Pub Date : 2024-08-30eCollection Date: 2024-01-01DOI: 10.1093/insilicoplants/diae015
Charles Hodgens, D T Flaherty, Anne-Marie Pullen, Imran Khan, Nolan J English, Lydia Gillan, Marcela Rojas-Pierce, Belinda S Akpa
Guard cell movements depend, in part, on the remodelling of vacuoles from a highly fragmented state to a fused morphology during stomata opening. Indeed, full opening of plant stomata requires vacuole fusion to occur. Fusion of vacuole membranes is a highly conserved process in eukaryotes, with key roles played by two multi-subunit complexes: HOPS (homotypic fusion and vacuolar protein sorting) and SNARE (soluble NSF attachment protein receptor). HOPS is a vacuole tethering factor that is thought to chaperone SNAREs from apposing vacuole membranes into a fusion-competent complex capable of rearranging membranes. In plants, recruitment of HOPS subunits to the tonoplast has been shown to require the presence of the phosphoinositide phosphatidylinositol 3-phosphate. However, chemically depleting this lipid induces vacuole fusion. To resolve this counter-intuitive observation regarding the role of HOPS in regulating plant vacuole morphology, we defined a quantitative model of vacuole fusion dynamics and used it to generate testable predictions about HOPS-SNARE interactions. We derived our model by using simulation-based inference to integrate prior knowledge about molecular interactions with limited, qualitative observations of emergent vacuole phenotypes. By constraining the model parameters to yield the emergent outcomes observed for stoma opening-as induced by two distinct chemical treatments-we predicted a dual role for HOPS and identified a stalled form of the SNARE complex that differs from phenomena reported in yeast. We predict that HOPS has contradictory actions at different points in the fusion signalling pathway, promoting the formation of SNARE complexes, but limiting their activity.
{"title":"Model-based inference of a dual role for HOPS in regulating guard cell vacuole fusion.","authors":"Charles Hodgens, D T Flaherty, Anne-Marie Pullen, Imran Khan, Nolan J English, Lydia Gillan, Marcela Rojas-Pierce, Belinda S Akpa","doi":"10.1093/insilicoplants/diae015","DOIUrl":"https://doi.org/10.1093/insilicoplants/diae015","url":null,"abstract":"<p><p>Guard cell movements depend, in part, on the remodelling of vacuoles from a highly fragmented state to a fused morphology during stomata opening. Indeed, full opening of plant stomata requires vacuole fusion to occur. Fusion of vacuole membranes is a highly conserved process in eukaryotes, with key roles played by two multi-subunit complexes: HOPS (homotypic fusion and vacuolar protein sorting) and SNARE (soluble NSF attachment protein receptor). HOPS is a vacuole tethering factor that is thought to chaperone SNAREs from apposing vacuole membranes into a fusion-competent complex capable of rearranging membranes. In plants, recruitment of HOPS subunits to the tonoplast has been shown to require the presence of the phosphoinositide phosphatidylinositol 3-phosphate. However, chemically depleting this lipid induces vacuole fusion. To resolve this counter-intuitive observation regarding the role of HOPS in regulating plant vacuole morphology, we defined a quantitative model of vacuole fusion dynamics and used it to generate testable predictions about HOPS-SNARE interactions. We derived our model by using simulation-based inference to integrate prior knowledge about molecular interactions with limited, qualitative observations of emergent vacuole phenotypes. By constraining the model parameters to yield the emergent outcomes observed for stoma opening-as induced by two distinct chemical treatments-we predicted a dual role for HOPS and identified a stalled form of the SNARE complex that differs from phenomena reported in yeast. We predict that HOPS has contradictory actions at different points in the fusion signalling pathway, promoting the formation of SNARE complexes, but limiting their activity.</p>","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"6 2","pages":"diae015"},"PeriodicalIF":2.6,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11599693/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142751899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-12DOI: 10.1093/insilicoplants/diad023
Simone Bregaglio, Giulia Carriero, Roberta Calone, Maddalena Romano, Sofia Bajocco
Simulation models are primary tools for synthesizing plant physiological knowledge, supporting farmers’ decisions, and predicting crop yields and functioning under climate change. The conventional approach within the scientific community consists of disseminating model outcomes through articles and technical reports, often impeding the share of knowledge among science, policy, and society. This work presents the mandala (modeled and abstracted plant), a simulation model translating crop phenology and physiology as a function of environmental drivers into symbols and sounds, focusing on plant responses to cold, drought, and heat stresses. The mandala has been realized with object-oriented (C#) and visual (vvvv) programming, and the source code is free for extension and improvement. We tested the mandala in six heterogeneous climates to show the potential to convey essential information on maize and wheat growth and responses to abiotic stresses. Despite lacking in artistic refinement, this work attempts to illustrate that visual and sound art can serve as unconventional means of disseminating crop model insights while showing their potential to enhance the breadth of information delivered to the public.
{"title":"Playing a crop simulation model using symbols and sounds: the ‘mandala’","authors":"Simone Bregaglio, Giulia Carriero, Roberta Calone, Maddalena Romano, Sofia Bajocco","doi":"10.1093/insilicoplants/diad023","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad023","url":null,"abstract":"\u0000 Simulation models are primary tools for synthesizing plant physiological knowledge, supporting farmers’ decisions, and predicting crop yields and functioning under climate change. The conventional approach within the scientific community consists of disseminating model outcomes through articles and technical reports, often impeding the share of knowledge among science, policy, and society. This work presents the mandala (modeled and abstracted plant), a simulation model translating crop phenology and physiology as a function of environmental drivers into symbols and sounds, focusing on plant responses to cold, drought, and heat stresses. The mandala has been realized with object-oriented (C#) and visual (vvvv) programming, and the source code is free for extension and improvement. We tested the mandala in six heterogeneous climates to show the potential to convey essential information on maize and wheat growth and responses to abiotic stresses. Despite lacking in artistic refinement, this work attempts to illustrate that visual and sound art can serve as unconventional means of disseminating crop model insights while showing their potential to enhance the breadth of information delivered to the public.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"18 9","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139010107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-08DOI: 10.1093/insilicoplants/diad022
D. Helmrich, F. Bauer, Mona Giraud, Andrea Schnepf, J. Göbbert, H. Scharr, E. Hvannberg, Morris Riedel
In plant science it is an established method to obtain structural parameters of crops using image analysis. In recent years, deep learning techniques have improved the underlying processes significantly. However, since data acquisition is time and resource consuming, reliable training data is currently limiting. To overcome this bottleneck, synthetic data is a promising option for not only enabling a higher order of correctness by offering more training data, but also for validation of results. However, the creation of synthetic data is complex and requires extensive knowledge in Computer Graphics, Visualization and High-Performance Computing. We address this by introducing Synavis, a framework that allows users to train networks on real-time generated data. We created a pipeline that integrates realistic plant structures, simulated by the functional-structural plant model framework CPlantBox, into the game engine Unreal Engine. For this purpose, we needed to extend CPlantBox by introducing a new leaf geometrization that results in realistic leafs. All parameterized geometries of the plant are directly provided by the plant model. In the Unreal Engine, it is possible to alter the environment. WebRTC enables the streaming of the final image composition, which in turn can then be directly used to train deep neural networks to increase parameter robustness, for further plant trait detection and validation of original parameters. We enable user-friendly ready-to-use pipelines, providing virtual plant experiment and field visualizations, a python-binding library to access synthetic data, and a ready-to-run example to train models.
{"title":"A Scalable Pipeline to Create Synthetic Datasets from Functional-Structural Plant Models for Deep Learning","authors":"D. Helmrich, F. Bauer, Mona Giraud, Andrea Schnepf, J. Göbbert, H. Scharr, E. Hvannberg, Morris Riedel","doi":"10.1093/insilicoplants/diad022","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad022","url":null,"abstract":"\u0000 In plant science it is an established method to obtain structural parameters of crops using image analysis. In recent years, deep learning techniques have improved the underlying processes significantly. However, since data acquisition is time and resource consuming, reliable training data is currently limiting. To overcome this bottleneck, synthetic data is a promising option for not only enabling a higher order of correctness by offering more training data, but also for validation of results. However, the creation of synthetic data is complex and requires extensive knowledge in Computer Graphics, Visualization and High-Performance Computing. We address this by introducing Synavis, a framework that allows users to train networks on real-time generated data. We created a pipeline that integrates realistic plant structures, simulated by the functional-structural plant model framework CPlantBox, into the game engine Unreal Engine. For this purpose, we needed to extend CPlantBox by introducing a new leaf geometrization that results in realistic leafs. All parameterized geometries of the plant are directly provided by the plant model. In the Unreal Engine, it is possible to alter the environment. WebRTC enables the streaming of the final image composition, which in turn can then be directly used to train deep neural networks to increase parameter robustness, for further plant trait detection and validation of original parameters.\u0000 We enable user-friendly ready-to-use pipelines, providing virtual plant experiment and field visualizations, a python-binding library to access synthetic data, and a ready-to-run example to train models.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"32 10","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138587674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-11DOI: 10.1093/insilicoplants/diad021
Serena Lotreck, Kenia Segura Abá, Melissa Lehti-Shiu, Abigail Seeger, Brianna N I Brown, Thilanka Ranaweera, Ally Schumacher, Mohammad Ghassemi, Shin-Han Shiu
Abstract Natural language processing (NLP) techniques can enhance our ability to interpret plant science literature. Many state-of-the-art algorithms for NLP tasks require high-quality labeled data in the target domain, in which entities like genes and proteins, as well as the relationships between entities are labeled according to a set of annotation guidelines. While there exist such datasets for other domains, these resources need development in the plant sciences. Here, we present the Plant ScIenCe KnowLedgE Graph (PICKLE) corpus, a collection of 250 plant science abstracts annotated with entities and relations, along with its annotation guidelines. The annotation guidelines were refined by iterative rounds of overlapping annotations, in which inter-annotator agreement was leveraged to improve the guidelines. To demonstrate PICKLE’s utility, we evaluated the performance of pretrained models from other domains and trained a new, PICKLE-based model for entity and relation extraction. The PICKLE-trained models exhibit the second-highest in-domain entity performance of all models evaluated, as well as a relation extraction performance that is on par with other models. Additionally, we found that computer science-domain models outperformed models trained on a biomedical corpus (GENIA) in entity extraction, which was unexpected given the intuition that biomedical literature is more similar to PICKLE than computer science. Upon further exploration, we established that the inclusion of new types on which the models were not trained substantially impacts performance. The PICKLE corpus is therefore an important contribution to training resources for entity and relation extraction in the plant sciences.
{"title":"In a PICKLE: A gold standard entity and relation corpus for the molecular plant sciences","authors":"Serena Lotreck, Kenia Segura Abá, Melissa Lehti-Shiu, Abigail Seeger, Brianna N I Brown, Thilanka Ranaweera, Ally Schumacher, Mohammad Ghassemi, Shin-Han Shiu","doi":"10.1093/insilicoplants/diad021","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad021","url":null,"abstract":"Abstract Natural language processing (NLP) techniques can enhance our ability to interpret plant science literature. Many state-of-the-art algorithms for NLP tasks require high-quality labeled data in the target domain, in which entities like genes and proteins, as well as the relationships between entities are labeled according to a set of annotation guidelines. While there exist such datasets for other domains, these resources need development in the plant sciences. Here, we present the Plant ScIenCe KnowLedgE Graph (PICKLE) corpus, a collection of 250 plant science abstracts annotated with entities and relations, along with its annotation guidelines. The annotation guidelines were refined by iterative rounds of overlapping annotations, in which inter-annotator agreement was leveraged to improve the guidelines. To demonstrate PICKLE’s utility, we evaluated the performance of pretrained models from other domains and trained a new, PICKLE-based model for entity and relation extraction. The PICKLE-trained models exhibit the second-highest in-domain entity performance of all models evaluated, as well as a relation extraction performance that is on par with other models. Additionally, we found that computer science-domain models outperformed models trained on a biomedical corpus (GENIA) in entity extraction, which was unexpected given the intuition that biomedical literature is more similar to PICKLE than computer science. Upon further exploration, we established that the inclusion of new types on which the models were not trained substantially impacts performance. The PICKLE corpus is therefore an important contribution to training resources for entity and relation extraction in the plant sciences.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"6 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135087098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-08DOI: 10.1093/insilicoplants/diad018
Maleana G Khoury, Kenneth S Berenhaut, Katherine E Moore, Edward E Allen, Alexandria F Harkey, Joëlle K Mühlemann, Courtney N Craven, Jiayi Xu, Suchi S Jain, David J John, James L Norris, Gloria K Muday
Abstract Transcriptome studies that provide temporal information about transcript abundance facilitate identification of gene regulatory networks (GRNs). Inferring GRNs from time series data using computational modeling remains a central challenge in systems biology. Commonly employed clustering algorithms identify modules of like-responding genes but do not provide information on how these modules are interconnected. These methods also require users to specify parameters such as cluster number and size, adding complexity to the analysis. To address these challenges, we employed a recently developed algorithm, Partitioned Local Depth (PaLD), to generate cohesive networks for 4 time series transcriptome datasets (3 hormone and 1 abiotic stress dataset) from the model plant Arabidopsis thaliana. PaLD provided a cohesive network representation of the data, revealing networks with distinct structures and varying numbers of connections between transcripts. We utilized the networks to make predictions about GRNs by examining local neighborhoods of transcripts with highly similar temporal responses. We also partitioned the networks into groups of like-responding transcripts and identified enriched functional and regulatory features in them. Comparison of groups to clusters generated by commonly used approaches indicated that these methods identified modules of transcripts that have similar temporal and biological features, but also identified unique groups, suggesting a PaLD-based approach (supplemented with a community detection algorithm) can complement existing methods. These results revealed that PaLD could sort like-responding transcripts into biologically meaningful neighborhoods and groups while requiring minimal user input and producing cohesive network structure, offering an additional tool to the systems biology community to predict GRNs.
转录组研究提供了转录丰度的时间信息，有助于识别基因调控网络(grn)。利用计算模型从时间序列数据推断grn仍然是系统生物学的核心挑战。常用的聚类算法识别相似响应基因的模块，但不提供这些模块如何相互连接的信息。这些方法还要求用户指定参数，如簇数和大小，这增加了分析的复杂性。为了解决这些挑战，我们采用了最近开发的一种算法，Partitioned Local Depth (PaLD)，为来自模式植物拟南芥的4个时间序列转录组数据集(3个激素和1个非生物胁迫数据集)生成内聚网络。PaLD提供了数据的内聚网络表示，揭示了具有不同结构和转录本之间不同数量连接的网络。我们利用该网络通过检查具有高度相似时间响应的转录本的局部邻域来预测grn。我们还将网络划分为类似响应的转录本组，并确定了其中丰富的功能和调控特征。将常用方法生成的组与聚类进行比较表明，这些方法识别出具有相似时间和生物学特征的转录本模块，但也识别出独特的组，这表明基于pald的方法(辅以群落检测算法)可以补充现有方法。这些结果表明，PaLD可以将类似响应的转录本分类到生物学上有意义的邻域和组中，同时需要最少的用户输入并产生内聚的网络结构，为系统生物学社区预测grn提供了额外的工具。
{"title":"Informative community structure revealed using Arabidopsis time series transcriptome data via Partitioned Local Depth","authors":"Maleana G Khoury, Kenneth S Berenhaut, Katherine E Moore, Edward E Allen, Alexandria F Harkey, Joëlle K Mühlemann, Courtney N Craven, Jiayi Xu, Suchi S Jain, David J John, James L Norris, Gloria K Muday","doi":"10.1093/insilicoplants/diad018","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad018","url":null,"abstract":"Abstract Transcriptome studies that provide temporal information about transcript abundance facilitate identification of gene regulatory networks (GRNs). Inferring GRNs from time series data using computational modeling remains a central challenge in systems biology. Commonly employed clustering algorithms identify modules of like-responding genes but do not provide information on how these modules are interconnected. These methods also require users to specify parameters such as cluster number and size, adding complexity to the analysis. To address these challenges, we employed a recently developed algorithm, Partitioned Local Depth (PaLD), to generate cohesive networks for 4 time series transcriptome datasets (3 hormone and 1 abiotic stress dataset) from the model plant Arabidopsis thaliana. PaLD provided a cohesive network representation of the data, revealing networks with distinct structures and varying numbers of connections between transcripts. We utilized the networks to make predictions about GRNs by examining local neighborhoods of transcripts with highly similar temporal responses. We also partitioned the networks into groups of like-responding transcripts and identified enriched functional and regulatory features in them. Comparison of groups to clusters generated by commonly used approaches indicated that these methods identified modules of transcripts that have similar temporal and biological features, but also identified unique groups, suggesting a PaLD-based approach (supplemented with a community detection algorithm) can complement existing methods. These results revealed that PaLD could sort like-responding transcripts into biologically meaningful neighborhoods and groups while requiring minimal user input and producing cohesive network structure, offering an additional tool to the systems biology community to predict GRNs.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"14 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135430060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-08DOI: 10.1093/insilicoplants/diad020
Achraf Mamassi, Marie Lang, Bernard Tychon, Mouanis Lahlou, Joost Wellens, Mohamed El Gharous, Hélène Marrou
Abstract In the context of climate change, in-season and longer-term yield predictions are needed to anticipate local and regional food crises and propose adaptations to farmers’ practices. Mechanistic models and machine learning are two modelling options to consider in this perspective. In this study, regression (MR) and Random Forest (RF) models were calibrated for wheat yield prediction in Morocco, using data collected from 125 farmers’ wheat fields. Additionally , MR and RF models were calibrated both with or without remotely-sensed leaf area index (LAI), while considering all farmers’ fields, or specifically to agroecological zoning in Morocco. The same farmers’ fields were simulated using a mechanistic model (APSIM-wheat). We compared the predictive performances of the empirical models and APSIM-wheat. Results showed that both MR and RF showed rather good predictive quality (NRMSEs below 35%), but were always outperformed by APSIM model. Both RF and MR selected remotely-sensed LAI at heading, climate variables (maximal temperatures at emergence and tillering), and fertilization practices (amount of nitrogen applied at heading) as major yield predictors. Integration of remotely-sensed LAI in the calibration process reduced NRMSE of 4.5% and 1.8 % on average for MR and RF models respectively. Calibration of region specific models did not significantly improve the predictive. These findings lead to the conclusion that mechanistic models are better at capturing the impacts of in-season climate variability and would be preferred to support short term tactical adjustments to farmers’ practices, while machine learning models are easier to use in the perspective of mid-term regional prediction.
{"title":"A comparison of empirical and mechanistic models for wheat yield prediction at field level in Moroccan rainfed areas","authors":"Achraf Mamassi, Marie Lang, Bernard Tychon, Mouanis Lahlou, Joost Wellens, Mohamed El Gharous, Hélène Marrou","doi":"10.1093/insilicoplants/diad020","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad020","url":null,"abstract":"Abstract In the context of climate change, in-season and longer-term yield predictions are needed to anticipate local and regional food crises and propose adaptations to farmers’ practices. Mechanistic models and machine learning are two modelling options to consider in this perspective. In this study, regression (MR) and Random Forest (RF) models were calibrated for wheat yield prediction in Morocco, using data collected from 125 farmers’ wheat fields. Additionally , MR and RF models were calibrated both with or without remotely-sensed leaf area index (LAI), while considering all farmers’ fields, or specifically to agroecological zoning in Morocco. The same farmers’ fields were simulated using a mechanistic model (APSIM-wheat). We compared the predictive performances of the empirical models and APSIM-wheat. Results showed that both MR and RF showed rather good predictive quality (NRMSEs below 35%), but were always outperformed by APSIM model. Both RF and MR selected remotely-sensed LAI at heading, climate variables (maximal temperatures at emergence and tillering), and fertilization practices (amount of nitrogen applied at heading) as major yield predictors. Integration of remotely-sensed LAI in the calibration process reduced NRMSE of 4.5% and 1.8 % on average for MR and RF models respectively. Calibration of region specific models did not significantly improve the predictive. These findings lead to the conclusion that mechanistic models are better at capturing the impacts of in-season climate variability and would be preferred to support short term tactical adjustments to farmers’ practices, while machine learning models are easier to use in the perspective of mid-term regional prediction.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"31 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135430034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-03DOI: 10.1093/insilicoplants/diad019
G L Hammer, G McLean, J Kholová, E van Oosterom
Abstract Tillering affects canopy leaf area, and hence crop growth via capture of light, water, and nutrients. Depending on the season, variation in tillering can result in increased or decreased yield. Reduced tillering has been associated with water-saving and enhanced yield in water-limited conditions. The objective of this study was to develop a generic model of the dynamics of tillering in sorghum incorporating key genetic and environmental controls. The dynamic of tillering was defined in four key phases – pre-tillering, tiller emergence, cessation of tiller emergence, and cessation of tiller growth. Tillering commenced at full expansion of leaf four and thereafter was synchronised with leaf appearance. The potential total number of tillers (TTN) was dependent on a genetic propensity to tiller and an index of assimilate availability dependent on the shoot source-sink balance. Cessation of tiller emergence could occur before TTN depending on extent of competition from neighbours. Subsequent cessation of growth of emerged tillers was related to the extent of internal competition for assimilate among plant organs, resulting in prediction of final fertile tiller number (FTN). The model predicted tillering dynamics well in an experiment with a range in plant density. Plausibility simulations of FTN conducted for diverse field conditions in the Australian sorghum belt reflected expectations. The model is able to predict fertile tiller number as an emergent property. Its utility to explore GxMxE crop adaptation landscapes, guide molecular discovery, provide a generic template for other cereals, and link to advanced methods for enhancing genetic gain in crops were discussed.
{"title":"Modelling the dynamics and phenotypic consequences of tiller outgrowth and cessation in sorghum","authors":"G L Hammer, G McLean, J Kholová, E van Oosterom","doi":"10.1093/insilicoplants/diad019","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad019","url":null,"abstract":"Abstract Tillering affects canopy leaf area, and hence crop growth via capture of light, water, and nutrients. Depending on the season, variation in tillering can result in increased or decreased yield. Reduced tillering has been associated with water-saving and enhanced yield in water-limited conditions. The objective of this study was to develop a generic model of the dynamics of tillering in sorghum incorporating key genetic and environmental controls. The dynamic of tillering was defined in four key phases – pre-tillering, tiller emergence, cessation of tiller emergence, and cessation of tiller growth. Tillering commenced at full expansion of leaf four and thereafter was synchronised with leaf appearance. The potential total number of tillers (TTN) was dependent on a genetic propensity to tiller and an index of assimilate availability dependent on the shoot source-sink balance. Cessation of tiller emergence could occur before TTN depending on extent of competition from neighbours. Subsequent cessation of growth of emerged tillers was related to the extent of internal competition for assimilate among plant organs, resulting in prediction of final fertile tiller number (FTN). The model predicted tillering dynamics well in an experiment with a range in plant density. Plausibility simulations of FTN conducted for diverse field conditions in the Australian sorghum belt reflected expectations. The model is able to predict fertile tiller number as an emergent property. Its utility to explore GxMxE crop adaptation landscapes, guide molecular discovery, provide a generic template for other cereals, and link to advanced methods for enhancing genetic gain in crops were discussed.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"40 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135874837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Crop yield is determined by potential harvest organ size, source organ photosynthesis, and carbohydrate partitioning. Filling the harvest organ efficiently remains a challenge. Here, we developed a kinetic model of rice grain filling, which scales from the primary biochemical and biophysical processes of photosynthesis to whole-plant carbon and nitrogen dynamics. The model reproduces the rice yield formation process under different environmental and genetic perturbations. In silico screening identified a range of post-anthesis targets—both established and novel—that can be manipulated to enhance rice yield. Remarkably, we pinpointed the stability of grain filling rate from flowering to harvest as a critical factor for maximizing grain yield. This finding was further validated in two independent super-high yielding rice cultivars, each yielding approximately 21 t ha -1 of rough rice at 14% moisture content. Furthermore, we revealed that stabilizing the grain filling rate could lead to a potential yield increase of around 30-40% in an elite rice cultivar. Notably, the cumulative grain filling rates around 15- and 38-days post-flowering significantly influence grain yield, and we introduced an innovative in situ approach using ear respiratory rates for precise quantification of these rates. We finally derived an equation to predict maximum dried brown rice yield (Y, t ha -1) of a cultivar based on its potential gross photosynthetic accumulation from flowering to harvest (Apc, t CO2 ha -1): Y = 0.74 * Apc + 1.9. Overall, this work establishes a framework for quantitatively dissecting crop physiology and designing high-yielding ideotypes.
{"title":"Bridging Photosynthesis and Crop Yield Formation with a Mechanistic Model of Whole Plant Carbon-Nitrogen Interaction","authors":"Tianxin Chang, Zhongwei Wei, Zai Shi, Yi Xiao, Honglong Zhao, Shuoqi Chang, Mingnan Qu, Qingfeng Song, Faming Chen, Fenfen Miao, Xinguang Zhu","doi":"10.1093/insilicoplants/diad011","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad011","url":null,"abstract":"\u0000 Crop yield is determined by potential harvest organ size, source organ photosynthesis, and carbohydrate partitioning. Filling the harvest organ efficiently remains a challenge. Here, we developed a kinetic model of rice grain filling, which scales from the primary biochemical and biophysical processes of photosynthesis to whole-plant carbon and nitrogen dynamics. The model reproduces the rice yield formation process under different environmental and genetic perturbations. In silico screening identified a range of post-anthesis targets—both established and novel—that can be manipulated to enhance rice yield. Remarkably, we pinpointed the stability of grain filling rate from flowering to harvest as a critical factor for maximizing grain yield. This finding was further validated in two independent super-high yielding rice cultivars, each yielding approximately 21 t ha -1 of rough rice at 14% moisture content. Furthermore, we revealed that stabilizing the grain filling rate could lead to a potential yield increase of around 30-40% in an elite rice cultivar. Notably, the cumulative grain filling rates around 15- and 38-days post-flowering significantly influence grain yield, and we introduced an innovative in situ approach using ear respiratory rates for precise quantification of these rates. We finally derived an equation to predict maximum dried brown rice yield (Y, t ha -1) of a cultivar based on its potential gross photosynthetic accumulation from flowering to harvest (Apc, t CO2 ha -1): Y = 0.74 * Apc + 1.9. Overall, this work establishes a framework for quantitatively dissecting crop physiology and designing high-yielding ideotypes.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47526459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-16DOI: 10.1093/insilicoplants/diad010
Michael Ottesen, Joseph Carter, Ryan Hall, Nan-Wei Liu, D. Cook
Maize stalk lodging is the structural failure of the stalk prior to harvest and is a major problem for maize (corn) producers and plant breeders. To address this problem, it is critical to understand precisely how geometric and material parameters of the maize stalk influence stalk strength. Computational models could be a powerful tool in such investigations, but current methods of creating computational models are costly, time-consuming, and most importantly, do not provide parameterized control of the maize stalk parameters. The purpose of this study was to develop and validate a parameterized three-dimensional model of the maize stalk. The parameterized model provides independent control over all aspects of the maize stalk geometry and material properties. The model accurately captures the shape of actual maize stalks and is predictive of maize stalk stiffness and strength. The model was validated using stochastic sampling of material properties to account for uncertainty in the values and influence of mechanical tissue properties. Results indicated that buckling is influenced by material properties to a greater extent that flexural stiffness. Finally, we demonstrate that this model can be used to create an unlimited number of synthetic stalks from within the parameter space. This model will enable the future implementation of parameter sweep studies, sensitivity analysis and optimization studies, and can be used to create computational models of maize stalks with any desired combination of geometric and material properties.
{"title":"Development and Stochastic Validation of a Parameterized Model of Maize Stalk Flexure and Buckling","authors":"Michael Ottesen, Joseph Carter, Ryan Hall, Nan-Wei Liu, D. Cook","doi":"10.1093/insilicoplants/diad010","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad010","url":null,"abstract":"\u0000 Maize stalk lodging is the structural failure of the stalk prior to harvest and is a major problem for maize (corn) producers and plant breeders. To address this problem, it is critical to understand precisely how geometric and material parameters of the maize stalk influence stalk strength. Computational models could be a powerful tool in such investigations, but current methods of creating computational models are costly, time-consuming, and most importantly, do not provide parameterized control of the maize stalk parameters. The purpose of this study was to develop and validate a parameterized three-dimensional model of the maize stalk. The parameterized model provides independent control over all aspects of the maize stalk geometry and material properties. The model accurately captures the shape of actual maize stalks and is predictive of maize stalk stiffness and strength. The model was validated using stochastic sampling of material properties to account for uncertainty in the values and influence of mechanical tissue properties. Results indicated that buckling is influenced by material properties to a greater extent that flexural stiffness. Finally, we demonstrate that this model can be used to create an unlimited number of synthetic stalks from within the parameter space. This model will enable the future implementation of parameter sweep studies, sensitivity analysis and optimization studies, and can be used to create computational models of maize stalks with any desired combination of geometric and material properties.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49379892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-17DOI: 10.1093/insilicoplants/diad009
M. Giraud, Samuel Le Gall, Moritz Harings, M. Javaux, D. Leitner, F. Meunier, Y. Rothfuss, D. van Dusschoten, J. Vanderborght, H. Vereecken, G. Lobet, A. Schnepf
A plant’s development is strongly linked to the water and carbon flows in the soil-plant-atmosphere continuum. Expected climate shifts will alter the water and carbon cycles and will affect plant phenotypes. Comprehensive models which simulate mechanistically and dynamically the feedback loops between a plant’s three-dimensional development and the water and carbon flows are useful tools to evaluate the sustainability of genotype-environment-management combinations which do not yet exist. In this study, we present the latest version of the open-source three-dimensional Functional-Structural Plant Model CPlantBox with PiafMunch and DuMu x coupling. This new implementation can be used to study the interactions between known or hypothetical processes at the plant scale. We simulated semi-mechanistically the development of generic C3 monocots from 10 to 25 days after sowing and undergoing an atmospheric dry spell of one week (no precipitation). We compared the results for dry spells starting on different days (day 11 or 18) against a wetter and colder baseline scenario. Compared with the baseline, the dry spells led to a lower instantaneous water use efficiency. Moreover, the temperature-induced increased enzymatic activity led to a higher maintenance respiration which diminished the amount of sucrose available for growth. Both of these effects were stronger for the later dry spell compared with the early dry spell. We could thus use CPlantBox to simulate diverging emerging processes (like carbon partitioning) defining the plants’ phenotypic plasticity response to their environment. The model remains to be validated against independent observations of the Soil-Plant-Atmosphere-Continuum.
{"title":"CPlantBox: a fully coupled modeling platform for the water and carbon fluxes in the Soil-Plant-Atmosphere-Continuum","authors":"M. Giraud, Samuel Le Gall, Moritz Harings, M. Javaux, D. Leitner, F. Meunier, Y. Rothfuss, D. van Dusschoten, J. Vanderborght, H. Vereecken, G. Lobet, A. Schnepf","doi":"10.1093/insilicoplants/diad009","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad009","url":null,"abstract":"\u0000 A plant’s development is strongly linked to the water and carbon flows in the soil-plant-atmosphere continuum. Expected climate shifts will alter the water and carbon cycles and will affect plant phenotypes. Comprehensive models which simulate mechanistically and dynamically the feedback loops between a plant’s three-dimensional development and the water and carbon flows are useful tools to evaluate the sustainability of genotype-environment-management combinations which do not yet exist. In this study, we present the latest version of the open-source three-dimensional Functional-Structural Plant Model CPlantBox with PiafMunch and DuMu x coupling. This new implementation can be used to study the interactions between known or hypothetical processes at the plant scale. We simulated semi-mechanistically the development of generic C3 monocots from 10 to 25 days after sowing and undergoing an atmospheric dry spell of one week (no precipitation). We compared the results for dry spells starting on different days (day 11 or 18) against a wetter and colder baseline scenario. Compared with the baseline, the dry spells led to a lower instantaneous water use efficiency. Moreover, the temperature-induced increased enzymatic activity led to a higher maintenance respiration which diminished the amount of sucrose available for growth. Both of these effects were stronger for the later dry spell compared with the early dry spell. We could thus use CPlantBox to simulate diverging emerging processes (like carbon partitioning) defining the plants’ phenotypic plasticity response to their environment. The model remains to be validated against independent observations of the Soil-Plant-Atmosphere-Continuum.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49402342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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