PMID- 26568633
OWN - NLM
STAT- MEDLINE
DCOM- 20170804
LR  - 20181202
IS  - 1367-4811 (Electronic)
IS  - 1367-4803 (Print)
IS  - 1367-4803 (Linking)
VI  - 32
IP  - 6
DP  - 2016 Mar 15
TI  - Optimal design of gene knockout experiments for gene regulatory network 
      inference.
PG  - 875-83
LID - 10.1093/bioinformatics/btv672 [doi]
AB  - MOTIVATION: We addressed the problem of inferring gene regulatory network (GRN) 
      from gene expression data of knockout (KO) experiments. This inference is known 
      to be underdetermined and the GRN is not identifiable from data. Past studies 
      have shown that suboptimal design of experiments (DOE) contributes significantly 
      to the identifiability issue of biological networks, including GRNs. However, 
      optimizing DOE has received much less attention than developing methods for GRN 
      inference. RESULTS: We developed REDuction of UnCertain Edges (REDUCE) algorithm 
      for finding the optimal gene KO experiment for inferring directed graphs 
      (digraphs) of GRNs. REDUCE employed ensemble inference to define uncertain gene 
      interactions that could not be verified by prior data. The optimal experiment 
      corresponds to the maximum number of uncertain interactions that could be 
      verified by the resulting data. For this purpose, we introduced the concept of 
      edge separatoid which gave a list of nodes (genes) that upon their removal would 
      allow the verification of a particular gene interaction. Finally, we proposed a 
      procedure that iterates over performing KO experiments, ensemble update and 
      optimal DOE. The case studies including the inference of Escherichia coli GRN and 
      DREAM 4 100-gene GRNs, demonstrated the efficacy of the iterative GRN inference. 
      In comparison to systematic KOs, REDUCE could provide much higher information 
      return per gene KO experiment and consequently more accurate GRN estimates. 
      CONCLUSIONS: REDUCE represents an enabling tool for tackling the underdetermined 
      GRN inference. Along with advances in gene deletion and automation technology, 
      the iterative procedure brings an efficient and fully automated GRN inference 
      closer to reality. AVAILABILITY AND IMPLEMENTATION: MATLAB and Python scripts of 
      REDUCE are available on www.cabsel.ethz.ch/tools/REDUCE CONTACT: 
      rudi.gunawan@chem.ethz.ch SUPPLEMENTARY INFORMATION: Supplementary data are 
      available at Bioinformatics online.
CI  - (c) The Author 2015. Published by Oxford University Press.
FAU - Ud-Dean, S M Minhaz
AU  - Ud-Dean SM
AD  - Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland and 
      Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland and.
FAU - Gunawan, Rudiyanto
AU  - Gunawan R
AD  - Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland and 
      Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland and.
LA  - eng
PT  - Journal Article
DEP - 20151114
PL  - England
TA  - Bioinformatics
JT  - Bioinformatics (Oxford, England)
JID - 9808944
SB  - IM
MH  - Algorithms
MH  - Escherichia coli
MH  - Gene Expression
MH  - *Gene Knockout Techniques
MH  - *Gene Regulatory Networks
PMC - PMC4803391
EDAT- 2015/11/17 06:00
MHDA- 2017/08/05 06:00
PMCR- 2015/11/14
CRDT- 2015/11/17 06:00
PHST- 2015/07/19 00:00 [received]
PHST- 2015/11/09 00:00 [accepted]
PHST- 2015/11/17 06:00 [entrez]
PHST- 2015/11/17 06:00 [pubmed]
PHST- 2017/08/05 06:00 [medline]
PHST- 2015/11/14 00:00 [pmc-release]
AID - btv672 [pii]
AID - 10.1093/bioinformatics/btv672 [doi]
PST - ppublish
SO  - Bioinformatics. 2016 Mar 15;32(6):875-83. doi: 10.1093/bioinformatics/btv672. 
      Epub 2015 Nov 14.