TY - GEN
T1 - Reconstruction of a GRN model of salt stress response in arabidopsis using genetic algorithms
AU - Ruz, Gonzalo A.
AU - Timmermann, Tania
AU - Goles, Eric
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/10/16
Y1 - 2015/10/16
N2 - Salinity is one of the main problems in agriculture, negatively influencing the survival, biomass production, and yield of food crops. Exposure to high salinity is connected with ionic stress due to accumulation of sodium ions, osmotic stress, and reactive oxygen species production. To develop crop plants with enhanced tolerance of saline stress, a basic understanding of physiological, biochemical and gene regulatory networks (GRN) is essential. In this paper, an approach to study the saline stress response and tolerance of plants through the GRN involved in this process is proposed. In particular, we reconstruct the GRN of Ara-bidopsis thaliana saline stress response using genetic algorithms and a Boolean network model. The proposed computational intelligence approach was able to successfully infer 1000 threshold Boolean networks that contained the desired Boolean trajectory. The inferred networks were used to build a consensus network, which was useful to identify the regulations or interactions among the genes that were more plausible.
AB - Salinity is one of the main problems in agriculture, negatively influencing the survival, biomass production, and yield of food crops. Exposure to high salinity is connected with ionic stress due to accumulation of sodium ions, osmotic stress, and reactive oxygen species production. To develop crop plants with enhanced tolerance of saline stress, a basic understanding of physiological, biochemical and gene regulatory networks (GRN) is essential. In this paper, an approach to study the saline stress response and tolerance of plants through the GRN involved in this process is proposed. In particular, we reconstruct the GRN of Ara-bidopsis thaliana saline stress response using genetic algorithms and a Boolean network model. The proposed computational intelligence approach was able to successfully infer 1000 threshold Boolean networks that contained the desired Boolean trajectory. The inferred networks were used to build a consensus network, which was useful to identify the regulations or interactions among the genes that were more plausible.
UR - http://www.scopus.com/inward/record.url?scp=84953432544&partnerID=8YFLogxK
U2 - 10.1109/CIBCB.2015.7300306
DO - 10.1109/CIBCB.2015.7300306
M3 - Conference contribution
AN - SCOPUS:84953432544
T3 - 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015
BT - 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015
Y2 - 12 August 2015 through 15 August 2015
ER -