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Eric H. Davidson - One of the best experts on this subject based on the ideXlab platform.

  • experimentally based sea urchin Gene Regulatory Network and the causal explanation of developmental phenomenology
    Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 2009
    Co-Authors: Smadar Bentabou Deleon, Eric H. Davidson
    Abstract:

    Gene Regulatory Networks for development underlie cell fate specification and differentiation. Network topology, logic and dynamics can be obtained by thorough experimental analysis. Our understanding of the Gene Regulatory Network controlling endomesoderm specification in the sea urchin embryo has attained an advanced level such that it explains developmental phenomenology. Here we review how the Network explains the mechanisms utilized in development to control the formation of dynamic expression patterns of transcription factors and signaling molecules. The Network represents the genomic program controlling timely activation of specification and differentiation Genes in the correct embryonic lineages. It can also be used to study evolution of body plans. We demonstrate how comparing the sea urchin Gene Regulatory Network to that of the sea star and to that of later developmental stages in the sea urchin, reveals mechanisms underlying the origin of evolutionary novelty. The experimentally based Gene Regulatory Network for endomesoderm specification in the sea urchin embryo provides unique insights into the system level properties of cell fate specification and its evolution.

  • a Gene Regulatory Network subcircuit drives a dynamic pattern of Gene expression
    Science, 2007
    Co-Authors: Joel Smith, Christina V Theodoris, Eric H. Davidson
    Abstract:

    Early specification of endomesodermal territories in the sea urchin embryo depends on a moving torus of Regulatory Gene expression. We show how this dynamic patterning function is encoded in a Gene Regulatory Network (GRN) subcircuit that includes the otx, wnt8, and blimp1 Genes, the cis-Regulatory control systems of which have all been experimentally defined. A cis-Regulatory reconstruction experiment revealed that blimp1 autorepression accounts for progressive extinction of expression in the center of the torus, whereas its outward expansion follows reception of the Wnt8 ligand by adjacent cells. GRN circuitry thus controls not only static spatial assignment in development but also dynamic Regulatory patterning.

  • Gene Regulatory Network controlling embryonic specification in the sea urchin
    Current Opinion in Genetics & Development, 2004
    Co-Authors: Paola Oliveri, Eric H. Davidson
    Abstract:

    The current state of the Gene Regulatory Network for endomesoderm specification in sea urchin embryos is reviewed. The Network was experimentally defined, and is presented as a predictive map of cis-Regulatory inputs and functional Regulatory Gene interconnections (updated versions of the Network and most of the underlying data are at http://sugp.caltech.edu/endomes/). The Network illuminates the ‘whys’ of many aspects of zygotic control in early sea urchin development, both spatial and temporal. The Network includes almost 50 Genes, and these are organized in subcircuits, each of which executes a particular Regulatory function.

Mark A Ragan - One of the best experts on this subject based on the ideXlab platform.

  • Gene Regulatory Network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets
    Genome Medicine, 2012
    Co-Authors: Piyush B Madhamshettiwar, Stefan R Maetschke, Melissa J Davis, Antonio Reverter, Mark A Ragan
    Abstract:

    Background Altered Networks of Gene regulation underlie many complex conditions, including cancer. Inferring Gene Regulatory Networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the Gene Regulatory Network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality. Methods We report a comprehensive comparative evaluation of nine state-of-the art Gene Regulatory Network inference methods encompassing the main algorithmic approaches (mutual information, correlation, partial correlation, random forests, support vector machines) using 38 simulated datasets and empirical serous papillary ovarian adenocarcinoma expression-microarray data. We then apply the best-performing method to infer normal and cancer Networks. We assess the druggability of the proteins encoded by our predicted target Genes using the CancerResource and PharmGKB webtools and databases. Results We observe large differences in the accuracy with which these methods predict the underlying Gene Regulatory Network depending on features of the data, Network size, topology, experiment type, and parameter settings. Applying the best-performing method (the supervised method SIRENE) to the serous papillary ovarian adenocarcinoma dataset, we infer and rank Regulatory interactions, some previously reported and others novel. For selected novel interactions we propose testable mechanistic models linking Gene regulation to cancer. Using Network analysis and visualization, we uncover cross-regulation of angioGenesis-specific Genes through three key transcription factors in normal and cancer conditions. Druggabilty analysis of proteins encoded by the 10 highest-confidence target Genes, and by 15 Genes with differential regulation in normal and cancer conditions, reveals 75% to be potential drug targets. Conclusions Our study represents a concrete application of Gene Regulatory Network inference to ovarian cancer, demonstrating the complete cycle of computational systems biology research, from genome-scale data analysis via Network inference, evaluation of methods, to the Generation of novel testable hypotheses, their prioritization for experimental validation, and discovery of potential drug targets.

  • Gene Regulatory Network inference evaluation and application to ovarian cancer allows the prioritization of drug targets
    Genome Medicine, 2012
    Co-Authors: Piyush B Madhamshettiwar, Stefan R Maetschke, Melissa J Davis, Antonio Reverter, Mark A Ragan
    Abstract:

    Background Altered Networks of Gene regulation underlie many complex conditions, including cancer. Inferring Gene Regulatory Networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the Gene Regulatory Network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality.

Enrico Nardelli - One of the best experts on this subject based on the ideXlab platform.

  • Modeling Gene Regulatory Network motifs using Statecharts.
    BMC Bioinformatics, 2012
    Co-Authors: Fabio Fioravanti, Manuela Helmer-citterich, Enrico Nardelli
    Abstract:

    Gene Regulatory Networks are widely used by biologists to describe the interactions among Genes, proteins and other components at the intra-cellular level. Recently, a great effort has been devoted to give Gene Regulatory Networks a formal semantics based on existing computational frameworks. For this purpose, we consider Statecharts, which are a modular, hierarchical and executable formal model widely used to represent software systems. We use Statecharts for modeling small and recurring patterns of interactions in Gene Regulatory Networks, called motifs. We present an improved method for modeling Gene Regulatory Network motifs using Statecharts and we describe the successful modeling of several motifs, including those which could not be modeled or whose models could not be distinguished using the method of a previous proposal. We model motifs in an easy and intuitive way by taking advantage of the visual features of Statecharts. Our modeling approach is able to simulate some interesting temporal properties of Gene Regulatory Network motifs: the delay in the activation and the deactivation of the "output" Gene in the coherent type-1 feedforward loop, the pulse in the incoherent type-1 feedforward loop, the bistability nature of double positive and double negative feedback loops, the oscillatory behavior of the negative feedback loop, and the "lock-in" effect of positive autoregulation. We present a Statecharts-based approach for the modeling of Gene Regulatory Network motifs in biological systems. The basic motifs used to build more complex Networks (that is, simple regulation, reciprocal regulation, feedback loop, feedforward loop, and autoregulation) can be faithfully described and their temporal dynamics can be analyzed.

  • Modeling Gene Regulatory Network motifs using statecharts
    BMC Bioinformatics, 2012
    Co-Authors: Fabio Fioravanti, Manuela Helmer-citterich, Enrico Nardelli
    Abstract:

    Abstract Background Gene Regulatory Networks are widely used by biologists to describe the interactions among Genes, proteins and other components at the intra-cellular level. Recently, a great effort has been devoted to give Gene Regulatory Networks a formal semantics based on existing computational frameworks. For this purpose, we consider Statecharts, which are a modular, hierarchical and executable formal model widely used to represent software systems. We use Statecharts for modeling small and recurring patterns of interactions in Gene Regulatory Networks, called motifs. Results We present an improved method for modeling Gene Regulatory Network motifs using Statecharts and we describe the successful modeling of several motifs, including those which could not be modeled or whose models could not be distinguished using the method of a previous proposal. We model motifs in an easy and intuitive way by taking advantage of the visual features of Statecharts. Our modeling approach is able to simulate some interesting temporal properties of Gene Regulatory Network motifs: the delay in the activation and the deactivation of the "output" Gene in the coherent type-1 feedforward loop, the pulse in the incoherent type-1 feedforward loop, the bistability nature of double positive and double negative feedback loops, the oscillatory behavior of the negative feedback loop, and the "lock-in" effect of positive autoregulation. Conclusions We present a Statecharts-based approach for the modeling of Gene Regulatory Network motifs in biological systems. The basic motifs used to build more complex Networks (that is, simple regulation, reciprocal regulation, feedback loop, feedforward loop, and autoregulation) can be faithfully described and their temporal dynamics can be analyzed.

Gerhard Schlosser - One of the best experts on this subject based on the ideXlab platform.

  • A Gene Regulatory Network underlying the formation of pre-placodal ectoderm in Xenopus laevis.
    BMC biology, 2018
    Co-Authors: Santosh Kumar Maharana, Gerhard Schlosser
    Abstract:

    The neural plate border ectoderm gives rise to key developmental structures during embryoGenesis, including the neural crest and the preplacodal ectoderm. Many sensory organs and ganglia of vertebrates develop from cranial placodes, which themselves arise from preplacodal ectoderm, defined by expression of transcription factor Six1 and its coactivator Eya1. Here we elucidate the Gene Regulatory Network underlying the specification of the preplacodal ectoderm in Xenopus, and the functional interactions among transcription factors that give rise to this structure. To elucidate the Gene Regulatory Network upstream of preplacodal ectoderm formation, we use gain- and loss-of-function studies to explore the role of early ectodermal transcription factors for establishing the preplacodal ectoderm and adjacent ectodermal territories, and the role of Six1 and Eya1 in feedback regulation of these transcription factors. Our findings suggest that transcription factors with expression restricted to ventral (non-neural) ectoderm (AP2, Msx1, FoxI1, Vent2, Dlx3, GATA2) and those restricted to dorsal (neural) ectoderm (Pax3, Hairy2b, Zic1) are required for specification of both preplacodal ectoderm and neural crest in a context-dependent fashion and are cross-regulated by Eya1 and Six1. These findings allow us to elucidate a detailed Gene Regulatory Network at the neural plate border upstream of preplacodal ectoderm formation based on functional interactions between ectodermal transcription factors. We propose a new model to explain the formation of immediately juxtaposed preplacodal ectoderm and neural crest territories at the neural plate border, uniting previous models.

Marianne Bronnerfraser - One of the best experts on this subject based on the ideXlab platform.

  • assembling neural crest Regulatory circuits into a Gene Regulatory Network
    Annual Review of Cell and Developmental Biology, 2010
    Co-Authors: Paola Betancur, Marianne Bronnerfraser, Tatjana Saukaspengler
    Abstract:

    The neural crest is a multipotent stem cell–like population that gives rise to a wide range of derivatives in the vertebrate embryo including elements of the craniofacial skeleton and peripheral nervous system as well as melanocytes. The neural crest forms in a series of Regulatory steps that include induction and specification of the prospective neural crest territory–neural plate border, specification of bona fide neural crest progenitors, and differentiation into diverse derivatives. These individual processes during neural crest ontogeny are controlled by Regulatory circuits that can be assembled into a hierarchical Gene Regulatory Network (GRN). Here we present an overview of the GRN that orchestrates the formation of cranial neural crest cells. Formulation of this Network relies on information largely inferred from Gene perturbation studies performed in several vertebrate model organisms. Our representation of the cranial neural crest GRN also includes information about direct Regulatory interactions obtained from the cis-Regulatory analyses performed to date, which increases the resolution of the architectural circuitry within the Network.

  • a Gene Regulatory Network orchestrates neural crest formation
    Nature Reviews Molecular Cell Biology, 2008
    Co-Authors: Tatjana Saukaspengler, Marianne Bronnerfraser
    Abstract:

    The neural crest is a multipotent, migratory cell population that is unique to vertebrate embryos and gives rise to many derivatives, ranging from the peripheral nervous system to the craniofacial skeleton and pigment cells. A multimodule Gene Regulatory Network mediates the complex process of neural crest formation, which involves the early induction and maintenance of the precursor pool, emigration of the neural crest progenitors from the neural tube via an epithelial to mesenchymal transition, migration of progenitor cells along distinct pathways and overt differentiation into diverse cell types. Here, we review our current understanding of these processes and discuss the molecular players that are involved in the neural crest Gene Regulatory Network.

  • ancient evolutionary origin of the neural crest Gene Regulatory Network
    Developmental Cell, 2007
    Co-Authors: Tatjana Saukaspengler, Daniel Meulemans, M Jones, Marianne Bronnerfraser
    Abstract:

    The vertebrate neural crest migrates from its origin, the neural plate border, to form diverse derivatives. We previously hypothesized that a neural crest Gene Regulatory Network (NC-GRN) guides neural crest formation. Here, we investigate when during evolution this hypothetical Network emerged by analyzing neural crest formation in lamprey, a basal extant vertebrate. We identify 50 NC-GRN homologs and use morpholinos to demonstrate a critical role for eight transcriptional regulators. The results reveal conservation in deployment of upstream factors, suggesting that proximal portions of the Network arose early in vertebrate evolution and have been conserved for >500 million years. We found biphasic expression of neural crest specifiers and differences in deployment of some specifiers and effectors expected to confer species-specific properties. By testing the collective expression and function of neural crest Genes in a single, basal vertebrate, we reveal the ground state of the NC-GRN and resolve ambiguities between model organisms.