scholarly journals AGENT: the Arabidopsis Gene Regulatory Network Tool for Exploring and Analyzing GRNs

2021 ◽  
Author(s):  
Vincent Lau ◽  
Rachel Woo ◽  
Bruno Pereira ◽  
Asher Pasha ◽  
Eddi Esteban ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Ad Hoc ◽  
Arabidopsis Gene ◽  
Nitrate Signaling ◽  
Multi Level ◽  
Gene Regulatory

AbstractGene regulatory networks (GRNs) are complex networks that capture multi-level regulatory events between one or more regulatory macromolecules, such as transcription factors (TFs), and their target genes. Advancements in screening technologies such as enhanced yeast-one-hybrid screens have allowed for high throughput determination of GRNs. However, visualization of GRNs in Arabidopsis has been limited to ad hoc networks and are not interactive. Here, we describe the Arabidopsis GEne Network Tool (AGENT) that houses curated GRNs and provides tools to visualize and explore them. AGENT features include expression overlays, subnetwork motif scanning, and network analysis. We show how to use AGENT’s multiple built-in tools to identify key genes that are involved in flowering and seed development along with identifying temporal multi-TF control of a key transporter in nitrate signaling. AGENT can be accessed at https://bar.utoronto.ca/AGENT.

A study of structural properties of gene network graphs for mathematical modeling of integrated mosaic gene networks

2017 ◽  
Vol 15 (02) ◽  
pp. 1650045 ◽  
Author(s):  
Olga V. Petrovskaya ◽  
Evgeny D. Petrovskiy ◽  
Inna N. Lavrik ◽  
Vladimir A. Ivanisenko
Keyword(s):  
Mathematical Modeling ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Network Modeling ◽  
Computer Experiments ◽  
Linear Control ◽  
Expression Of Genes ◽  
Gene Regulatory

Gene network modeling is one of the widely used approaches in systems biology. It allows for the study of complex genetic systems function, including so-called mosaic gene networks, which consist of functionally interacting subnetworks. We conducted a study of a mosaic gene networks modeling method based on integration of models of gene subnetworks by linear control functionals. An automatic modeling of 10,000 synthetic mosaic gene regulatory networks was carried out using computer experiments on gene knockdowns/knockouts. Structural analysis of graphs of generated mosaic gene regulatory networks has revealed that the most important factor for building accurate integrated mathematical models, among those analyzed in the study, is data on expression of genes corresponding to the vertices with high properties of centrality.

scholarly journals Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets

2016 ◽  
Vol 113 (13) ◽  
pp. E1835-E1843 ◽  
Author(s):  
Mina Fazlollahi ◽  
Ivor Muroff ◽  
Eunjee Lee ◽  
Helen C. Causton ◽  
Harmen J. Bussemaker
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Nonsynonymous Mutation ◽  
Gene Regulatory ◽  
Genetic Modulators

Regulation of gene expression by transcription factors (TFs) is highly dependent on genetic background and interactions with cofactors. Identifying specific context factors is a major challenge that requires new approaches. Here we show that exploiting natural variation is a potent strategy for probing functional interactions within gene regulatory networks. We developed an algorithm to identify genetic polymorphisms that modulate the regulatory connectivity between specific transcription factors and their target genes in vivo. As a proof of principle, we mapped connectivity quantitative trait loci (cQTLs) using parallel genotype and gene expression data for segregants from a cross between two strains of the yeast Saccharomyces cerevisiae. We identified a nonsynonymous mutation in the DIG2 gene as a cQTL for the transcription factor Ste12p and confirmed this prediction empirically. We also identified three polymorphisms in TAF13 as putative modulators of regulation by Gcn4p. Our method has potential for revealing how genetic differences among individuals influence gene regulatory networks in any organism for which gene expression and genotype data are available along with information on binding preferences for transcription factors.

scholarly journals State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tuqyah Abdullah Al Qazlan ◽  
Aboubekeur Hamdi-Cherif ◽  
Chafia Kara-Mohamed
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Ad Hoc ◽  
Cellular Level ◽  
Biological Information ◽  
Cellular Functions ◽  
Fuzzy Methods ◽  
Gene Regulatory ◽  
Living Organisms ◽  
Different Levels

To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference. GRNs represent causal relationships between genes that have a direct influence, trough protein production, on the life and the development of living organisms and provide a useful contribution to the understanding of the cellular functions as well as the mechanisms of diseases. Fuzzy systems are based on handling imprecise knowledge, such as biological information. They provide viable computational tools for inferring GRNs from gene expression data, thus contributing to the discovery of gene interactions responsible for specific diseases and/orad hoccorrecting therapies. Increasing computational power and high throughput technologies have provided powerful means to manage these challenging digital ecosystems at different levels from cell to society globally. The main aim of this paper is to report, present, and discuss the main contributions of this multidisciplinary field in a coherent and structured framework.

scholarly journals Evolution of Mendelian Dominance in Gene Regulatory Networks Associated With Phenotypic Robustness

2021 ◽  
Author(s):  
Kenji Okubo ◽  
Kunihiko Kaneko
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Classical Case ◽  
Mendelian Inheritance ◽  
Degree Of Dominance ◽  
Gene Expression Dynamics ◽  
Phenotypic Robustness ◽  
Robustness To Noise ◽  
Gene Regulatory

Abstract Background: Mendelian inheritance is a fundamental law of genetics. Considering two alleles in a diploid, a phenotype of a heterotype is dominated by a particular homotype according to the law of dominance. This picture is usually based on simple genotype-phenotype mapping in which one gene regulates one phenotype. However, in reality, some interactions between genes can result in deviation from Mendelian dominance. Result: Here, by using the numerical evolution of diploid gene regulatory networks (GRNs), we discuss whether Mendelian dominance evolves beyond the classical case of one-to-one genotype-phenotype mapping. We examine whether complex genotype-phenotype mapping can achieve Mendelian dominance through the evolution of the GRN with interacting genes. Specifically, we extend the GRN model to a diploid case, in which two GRN matrices are added to give gene expression dynamics, and simulate evolution with meiosis and recombination. Our results reveal that Mendelian dominance evolves even under complex genotype-phenotype mapping. This dominance is achieved via a group of genotypes that differ from each other but have a common phenotype given by the expression of target genes. Calculating the degree of dominance shows that it increases through the evolution, correlating closely with the decrease in phenotypic fluctuations and the increase in robustness to initial noise. This evolution of Mendelian dominance is associated with phenotypic robustness against meiosis-induced genome mixing, whereas sexual recombination arising from the mixing of chromosomes from the parents further enhances dominance and robustness. Owing to this dominance, the robustness to genetic differences increases, while the optimal fitness is sustained up to a large difference between the two genomes. Conclusion: Mendelian dominance is achieved by groups of genotypes that are associated with the increase in phenotypic robustness to noise.

scholarly journals Neural Gene Network Constructor: A Neural Based Model for Reconstructing Gene Regulatory Network

2019 ◽  
Author(s):  
Zhang Zhang ◽  
Lifei Wang ◽  
Shuo Wang ◽  
Ruyi Tao ◽  
Jingshu Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Network ◽  
Gene Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Regulatory

SummaryReconstructing gene regulatory networks (GRNs) and inferring the gene dynamics are important to understand the behavior and the fate of the normal and abnormal cells. Gene regulatory networks could be reconstructed by experimental methods or from gene expression data. Recent advances in Single Cell RNA sequencing technology and the computational method to reconstruct trajectory have generated huge scRNA-seq data tagged with additional time labels. Here, we present a deep learning model “Neural Gene Network Constructor” (NGNC), for inferring gene regulatory network and reconstructing the gene dynamics simultaneously from time series gene expression data. NGNC is a model-free heterogenous model, which can reconstruct any network structure and non-linear dynamics. It consists of two parts: a network generator which incorporating gumbel softmax technique to generate candidate network structure, and a dynamics learner which adopting multiple feedforward neural networks to predict the dynamics. We compare our model with other well-known frameworks on the data set generated by GeneNetWeaver, and achieve the state of the arts results both on network reconstruction and dynamics learning.

scholarly journals Nuclear Transfer Arrest Embryos Show Massive Dysregulation of Genes Involved in Transcription Pathways

2021 ◽  
Vol 22 (15) ◽  
pp. 8187
Author(s):  
Chunshen Long ◽  
Hanshuang Li ◽  
Xinru Li ◽  
Wuritu Yang ◽  
Yongchun Zuo
Keyword(s):  
Gene Regulatory Networks ◽  
Nuclear Transfer ◽  
Regulatory Networks ◽  
Target Genes ◽  
Epigenetic Modification ◽  
Cell Nuclei ◽  
Activation Of Transcription ◽  
Gene Regulatory ◽  
Catabolic Process

Somatic cell nuclear transfer (SCNT) technology can reprogram terminally differentiated cell nuclei into a totipotent state. However, the underlying molecular barriers of SCNT embryo development remain incompletely elucidated. Here, we observed that transcription-related pathways were incompletely activated in nuclear transfer arrest (NTA) embryos compared to normal SCNT embryos and in vivo fertilized (WT) embryos, which hinders the development of SCNT embryos. We further revealed the transcription pathway associated gene regulatory networks (GRNs) and found the aberrant transcription pathways can lead to the massive dysregulation of genes in NTA embryos. The predicted target genes of transcription pathways contain a series of crucial factors in WT embryos, which play an important role in catabolic process, pluripotency regulation, epigenetic modification and signal transduction. In NTA embryos, however, these genes were varying degrees of inhibition and show a defect in synergy. Overall, our research found that the incomplete activation of transcription pathways is another potential molecular barrier for SCNT embryos besides the incomplete reprogramming of epigenetic modifications, broadening the understanding of molecular mechanism of SCNT embryonic development.

Data-driven Gene Regulatory Networks Inference Based on Classification Algorithms

2021 ◽  
Vol 30 (04) ◽  
pp. 2150022
Author(s):  
Sergio Peignier ◽  
Pauline Schmitt ◽  
Federica Calevro
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Data Driven ◽  
Classification Algorithms ◽  
New Family ◽  
Gene Regulatory ◽  
High Throughput Gene Expression ◽  
Inference Methods

Inferring Gene Regulatory Networks from high-throughput gene expression data is a challenging problem, addressed by the systems biology community. Most approaches that aim at unraveling the gene regulation mechanisms in a data-driven way, analyze gene expression datasets to score potential regulatory links between transcription factors and target genes. So far, three major families of approaches have been proposed to score regulatory links. These methods rely respectively on correlation measures, mutual information metrics, and regression algorithms. In this paper we present a new family of data-driven inference methods. This new family, inspired by the regression-based paradigm, relies on the use of classification algorithms. This paper assesses and advocates for the use of this paradigm as a new promising approach to infer gene regulatory networks. Indeed, the development and assessment of five new inference methods based on well-known classification algorithms shows that the classification-based inference family exhibits good results when compared to well-established paradigms.

scholarly journals Randomization Strategies Affect Motif Significance Analysis in TF-miRNA-Gene Regulatory Networks

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Sepideh Sadegh ◽  
Maryam Nazarieh ◽  
Christian Spaniol ◽  
Volkhard Helms
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Networks ◽  
Degree Distribution ◽  
Regulatory Networks ◽  
Target Genes ◽  
Mirna Gene ◽  
Biological Cells ◽  
Significance Analysis ◽  
Gene Regulatory ◽  
Randomized Networks

AbstractGene-regulatory networks are an abstract way of capturing the regulatory connectivity between transcription factors, microRNAs, and target genes in biological cells. Here, we address the problem of identifying enriched co-regulatory three-node motifs that are found significantly more often in real network than in randomized networks. First, we compare two randomization strategies, that either only conserve the degree distribution of the nodes’ in- and out-links, or that also conserve the degree distributions of different regulatory edge types. Then, we address the issue how convergence of randomization can be measured. We show that after at most 10 × |E| edge swappings, converged motif counts are obtained and the memory of initial edge identities is lost.

EVIDENCE OF SCALE-FREE TOPOLOGY IN GENE REGULATORY NETWORK OF HUMAN TISSUES

2008 ◽  
Vol 19 (02) ◽  
pp. 283-290 ◽  
Author(s):  
M. ANDRECUT ◽  
S. A. KAUFFMAN ◽  
A. M. MADNI
Keyword(s):  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Gene Network ◽  
Regulatory Networks ◽  
Regulatory Gene ◽  
Human Tissues ◽  
Scale Free ◽  
The Common ◽  
Gene Regulatory

We report the reconstruction of the topology of gene regulatory network in human tissues. The results show that the connectivity of the regulatory gene network is characterized by a scale-free distribution. This result supports the hypothesis that scale-free networks may represent the common blueprint for gene regulatory networks.

scholarly journals Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Christopher A Jackson ◽  
Dayanne M Castro ◽  
Giuseppe-Antonio Saldi ◽  
Richard Bonneau ◽  
David Gresham
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

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