Dataset Information

Kuhn2009_EndoMesodermNetwork

ABSTRACT: This a model from the article: 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, Poustka AJ. BMC Syst Biol.2009 Aug 23;3:83. 19698179, Abstract: 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. The paper describes several models, Mi, i=1...n, where M0 correspond to the unperturbed model and all the others correspond to the perturbed model. This model is the unperturbed model. The model reproduces figure 5 of the reference publication. The figures were generated by running 1 simulation, whereas in the paper the plotted values are the means of 800 simulations using randomly samples parameter sets. Additional information from the Author: The parameter that were randomly samples are the transcription parameters c_Proteins... and k_Proteins. The parameter were sampled from a lognormal distribution with sigma = 1.5 and mu = 0.5 This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.For more information see the terms of use.To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

SUBMITTER: Clemens Kühn

PROVIDER: BIOMD0000000235 | BioModels | 2010-01-15

REPOSITORIES: BioModels

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Publications

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

Kühn Clemens C Wierling Christoph C Kühn Alexander A Klipp Edda E Panopoulou Georgia G Lehrach Hans H Poustka Albert J AJ

BMC systems biology 20090823

<h4>Background</h4>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 mod ...[more]

PMID: 19698179

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Project description:Kummer2000 - Oscillations in Calcium Signalling Simplified (3-variable) calcium oscillation model Kummer et al. (2000) Biophys. J. 79, 1188-1195 This model is defined in a small compartment with low concentrations. You can run it first with the LSODA ODE solver and then with the Gillespie Monte Carlo method (in Time Course widget). This illustrates that at low particle numbers, as here, the stochastic simulation and the ODE approach produce different results (the stochastic approach is more correct in these circumstances). This file also demonstrates the use of several different plots to visualize results, including a histogram. This model is described in the article: Switching from simple to complex oscillations in calcium signaling. Kummer U, Olsen LF, Dixon CJ, Green AK, Bornberg-Bauer E, Baier G. Biophys. J. 2000 Sep; 79(3): 1188-1195 Abstract: We present a new model for calcium oscillations based on experiments in hepatocytes. The model considers feedback inhibition on the initial agonist receptor complex by calcium and activated phospholipase C, as well as receptor type-dependent self-enhanced behavior of the activated G(alpha) subunit. It is able to show simple periodic oscillations and periodic bursting, and it is the first model to display chaotic bursting in response to agonist stimulations. Moreover, our model offers a possible explanation for the differences in dynamic behavior observed in response to different agonists in hepatocytes. This model is hosted on BioModels Database and identified by: BIOMD0000000329. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Dataset Information

Kuhn2009_EndoMesodermNetwork

Publications

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

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