scholarly journals Integration of TP53, DREAM, MMB-FOXM1 and RB-E2F target gene analyses identifies cell cycle gene regulatory networks

2016 ◽  
Vol 44 (13) ◽  
pp. 6070-6086 ◽  
Author(s):  
Martin Fischer ◽  
Patrick Grossmann ◽  
Megha Padi ◽  
James A. DeCaprio
Keyword(s):  
Cell Cycle ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Gene ◽  
Cell Cycle Gene ◽  
Gene Regulatory

Using Bayesian Networks to Construct Gene Regulatory Networks from Microarray Data

2012 ◽  
pp. 1-6
Author(s):  
Ai Kung Tan ◽  
Mohd Saberi Mohamad
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Cell Cycle ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Cell Cycle Gene ◽  
Gene Regulatory ◽  
Cell Cycle Gene Expression

In this research, Bayesian network is proposed as the model to construct gene regulatory networks from Saccharomyces cerevisiae cell-cycle gene expression dataset and Escherichia coli dataset due to its capability of handling microarray datasets with missing values. The goal of this research is to study and to understand the framework of the Bayesian networks, and then to construct gene regulatory networks from Saccharomyces cerevisiae cell-cycle gene expression dataset and Escherichia coli dataset by developing Bayesian networks using hill-climbing algorithm and Efron’s bootstrap approach and then the performance of the constructed gene networks of Saccharomyces cerevisiae are evaluated and are compared with the previously constructed sub-networks by Dejori [14]. At the end of this research, the gene networks constructed for Saccharomyces cerevisiae not only have achieved high True Positive Rate (more than 90%), but the networks constructed also have discovered more potential interactions between genes. Therefore, it can be concluded that the performance of the gene regulatory networks constructed using Bayesian networks in this research is proved to be better because it can reveal more gene relationships. Dalam penyelidikan ini, Bayesian network adalah dicadangkan sebagai model untuk membina gene regulatory networks dari kitar sel S. cerevisiae set data disebabkan keupayaannya untuk mengendali set data microarray yang mempunyai nilai-nilai yang hilang. Tujuan penyelidikan ini adalah untuk mempelajari dan memahami rekabentuk untuk Bayesian network, dan kemudian untuk membina gene regulatory networks dari data Saccharomyces cerevisiae cell-cycle gene expression dan data Escherichia coli dengan membina model Bayesian networks dengan menggunakan algoritma hill-climbing serta Efron’s bootstrap approach dan gene networks yang dibina untuk Saccharomyces cerevisiae dibandingkan dengan sub-networks yang dibina oleh Dejori [14]. Pada akhir kajian ini, gene networks yang dibina untuk Saccharomyces cerevisiae bukan sahaja telah mencapai True Positive Rate yang tinggi (lebih dari 90%), tetapi gene networks yang dibina juga telah menemui lebih banyak interaksi berpotensi antara gen. Oleh kerana itu, dapat disimpulkan bahawa prestasi gene networks yang dibina menggunakan Bayesian network dalam kajian ini adalah terbukti lebih baik kerana ia boleh mendedahkan lebih banyak hubungan antara gen.

Modelling Gene Regulatory Networks Using Computational Intelligence Techniques

2010 ◽  
pp. 244-265
Author(s):  
Ramesh Ram ◽  
Madhu Chetty
Keyword(s):  
Genetic Algorithm ◽  
Cell Cycle ◽  
Gene Regulatory Networks ◽  
Computational Intelligence ◽  
Regulatory Networks ◽  
Causal Model ◽  
Yeast Cell Cycle ◽  
Computational Perspective ◽  
Modelling Techniques ◽  
Gene Regulatory

This chapter presents modelling gene regulatory networks (GRNs) using probabilistic causal model and the guided genetic algorithm. The problem of modelling is explained from both a biological and computational perspective. Further, a comprehensive methodology for developing a GRN model is presented where the application of computation intelligence (CI) techniques can be seen to be significantly important in each phase of modelling. An illustrative example of the causal model for GRN modelling is also included and applied to model the yeast cell cycle dataset. The results obtained are compared for providing biological relevance to the findings which thereby underpins the CI based modelling techniques.

MicroRNA profiling reveals dysregulated microRNAs and their target gene regulatory networks in cemento-ossifying fibroma

2017 ◽  
Vol 47 (1) ◽  
pp. 78-85 ◽  
Author(s):  
Thaís dos Santos Fontes Pereira ◽  
João Artur Ricieri Brito ◽  
André Luiz Sena Guimarães ◽  
Carolina Cavaliéri Gomes ◽  
Júlio Cesar Tanos de Lacerda ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Gene ◽  
Ossifying Fibroma ◽  
Microrna Profiling ◽  
Gene Regulatory

scholarly journals Boolean factor graph model for biological systems: the yeast cell-cycle network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Stephen Kotiang ◽  
Ali Eslami
Keyword(s):  
Cell Cycle ◽  
Yeast Cell ◽  
Gene Regulatory Networks ◽  
Biological Networks ◽  
Error Propagation ◽  
Regulatory Networks ◽  
Biological Systems ◽  
Yeast Cell Cycle ◽  
Computational Framework ◽  
Gene Regulatory

Abstract Background The desire to understand genomic functions and the behavior of complex gene regulatory networks has recently been a major research focus in systems biology. As a result, a plethora of computational and modeling tools have been proposed to identify and infer interactions among biological entities. Here, we consider the general question of the effect of perturbation on the global dynamical network behavior as well as error propagation in biological networks to incite research pertaining to intervention strategies. Results This paper introduces a computational framework that combines the formulation of Boolean networks and factor graphs to explore the global dynamical features of biological systems. A message-passing algorithm is proposed for this formalism to evolve network states as messages in the graph. In addition, the mathematical formulation allows us to describe the dynamics and behavior of error propagation in gene regulatory networks by conducting a density evolution (DE) analysis. The model is applied to assess the network state progression and the impact of gene deletion in the budding yeast cell cycle. Simulation results show that our model predictions match published experimental data. Also, our findings reveal that the sample yeast cell-cycle network is not only robust but also consistent with real high-throughput expression data. Finally, our DE analysis serves as a tool to find the optimal values of network parameters for resilience against perturbations, especially in the inference of genetic graphs. Conclusion Our computational framework provides a useful graphical model and analytical tools to study biological networks. It can be a powerful tool to predict the consequences of gene deletions before conducting wet bench experiments because it proves to be a quick route to predicting biologically relevant dynamic properties without tunable kinetic parameters.

scholarly journals RUNX1-EVI1 disrupts lineage determination and the cell cycle by interfering with RUNX1 and EVI1 driven gene regulatory networks

Haematologica ◽  
2020 ◽  
pp. haematol.2019.241885
Author(s):  
Sophie G. Kellaway ◽  
Peter Keane ◽  
Ella Kennett ◽  
Constanze Bonifer
Keyword(s):  
Cell Cycle ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Gene Regulatory

scholarly journals Inductive Inference of Gene Regulatory Network Using Supervised and Semi-supervised Graph Neural Networks

2020 ◽  
Author(s):  
Juexin Wang ◽  
Anjun Ma ◽  
Qin Ma ◽  
Dong Xu ◽  
Trupti Joshi
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Gene ◽  
Graph Embedding ◽  
Expression Data ◽  
Graph Classification ◽  
Gene Regulatory

AbstractDiscovering gene regulatory relationships and reconstructing gene regulatory networks (GRN) based on gene expression data is a classical, long-standing computational challenge in bioinformatics. Computationally inferring a possible regulatory relationship between two genes can be formulated as a link prediction problem between two nodes in a graph. Graph neural network (GNN) provides an opportunity to construct GRN by integrating topological neighbor propagation through the whole gene network. We propose an end-to-end gene regulatory graph neural network (GRGNN) approach to reconstruct GRNs from scratch utilizing the gene expression data, in both a supervised and a semi-supervised framework. To get better inductive generalization capability, GRN inference is formulated as a graph classification problem, to distinguish whether a subgraph centered at two nodes contains the link between the two nodes. A linked pair between a transcription factor (TF) and a target gene, and their neighbors are labeled as a positive subgraph, while an unlinked TF and target gene pair and their neighbors are labeled as a negative subgraph. A GNN model is constructed with node features from both explicit gene expression and graph embedding. We demonstrate a noisy starting graph structure built from partial information, such as Pearson’s correlation coefficient and mutual information can help guide the GRN inference through an appropriate ensemble technique. Furthermore, a semi-supervised scheme is implemented to increase the quality of the classifier. When compared with established methods, GRGNN achieved state-of-the-art performance on the DREAM5 GRN inference benchmarks. GRGNN is publicly available at https://github.com/juexinwang/GRGNN.HighlightsWe present a novel formulation of graph classification in inferring gene regulatory relationships from gene expression and graph embedding.Our method leverages a powerful framework, gene regulatory graph neural network (GRGNN), which is flexible and powerful to ensemble statistical powers from a number of heuristic skeletons.Our results show GRGRNN outperforms previous supervised and unsupervised methods inductively on benchmarks.GRGNN can be interpreted and explained following the biological network motif hypothesis in gene regulatory networks.

scholarly journals DNA methylation and gene expression profiles to identify childhood atopic asthma associated genes

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rui Chen ◽  
Li-Zhen Piao ◽  
Ling Liu ◽  
Xiao-Fei Zhang
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Mirna Target ◽  
Target Gene ◽  
Atopic Asthma ◽  
Mirna Target Gene ◽  
Kegg Pathways ◽  
Gene Regulatory

Abstract Background Asthma is a chronic inflammatory disorder of the airways involving many different factors. This study aimed to screen for the critical genes using DNA methylation/CpGs and miRNAs involved in childhood atopic asthma. Methods DNA methylation and gene expression data (Access Numbers GSE40732 and GSE40576) were downloaded from the Gene Expression Omnibus database. Each set contains 194 peripheral blood mononuclear cell (PBMC) samples of 97 children with atopic asthma and 97 control children. Differentially expressed genes (DEGs) with DNA methylation changes were identified. Pearson correlation analysis was used to select genes with an opposite direction of expression and differences in methylation levels, and then Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Protein–protein interaction network and miRNA–target gene regulatory networks were then constructed. Finally, important genes related to asthma were screened. Results A total of 130 critical DEGs with DNA methylation changes were screened from children with atopic asthma and compared with control samples from healthy children. GO and KEGG pathway enrichment analysis found that critical genes were primarily related to 24 GO terms and 10 KEGG pathways. In the miRNA–target gene regulatory networks, 9 KEGG pathways were identified. Analysis of the miRNA–target gene network noted an overlapping KEGG signaling pathway, hsa04060: cytokine-cytokine receptor interaction, in which the gene CCL2, directly related to asthma, was involved. This gene is targeted by eight asthma related miRNAs (hsa-miR-206, hsa-miR-19a, hsa-miR-9,hsa-miR-22, hsa-miR-33b, hsa-miR-122, hsa-miR-1, and hsa-miR-23b). The genes IL2RG and CCl4 were also involved in this pathway. Conclusions The present study provides a novel insight into the underlying molecular mechanism of childhood atopic asthma.

Modelling Gene Regulatory Networks Using Computational Intelligence Techniques

2012 ◽  
pp. 333-352
Author(s):  
Ramesh Ram ◽  
Madhu Chetty
Keyword(s):  
Genetic Algorithm ◽  
Cell Cycle ◽  
Gene Regulatory Networks ◽  
Computational Intelligence ◽  
Regulatory Networks ◽  
Causal Model ◽  
Yeast Cell Cycle ◽  
Computational Perspective ◽  
Modelling Techniques ◽  
Gene Regulatory

This chapter presents modelling gene regulatory networks (GRNs) using probabilistic causal model and the guided genetic algorithm. The problem of modelling is explained from both a biological and computational perspective. Further, a comprehensive methodology for developing a GRN model is presented where the application of computation intelligence (CI) techniques can be seen to be significantly important in each phase of modelling. An illustrative example of the causal model for GRN modelling is also included and applied to model the yeast cell cycle dataset. The results obtained are compared for providing biological relevance to the findings which thereby underpins the CI based modelling techniques.

scholarly journals Identification of Gene Regulatory Networks Governing Stemness Properties of Human HSC and LSC

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3832-3832
Author(s):  
Stanley W.K. Ng ◽  
Stephanie Zhi-Juan Xie ◽  
Elvin Wagenblast ◽  
Naoya Takayama ◽  
Liqing Jin ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Protein Interactions ◽  
Regulatory Networks ◽  
Target Gene ◽  
Gene Interactions ◽  
Statistical Regression ◽  
Hematopoietic Stem ◽  
Pathway Activity ◽  
The Core ◽  
Gene Regulatory

Abstract The gene regulatory networks (GRN) governing maintenance and expansion of normal and leukemic human hematopoietic stem-cells (HSC and LSC) are not well understood. Typically, GRNs are inferred from gene expression (GE) data of a limited subset of pre-selected genes implicated to be relevant to the cell types being studied. Such data are commonly derived from relatively homogeneous cell populations or cell lines, which do not reflect the heterogeneity of primary human samples. Importantly, there are currently no GRNs that directly interrogate the transcriptional circuitry controlling human HSC/LSC. To gain insight into the determinants of stem cell function in human HSC/LSC, we developed a unique method for building GRNs that employs GE and chromatin accessibility (ATAC-Seq) data derived from n=17 highly purified human umbilical cord blood hematopoietic stem and progenitor cell populations (hUCB-HSPC) and n=64 functionally-validated LSC-enriched and LSC-depleted cell fractions sorted from AML patient samples. Estimates of HSC/LSC frequencies based on limiting dilution xenotransplantation assays were also incorporated with statistical learning approaches to infer GRN models. Specifically, we determined transcription factor (TF) motif occurrence in HSC/LSC-enriched open chromatin regions near genes that are more highly expressed in stem versus non-stem profiles (P<0.05) to identify TF-target gene interactions in HSCs and LSCs. The effect of specific TF binding on target GE was modelled using statistical regression. A database comprising n=8,927 and n=7,916 HSC and LSC specific TF-target gene relationships, respectively, was constructed. Importantly, only a small set of n=95 TF-target gene interactions overlapped between HSC and LSC, suggesting divergent regulatory rules governing stemness maintenance, as well as differential downstream effects upon targeting of specific genes. Self-sustaining transcriptional loops between subsets of TFs were detected in HSC (ETS1, EGR1, RUNX2, FOSL1, ZNF274, ZNF683) and LSC (MEIS1, FOXK1) data, representing core regulatory hubs that are likely to be important to the maintenance of the HSC/LSC state. To determine how each gene in the transcriptome may interact with the core HSC and LSC networks, n=284,606 protein-protein interactions (PPI) between n=16,540 proteins were analyzed to define n=103,516 shortest PPI pathways connecting to the core HSC/LSC TFs. Statistical regression guided by functional data was used to identify likely HSC/LSC-relevant PPI pathway activity scores, defined as weighted combinations of constituent pathway component GE values, that were highly correlated to HSC/LSC frequency estimates from xenotransplantation assays. This generated 2 lists of n=9,948 and n=45,063 HSC- and LSC-relevant PPI pathways, respectively. We next analyzed these putative HSC/LSC-relevant pathways for points of perturbation (i.e. through gene knockdown (KD) or overexpression (OE)) that could lead to changes in stemness pathway activity scores and therefore potential HSC expansion or LSC eradication, resulting in a catalogue comprising n=976 and n=3,819 HSC and LSC targets, respectively. Prediction of several anti-LSC targets, including CDK6, XPO1, mir-126, CD47, and CD123, was supported by serial xenotransplantation data from our group and others. Furthermore, the HSC GRN correctly predicted increased HSC frequency as a consequence of mir-126 or CDK6 KD, or addition of a PROCR agonist to HSC-enriched hUCB or bone marrow. These functional validations of several GRN predictions support the overall validity of our model and accuracy of untested predictions. Collectively, we report a comprehensive resource for exploring the gene regulatory wiring and extended protein interactions that define the functional state of human HSC and LSC. The constructed GRNs can also serve as an in-silico screening platform for the systematic identification of gene/protein targets that can be exploited for clinical applications, including HSC expansion and LSC eradication. Disclosures Takayama: Megakaryon co. Ltd.: Research Funding. Zandstra:ExCellThera: Equity Ownership.

scholarly journals Discovering and Constructing ceRNA-miRNA-Target Gene Regulatory Networks during Anther Development in Maize

2019 ◽  
Vol 20 (14) ◽  
pp. 3480 ◽  
Author(s):  
Ziwen Li ◽  
Xueli An ◽  
Taotao Zhu ◽  
Tingwei Yan ◽  
Suowei Wu ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Mirna Target ◽  
Target Gene ◽  
Target Genes ◽  
Anther Development ◽  
Small Rna Sequencing ◽  
Mirna Target Gene ◽  
Gene Pairs ◽  
Gene Regulatory

The “competing endogenous RNA (ceRNA) hypothesis” has recently been proposed for a new type of gene regulatory model in many organisms. Anther development is a crucial biological process in plant reproduction, and its gene regulatory network (GRN) has been gradually revealed during the past two decades. However, it is still unknown whether ceRNAs contribute to anther development and sexual reproduction in plants. We performed RNA and small RNA sequencing of anther tissues sampled at three developmental stages in two maize lines. A total of 28,233 stably transcribed loci, 61 known and 51 potentially novel microRNAs (miRNAs) were identified from the transcriptomes. Predicted ceRNAs and target genes were found to conserve in sequences of recognition sites where their corresponding miRNAs bound. We then reconstructed 79 ceRNA-miRNA-target gene regulatory networks consisting of 51 known miRNAs, 28 potentially novel miRNAs, 619 ceRNA-miRNA pairs, and 869 miRNA-target gene pairs. More than half of the regulation pairs showed significant negative correlations at transcriptional levels. Several well-studied miRNA-target gene pairs associated with plant flower development were located in some networks, including miR156-SPL, miR159-MYB, miR160-ARF, miR164-NAC, miR172-AP2, and miR319-TCP pairs. Six target genes in the networks were found to be orthologs of functionally confirmed genes participating in anther development in plants. Our results provide an insight that the ceRNA-miRNA-target gene regulatory networks likely contribute to anther development in maize. Further functional studies on a number of ceRNAs, miRNAs, and target genes will facilitate our deep understanding on mechanisms of anther development and sexual plants reproduction.

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