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International Journal of Genomics
Volume 2017 (2017), Article ID 4858173, 12 pages
Research Article

The Transcriptional Network Structure of a Myeloid Cell: A Computational Approach

1Computational Genomics Division, National Institute of Genomic Medicine, 14610 México City, Mexico
2Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, 04510 México City, Mexico

Correspondence should be addressed to Enrique Hernández-Lemus

Received 2 February 2017; Revised 28 July 2017; Accepted 9 August 2017; Published 30 September 2017

Academic Editor: Graziano Pesole

Copyright © 2017 Jesús Espinal-Enríquez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Understanding the general principles underlying genetic regulation in eukaryotes is an incomplete and challenging endeavor. The lack of experimental information regarding the regulation of the whole set of transcription factors and their targets in different cell types is one of the main reasons to this incompleteness. So far, there is a small set of curated known interactions between transcription factors and their downstream genes. Here, we built a transcription factor network for human monocytic THP-1 myeloid cells based on the experimentally curated FANTOM4 database where nodes are genes and the experimental interactions correspond to links. We present the topological parameters which define the network as well as some global structural features and introduce a relative inuence parameter to quantify the relevance of a transcription factor in the context of induction of a phenotype. Genes like ZHX2, ADNP, or SMAD6 seem to be highly regulated to avoid an avalanche transcription event. We compare these results with those of RegulonDB, a highly curated transcriptional network for the prokaryotic organism E. coli, finding similarities between general hallmarks on both transcriptional programs. We believe that an approach, such as the one shown here, could help to understand the one regulation of transcription in eukaryotic cells.