Detecting Drought Regulators using Stochastic Inference in Bayesian Networks Article Swipe
Aditya Lahiri
,
Lin Zhou
,
Ping He
,
Aniruddha Datta
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-73056/v1
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-73056/v1
Background: 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. Result: 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. Conclusion: 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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-73056/v1
- https://www.researchsquare.com/article/rs-73056/v1.pdf?c=1619549993000
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4246315310
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4246315310Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-73056/v1Digital Object Identifier
- Title
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Detecting Drought Regulators using Stochastic Inference in Bayesian NetworksWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-10-06Full publication date if available
- Authors
-
Aditya Lahiri, Lin Zhou, Ping He, Aniruddha DattaList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-73056/v1Publisher landing page
- PDF URL
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https://www.researchsquare.com/article/rs-73056/v1.pdf?c=1619549993000Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-73056/v1.pdf?c=1619549993000Direct OA link when available
- Concepts
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Inference, Bayesian network, Bayesian probability, Bayesian inference, Computer science, Econometrics, Artificial intelligence, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2021: 2Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Validation | 179 |
| abstract_inverted_index.activating | 131, 153 |
| abstract_inverted_index.biological | 93 |
| abstract_inverted_index.increasing | 34 |
| abstract_inverted_index.inhibiting | 134, 156 |
| abstract_inverted_index.likelihood | 116 |
| abstract_inverted_index.literature | 94 |
| abstract_inverted_index.negatively | 184 |
| abstract_inverted_index.parameters | 98 |
| abstract_inverted_index.regulators | 69, 78, 122, 174 |
| abstract_inverted_index.simulation | 112 |
| abstract_inverted_index.stochastic | 111 |
| abstract_inverted_index.strategies | 142 |
| abstract_inverted_index.worldwide, | 40 |
| abstract_inverted_index.Arabidopsis | 62 |
| abstract_inverted_index.Background: | 1 |
| abstract_inverted_index.Conclusion: | 162 |
| abstract_inverted_index.Genetically | 74 |
| abstract_inverted_index.experiments | 180 |
| abstract_inverted_index.intervening | 75, 190 |
| abstract_inverted_index.intervention | 141 |
| abstract_inverted_index.Additionally, | 149 |
| abstract_inverted_index.simultaneously | 152 |
| abstract_inverted_index.drought-resistant | 46, 82, 198 |
| abstract_inverted_index.drought-responsive | 71, 124, 146 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 93 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.59143401 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |