Workshop venue: The workshop will take place at the IT-room on the main floor of the BioQuant Center, Im Neuenheimer Feld 267, D-69120 Heidelberg! The venue is located on the campus of the University of Heidelberg.

IOMPA 2011, a workshop dedicated at the Integration of OMICS Datasets into Metabolic Pathway Analysis will be held in Heidelberg, Germany on September 02, 2011. This workshop is a sequel of IOMPA 2010, that was held on October 15, 2010 in Edinburgh.

Aims and scope

The aim of this workshop is to bring together people working in the field, discuss different perspectives on the topic of metabolic pathway analysis and to develop new ideas for the integration of experimental data. The workshop is planed to have the duration of one day. It starts with a review on the topic of reconstruction of genome-scale networks, followed by the presentation of recent developments in metabolic pathway analysis. The second part of the workshop focuses on existing works dealing with the integration of Omics data. In a concluding session, the speakers are invited to share and discuss their experiences in order to develop new ideas how to achieve a more comprehensive way of modeling metabolism. An audience of 30 to 60 people is expected.

The number of published genome-scale metabolic network reconstructions is growing exponentially [1]. Moreover, the representation of metabolic networks is becoming more and more detailed and complex [2–4]. This new way of representing metabolism of microorganisms and human cells poses new challenges in the field of metabolic pathway analysis. The classical concept of a metabolic pathway has to be revised and adapted to such large models [5, 6] and there is a strong need to take into account experimental data to guide pathway analysis [7]. This allows the integration of diverse types of experimental data within a comprehensive model of metabolism and should facilitate the otherwise cumbersome reconstruction of treatment, organism and tissue specific metabolic fluxes. Such a model facilitates the planning of new experiments that give further insight into the structure and regulation of metabolic networks and also possible treatment strategies interfering e.g. malignant cell growth. Recently, many graph based tools have been developed to predict metabolic pathways [8–10]. Alternatively, the concept of elementary modes, formerly applied exclusively to small-scale networks, has been extended to large models [11–13]. Both concepts have been a matter of debate and the challenge is to combine them in order to make use of the advantages of both methodologies [14, 15]. One of the most important outcomes of convex-basis based methods is the unbiased representation of the metabolic capabilities of an organism. However, experimental data are required to reduce the large number of possible pathways to those that are active in a certain environment and consequently, to determine the physiological state of the organism. Some first important steps have already been taken for the integration of expression data [16–20] and metabolic flux data [16, 21, 20]. However, these approaches need further development and new methods have to come up to be applied for a large variety of different settings and integration of different types of Omics data.


The registration takes place through the ICSB registration form. The registration fee is € 70.


Dr. Christoph Kaleta (University of Jena) (Christoph.Kaleta at uni-jena dot de)

Sascha Schäuble (University of Jena) (sascha.schaeuble at uni-jena dot de)

Martin Kötzing(University of Jena) (martin.koetzing at uni-jena dot de)

PD Dr. Rainer König(University of Heidelberg) (koenig at uni-heidelberg dot de)

Prof. Dr. Stefan Schuster (University of Jena) (stefan.schu at uni-jena dot de)


[1] Adam M Feist, Markus J Herrgård Ines Thiele, Jennie L Reed, and Bernhard Ø Palsson. Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol, 7(2):129–143, Feb 2009.
[2] Adam M Feist and Bernhard Ø Palsson. The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nat Biotechnol, 26(6):659–667, Jun 2008.
[3] Livnat Jerby, Tomer Shlomi, and Eytan Ruppin. Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol Syst Biol, 6:401, Sep 2010.
[4] Nathan E Lewis, Kim K Hixson, Tom M Conrad, Joshua A Lerman, Pep Charusanti, Ashoka D Polpitiya, Joshua N Adkins, Gunnar Schramm, Samuel O Purvine, Daniel Lopez-Ferrer, Karl K Weitz, Roland Eils, Rainer König, Richard D Smith, and Bernhard Ø Palsson. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol Syst Biol, 6:390, Jul 2010.
[5] Uwe Sauer. Metabolic networks in motion: 13c-based flux analysis. Mol Syst Biol, 2:62, 2006.
[6] Berbhard Ø. Palsson. Systems Biology - Properties of Reconstructed Networks. Cambridge University Press, New York, 2006.
[7] Uwe Sauer, Matthias Heinemann, and Nicola Zamboni. Genetics. getting closer to the whole picture. Science, 316(5824):550–551, Apr 2007.
[8] Torsten Blum and Oliver Kohlbacher. Metaroute: fast search for relevant metabolic routes for interactive network navigation and visualization. Bioinformatics, 24(18):2108–2109, Sep 2008.
[9] Karoline Faust, Didier Croes, and Jacques van Helden. Metabolic pathfinding using RPAIR annotation. J Mol Biol, 388(2):390–414, May 2009.
[10] S. A. Rahman, P. Advani, R. Schunk, R. Schrader, and Dietmar Schomburg. Metabolic pathway analysis web service (pathway hunter tool at cubic). Bioinformatics, 21(7):1189–1193, Apr 2005.
[11] Luis F de Figueiredo, Adam Podhorski, Angel Rubio, Christoph Kaleta, John E Beasley, Stefan Schuster, and Francisco J Planes. Computing the shortest elementary flux modes in genome-scale metabolic networks. Bioinformatics, 25(23):3158–3165, Dec 2009.
[12] Christoph Kaleta, Lu´ Filipe de Figueiredo, and Stefan Schuster. Can the whole be less than the sum of its parts? pathway analysis in genomescale metabolic networks using elementary flux patterns. Genome Res, 19(10):1872–1883, Oct 2009.
[13] Marco Terzer and J¨rg Stelling. Large-scale computation of elementary flux modes with bit pattern trees. Bioinformatics, 24(19):2229–2235, Oct 2008.
[14] Karoline Faust, Didier Croes, and Jacques van Helden. In response to ’can sugars be produced from fatty acids? a test case for pathway analysis tools’. Bioinformatics, 25(23):3202–3205, Dec 2009.
[15] Luis F de Figueiredo, Stefan Schuster, Christoph Kaleta, and David A Fell. Response to comment on ’can sugars be produced from fatty acids? a test case for pathway analysis tools’. Bioinformatics, 25(24):3330–3331, Dec 2009.
[16] Joel F Moxley, Michael C Jewett, Maciek R Antoniewicz, Silas G Villas-Boas, Hal Alper, Robert T Wheeler, Lily Tong, Alan G Hinnebusch, Trey Ideker, Jens Nielsen, and Gregory Stephanopoulos. Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator gcn4p. Proc Natl Acad Sci U S A, 106(16):6477–6482, Apr 2009.
[17] Gunnar Schramm, Stefan Wiesberg, Nicolle Diessl, Anna-Lena Kranz, Vitalia Sagulenko, Marcus Oswald, Gerhard Reinelt, Frank Westermann, Roland Eils, and Rainer König. PathWave: discovering patterns of differentially regulated enzymes in metabolic pathways. Bioinformatics, 26(9):1225–1231, May 2010.
[18] Richard A Notebaart, Bas Teusink, Roland J Siezen, and Bal´zs Papp. a Co-regulation of metabolic genes is better explained by flux coupling than by network distance. PLoS Comput Biol, 4(1):e26, Jan 2008.
[19] Jean-Marc Schwartz, Claire Gaugain, Jose C Nacher, Antoine de Daruvar, and Minoru Kanehisa. Observing metabolic functions at the genome scale. Genome Biol, 8(6):R123, 2007.
[20] Tomer Shlomi, Moran N Cabili, Markus J Herrgård, Bernhard Ø Palsson, and Eytan Ruppin. Network-based prediction of human tissue-specific metabolism. Nat Biotechnol, 26(9):1003–1010, Sep 2008.
[21] Tomer Shlomi, Yariv Eisenberg, Roded Sharan, and Eytan Ruppin. A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Mol Syst Biol, 3:101, 2007.