Drosophila melanogaster
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA118037 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Describing the architecture of robust, nonlinear cell signaling networks is essential to gain a predictive understanding of cellular behavior. The structure of the Drosophila Rho-signaling network, comprised of Rho-family GTPases, RhoGTP Exchange Factors (RhoGEFs), and RhoGTPase Activating Proteins (RhoGAPs), has been particularly difficult to infer due to the highly overlapping function and substrate specificity of network components. We developed a parameterized modeling approach to predict connectivity amongst components of the Rho-signaling network that was driven by hundreds of mRNA expression profiles derived from RNAi-mediated inhibition or overexpression of component genes. Our model incorporated rate kinetics, transcriptional feedback, and noise. We biochemically validated several novel predicted connections, and used this model to predict Rho-signaling response to particular conditions. While functional redundancy is a feature of all signaling systems that often prevents classical genetic methods from elucidating relationships between components, the methods described here provide the basis for describing any complex network architecture. Keywords: Genetic modification, RNAi-mediated gene inhibition 129 samples analyzed. Experiments were peformed in batches of 4 containing 1 control/reference sample (transfection of GFP alone) that was prepared in parallel with experimental samples. There are 30 reference samples. The majority of experiments were replicated 2-6 times.
2012-12-31 | E-GEOD-18307 | biostudies-arrayexpress
Project description:Describing the architecture of robust, nonlinear cell signaling networks is essential to gain a predictive understanding of cellular behavior. The structure of the Drosophila Rho-signaling network, comprised of Rho-family GTPases, RhoGTP Exchange Factors (RhoGEFs), and RhoGTPase Activating Proteins (RhoGAPs), has been particularly difficult to infer due to the highly overlapping function and substrate specificity of network components. We developed a parameterized modeling approach to predict connectivity amongst components of the Rho-signaling network that was driven by hundreds of mRNA expression profiles derived from RNAi-mediated inhibition or overexpression of component genes. Our model incorporated rate kinetics, transcriptional feedback, and noise. We biochemically validated several novel predicted connections, and used this model to predict Rho-signaling response to particular conditions. While functional redundancy is a feature of all signaling systems that often prevents classical genetic methods from elucidating relationships between components, the methods described here provide the basis for describing any complex network architecture. Keywords: Genetic modification, RNAi-mediated gene inhibition
2012-12-31 | GSE18307 | GEO
Project description:Even though proteins are produced from mRNA, the correlation between mRNA levels and protein abundances is moderate in most studies, occasionally attributed to complex post-transcriptional regulation. To address this, we generated a paired transcriptome/proteome time course dataset with 14 time points during Drosophila embryogenesis. Despite a limited mRNA-protein correlation (ρ = 0.54), mathematical models describing protein translation and degradation explain 84% of protein time-courses based on the measured mRNA dynamics without assuming complex post-transcriptional regulation, and allow for classification of most proteins into four distinct regulatory scenarios. By performing an in-depth characterization of the putatively post-transcriptionally regulated genes, we postulated that the RNA-binding protein Hrb98DE is involved in post-transcriptional control of sugar metabolism in early embryogenesis and partially validated this hypothesis using Hrb98DE knockdown. In summary, we present a systems biology framework for the identification of post-transcriptional gene regulation for large-scale time-resolved transcriptome and proteome data.
2018-10-26 | PXD011238 | Pride
Project description:An ectopic network of transcription factors regulated by Hippo signaling drives growth and invasion of a malignant tumor model [larval imaginal discs]
| PRJNA291028 | ENA
Project description:An ectopic network of transcription factors regulated by Hippo signaling drives growth and invasion of a malignant tumor model [larval wild type discs]
| PRJNA291196 | ENA
Project description:The network of targeting interactions in chromatin
| PRJNA116717 | ENA
Project description:Transcriptome comparison between w1118 and glass 3 drosophila visual systems.
| PRJNA299690 | ENA
Project description:An ectopic network of transcription factors regulated by Hippo signaling drives growth and invasion of a malignant tumor model [larval imaginal discs (eye, wing and leg)]
| PRJNA321171 | ENA
Project description:The Drosophila transcriptional network is structured by microbiota
| PRJNA347655 | ENA
Project description:Interplay between RNA interference and heat shock response systems in Drosophila melanogaster
| PRJNA308750 | ENA