Dataset Information

Information processing at the foxa node of the sea urchin endomesoderm specification network.

ABSTRACT: The foxa regulatory gene is of central importance for endoderm specification across Bilateria, and this gene lies at an essential node of the well-characterized sea urchin endomesoderm gene regulatory network (GRN). Here we experimentally dissect the cis-regulatory system that controls the complex pattern of foxa expression in these embryos. Four separate cis-regulatory modules (CRMs) cooperate to control foxa expression in different spatial domains of the endomesoderm, and at different times. A detailed mutational analysis revealed the inputs to each of these cis-regulatory modules. The complex and dynamic expression of foxa is regulated by a combination of repressors, a permissive switch, and multiple activators. A mathematical kinetic model was applied to study the dynamic response of foxa cis-regulatory modules to transient inputs. This study shed light on the mesoderm-endoderm fate decision and provides a functional explanation, in terms of the genomic regulatory code, for the spatial and temporal expression of a key developmental control gene.

SUBMITTER: de-Leon SB

PROVIDER: S-EPMC2890477 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature

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Information processing at the foxa node of the sea urchin endomesoderm specification network.

de-Leon Smadar Ben-Tabou SB Davidson Eric H EH

Proceedings of the National Academy of Sciences of the United States of America 20100517 22

The foxa regulatory gene is of central importance for endoderm specification across Bilateria, and this gene lies at an essential node of the well-characterized sea urchin endomesoderm gene regulatory network (GRN). Here we experimentally dissect the cis-regulatory system that controls the complex pattern of foxa expression in these embryos. Four separate cis-regulatory modules (CRMs) cooperate to control foxa expression in different spatial domains of the endomesoderm, and at different times. A ...[more]

PMID: 20479235

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Project 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.

Dataset Information

Information processing at the foxa node of the sea urchin endomesoderm specification network.

Publications

Information processing at the foxa node of the sea urchin endomesoderm specification network.

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