scholarly journals On the Impact of Entropy Estimation on Transcriptional Regulatory Network Inference Based on Mutual Information

2009 ◽  
Vol 2009 ◽  
pp. 1-9 ◽  
Author(s):  
Catharina Olsen ◽  
Patrick E. Meyer ◽  
Gianluca Bontempi
Keyword(s):  
Mutual Information ◽  
Regulatory Network ◽  
Network Inference ◽  
Transcriptional Regulatory Network ◽  
Entropy Estimation ◽  
Transcriptional Regulatory ◽  
The Impact

scholarly journals Mutual information estimation for transcriptional regulatory network inference

2017 ◽  
Author(s):  
Jonathan Ish-Horowicz ◽  
John Reid
Keyword(s):  
Mutual Information ◽  
Regulatory Networks ◽  
Network Inference ◽  
Transcriptional Regulatory Network ◽  
Estimation Method ◽  
Computation Time ◽  
Estimation Methods ◽  
Inference Algorithm ◽  
Inference Algorithms ◽  
Transcriptional Regulatory

AbstractMutual information-based network inference algorithms are an important tool in the reverse-engineering of transcriptional regulatory networks, but all rely on estimates of the mutual information between the expression of pairs of genes. Various methods exist to compute estimates of the mutual information, but none have been firmly established as optimal for network inference. The performance of 9 mutual information estimation methods are compared using three popular network inference algorithms: CLR, MRNET and ARACNE. The performance of the estimators is compared on one synthetic and two real datasets. For estimators that discretise data, the effect of discretisation parameters are also studied in detail. Implementations of 5 estimators are provided in parallelised C++ with an R interface. These are faster than alternative implementations, with reductions in computation time up to a factor of 3,500.ResultsThe B-spline estimator consistently performs well on real and synthetic datasets. CLR was found to be the best performing inference algorithm, corroborating previous results indicating that it is the state of the art mutual inference algorithm. It is also found to be robust to the mutual information estimation method and their parameters. Furthermore, when using an estimator that discretises expression data, using N1/3 bins for N samples gives the most accurate inferred network. This contradicts previous findings that suggested using N1/2 bins.

scholarly journals Identification of transcriptional regulatory network associated with response of host epithelial cells to SARS-CoV-2

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chen Su ◽  
Simon Rousseau ◽  
Amin Emad
Keyword(s):  
Gene Expression ◽  
Epithelial Cells ◽  
Regulatory Network ◽  
Transcriptional Regulatory Network ◽  
Organ Damage ◽  
Host Cells ◽  
Regulatory Mechanisms ◽  
Transcriptional Regulatory ◽  
Transcriptional Regulatory Mechanisms ◽  
The Impact

AbstractIdentification of transcriptional regulatory mechanisms and signaling networks involved in the response of host cells to infection by SARS-CoV-2 is a powerful approach that provides a systems biology view of gene expression programs involved in COVID-19 and may enable the identification of novel therapeutic targets and strategies to mitigate the impact of this disease. In this study, our goal was to identify a transcriptional regulatory network that is associated with gene expression changes between samples infected by SARS-CoV-2 and those that are infected by other respiratory viruses to narrow the results on those enriched or specific to SARS-CoV-2. We combined a series of recently developed computational tools to identify transcriptional regulatory mechanisms involved in the response of epithelial cells to infection by SARS-CoV-2, and particularly regulatory mechanisms that are specific to this virus when compared to other viruses. In addition, using network-guided analyses, we identified kinases associated with this network. The results identified pathways associated with regulation of inflammation (MAPK14) and immunity (BTK, MBX) that may contribute to exacerbate organ damage linked with complications of COVID-19. The regulatory network identified herein reflects a combination of known hits and novel candidate pathways supporting the novel computational pipeline presented herein to quickly narrow down promising avenues of investigation when facing an emerging and novel disease such as COVID-19.

scholarly journals An Overview of NCA-Based Algorithms for Transcriptional Regulatory Network Inference

Microarrays ◽  
2015 ◽  
Vol 4 (4) ◽  
pp. 596-617 ◽  
Author(s):  
Xu Wang ◽  
Mustafa Alshawaqfeh ◽  
Xuan Dang ◽  
Bilal Wajid ◽  
Amina Noor ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Network Inference ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Regulatory

scholarly journals Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells

2019 ◽  
Vol 29 (3) ◽  
pp. 449-463 ◽  
Author(s):  
Emily R. Miraldi ◽  
Maria Pokrovskii ◽  
Aaron Watters ◽  
Dayanne M. Castro ◽  
Nicholas De Veaux ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Network Inference ◽  
Transcriptional Regulatory Network ◽  
Chromatin Accessibility ◽  
T Helper ◽  
T Helper 17 ◽  
T Helper 17 Cells ◽  
Transcriptional Regulatory

scholarly journals Reconstructing blood stem cell regulatory network models from single-cell molecular profiles

2017 ◽  
Vol 114 (23) ◽  
pp. 5822-5829 ◽  
Author(s):  
Fiona K. Hamey ◽  
Sonia Nestorowa ◽  
Sarah J. Kinston ◽  
David G. Kent ◽  
Nicola K. Wilson ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Network ◽  
Network Inference ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Network Models ◽  
Cell Types ◽  
Mature Cell ◽  
Hematopoietic Stem ◽  
Transcriptional Regulatory

Adult blood contains a mixture of mature cell types, each with specialized functions. Single hematopoietic stem cells (HSCs) have been functionally shown to generate all mature cell types for the lifetime of the organism. Differentiation of HSCs toward alternative lineages must be balanced at the population level by the fate decisions made by individual cells. Transcription factors play a key role in regulating these decisions and operate within organized regulatory programs that can be modeled as transcriptional regulatory networks. As dysregulation of single HSC fate decisions is linked to fatal malignancies such as leukemia, it is important to understand how these decisions are controlled on a cell-by-cell basis. Here we developed and applied a network inference method, exploiting the ability to infer dynamic information from single-cell snapshot expression data based on expression profiles of 48 genes in 2,167 blood stem and progenitor cells. This approach allowed us to infer transcriptional regulatory network models that recapitulated differentiation of HSCs into progenitor cell types, focusing on trajectories toward megakaryocyte–erythrocyte progenitors and lymphoid-primed multipotent progenitors. By comparing these two models, we identified and subsequently experimentally validated a difference in the regulation of nuclear factor, erythroid 2 (Nfe2) and core-binding factor, runt domain, alpha subunit 2, translocated to, 3 homolog (Cbfa2t3h) by the transcription factor Gata2. Our approach confirms known aspects of hematopoiesis, provides hypotheses about regulation of HSC differentiation, and is widely applicable to other hierarchical biological systems to uncover regulatory relationships.

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