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

Kühn, C., Wierling, C., Kühn, A., Klipp, E., Panopoulou, G., Lehrach, H., et al. (2009). Monte-Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network. BMC Systems Biology, 3, 83-83. doi:10.1186/1752-0509-3-83.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-7D33-3 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-7D34-1
Genre: Journal Article
Alternative Title : BMC Syst. Biol.

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 Creators:
Kühn, Clemens1, Author
Wierling, Christoph2, Author              
Kühn, Alexander2, Author              
Klipp, Edda3, Author              
Panopoulou, Georgia4, Author              
Lehrach, Hans5, Author              
Poustka, Albert J.4, Author              
Affiliations:
1Max Planck Society, ou_persistent13              
2Systems Biology (Christoph Wierling), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479656              
3Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433554              
4Evolution and Development (Albert Poustka), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479650              
5Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433550              

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 Abstract: Gene Regulatory Networks (GRNs) control the differentiation, specification and function of cells at the genomic level. The levels of interactions within large GRNs are of enormous depth and complexity. Details about many GRNs are emerging, but in most cases it is unknown to what extent they control a given process, i.e. the grade of completeness is uncertain. This uncertainty stems from limited experimental data, which is the main bottleneck for creating detailed dynamical models of cellular processes. Parameter estimation for each node is often infeasible for very large GRNs. We propose a method, based on random parameter estimations through Monte-Carlo simulations to measure completeness grades of GRNs. Results We developed a heuristic to assess the completeness of large GRNs, using ODE simulations under different conditions and randomly sampled parameter sets to detect parameter-invariant effects of perturbations. To test this heuristic, we constructed the first ODE model of the whole sea urchin endomesoderm GRN, one of the best studied large GRNs. We find that nearly 48% of the parameter-invariant effects correspond with experimental data, which is 65% of the expected optimal agreement obtained from a submodel for which kinetic parameters were estimated and used for simulations. Randomized versions of the model reproduce only 23.5% of the experimental data. Conclusion The method described in this paper enables an evaluation of network topologies of GRNs without requiring any parameter values. The benefit of this method is exemplified in the first mathematical analysis of the complete Endomesoderm Network Model. The predictions we provide deliver candidate nodes in the network that are likely to be erroneous or miss unknown connections, which may need additional experiments to improve the network topology. This mathematical model can serve as a scaffold for detailed and more realistic models. We propose that our method can be used to assess a completeness grade of any GRN. This could be especially useful for GRNs involved in human diseases, where often the amount of connectivity is unknown and/or many genes/interactions are missing.

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Language(s): eng - English
 Dates: 2009-08-23
 Publication Status: Published in print
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Title: BMC Systems Biology
  Alternative Title : BMC Syst. Biol.
Source Genre: Journal
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Pages: - Volume / Issue: 3 Sequence Number: - Start / End Page: 83 - 83 Identifier: ISSN: 1752-0509