title: Bottom-up GGM algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways or processes.
abstract: BACKGROUND
  Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways.
  
  
  RESULTS
  A bottom-up graphic Gaussian model (GGM) algorithm was developed for constructing ML-hGRN operating above a biological pathway using small- to medium-sized microarray or RNA-seq data sets. The algorithm first placed genes of a pathway at the bottom layer and began to construct a ML-hGRN by evaluating all combined triple genes: two pathway genes and one regulatory gene. The algorithm retained all triple genes where a regulatory gene significantly interfered two paired pathway genes. The regulatory genes with highest interference frequency were kept as the second layer and the number kept is based on an optimization function. Thereafter, the algorithm was used recursively to build a ML-hGRN in layer-by-layer fashion until the defined number of layers was obtained or terminated automatically.
  
  
  CONCLUSIONS
  We validated the algorithm and demonstrated its high efficiency in constructing ML-hGRNs governing biological pathways. The algorithm is instrumental for biologists to learn the hierarchical regulators associated with a given biological pathway from even small-sized microarray or RNA-seq data sets.
authors.affiliation: School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, 49931, USA.
authors.forename: Sapna
authors.initials: S
authors.lastname: Kumari
authors.affiliation: School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, 49931, USA.
authors.forename: Wenping
authors.initials: W
authors.lastname: Deng
authors.affiliation: School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, 49931, USA.
authors.forename: Chathura
authors.initials: C
authors.lastname: Gunasekara
authors.affiliation: Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, 27695, USA.
authors.forename: Vincent
authors.initials: V
authors.lastname: Chiang
authors.affiliation: Statistical Methodology and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD, 20850, USA.
authors.forename: Huann-Sheng
authors.initials: HS
authors.lastname: Chen
authors.affiliation: NCCWA, USDA ARS, Kearneysville, WV, 25430, USA.
authors.forename: Hao
authors.initials: H
authors.lastname: Ma
authors.affiliation: Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, 27695, USA.
authors.forename: Xin
authors.initials: X
authors.lastname: Davis
authors.affiliation: School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, 49931, USA. hairong@mtu.edu.
authors.forename: Hairong
authors.initials: H
authors.lastname: Wei
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics / Q000378:metabolism; D029681:Arabidopsis Proteins / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D015398:Signal Transduction / Q000235:genetics*; D059467:Transcriptome
chemical_list: D029681:Arabidopsis Proteins
keywords: Microarray or RNA-seq data; Multilayered gene regulatory network; Pathway