TY - JOUR
T1 - Regulostat Inferelator
T2 - a novel network biology platform to uncover molecular devices that predetermine cellular response phenotypes
AU - Ung, Choong Yong
AU - Bari, Mehrab Ghanat
AU - Zhang, Cheng
AU - Liang, Jingjing
AU - Correia, Cristina
AU - Li, Hu
N1 - Publisher Copyright:
© 2019 Oxford University Press. All rights reserved.
PY - 2019/8/22
Y1 - 2019/8/22
N2 - With the emergence of genome editing technologies and synthetic biology, it is now possible to engineer genetic circuits driving a cell's phenotypic response to a stressor. However, capturing a continuous response, rather than simply a binary 'on' or 'off' response, remains a bioengineering challenge. No tools currently exist to identify gene candidates responsible for predetermining and fine-tuning cell response phenotypes. To address this gap, we devised a novel Regulostat Inferelator (RSI) algorithm to decipher intrinsic molecular devices or networks that predetermine cellular phenotypic responses. The RSI algorithm is designed to extract gene expression patterns from basal transcriptomic data in order to identify 'regulostat' constituent gene pairs, which exhibit rheostat-like mode-of-cooperation capable of finetuning cellular response. Our proof-of-concept study provides computational evidence for the existence of regulostats and that these networks predetermine cellular response prior to exposure to a stressor or drug. In addition, our work, for the first time, provides evidence of context-specific, drug-regulostat interactions in predetermining drug response phenotypes in cancer cells. Given RSI-inferred regulostat networks offer insights for prioritizing gene candidates capable of rendering a resistant phenotype sensitive to a given drug, we envision that this tool will be of great value in bioengineering and medicine.
AB - With the emergence of genome editing technologies and synthetic biology, it is now possible to engineer genetic circuits driving a cell's phenotypic response to a stressor. However, capturing a continuous response, rather than simply a binary 'on' or 'off' response, remains a bioengineering challenge. No tools currently exist to identify gene candidates responsible for predetermining and fine-tuning cell response phenotypes. To address this gap, we devised a novel Regulostat Inferelator (RSI) algorithm to decipher intrinsic molecular devices or networks that predetermine cellular phenotypic responses. The RSI algorithm is designed to extract gene expression patterns from basal transcriptomic data in order to identify 'regulostat' constituent gene pairs, which exhibit rheostat-like mode-of-cooperation capable of finetuning cellular response. Our proof-of-concept study provides computational evidence for the existence of regulostats and that these networks predetermine cellular response prior to exposure to a stressor or drug. In addition, our work, for the first time, provides evidence of context-specific, drug-regulostat interactions in predetermining drug response phenotypes in cancer cells. Given RSI-inferred regulostat networks offer insights for prioritizing gene candidates capable of rendering a resistant phenotype sensitive to a given drug, we envision that this tool will be of great value in bioengineering and medicine.
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U2 - 10.1093/nar/gkz417
DO - 10.1093/nar/gkz417
M3 - Article
C2 - 31114928
AN - SCOPUS:85071708111
SN - 0305-1048
VL - 47
SP - E82
JO - Nucleic acids research
JF - Nucleic acids research
IS - 14
ER -