001
[17] Yonekura-Sakakibara, K., Tanaka, Y., Fukuchi-Mizutani, M., Fujiwara, H., Fukui, Y., Ashikari, T., Murakami, Y., Yamaguchi, M. and Kusumi, T. (2000) Molecular and Biochemical Characterization of a Novel Hydroxycinnamoyl-CoA: Anthocyanin 3-O-Glucoside-6”-O-acyltransferase from Perilla frutescens. Plant and Cell Physiology, 41, 495-502. http://dx.doi.org/10.1093/pcp/41.4.495
[18] Clark, S. and Verwoerd, W. (2011) A Systems Approach to Identifying Correlated Gene Targets for the Loss of Colour Pigmentation in Plants. BMC Bioinformatics, 12, 343.
[19] Klamt, S., Sae-Rodriguez, J. and Gilles, E.D. (2007) Structural and Functional Analysis of Cellular Networks with CellNetAnalyzer. BMC Systems Biology, 1, 2.

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