Pub Date : 2022-05-12DOI: 10.1093/insilicoplants/diac008
S. K. Obayes, Luke C. Timber, M. Head, Erin E. Sparks
Plant mechanical failure (lodging) causes significant yield loss for crops such as maize. Understanding this failure has relied on static measurements of plant biomechanics. In this study, digital image correlation techniques are used to capture dynamic motion to understand the plant mechanical behavior of maize stalks in the presence and absence of brace roots, which are stem-borne aerial roots known to stabilize the maize stalks. The data show that brace roots function to secure the maize stalk, limiting both deflection and uplift. A finite element (FE) model is developed using ABAQUS software to validate the non-contact, video-based measured deflections captured by the dynamic motion and confirm the linear elastic behavior of the stem, following fundamental principles of engineering mechanics. Good agreement is found between the field data captured using video-based measurements and the physics-based FE model when a rotational connector element is connected at the base to quantify the 1) relative contribution and moment resistance provided by the root system, 2) displacement at any location along the stalk, and 3) flexural rigidity of the brace-stem system, where the rigidity can be associated with various phenotypes to design plant systems that are more resilient to lateral loading.
{"title":"Evaluation of Brace Root Parameters and Its Effect on the Stiffness of Maize","authors":"S. K. Obayes, Luke C. Timber, M. Head, Erin E. Sparks","doi":"10.1093/insilicoplants/diac008","DOIUrl":"https://doi.org/10.1093/insilicoplants/diac008","url":null,"abstract":"\u0000 Plant mechanical failure (lodging) causes significant yield loss for crops such as maize. Understanding this failure has relied on static measurements of plant biomechanics. In this study, digital image correlation techniques are used to capture dynamic motion to understand the plant mechanical behavior of maize stalks in the presence and absence of brace roots, which are stem-borne aerial roots known to stabilize the maize stalks. The data show that brace roots function to secure the maize stalk, limiting both deflection and uplift. A finite element (FE) model is developed using ABAQUS software to validate the non-contact, video-based measured deflections captured by the dynamic motion and confirm the linear elastic behavior of the stem, following fundamental principles of engineering mechanics. Good agreement is found between the field data captured using video-based measurements and the physics-based FE model when a rotational connector element is connected at the base to quantify the 1) relative contribution and moment resistance provided by the root system, 2) displacement at any location along the stalk, and 3) flexural rigidity of the brace-stem system, where the rigidity can be associated with various phenotypes to design plant systems that are more resilient to lateral loading.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43157309","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 : 2022-04-19DOI: 10.1093/insilicoplants/diac005
Clément Saint Cast, G. Lobet, Llorenç Cabrera-Bosquet, V. Couvreur, C. Pradal, F. Tardieu, X. Draye
Plant phenotyping platforms generate large amounts of high dimensional data at different scales of plant organization. The possibility to use this information as inputs of models is an opportunity to develop models that integrate new processes and genetic inputs. We assessed to what extent the phenomics and modelling communities can address the issues of interoperability and data exchange, using a science mapping approach (i.e. visualization and analysis of a broad range of scientific and technological activities as a whole). In this paper, we (i) evaluate connections, (ii) identify compatible and connectable research topics, and (iii) propose strategies to facilitate connection across communities. We applied a science mapping approach based on reference and term analyses to a set of 4332 scientific papers published by the plant phenomics and modelling communities from 1980 to 2019, retrieved using the Elsevier’s Scopus database and the quantitative-plant.org website. The number of papers on phenotyping and modelling dramatically increased during the past decade, boosted by progress in phenotyping technologies and by key developments at hard- and software levels. The science mapping approach indicated a large diversity of research topics studied in each community. Despite compatibilities of research topics, the level of connection between the phenomics and modelling communities was low. Although phenomics and modelling crucially need to exchange data, the two communities appeared to be weakly connected. We encourage these communities to work on ontologies, harmonized formats, translators and connectors to facilitate transparent data exchange.
{"title":"Connecting plant phenotyping and modelling communities: lessons from science mapping and operational perspectives","authors":"Clément Saint Cast, G. Lobet, Llorenç Cabrera-Bosquet, V. Couvreur, C. Pradal, F. Tardieu, X. Draye","doi":"10.1093/insilicoplants/diac005","DOIUrl":"https://doi.org/10.1093/insilicoplants/diac005","url":null,"abstract":"\u0000 Plant phenotyping platforms generate large amounts of high dimensional data at different scales of plant organization. The possibility to use this information as inputs of models is an opportunity to develop models that integrate new processes and genetic inputs. We assessed to what extent the phenomics and modelling communities can address the issues of interoperability and data exchange, using a science mapping approach (i.e. visualization and analysis of a broad range of scientific and technological activities as a whole). In this paper, we (i) evaluate connections, (ii) identify compatible and connectable research topics, and (iii) propose strategies to facilitate connection across communities. We applied a science mapping approach based on reference and term analyses to a set of 4332 scientific papers published by the plant phenomics and modelling communities from 1980 to 2019, retrieved using the Elsevier’s Scopus database and the quantitative-plant.org website. The number of papers on phenotyping and modelling dramatically increased during the past decade, boosted by progress in phenotyping technologies and by key developments at hard- and software levels. The science mapping approach indicated a large diversity of research topics studied in each community. Despite compatibilities of research topics, the level of connection between the phenomics and modelling communities was low. Although phenomics and modelling crucially need to exchange data, the two communities appeared to be weakly connected. We encourage these communities to work on ontologies, harmonized formats, translators and connectors to facilitate transparent data exchange.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44751733","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 : 2022-04-14DOI: 10.1093/insilicoplants/diac004
M. Yanagisawa, S. Keynia, S. Belteton, J. A. Turner, D. Szymanski
Highly polarized cotton fiber cells that develop from the seed coat surface are the foundation of a multi-billion-dollar international textile industry. The unicellular trichoblast emerges as a hemispherical bulge that is efficiently converted to a narrower and elongated shape that extends for about two weeks before transitioning into a cellulose-generating machine. The polarized elongation phase employs an evolutionarily conserved microtubule-cellulose synthase control module that patterns the cell wall and enables highly anisotropic diffuse growth. As the multi-scale interactions and feedback controls among cytoskeletal systems, morphologically potent cell wall properties, and a changing cell geometry are uncovered, opportunities emerge to engineer architectural traits. However, in cotton such efforts are hampered by insufficient knowledge about the underlying morphogenetic control mechanisms. For example, fiber diameter is an important trait that is determined during the earliest stages of development, but the basic growth mode and the mechanisms by which cytoskeletal and cell wall systems mediate fiber tapering are not known. This paper combines multiparametric and multiscale fiber phenotyping and finite element computational modeling of a growing cell to discover an evolutionarily conserved tapering mechanism. The actin network interconverts between two distinct longitudinal organizations that broadly distributes organelles and likely enables matrix secretion patterns that maintain cell wall thickness during growth. Based on plausible finite element models and quantitative analyses of the microtubule cytoskeleton, tapering and anisotropic growth is programmed by a constricting apical microtubule depletion zone and highly aligned microtubules along the fiber shaft. The finite element model points to a central role for tensile forces in the cell wall to dictate the densities and orientations of morphologically potent microtubules.
{"title":"A conserved cellular mechanism for cotton fiber diameter and length control","authors":"M. Yanagisawa, S. Keynia, S. Belteton, J. A. Turner, D. Szymanski","doi":"10.1093/insilicoplants/diac004","DOIUrl":"https://doi.org/10.1093/insilicoplants/diac004","url":null,"abstract":"\u0000 Highly polarized cotton fiber cells that develop from the seed coat surface are the foundation of a multi-billion-dollar international textile industry. The unicellular trichoblast emerges as a hemispherical bulge that is efficiently converted to a narrower and elongated shape that extends for about two weeks before transitioning into a cellulose-generating machine. The polarized elongation phase employs an evolutionarily conserved microtubule-cellulose synthase control module that patterns the cell wall and enables highly anisotropic diffuse growth. As the multi-scale interactions and feedback controls among cytoskeletal systems, morphologically potent cell wall properties, and a changing cell geometry are uncovered, opportunities emerge to engineer architectural traits. However, in cotton such efforts are hampered by insufficient knowledge about the underlying morphogenetic control mechanisms. For example, fiber diameter is an important trait that is determined during the earliest stages of development, but the basic growth mode and the mechanisms by which cytoskeletal and cell wall systems mediate fiber tapering are not known. This paper combines multiparametric and multiscale fiber phenotyping and finite element computational modeling of a growing cell to discover an evolutionarily conserved tapering mechanism. The actin network interconverts between two distinct longitudinal organizations that broadly distributes organelles and likely enables matrix secretion patterns that maintain cell wall thickness during growth. Based on plausible finite element models and quantitative analyses of the microtubule cytoskeleton, tapering and anisotropic growth is programmed by a constricting apical microtubule depletion zone and highly aligned microtubules along the fiber shaft. The finite element model points to a central role for tensile forces in the cell wall to dictate the densities and orientations of morphologically potent microtubules.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41601411","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 : 2022-02-24DOI: 10.1093/insilicoplants/diac002
Faustino Chi, Katarína Streit, Aleksi Tavkhelidze, W. Kurth
The function of the arrangement of leaves (phyllotaxy) in a plant, increases its ability to perform photosynthesis by positioning the leaves to maximize the surface area available to intercept sunlight. In mangroves species, phyllotaxis is an unexplored phenomenon with the exception of early work from Tomlinson and Wheat. Available red mangrove models do not provide the needed flexibility in representation of tree architecture, which is needed to analyze and reconstruct the detailed architecture of saplings. The objective of the current study was to generate the phyllotactic pattern for red mangrove saplings (Rhizophora mangle L) based on our 3D digitized model, in situ measurements, photographic analysis, and using an algorithm. Onsite mangrove saplings (between 1 and 2.5 m) from Turneffe Atoll, Belize, were photographed. The above-ground part was digitized using the electromagnetic digitizing equipment (FASTRAK ® Polhemus Inc.), high-resolution photos of the leaf arrangements were taken along with field notes, and the model was constructed using the software GroIMP. Our algorithm, enriched by probabilistic approaches for the purpose of handling perturbations in phyllotactic patterns, was able to closely refoliate our 3D model. We then used the resulting hybrid model, composed of the digitized branching structure and the algorithmically-generated leaves, to simulate the interception of light by individual leaves, employing the stochastic raytracing-based radiation model. This preliminary result allows us to assess and visualize the photosynthetic contributions of single leaves throughout the canopy. Simulations of other processes (flows in xylem and phloem; mechanical behavior) could be based on such a model.
{"title":"Reconstruction of phyllotaxis at the example of digitized red mangrove (Rhizophora mangle) and application to light interception simulation","authors":"Faustino Chi, Katarína Streit, Aleksi Tavkhelidze, W. Kurth","doi":"10.1093/insilicoplants/diac002","DOIUrl":"https://doi.org/10.1093/insilicoplants/diac002","url":null,"abstract":"\u0000 The function of the arrangement of leaves (phyllotaxy) in a plant, increases its ability to perform photosynthesis by positioning the leaves to maximize the surface area available to intercept sunlight. In mangroves species, phyllotaxis is an unexplored phenomenon with the exception of early work from Tomlinson and Wheat. Available red mangrove models do not provide the needed flexibility in representation of tree architecture, which is needed to analyze and reconstruct the detailed architecture of saplings.\u0000 The objective of the current study was to generate the phyllotactic pattern for red mangrove saplings (Rhizophora mangle L) based on our 3D digitized model, in situ measurements, photographic analysis, and using an algorithm. Onsite mangrove saplings (between 1 and 2.5 m) from Turneffe Atoll, Belize, were photographed. The above-ground part was digitized using the electromagnetic digitizing equipment (FASTRAK ® Polhemus Inc.), high-resolution photos of the leaf arrangements were taken along with field notes, and the model was constructed using the software GroIMP.\u0000 Our algorithm, enriched by probabilistic approaches for the purpose of handling perturbations in phyllotactic patterns, was able to closely refoliate our 3D model. We then used the resulting hybrid model, composed of the digitized branching structure and the algorithmically-generated leaves, to simulate the interception of light by individual leaves, employing the stochastic raytracing-based radiation model. This preliminary result allows us to assess and visualize the photosynthetic contributions of single leaves throughout the canopy. Simulations of other processes (flows in xylem and phloem; mechanical behavior) could be based on such a model.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46501467","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 : 2022-02-12DOI: 10.1093/insilicoplants/diac003
E. Lochocki, Scott Rohde, D. Jaiswal, Megan L Matthews, F. Miguez, S. Long, J. McGrath
The central motivation for mechanistic crop growth simulation has remained the same for decades: to reliably predict changes in crop yields and water usage in response to previously unexperienced increases in air temperature and CO2 concentration across different environments, species, and genotypes. Over the years, individual process-based model components have become more complex and specialized, increasing their fidelity but posing a challenge for integrating them into powerful multiscale models. Combining models is further complicated by the common strategy of hard-coding intertwined parameter values, equations, solution algorithms, and user interfaces, rather than treating these each as separate components. It is clear that a more flexible approach is now required. Here we describe a modular crop growth simulator, BioCro II. At its core, BioCro II is a cross-platform representation of models as sets of equations. This facilitates modularity in model building and allows it to harness modern techniques for numerical integration and data visualization. Several crop models have been implemented using the BioCro II framework, but it is a general purpose tool and can be used to model a wide variety of processes.
{"title":"BioCro II: a Software Package for Modular Crop Growth Simulations","authors":"E. Lochocki, Scott Rohde, D. Jaiswal, Megan L Matthews, F. Miguez, S. Long, J. McGrath","doi":"10.1093/insilicoplants/diac003","DOIUrl":"https://doi.org/10.1093/insilicoplants/diac003","url":null,"abstract":"\u0000 The central motivation for mechanistic crop growth simulation has remained the same for decades: to reliably predict changes in crop yields and water usage in response to previously unexperienced increases in air temperature and CO2 concentration across different environments, species, and genotypes. Over the years, individual process-based model components have become more complex and specialized, increasing their fidelity but posing a challenge for integrating them into powerful multiscale models. Combining models is further complicated by the common strategy of hard-coding intertwined parameter values, equations, solution algorithms, and user interfaces, rather than treating these each as separate components. It is clear that a more flexible approach is now required. Here we describe a modular crop growth simulator, BioCro II. At its core, BioCro II is a cross-platform representation of models as sets of equations. This facilitates modularity in model building and allows it to harness modern techniques for numerical integration and data visualization. Several crop models have been implemented using the BioCro II framework, but it is a general purpose tool and can be used to model a wide variety of processes.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48912226","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 : 2022-02-02DOI: 10.1093/insilicoplants/diac001
Miao Lin Pay, Dae Wook Kim, D. E. Somers, Jae Kyoung Kim, Mathias Foo
Abstract To meet the ever-increasing global food demand, the food production rate needs to be increased significantly in the near future. Speed breeding is considered as a promising agricultural technology solution to achieve the zero-hunger vision as specified in the United Nations Sustainable Development Goal 2. In speed breeding, the photoperiod of the artificial light has been manipulated to enhance crop productivity. In particular, regulating the photoperiod of different light qualities rather than solely white light can further improve speed breading. However, identifying the optimal light quality and the associated photoperiod simultaneously remains a challenging open problem due to complex interactions between multiple photoreceptors and proteins controlling plant growth. To tackle this, we develop a first comprehensive model describing the profound effect of multiple light qualities with different photoperiods on plant growth (i.e. hypocotyl growth). The model predicts that hypocotyls elongated more under red light compared to both red and blue light. Drawing similar findings from previous related studies, we propose that this might result from the competitive binding of red and blue light receptors, primarily Phytochrome B (phyB) and Cryptochrome 1 (cry1) for the core photomorphogenic regulator, CONSTITUTIVE PHOTOMORPHOGENIC 1 (COP1). This prediction is validated through an experimental study on Arabidopsis thaliana. Our work proposes a potential molecular mechanism underlying plant growth under different light qualities and ultimately suggests an optimal breeding protocol that takes into account light quality.
{"title":"Modelling of plant circadian clock for characterizing hypocotyl growth under different light quality conditions","authors":"Miao Lin Pay, Dae Wook Kim, D. E. Somers, Jae Kyoung Kim, Mathias Foo","doi":"10.1093/insilicoplants/diac001","DOIUrl":"https://doi.org/10.1093/insilicoplants/diac001","url":null,"abstract":"Abstract To meet the ever-increasing global food demand, the food production rate needs to be increased significantly in the near future. Speed breeding is considered as a promising agricultural technology solution to achieve the zero-hunger vision as specified in the United Nations Sustainable Development Goal 2. In speed breeding, the photoperiod of the artificial light has been manipulated to enhance crop productivity. In particular, regulating the photoperiod of different light qualities rather than solely white light can further improve speed breading. However, identifying the optimal light quality and the associated photoperiod simultaneously remains a challenging open problem due to complex interactions between multiple photoreceptors and proteins controlling plant growth. To tackle this, we develop a first comprehensive model describing the profound effect of multiple light qualities with different photoperiods on plant growth (i.e. hypocotyl growth). The model predicts that hypocotyls elongated more under red light compared to both red and blue light. Drawing similar findings from previous related studies, we propose that this might result from the competitive binding of red and blue light receptors, primarily Phytochrome B (phyB) and Cryptochrome 1 (cry1) for the core photomorphogenic regulator, CONSTITUTIVE PHOTOMORPHOGENIC 1 (COP1). This prediction is validated through an experimental study on Arabidopsis thaliana. Our work proposes a potential molecular mechanism underlying plant growth under different light qualities and ultimately suggests an optimal breeding protocol that takes into account light quality.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47681498","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 : 2022-01-18DOI: 10.1093/insilicoplants/diab038
T. De Swaef, Olivier Pieters, S. Appeltans, I. Borra‐Serrano, Willem Coudron, V. Couvreur, S. Garré, P. Lootens, B. Nicolaï, L. Pols, Clément Saint Cast, J. Šalagovič, Maxime Van Haeverbeke, Michiel Stock, F. Wyffels
Water potential explains water transport in the Soil-Plant-Atmosphere Continuum (SPAC), and is gaining interest as connecting variable between ‘pedo-, bio- and atmosphere’. It is primarily used to simulate hydraulics in the SPAC, and is thus essential for studying drought effects. Recent implementations of hydraulics in large-scale Terrestrial Biosphere Models (TBMs) improved their performance under water-limited conditions, while hydraulic features of recent detailed Functional-Structural Plant Models (FSPMs) open new possibilities for dissecting complex traits for drought tolerance. These developments in models across scales deserve a critical appraisal to evaluate its potential for wider use in FSPMs, but also in crop systems models (CSMs), where hydraulics are currently still absent. After refreshing the physical basis, we first address models where water potential is primarily used for describing water transport along the transpiration pathway from the soil to the leaves, through the roots, the xylem and the leaf mesophyll. Then, we highlight models for three ecophysiological processes, which have well-recognised links to water potential: phloem transport, stomatal conductance and organ growth. We identify water potential as the bridge between soil, root and shoot models, as the physiological variable integrating below- and above-ground abiotic drivers, but also as the link between water status and growth. Models making these connections enable identifying crucial traits for ecosystem resilience to drought and for breeding towards improved drought tolerance in crops. Including hydraulics often increases model complexity, and thus requires experimental data on soil and plant hydraulics. Nevertheless, modelling hydraulics is insightful at different scales (FSPMs, CSMs and TBMs).
{"title":"On the pivotal role of water potential to model plant physiological processes","authors":"T. De Swaef, Olivier Pieters, S. Appeltans, I. Borra‐Serrano, Willem Coudron, V. Couvreur, S. Garré, P. Lootens, B. Nicolaï, L. Pols, Clément Saint Cast, J. Šalagovič, Maxime Van Haeverbeke, Michiel Stock, F. Wyffels","doi":"10.1093/insilicoplants/diab038","DOIUrl":"https://doi.org/10.1093/insilicoplants/diab038","url":null,"abstract":"\u0000 Water potential explains water transport in the Soil-Plant-Atmosphere Continuum (SPAC), and is gaining interest as connecting variable between ‘pedo-, bio- and atmosphere’. It is primarily used to simulate hydraulics in the SPAC, and is thus essential for studying drought effects. Recent implementations of hydraulics in large-scale Terrestrial Biosphere Models (TBMs) improved their performance under water-limited conditions, while hydraulic features of recent detailed Functional-Structural Plant Models (FSPMs) open new possibilities for dissecting complex traits for drought tolerance. These developments in models across scales deserve a critical appraisal to evaluate its potential for wider use in FSPMs, but also in crop systems models (CSMs), where hydraulics are currently still absent. After refreshing the physical basis, we first address models where water potential is primarily used for describing water transport along the transpiration pathway from the soil to the leaves, through the roots, the xylem and the leaf mesophyll. Then, we highlight models for three ecophysiological processes, which have well-recognised links to water potential: phloem transport, stomatal conductance and organ growth. We identify water potential as the bridge between soil, root and shoot models, as the physiological variable integrating below- and above-ground abiotic drivers, but also as the link between water status and growth. Models making these connections enable identifying crucial traits for ecosystem resilience to drought and for breeding towards improved drought tolerance in crops. Including hydraulics often increases model complexity, and thus requires experimental data on soil and plant hydraulics. Nevertheless, modelling hydraulics is insightful at different scales (FSPMs, CSMs and TBMs).","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45835485","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 : 2021-12-14DOI: 10.1093/insilicoplants/diab040
Jan Vaillant, I. Grechi, F. Normand, F. Boudon
Functional-Structural Plant Models (FSPMs) are powerful tools to explore the complex interplays between plant growth, underlying physiological processes and the environment. Various modeling platforms dedicated to FSPMs have been developed with limited support for collaborative and distributed model design, reproducibility and dissemination. With the objective to alleviate these problems, we used the Jupyter project, an open-source computational notebook ecosystem, to create virtual modeling environments for plant models. These environments combined Python scientific modules, L-systems formalism, multidimensional arrays and 3D plant architecture visualization in Jupyter notebooks. As a case study, we present an application of such an environment by reimplementing V-Mango, a model of mango tree development and fruit production built on interrelated processes of architectural development and fruit growth that are affected by temporal, structural and environmental factors. This new implementation increased model modularity, with modules representing single processes and the workflows between them. The model modularity allowed us to run simulations for a subset of processes only, on simulated or empirical architectures. The exploration of carbohydrate source-sink relationships on a measured mango branch architecture illustrates this possibility. We also proposed solutions for visualization, distant distributed computation and parallel simulations of several independent mango trees during a growing season. The development of models on locations far from computational resources makes collaborative and distributed model design and implementation possible, and demonstrates the usefulness and efficiency of a customizable virtual modeling environment.
{"title":"Towards virtual modeling environments for functional structural plant models based on Jupyter notebooks: Application to the modeling of mango tree growth and development","authors":"Jan Vaillant, I. Grechi, F. Normand, F. Boudon","doi":"10.1093/insilicoplants/diab040","DOIUrl":"https://doi.org/10.1093/insilicoplants/diab040","url":null,"abstract":"\u0000 Functional-Structural Plant Models (FSPMs) are powerful tools to explore the complex interplays between plant growth, underlying physiological processes and the environment. Various modeling platforms dedicated to FSPMs have been developed with limited support for collaborative and distributed model design, reproducibility and dissemination. With the objective to alleviate these problems, we used the Jupyter project, an open-source computational notebook ecosystem, to create virtual modeling environments for plant models. These environments combined Python scientific modules, L-systems formalism, multidimensional arrays and 3D plant architecture visualization in Jupyter notebooks. As a case study, we present an application of such an environment by reimplementing V-Mango, a model of mango tree development and fruit production built on interrelated processes of architectural development and fruit growth that are affected by temporal, structural and environmental factors. This new implementation increased model modularity, with modules representing single processes and the workflows between them. The model modularity allowed us to run simulations for a subset of processes only, on simulated or empirical architectures. The exploration of carbohydrate source-sink relationships on a measured mango branch architecture illustrates this possibility. We also proposed solutions for visualization, distant distributed computation and parallel simulations of several independent mango trees during a growing season. The development of models on locations far from computational resources makes collaborative and distributed model design and implementation possible, and demonstrates the usefulness and efficiency of a customizable virtual modeling environment.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47657156","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 : 2021-12-10DOI: 10.1093/insilicoplants/diab039
M. Cieslak, N. Khan, Pascal Ferraro, R. Soolanayakanahally, S. J. Robinson, I. Parkin, Ian McQuillan, P. Prusinkiewicz
Artificial neural networks that recognize and quantify relevant aspects of crop plants show great promise in image-based phenomics, but their training requires many annotated images. The acquisition of these images is comparatively simple, but their manual annotation is time-consuming. Realistic plant models, which can be annotated automatically, thus present an attractive alternative to real plant images for training purposes. Here we show how such models can be constructed and calibrated quickly, using maize and canola as case studies.
{"title":"L-system models for image-based phenomics: case studies of maize and canola","authors":"M. Cieslak, N. Khan, Pascal Ferraro, R. Soolanayakanahally, S. J. Robinson, I. Parkin, Ian McQuillan, P. Prusinkiewicz","doi":"10.1093/insilicoplants/diab039","DOIUrl":"https://doi.org/10.1093/insilicoplants/diab039","url":null,"abstract":"\u0000 Artificial neural networks that recognize and quantify relevant aspects of crop plants show great promise in image-based phenomics, but their training requires many annotated images. The acquisition of these images is comparatively simple, but their manual annotation is time-consuming. Realistic plant models, which can be annotated automatically, thus present an attractive alternative to real plant images for training purposes. Here we show how such models can be constructed and calibrated quickly, using maize and canola as case studies.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42666334","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 : 2021-12-05DOI: 10.1093/insilicoplants/diab032
Megan L. Matthews, Amy Marshall-Colón, J. McGrath, E. Lochocki, S. Long
Soybean is a major global source of protein and oil. Understanding how soybean crops will respond to the changing climate and identifying the responsible molecular machinery, are important for facilitating bioengineering and breeding to meet the growing global food demand. The BioCro family of crop models are semi-mechanistic models scaling from biochemistry to whole crop growth and yield. BioCro was previously parameterized and proved effective for the biomass crops miscanthus, coppice willow, and Brazilian sugarcane. Here, we present Soybean-BioCro, the first food crop to be parameterized for BioCro. Two new module sets were incorporated into the BioCro framework describing the rate of soybean development and carbon partitioning and senescence. The model was parameterized using field measurements collected over the 2002 and 2005 growing seasons at the open air [CO2] enrichment (SoyFACE) facility under ambient atmospheric [CO2]. We demonstrate that Soybean-BioCro successfully predicted how elevated [CO2] impacted field-grown soybean growth without a need for re-parameterization, by predicting soybean growth under elevated atmospheric [CO2] during the 2002 and 2005 growing seasons, and under both ambient and elevated [CO2] for the 2004 and 2006 growing seasons. Soybean-BioCro provides a useful foundational framework for incorporating additional primary and secondary metabolic processes or gene regulatory mechanisms that can further aid our understanding of how future soybean growth will be impacted by climate change.
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