Gene Regulatory Networks for the characterization of the effects of the catalytic inhibition of DNA topoisomerase II
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE198093 | GEO | 2022/03/14
REPOSITORIES: GEO
Similar Datasets
Project description:Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to “static” transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach that integrates kinetic transcription data and the theory of attractor dynamics analysis to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, all of which do not incorporate kinetic transcriptional data in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in S. cerevisiae. Moreover, we have shown the potential of our method to predict unknown transcription profiles that would be produced upon genetic perturbation of the GRN governing a two-state phenotypic switch in C. albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation and the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that accurately describes the structure and dynamics of the in vivo GRN.
2023-06-23 | GSE217461 | GEO
Project description:Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to “static” transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach that integrates kinetic transcription data and the theory of attractor dynamics analysis to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, all of which do not incorporate kinetic transcriptional data in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in S. cerevisiae. Moreover, we have shown the potential of our method to predict unknown transcription profiles that would be produced upon genetic perturbation of the GRN governing a two-state phenotypic switch in C. albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation and the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that accurately describes the structure and dynamics of the in vivo GRN.
2023-06-23 | GSE217383 | GEO
Project description:Transcriptional control of any biological process occurs through the action of one to many gene regulatory networks (GRNs), but studies analyzing GRN interaction are lacking. To address this, we tested for the first time in vivo the hypothesis that two independent GRNs, which are active during formation of two discrete skeletal cell types (immature chondrocytes and osteoblasts), interact during differentiation of a third skeletal cell type (mature chondrocytes). These three cell types were isolated from the mouse embryo specifically using laser capture microdissection, and then RNA was extracted. Multiple analyses of corresponding RNA-seq data supported the hypothesis. Gene co-expression network analyses of differentially expressed genes suggested that one GRN containing the chondrogenic transcription factor Sox9 characterizes immature chondrocytes, one GRN containing the osteogenic transcription factor Runx2 characterizes osteoblasts, and both GRNs operate in mature chondrocytes. Indeed, mature chondrocytes differentially expressed fewer genes than the other cell types, consistent with the idea that overlapping actions of the immature chondrocyte and osteoblast GRNs regulate mature chondrocytes. Clustering analyses provided molecular insights into potential GRN interactions. Several genes in mature chondrocytes had expression levels that represented an averaging between levels in the immature chondrocyte and osteoblast. Interestingly, expression levels of one gene cluster, containing the hallmark mature chondrocyte genes Collagen type 10a1 and Indian hedgehog, suggested a synergistic interaction between immature chondrocyte and osteoblast GRNs. In addition to identifying novel genes expressed in mature chondrocytes, these results outline a novel in vivo experimental system through which to understand GRN organization and interaction.
2018-03-01 | GSE110051 | GEO
Project description:Deciphering gene regulatory networks (GRNs) is a key for understanding gene expression regulations in living systems. Here, we describe the investigation of the ABSCISIC ACID INSENSITIVE 3 (ABI3) plant transcription factor GRN vicinity by a technique called Network Walking. The method involves transient transformation of protoplasts and inducible nuclear re-localization of transcription factors along with transcriptomic analysis. This genome-wide approach allowed the de novo recovery of i) direct and indirect ABI3 target genes, ii) cis-binding site preference, and iii) biological processes regulated by this canonical abscisic acid response factor. This work improves our knowledge of ABI3 action by inferring network motifs (such as Feed Forwar Loops) under its influence. The novel high-throughput-oriented technique will help accelerate GRN systems investigations in plants, as well as in other organisms. This work studies ABI3 direct and indirect targets by a technique named Network Walking. Root/protoplasts were treated with or without dexamethasone (DEX) and cycloheximide (CHX). 3 reps each.
2013-01-01 | E-GEOD-33344 | biostudies-arrayexpress
Project description:Genome control is operated by transcription factors (TF) controlling their target genes by binding to promoters and enhancers. Conceptually, the interactions between TFs, their binding sites, and their functional targets are represented by gene regulatory networks (GRN). Deciphering in vivo GRNs underlying organ development in an unbiased genome-wide setting involves identifying both functional TF-gene interactions and physical TF-DNA interactions. To reverse-engineer the GRN of eye development in Drosophila, we performed RNA-seq across 72 genetic perturbations and sorted cell types, and inferred a co-expression network. Next, we derived direct TF-DNA interactions using computational motif inference, ultimately connecting 241 TFs to 5632 direct target genes through 24926 enhancers. Using this network we found network motifs, cis-regulatory codes, and new regulators of eye development. We validate the predicted target regions of Grainyhead by ChIP-seq and identify this factor as a general co-factor in the eye network, being bound to thousands of nucleosome-free regions.
2014-12-01 | GSE59059 | GEO
Project description:Genome control is operated by transcription factors (TF) controlling their target genes by binding to promoters and enhancers. Conceptually, the interactions between TFs, their binding sites, and their functional targets are represented by gene regulatory networks (GRN). Deciphering in vivo GRNs underlying organ development in an unbiased genome-wide setting involves identifying both functional TF-gene interactions and physical TF-DNA interactions. To reverse-engineer the GRN of eye development in Drosophila, we performed RNA-seq across 72 genetic perturbations and sorted cell types, and inferred a co-expression network. Next, we derived direct TF-DNA interactions using computational motif inference, ultimately connecting 241 TFs to 5632 direct target genes through 24926 enhancers. Using this network we found network motifs, cis-regulatory codes, and new regulators of eye development. We validate the predicted target regions of Grainyhead by ChIP-seq and identify this factor as a general co-factor in the eye network, being bound to thousands of nucleosome-free regions.
2014-12-01 | GSE62557 | GEO
Project description: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.
2010-01-15 | BIOMD0000000235 | BioModels
Project description:The gene regulatory network (GRN) underlying CTL differentiation and whether Tex responses can be functionally reinvigorated are incompletely understood. Here, we systematically mapped causal GRNs via single-cell CRISPR (scCRISPR) screens in vivo and discovered checkpoints for CTL differentiation.
2023-11-15 | GSE239802 | GEO
Project description:The gene regulatory network (GRN) underlying CTL differentiation and whether Tex responses can be functionally reinvigorated are incompletely understood. Here, we systematically mapped causal GRNs via single-cell CRISPR (scCRISPR) screens in vivo and discovered checkpoints for CTL differentiation.
2023-11-15 | GSE239801 | GEO
Project description:The gene regulatory network (GRN) underlying CTL differentiation and whether Tex responses can be functionally reinvigorated are incompletely understood. Here, we systematically mapped causal GRNs via single-cell CRISPR (scCRISPR) screens in vivo and discovered checkpoints for CTL differentiation.
2023-11-15 | GSE216909 | GEO