KFGRNI: A Robust method to inference gene regulatory network from time-course gene data based on Ensemble Kalman filter

During early vertebrate development, signals from a special region of the embryo, the organizer, can re-direct the fate of non-neural ectoderm cells to form a complete, patterned nervous system. This is called neural induction and has generally been imagined as a single signaling event, causing a switch of fate. Here we undertake a comprehensive analysis, in very fine time-course, of the events following exposure of ectoderm to the organizer. Using transcriptomics and epigenomics we generate a Gene Regulatory Network comprising 175 transcriptional regulators and 5,614 predicted interactions between them, with fine temporal dynamics from initial exposure to the signals to expression of mature neural plate markers. Using in situ hybridization, single-cell RNA-sequencing and reporter assays we show that neural induction by a grafted organizer mimics normal neural plate development. The study is accompanied by a comprehensive resource including information about conservation of the predicted enhancers in different vertebrate systems.

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Dynamic gene regulatory network analysis using Saccharomyces cerevisiae large-scale time-course microarray data

2017 IEEE International Symposium on Circuits and Systems (ISCAS) ◽

10.1109/iscas.2017.8050662 ◽

2017 ◽

Author(s):

L. Zhang ◽

H. C. Wu ◽

J. Q. Lin ◽

S. C. Chan

Keyword(s):

Saccharomyces Cerevisiae ◽

Network Analysis ◽

Gene Regulatory Network ◽

Microarray Data ◽

Regulatory Network ◽

Large Scale ◽

Time Course ◽

Gene Regulatory

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Optimal gene regulatory network inference using the Boolean Kalman filter and multiple model adaptive estimation

2015 49th Asilomar Conference on Signals, Systems and Computers ◽

10.1109/acssc.2015.7421162 ◽

2015 ◽

Cited By ~ 20

Author(s):

Mahdi Imani ◽

Ulisses Braga-Neto

Keyword(s):

Kalman Filter ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Network Inference ◽

Adaptive Estimation ◽

Multiple Model ◽

Gene Regulatory Network Inference ◽

Gene Regulatory ◽

Multiple Model Adaptive Estimation

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Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq

Frontiers in Bioinformatics ◽

10.3389/fbinf.2021.777299 ◽

2021 ◽

Vol 1 ◽

Author(s):

Makoto Kashima ◽

Yuki Shida ◽

Takashi Yamashiro ◽

Hiromi Hirata ◽

Hiroshi Kurosaka

Keyword(s):

Transcription Factors ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Time Course ◽

Network Inference ◽

Molecular Mechanisms ◽

Rna Seq ◽

Gene Regulatory Network Inference ◽

Gene Regulatory ◽

Multicellular Organisms

Gene regulatory network (GRN) inference is an effective approach to understand the molecular mechanisms underlying biological events. Generally, GRN inference mainly targets intracellular regulatory relationships such as transcription factors and their associated targets. In multicellular organisms, there are both intracellular and intercellular regulatory mechanisms. Thus, we hypothesize that GRNs inferred from time-course individual (whole embryo) RNA-Seq during development can reveal intercellular regulatory relationships (signaling pathways) underlying the development. Here, we conducted time-course bulk RNA-Seq of individual mouse embryos during early development, followed by pseudo-time analysis and GRN inference. The results demonstrated that GRN inference from RNA-Seq with pseudo-time can be applied for individual bulk RNA-Seq similar to scRNA-Seq. Validation using an experimental-source-based database showed that our approach could significantly infer GRN for all transcription factors in the database. Furthermore, the inferred ligand-related and receptor-related downstream genes were significantly overlapped. Thus, the inferred GRN based on whole organism could include intercellular regulatory relationships, which cannot be inferred from scRNA-Seq based only on gene expression data. Overall, inferring GRN from time-course bulk RNA-Seq is an effective approach to understand the regulatory relationships underlying biological events in multicellular organisms.

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A Method to Construct Gene Regulatory Networks to Estimate and Calculate Time Delays

Jurnal Teknologi ◽

10.11113/jt.v62.1755 ◽

2013 ◽

Vol 62 (1) ◽

Author(s):

Mohd Saberi Mohamad ◽

Chai Suk Phin

Keyword(s):

Gene Expression ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Time Delays ◽

Time Course ◽

Target Gene ◽

Biological Research ◽

Expression Change ◽

Gene Regulatory ◽

Regulator Genes

In general, the motive of this research is to infer gene regulatory network in order to clarify the basis consequences of biological process at the molecular level. Time course gene expression profiling dataset has been widely used in basic biological research, especially in transcription regulation studies since the microarray dataset is a short time course gene expression dataset and have lots of errors, missing value, and noise. In this research, R library is implemented in this method to construct gene regulatory which aims to estimate and calculate the time delays between genes and transcription factor. Time delay is the parameters of the modeled time delay linear regression models and a time lag during gene expression change of the regulator genes toward target gene expression. The constructed gene regulatory network provided information of time delays between expression change in regulator genes and its target gene which can be applied to investigate important time-related biological process in cells. The result of time delays and regulation patterns in gene regulatory network may contribute into biological research such as cell development, cell cycle, and cell differentiation in any of living cells.

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DeltaNeTS+: Elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks

10.1101/788968 ◽

2019 ◽

Author(s):

Heeju Noh ◽

Ziyi Hua ◽

Panagiotis Chrysinas ◽

Jason E. Shoemaker ◽

Rudiyanto Gunawan

Keyword(s):

Gene Expression ◽

Gene Regulatory Network ◽

Network Structure ◽

Regulatory Network ◽

Gene Network ◽

Time Course ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Direct Gene ◽

Gene Regulatory

AbstractBackgroundKnowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes.ResultsIn this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ relies on a gene regulatory network model to identify direct perturbations to the transcription of genes. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course gene expression profiles, as well as to leverage information on the gene network structure that is increasingly becoming available for a multitude of organisms, including human. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains.ConclusionDeltaNeTS+ is an enabling tool to infer gene transcriptional perturbations caused by diseases and drugs from gene transcriptional profiles. By incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~10s). DeltaNeTS+ can freely downloaded from http://www.github.com/cabsel/deltanetsplus.

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