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

BACKGROUND: 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 t...

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Autores principales: Kühn, Clemens, Wierling, Christoph, Kühn, Alexander, Klipp, Edda, Panopoulou, Georgia, Lehrach, Hans, Poustka, Albert J
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2739852/
https://www.ncbi.nlm.nih.gov/pubmed/19698179
http://dx.doi.org/10.1186/1752-0509-3-83
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author Kühn, Clemens
Wierling, Christoph
Kühn, Alexander
Klipp, Edda
Panopoulou, Georgia
Lehrach, Hans
Poustka, Albert J
author_facet Kühn, Clemens
Wierling, Christoph
Kühn, Alexander
Klipp, Edda
Panopoulou, Georgia
Lehrach, Hans
Poustka, Albert J
author_sort Kühn, Clemens
collection PubMed
description BACKGROUND: 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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spelling pubmed-27398522009-09-09 Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network Kühn, Clemens Wierling, Christoph Kühn, Alexander Klipp, Edda Panopoulou, Georgia Lehrach, Hans Poustka, Albert J BMC Syst Biol Research Article BACKGROUND: 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. BioMed Central 2009-08-23 /pmc/articles/PMC2739852/ /pubmed/19698179 http://dx.doi.org/10.1186/1752-0509-3-83 Text en Copyright © 2009 Kühn et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Kühn, Clemens
Wierling, Christoph
Kühn, Alexander
Klipp, Edda
Panopoulou, Georgia
Lehrach, Hans
Poustka, Albert J
Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network
title Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network
title_full Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network
title_fullStr Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network
title_full_unstemmed Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network
title_short Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network
title_sort monte carlo analysis of an ode model of the sea urchin endomesoderm network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2739852/
https://www.ncbi.nlm.nih.gov/pubmed/19698179
http://dx.doi.org/10.1186/1752-0509-3-83
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