Estimation of Dynamic Systems for Gene Regulatory Networks from Dependent Time-Course Data

2018 ◽  
Vol 25 (9) ◽  
pp. 987-996 ◽  
Author(s):  
Yoonji Kim ◽  
Jaejik Kim
Keyword(s):  
Gene Regulatory Networks ◽  
Dynamic Systems ◽  
Regulatory Networks ◽  
Time Course ◽  
Time Course Data ◽  
Gene Regulatory

scholarly journals Uncovering the Gene Regulatory Networks Underlying Macrophage Polarization Through Comparative Analysis of Bulk and Single-Cell Data

2021 ◽  
Author(s):  
Klebea Carvalho ◽  
Elisabeth Rebboah ◽  
Camden Jansen ◽  
Katherine Williams ◽  
Andrew Dowey ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Course ◽  
Macrophage Polarization ◽  
Cell Polarization ◽  
Rna Seq ◽  
Gene Regulatory ◽  
Cell Subpopulations ◽  
Cell Data

SummaryGene regulatory networks (GRNs) provide a powerful framework for studying cellular differentiation. However, it is less clear how GRNs encode cellular responses to everyday microenvironmental cues. Macrophages can be polarized and potentially repolarized based on environmental signaling. In order to identify the GRNs that drive macrophage polarization and the heterogeneous single-cell subpopulations that are present in the process, we used a high-resolution time course of bulk and single-cell RNA-seq and ATAC-seq assays of HL-60-derived macrophages polarized towards M1 or M2 over 24 hours. We identified transient M1 and M2 markers, including the main transcription factors that underlie polarization, and subpopulations of naive, transitional, and terminally polarized macrophages. We built bulk and single-cell polarization GRNs to compare the recovered interactions and found that each technology recovered only a subset of known interactions. Our data provide a resource to study the GRN of cellular maturation in response to microenvironmental stimuli in a variety of contexts in homeostasis and disease.

scholarly journals A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection

2018 ◽  
Vol 27 (7) ◽  
pp. 1930-1955 ◽  
Author(s):  
Michelle Carey ◽  
Juan Camilo Ramírez ◽  
Shuang Wu ◽  
Hulin Wu
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Host Response ◽  
Regulatory Networks ◽  
Time Course ◽  
Influenza Infection ◽  
Gene Expression Omnibus ◽  
Expression Data ◽  
Gene Regulatory

A biological host response to an external stimulus or intervention such as a disease or infection is a dynamic process, which is regulated by an intricate network of many genes and their products. Understanding the dynamics of this gene regulatory network allows us to infer the mechanisms involved in a host response to an external stimulus, and hence aids the discovery of biomarkers of phenotype and biological function. In this article, we propose a modeling/analysis pipeline for dynamic gene expression data, called Pipeline4DGEData, which consists of a series of statistical modeling techniques to construct dynamic gene regulatory networks from the large volumes of high-dimensional time-course gene expression data that are freely available in the Gene Expression Omnibus repository. This pipeline has a consistent and scalable structure that allows it to simultaneously analyze a large number of time-course gene expression data sets, and then integrate the results across different studies. We apply the proposed pipeline to influenza infection data from nine studies and demonstrate that interesting biological findings can be discovered with its implementation.

scholarly journals Detecting shifts in gene regulatory networks during time-course experiments at single-time-point temporal resolution

2015 ◽  
Vol 13 (05) ◽  
pp. 1543002 ◽  
Author(s):  
Yoichi Takenaka ◽  
Shigeto Seno ◽  
Hideo Matsuda
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Course ◽  
Diauxic Shift ◽  
Gene Regulatory ◽  
Gene Regulations ◽  
Single Time Point ◽  
Time Point

Comprehensively understanding the dynamics of biological systems is one of the greatest challenges in biology. Vastly improved biological technologies have provided vast amounts of information that must be understood by bioinformatics and systems biology researchers. Gene regulations have been frequently modeled by ordinary differential equations or graphical models based on time-course gene expression profiles. The state-of-the-art computational approaches for analyzing gene regulations assume that their models are same throughout time-course experiments. However, these approaches cannot easily analyze transient changes at a time point, such as diauxic shift. We propose a score that analyzes the gene regulations at each time point. The score is based on the information gains of information criterion values. The method detects the shifts in gene regulatory networks (GRNs) during time-course experiments with single-time-point resolution. The effectiveness of the method is evaluated on the diauxic shift from glucose to lactose in Escherichia coli. Gene regulation shifts were detected at two time points: the first corresponding to the time at which the growth of E. coli ceased and the second corresponding to the end of the experiment, when the nutrient sources (glucose and lactose) had become exhausted. According to these results, the proposed score and method can appropriately detect the time of gene regulation shifts. The method based on the proposed score provides a new tool for analyzing dynamic biological systems. Because the score value indicates the strength of gene regulation at each time point in a gene expression profile, it can potentially infer hidden GRNs from time-course experiments.

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