BMC Systems Biology

official impact factor 3.57

Open Access Research article

Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network

Clemens Kühn1,2, Christoph Wierling1, Alexander Kühn1, Edda Klipp2, Georgia Panopoulou1, Hans Lehrach1 and Albert J Poustka1*

Author Affiliations

1 Max-Planck-Institute for Molecular Genetics, Ihnestr 63-73, 14195 Berlin, Germany

2 Humboldt Universität zu Berlin, Institute for Biology, Invalidenstr 42, 10115 Berlin, Germany

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BMC Systems Biology 2009, 3:83 doi:10.1186/1752-0509-3-83

Published: 23 August 2009

Additional files

Additional file 1:

Differential equations of the model. Differential equations as used in the model.

Format: PDF Size: 106KB Download file

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Additional file 2:

All detected TPEs. All TPEs for all timepoints and territories.

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Additional file 3:

Detailed comparison of TPEs and exp. Data. All matches and mismatches between detected TPEs and exp. data. Genes in the network for which no unambiguous expression change due to perturbations could be established from data as of December 2007 are not included.

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Additional file 4:

Robustness to parameter variations. Method used to compute the robustness of gene expression against random parameter variations, results and discussion thereof.

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Additional file 5:

Events used to initiate differential expression. Listing the events used in setting static inputs to initiate differential expression. For the Hill-Kinetics controlling inputs to the network in each territory (formula 9), we set S1 = 1, S2 = 0 at the time given in the respective 'on'-column and we set S1 = 0, S2 = 1 at the time given in the respective 'off'-column. Empty cells indicate inputs that are off (S1 = 0, S2 = 1) for the whole simulation time.

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Additional file 6:

SBML Model. Model in SBML format. Parameter values are set to arbitrary values. Also available on BioModels under ID MODEL2133240427.

Format: PDF Size: 59KB Download file

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