ArticleJournal of experimental botany2019
A framework for genomics-informed ecophysiological modeling in plants.
Article in Journal of experimental botany, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Genome-Enabled Prediction Methods Based on Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2022Pooled it
- Characterization of transcriptional and metabolic responses to a complex plant growth-promoting soil inoculum.Plant biology (Stuttgart, Germany) · 2025Article
- Incorporating information of causal variants in genomic prediction using GBLUP or machine learning models in a simulated livestock population.Journal of animal science and biotechnology · 2025Article
- Dynamic relationships among pathways producing hydrocarbons and fatty acids of maize silk cuticular waxes.Plant physiology · 2024Article
- Positive effects of public breeding on US rice yields under future climate scenarios.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Development and applications of metabolic models in plant multi-omics research.Frontiers in plant science · 2024Review
- Article
- Using Genomic Selection to Develop Performance-Based Restoration Plant Materials.International journal of molecular sciences · 2022Review
- Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling.Frontiers in plant science · 2021Review
- Use of hydraulic traits for modeling genotype-specific acclimation in cotton under drought.The New phytologist · 2020Article
- Leveraging genome-enabled growth models to study shoot growth responses to water deficit in rice.Journal of experimental botany · 2020Article
- Rapid ChlorophyllPlant physiology · 2020Article
- Emerging Advanced Technologies to Mitigate the Impact of Climate Change in Africa.Plants (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dynamic process-based plant models capture complex physiological response across time, carrying the potential to extend simulations out to novel environments and lend mechanistic insight to observed phenotypes. Despite the translational opportunities for varietal crop improvement that could be unlocked by linking natural genetic variation to first principles-based modeling, these models are challenging to apply to large populations of related individuals. Here we use a combination of model development, experimental evaluation, and genomic prediction in Brassica rapa L. to set the stage for future large-scale process-based modeling of intraspecific variation. We develop a new canopy growth submodel for B. rapa within the process-based model Terrestrial Regional Ecosystem Exchange Simulator (TREES), test input parameters for feasibility of direct estimation with observed phenotypes across cultivated morphotypes and indirect estimation using genomic prediction on a recombinant inbred line population, and explore model performance on an in silico population under non-stressed and mild water-stressed conditions. We find evidence that the updated whole-plant model has the capacity to distill genotype by environment interaction (G×E) into tractable components. The framework presented offers a means to link genetic variation with environment-modulated plant response and serves as a stepping stone towards large-scale prediction of unphenotyped, genetically related individuals under untested environmental scenarios.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.