Temporal gene interaction regulatory networks model using temporal relation rules and 3D cube mining

2009 ◽  
Author(s):  
Meijing Li ◽  
Keun Ho Ryu
Keyword(s):  
Regulatory Networks ◽  
Gene Interaction ◽  
Temporal Relation

scholarly journals Biological network analysis with deep learning

2020 ◽  
Author(s):  
Giulia Muzio ◽  
Leslie O’Bray ◽  
Karsten Borgwardt
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Biological Networks ◽  
Protein Function ◽  
Regulatory Networks ◽  
Biological Network ◽  
Gene Interaction ◽  
Disease Diagnosis ◽  
Interaction Prediction ◽  
Graph Neural Networks

Abstract Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). In this article, we describe biological networks and review the principles and underlying algorithms of GNNs. We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein–protein interaction prediction and in silico drug discovery and development. Finally, we highlight application areas such as gene regulatory networks and disease diagnosis where deep learning is emerging as a new tool to answer classic questions like gene interaction prediction and automatic disease prediction from data.

scholarly journals Multi-Omics Data Analysis Uncovers Molecular Networks and Gene Regulators for Metabolic Biomarkers

Biomolecules ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 406
Author(s):  
Su Yon Jung
Keyword(s):  
Regulatory Networks ◽  
Full Range ◽  
Gene Interaction ◽  
Molecular Networks ◽  
Specific Gene ◽  
Gene Interaction Networks ◽  
Igf I ◽  
Genomic Studies ◽  
Metabolic Biomarkers

The insulin-like growth factors (IGFs)/insulin resistance (IR) axis is the major metabolic hormonal pathway mediating the biologic mechanism of several complex human diseases, including type 2 diabetes (T2DM) and cancers. The genomewide association study (GWAS)-based approach has neither fully characterized the phenotype variation nor provided a comprehensive understanding of the regulatory biologic mechanisms. We applied systematic genomics to integrate our previous GWAS data for IGF-I and IR with multi-omics datasets, e.g., whole-blood expression quantitative loci, molecular pathways, and gene network, to capture the full range of genetic functionalities associated with IGF-I/IR and key drivers (KDs) in gene-regulatory networks. We identified both shared (e.g., T2DM, lipid metabolism, and estimated glomerular filtration signaling) and IR-specific (e.g., mechanistic target of rapamycin, phosphoinositide 3-kinases, and erb-b2 receptor tyrosine kinase 4 signaling) molecular biologic processes of IGF-I/IR axis regulation. Next, by using tissue-specific gene–gene interaction networks, we identified both well-established (e.g., IRS1 and IGF1R) and novel (e.g., AKT1, HRAS, and JAK1) KDs in the IGF-I/IR-associated subnetworks. Our results, if validated in additional genomic studies, may provide robust, comprehensive insights into the mechanisms of IGF-I/IR regulation and highlight potential novel genetic targets as preventive and therapeutic strategies for the associated diseases, e.g., T2DM and cancers.

Construction of gene interaction and regulatory networks in bovine skeletal muscle from expression data

2005 ◽  
Vol 45 (8) ◽  
pp. 821 ◽  
Author(s):  
A. Reverter ◽  
W. Barris ◽  
N. Moreno-Sánchez ◽  
S. McWilliam ◽  
Y. H. Wang ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Transcription Factors ◽  
Regulatory Networks ◽  
Mixed Model ◽  
Gene Interaction ◽  
Expression Data ◽  
Experimental Conditions ◽  
The Usa ◽  
Biological Interpretation ◽  
Bovine Skeletal Muscle

We propose a data-driven reverse engineering approach to isolate the components of a gene interaction and regulatory network. We apply this method to the construction of a network for bovine skeletal muscle. Key nodes in the network include muscle-specific genes and transcription factors. muscle-specific genes are identified from data mining the USA National Cancer Institute, Cancer Genome Anatomy Project database, while transcription factors are predicted by accurate function annotation. A total of 5 microarray studies spanning 78 hybridisations and 23 different experimental conditions provided raw expression data. A recently-reported analytical method based on multivariate mixed-model equations is used to compute gene co-expression measures across 624 genes. The resulting network included 102 genes (of which 40 were muscle-specific genes and 7 were transcription factors) that clustered in 7 distinct modules with clear biological interpretation.

scholarly journals Potential Breast Anticancer Drug Targets Revealed by Differential Gene Regulatory Network Analysis and Molecular Docking: Neoadjuvant Docetaxel Drug as a Case Study

2018 ◽  
Vol 17 ◽  
pp. 117693511875535 ◽  
Author(s):  
Adel Aloraini ◽  
Karim M ElSawy
Keyword(s):  
Anticancer Drug ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Gene Interaction ◽  
Protein Docking ◽  
Gene Interactions ◽  
Ras Gene ◽  
Individual Gene ◽  
Gene Regulatory

Understanding gene-gene interaction and its causal relationship to protein-protein interaction is a viable route for understanding drug action at the genetic level, which is largely hindered by inability to robustly map gene regulatory networks. Here, we use biological prior knowledge of family-to-family gene interactions available in the KEGG database to reveal individual gene-to-gene interaction networks that underlie the gene expression profiles of 2 cell line data sets, sensitive and resistive to neoadjuvant docetaxel breast anticancer drug. Comparison of the topology of the 2 networks revealed that the resistant network is highly connected with 2 large domains of connectivity: one in which the RAF1 and MAP2K2 genes form hubs of connectivity and another in which the RAS gene is highly connected. On the contrary, the sensitive network is highly disrupted with a lower degree of connectivity. We investigated the interactions of the neoadjuvant docetaxel drug with the protein chains encoded by gene-gene interactions that underlie the disruption of the sensitive network topology using protein-protein and drug-protein docking techniques. We found that the sensitive network is likely to be disrupted by interaction of the neoadjuvant docetaxel drug with the DAXX and FGR1 proteins, which is consistent with the observed accumulation of cytoplasmic DAXX and overexpression of FGR1 precursors in cancer cell lines. This indicates that the DAXX and FGR1 proteins could be potential targets for the neoadjuvant docetaxel drug. The work, therefore, provides a new route for understanding the effect of the drug mode of action from the viewpoint of the change in the topology of gene-gene regulatory networks and provides a new avenue for bridging the gap between gene-gene interactions and protein-protein interactions which could have deep implications on mainstream drug development protocols.

scholarly journals Analysis of single-cell gene pair coexpression landscapes by stochastic kinetic modeling reveals gene-pair interactions in development

2019 ◽  
Author(s):  
Cameron P. Gallivan ◽  
Honglei Ren ◽  
Elizabeth L. Read
Keyword(s):  
Single Cell ◽  
Gene Pair ◽  
Gene Network ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Complex Dynamics ◽  
Gene Interaction ◽  
State Transitions ◽  
Sequencing Data ◽  
Cell Gene

ABSTRACTSingle-cell transcriptomics is advancing discovery of the molecular determinants of cell identity, while spurring development of novel data analysis methods. Stochastic mathematical models of gene regulatory networks help unravel the dynamic, molecular mechanisms underlying cell-to-cell heterogeneity, and can thus aid interpretation of heterogeneous cell-states revealed by single-cell measurements. However, integrating stochastic gene network models with single cell data is challenging. Here, we present a method for analyzing single-cell gene-pair coexpression patterns, based on biophysical models of stochastic gene expression and interaction dynamics. We first developed a high-computational-throughput approach to stochastic modeling of gene-pair coexpression landscapes, based on numerical solution of gene network Master Equations. We then comprehensively catalogued coexpression patterns arising from tens of thousands of gene-gene interaction models with different biochemical kinetic parameters and regulatory interactions. From the computed landscapes, we obtain a low-dimensional “shape-space” describing distinct types of coexpression patterns. We applied the theoretical results to analysis of published single cell RNA sequencing data and uncovered complex dynamics of coexpression among gene pairs during embryonic development. Our approach provides a generalizable framework for inferring evolution of gene-gene interactions during critical cell-state transitions.

scholarly journals Molecular Biology Networks and Key Gene Regulators for Inflammatory Biomarkers Shared by Breast Cancer Development: Multi-Omics Systems Analysis

Biomolecules ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1379
Author(s):  
Su Yon Jung ◽  
Jeanette C. Papp ◽  
Matteo Pellegrini ◽  
Herbert Yu ◽  
Eric M. Sobel
Keyword(s):  
Breast Cancer ◽  
Regulatory Networks ◽  
Inflammatory Diseases ◽  
Association Studies ◽  
Full Range ◽  
Gene Interaction ◽  
Interferon Γ ◽  
Inflammatory Biomarkers ◽  
Genome Wide Association Studies ◽  
Tissue Specific

As key inflammatory biomarkers C-reactive protein (CRP) and interleukin-6 (IL6) play an important role in the pathogenesis of non-inflammatory diseases, including specific cancers, such as breast cancer (BC). Previous genome-wide association studies (GWASs) have neither explained the large proportion of genetic heritability nor provided comprehensive understanding of the underlying regulatory mechanisms. We adopted an integrative genomic network approach by incorporating our previous GWAS data for CRP and IL6 with multi-omics datasets, such as whole-blood expression quantitative loci, molecular biologic pathways, and gene regulatory networks to capture the full range of genetic functionalities associated with CRP/IL6 and tissue-specific key drivers (KDs) in gene subnetworks. We applied another systematic genomics approach for BC development to detect shared gene sets in enriched subnetworks across BC and CRP/IL6. We detected the topmost significant common pathways across CRP/IL6 (e.g., immune regulatory; chemokines and their receptors; interferon γ, JAK-STAT, and ERBB4 signaling), several of which overlapped with BC pathways. Further, in gene–gene interaction networks enriched by those topmost pathways, we identified KDs—both well-established (e.g., JAK1/2/3, STAT3) and novel (e.g., CXCR3, CD3D, CD3G, STAT6)—in a tissue-specific manner, for mechanisms shared in regulating CRP/IL6 and BC risk. Our study may provide robust, comprehensive insights into the mechanisms of CRP/IL6 regulation and highlight potential novel genetic targets as preventive and therapeutic strategies for associated disorders, such as BC.

Homology, Genes, and Evolutionary Innovation

2014 ◽  
Author(s):  
Günter P. Wagner
Keyword(s):  
Evolutionary Biology ◽  
Regulatory Networks ◽  
Biological Diversity ◽  
Cell Types ◽  
Origin And Evolution ◽  
Major Transitions ◽  
Form And Function ◽  
Historical Continuity ◽  
Evolutionary Innovation ◽  
Character Identity

Homology—a similar trait shared by different species and derived from common ancestry, such as a seal's fin and a bird's wing—is one of the most fundamental yet challenging concepts in evolutionary biology. This book provides the first mechanistically based theory of what homology is and how it arises in evolution. The book argues that homology, or character identity, can be explained through the historical continuity of character identity networks—that is, the gene regulatory networks that enable differential gene expression. It shows how character identity is independent of the form and function of the character itself because the same network can activate different effector genes and thus control the development of different shapes, sizes, and qualities of the character. Demonstrating how this theoretical model can provide a foundation for understanding the evolutionary origin of novel characters, the book applies it to the origin and evolution of specific systems, such as cell types; skin, hair, and feathers; limbs and digits; and flowers. The first major synthesis of homology to be published in decades, this book reveals how a mechanistically based theory can serve as a unifying concept for any branch of science concerned with the structure and development of organisms, and how it can help explain major transitions in evolution and broad patterns of biological diversity.

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