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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