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Analysis of gene expression data on metabolic networks

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Analysis of gene expression data on metabolic networks
Anna-Lena Kranz1, Marcus Oswald3, Thorsten Bonato3, Hanna Seitz3, Gerhard Reinelt3,
Heiko Runz4, Johannes Zschocke4, Roland Eils1,2 and Rainer König1,2
1Department of Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology, University of
Heidelberg, 69120 Heidelberg, Germany. 2Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69120
Heidelberg, Germany. 3 Institute of Computer Science, University of Heidelberg, 69120 Heidelberg, Germany. 4Institute for
Human Genetics, University of Heidelberg, 69120 Heidelberg, Germany.
When analysing gene expression data it is often not enough to examine single genes but rather
to evaluate groups of genes [1]. As the modular structure of complex networks plays a critical
role in functionality, the use of metabolic networks provides a more structural approach to the
analysis of gene expression data. Determining significant expression patterns of topologically
associated genes enables the identification of functionally relevant central components in the
network with respect to different conditions of interest.
We invented a novel technique: The identification of sub-graphs in the metabolic network and
thereby the grouping of reactions into parts with their major connections is achieved with
clustering procedures on the network [2, 3]. Several clustering heuristics are existent that have
been applied to the clustering of metabolic networks by our group, i.e. simulated annealing [4], a
greedy and a consecutive ones heuristic [5, 6]. The overall modularity of the clustering as a
quality control is determined and optimised [7].
Once the clusters are identified, the gene expression data is mapped onto the corresponding
enzymatic reactions of its metabolic network. It is then possible to extract expression patterns for
each cluster using a combinatorial approach that has been developed in our group. Thereby,
values for every possible expression pattern of genes within a cluster are calculated. These
values show essential differences between samples of different
conditions and identify regions with a varying pattern between
different states.
As a case study our approach is applied to gene expression
data of HeLa cells under different concentrations of cholesterol.
This has high clinical relevance as impaired sterol biosynthesis
can cause severe human diseases (see Figure 1) [8]. Our
results promise new insights into sterol biosynthesis when
applied to the human metabolic network. With our method it is
not only possible to detect broken enzymes but also to discover
crucial imbalances of affected pathways in the cell.
Figure 1: Section of the cholesterol biosynthesis pathway showing interrupts causing the genetic diseases Lathosterolosis,
Conradi-Hünemann Syndrome and Smith-Lemli-Opitz Syndrome. [8]
1. Rapaport, F., et al., Classification of microarray data using gene networks. BMC Bioinformatics, 2007. 8: p. 35.
2. König, R., et al., Discovering functional gene expression patterns in the metabolic network of Escherichia coli with wavelets
transforms. BMC Bioinformatics, 2006. 7: p. 119.
3. König, R. and R. Eils, Gene expression analysis on biochemical networks using the Potts spin model. Bioinformatics, 2004. 20(10):
p. 1500-5.
4. Guimera, R. and L.A. Nunes Amaral, Functional cartography of complex metabolic networks. Nature, 2005. 433(7028): p. 895-900.
5. Christof, T., M. Oswald, and G. Reinelt, Consecutive Ones and a Betweenness Problem in Computational Biology, in Proceedings
of the 6th International IPCO Conference on Integer Programming and Combinatorial Optimization. 1998, Springer-Verlag.
6. Oswald, M. and G. Reinelt, Polyhedral Aspects of the Consecutive Ones Problem, in Proceedings of the 6th Annual International
Conference on Computing and Combinatorics. 2000, Springer-Verlag.
7. Newman, M.E. and M. Girvan, Finding and evaluating community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys,
2004. 69(2 Pt 2): p. 026113.
8. Haas, D., et al., Abnormal sterol metabolism in holoprosencephaly: Studies in cultured lymphoblasts. J Med Genet, 2007.
... Penghitungan menggunakan fungsi jarak Euclidean bergantung kepada magnituda level ekspresi gen. Hasil penghitungan koefisien dengan Euclidean adalah matriks dissimilarity, semakin kecil koefisien dua gen maka semakin identik kedua gen tersebut [15] . ...
... Dengan demikian E[xy] = E[x]E[y]. Koefiesien korelasi Pearson menghasilkan matriks similarity, semakin kecil koefisien antara dua gen, maka semakin tidak identik kedua gen tersebut [15] . ...
... Terdapat beberapa jenis algoritma HC yang dapat diaplikasikan untuk data microarray yaitu [15] : ...
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