Detecting Drought Regulators using Stochastic Inference in Bayesian Networks
| Autor: | Aditya Lahiri, Lin Zhou, Ping He, Aniruddha Datta |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Fruit and Seed Anatomy
Arabidopsis Inference Gene Expression Plant Science Biochemistry Gene Expression Regulation Plant Stochastic simulation Plant Growth and Development Multidisciplinary biology Transcriptional Control Plant Anatomy Eukaryota food and beverages Plants Plant Cotyledon Droughts Experimental Organism Systems Embryogenesis Medicine Network Analysis Research Article Crops Agricultural Computer and Information Sciences Science Arabidopsis Thaliana Plant Development Computational biology Brassica Research and Analysis Methods Single node Model Organisms Plant and Algal Models Gene Types Stress Physiological Natural hazard DNA-binding proteins parasitic diseases Genetics Gene Regulation Biology and life sciences Plant Embryo Anatomy Arabidopsis Proteins Water stress fungi Organisms Bayesian network Proteins Bayes Theorem biology.organism_classification Weighting Regulatory Proteins Animal Studies Regulator Genes Plant Embryogenesis Transcription Factors Reporter Genes Developmental Biology |
| Zdroj: | PLoS ONE PLoS ONE, Vol 16, Iss 8, p e0255486 (2021) |
| Popis: | Drought is a natural hazard that affects crops by inducing water stress. Water stress, induced by drought, accounts for more loss in crop yield than all the other causes combined. With the increasing frequency and intensity of droughts worldwide, it is essential to develop drought-resistant crops to ensure food security. In this paper, we model multiple drought signaling pathways in Arabidopsis using Bayesian networks to identify potential regulators of drought-responsive reporter genes. Genetically intervening at these regulators can help develop drought-resistant crops. We create the Bayesian network model from the biological literature and determine its parameters from publicly available data. We conduct inference on this model using a stochastic simulation technique known as likelihood weighting to determine the best regulators of drought-responsive reporter genes. Our analysis reveals that activating MYC2 or inhibiting ATAF1 are the best single node intervention strategies to regulate the drought-responsive reporter genes. Additionally, we observe simultaneously activating MYC2 and inhibiting ATAF1 is a better strategy. The Bayesian network model indicated that MYC2 and ATAF1 are possible regulators of the drought response. Validation experiments showed that ATAF1 negatively regulated the drought response. Thus intervening at ATAF1 has the potential to create drought-resistant crops. |
| Databáze: | OpenAIRE |
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