Pub Date : 2019-01-01DOI: 10.1093/INSILICOPLANTS/DIY002
S. Long
The rise of mathematical modelling represents a transition in any scientific area towards quantitative and unequivocal presentation of hypotheses and theory. In Physics, phenomena are now predicted from mathematical models and computer simulations of those models well before observations are made to confirm these predictions. Indeed the largest endeavours in Physics, such as particle physics accelerators and astrophysics observational platforms, are created to test the most profound predictions of such models. Given the huge complexity of living organisms coupled with massive species and even phenotype within genotype diversity, we are far from the same level of advancement, yet need to approach it. Complexity of our systems means that many biological modelling efforts will remain, largely, based on emergent properties and phenomena. Nevertheless, complete models of the full complexity of single-celled organisms are beginning to transition to eukaryotes (Beard et al. 2012; Service 2016). Within narrow areas of gene function, we are already seeing successful projections from gene expression to prediction of growth and development of whole plants (Chew et al. 2014). Modelling provides a framework in which we can precisely organize and test our quantitative knowledge and hypotheses about how a plant process or combination of processes works and then test these against reality. As such, it provides a data-hypothesis-test-learn cycle to improve our understanding of plants and their use. Equally, the rapid growth of high-throughput ‘omics facilities is delivering ever-increasing amounts of data for which our capacity and ability to analyse and interpret lags. Mathematical models coupled with high-performance computing provide a means to deliver this needed acceleration. Simultaneously it should provide the means to predict which data is needed most, so providing feedback and focus for ‘omics approaches. This wealth of data also provides unprecedented opportunities for improving the precision of models by high-speed data to model linkage. Similarly, numerical and text mining knowledge discovery offer much to improving mathematical modelling of plant processes, with opportunities for automated improvement of representation and parameterization (Fer et al. 2018). In parallel, computer simulation of mathematical models has evolved from printouts of numbers to 3D representations of the growth and development of organs, whole plants and even communities of plants that can be indistinguishable from the real thing (Fig. 1). This facilitates identification of emergent phenomena while providing unprecedented opportunities in revolutionizing plant science education (Prusinkiewicz et al. 2007; Prusinkiewicz and Runions 2012; Runions et al. 2017).
{"title":"Making our plant modelling community more than the sum of its parts: a personal perspective","authors":"S. Long","doi":"10.1093/INSILICOPLANTS/DIY002","DOIUrl":"https://doi.org/10.1093/INSILICOPLANTS/DIY002","url":null,"abstract":"The rise of mathematical modelling represents a transition in any scientific area towards quantitative and unequivocal presentation of hypotheses and theory. In Physics, phenomena are now predicted from mathematical models and computer simulations of those models well before observations are made to confirm these predictions. Indeed the largest endeavours in Physics, such as particle physics accelerators and astrophysics observational platforms, are created to test the most profound predictions of such models. Given the huge complexity of living organisms coupled with massive species and even phenotype within genotype diversity, we are far from the same level of advancement, yet need to approach it. Complexity of our systems means that many biological modelling efforts will remain, largely, based on emergent properties and phenomena. Nevertheless, complete models of the full complexity of single-celled organisms are beginning to transition to eukaryotes (Beard et al. 2012; Service 2016). Within narrow areas of gene function, we are already seeing successful projections from gene expression to prediction of growth and development of whole plants (Chew et al. 2014). Modelling provides a framework in which we can precisely organize and test our quantitative knowledge and hypotheses about how a plant process or combination of processes works and then test these against reality. As such, it provides a data-hypothesis-test-learn cycle to improve our understanding of plants and their use. Equally, the rapid growth of high-throughput ‘omics facilities is delivering ever-increasing amounts of data for which our capacity and ability to analyse and interpret lags. Mathematical models coupled with high-performance computing provide a means to deliver this needed acceleration. Simultaneously it should provide the means to predict which data is needed most, so providing feedback and focus for ‘omics approaches. This wealth of data also provides unprecedented opportunities for improving the precision of models by high-speed data to model linkage. Similarly, numerical and text mining knowledge discovery offer much to improving mathematical modelling of plant processes, with opportunities for automated improvement of representation and parameterization (Fer et al. 2018). In parallel, computer simulation of mathematical models has evolved from printouts of numbers to 3D representations of the growth and development of organs, whole plants and even communities of plants that can be indistinguishable from the real thing (Fig. 1). This facilitates identification of emergent phenomena while providing unprecedented opportunities in revolutionizing plant science education (Prusinkiewicz et al. 2007; Prusinkiewicz and Runions 2012; Runions et al. 2017).","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/INSILICOPLANTS/DIY002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44411236","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}
Recent years witnessed a stagnation in yield enhancement in major staple crops, which leads plant biologists and breeders to focus on an urgent challenge to dramatically increase crop yield to meet the growing food demand. Systems models have started to show their capacity in guiding crops improvement for greater biomass and grain yield production. Here we argue that systems models, phenomics and genomics combined are three pillars for the future breeding for high-yielding photosynthetically efficient crops (HYPEC). Briefly, systems models can be used to guide identification of breeding targets for a particular cultivar and define optimal physiological and architectural parameters for a particular crop to achieve high yield under defined environments. Phenomics can support collection of architectural, physiological, biochemical and molecular parameters in a high-throughput manner, which can be used to support both model validation and model parameterization. Genomic techniques can be used to accelerate crop breeding by enabling more efficient mapping between genotypic and phenotypic variation, and guide genome engineering or editing for model-designed traits. In this paper, we elaborate on these roles and how they can work synergistically to support future HYPEC breeding.
{"title":"Systems models, phenomics and genomics: three pillars for developing high-yielding photosynthetically efficient crops.","authors":"Tian-Gen Chang, Shuoqi Chang, Qing-Feng Song, Shahnaz Perveen, Xin-Guang Zhu","doi":"10.1093/insilicoplants/diy003","DOIUrl":"https://doi.org/10.1093/insilicoplants/diy003","url":null,"abstract":"<p><p>Recent years witnessed a stagnation in yield enhancement in major staple crops, which leads plant biologists and breeders to focus on an urgent challenge to dramatically increase crop yield to meet the growing food demand. Systems models have started to show their capacity in guiding crops improvement for greater biomass and grain yield production. Here we argue that systems models, phenomics and genomics combined are three pillars for the future breeding for high-yielding photosynthetically efficient crops (HYPEC). Briefly, systems models can be used to guide identification of breeding targets for a particular cultivar and define optimal physiological and architectural parameters for a particular crop to achieve high yield under defined environments. Phenomics can support collection of architectural, physiological, biochemical and molecular parameters in a high-throughput manner, which can be used to support both model validation and model parameterization. Genomic techniques can be used to accelerate crop breeding by enabling more efficient mapping between genotypic and phenotypic variation, and guide genome engineering or editing for model-designed traits. In this paper, we elaborate on these roles and how they can work synergistically to support future HYPEC breeding.</p>","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/insilicoplants/diy003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39115099","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 : 2019-01-01DOI: 10.1093/INSILICOPLANTS/DIZ003
C. Messina, G. Hammer, G. McLean, M. Cooper, E. V. van Oosterom, F. Tardieu, S. Chapman, A. Doherty, C. Gho
Reproductive failure under drought in maize (Zea mays) is a major cause of instability in global food systems. While there has been extensive research on maize reproductive physiology, it has not been formalized in mathematical form to enable the study and prediction of emergent phenotypes, physiological epistasis and pleiotropy. We developed a quantitative synthesis organized as a dynamical model for cohorting of reproductive structures along the ear while accounting for carbon and water supply and demand balances. The model can simulate the dynamics of silk initiation, elongation, fertilization and kernel growth, and can generate well-known emergent phenotypes such as the relationship between plant growth, anthesis-silking interval, kernel number and yield, as well as ear phenotypes under drought (e.g. tip kernel abortion). Simulation of field experiments with controlled drought conditions showed that predictions tracked well the observed response of yield and yield components to timing of water deficit. This framework represents a significant improvement from previous approaches to simulate reproductive physiology in maize. We envisage opportunities for this predictive capacity to advance our understanding of maize reproductive biology by informing experimentation, supporting breeding and increasing productivity in maize.
{"title":"On the dynamic determinants of reproductive failure under drought in maize","authors":"C. Messina, G. Hammer, G. McLean, M. Cooper, E. V. van Oosterom, F. Tardieu, S. Chapman, A. Doherty, C. Gho","doi":"10.1093/INSILICOPLANTS/DIZ003","DOIUrl":"https://doi.org/10.1093/INSILICOPLANTS/DIZ003","url":null,"abstract":"Reproductive failure under drought in maize (Zea mays) is a major cause of instability in global food systems. While there has been extensive research on maize reproductive physiology, it has not been formalized in mathematical form to enable the study and prediction of emergent phenotypes, physiological epistasis and pleiotropy. We developed a quantitative synthesis organized as a dynamical model for cohorting of reproductive structures along the ear while accounting for carbon and water supply and demand balances. The model can simulate the dynamics of silk initiation, elongation, fertilization and kernel growth, and can generate well-known emergent phenotypes such as the relationship between plant growth, anthesis-silking interval, kernel number and yield, as well as ear phenotypes under drought (e.g. tip kernel abortion). Simulation of field experiments with controlled drought conditions showed that predictions tracked well the observed response of yield and yield components to timing of water deficit. This framework represents a significant improvement from previous approaches to simulate reproductive physiology in maize. We envisage opportunities for this predictive capacity to advance our understanding of maize reproductive biology by informing experimentation, supporting breeding and increasing productivity in maize.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/INSILICOPLANTS/DIZ003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43411079","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 : 2019-01-01DOI: 10.1093/INSILICOPLANTS/DIZ001
Meagan Lang
Thousands of computational models have been created within both the plant biology community and broader scientific communities in the past two decades that have the potential to be combined into complex integration networks capable of capturing more complex biological processes than possible with isolated models. However, the technological barriers introduced by differences in language and data formats have slowed this progress. We present yggdrasil (previously cis_interface), a Python package for running integration networks with connections between models across languages and scales. yggdrasil coordinates parallel execution of models in Python, C, C++, and Matlab on Linux, Mac OS, and Windows operating systems, and handles communication in a number of data formats common to computational plant modelling. yggdrasil is designed to be user-friendly and can be accessed at https://github.com/cropsinsilico/yggdrasil. Although originally developed for plant models, yggdrasil can be used to connect computational models from any domain.
{"title":"yggdrasil: a Python package for integrating computational models across languages and scales","authors":"Meagan Lang","doi":"10.1093/INSILICOPLANTS/DIZ001","DOIUrl":"https://doi.org/10.1093/INSILICOPLANTS/DIZ001","url":null,"abstract":"Thousands of computational models have been created within both the plant biology community and broader scientific communities in the past two decades that have the potential to be combined into complex integration networks capable of capturing more complex biological processes than possible with isolated models. However, the technological barriers introduced by differences in language and data formats have slowed this progress. We present yggdrasil (previously cis_interface), a Python package for running integration networks with connections between models across languages and scales. yggdrasil coordinates parallel execution of models in Python, C, C++, and Matlab on Linux, Mac OS, and Windows operating systems, and handles communication in a number of data formats common to computational plant modelling. yggdrasil is designed to be user-friendly and can be accessed at https://github.com/cropsinsilico/yggdrasil. Although originally developed for plant models, yggdrasil can be used to connect computational models from any domain.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/INSILICOPLANTS/DIZ001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46060570","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 : 2019-01-01DOI: 10.1093/INSILICOPLANTS/DIZ005
Alexandr Koryachko, Anna Matthiadis, Samiul Haque, D. Muhammad, J. Ducoste, James M. Tuck, Terri A. Long, Cranos M. Williams
The iron deficiency response in plants is a complex biological process with a host of influencing factors. The ability to precisely modulate this process at the transcriptome level would enable genetic manipulations allowing plants to survive in nutritionally poor soils and accumulate increased iron content in edible tissues. Despite the collected experimental data describing different aspects of the iron deficiency response in plants, no attempts have been made towards aggregating this information into a descriptive and predictive model of gene expression changes over time. We formulated and trained a dynamic model of the iron deficiency induced transcriptional response in Arabidopsis thaliana. Gene activity dynamics were modelled with a set of ordinary differential equations that contain biologically tractable parameters. The trained model was able to capture and account for a significant difference in mRNA decay rates under iron sufficient and iron deficient conditions, approximate the expression behaviour of currently unknown gene regulators, unveil potential synergistic effects between the modulating transcription factors and predict the effect of double regulator mutants. The presented modelling approach illustrates a framework for experimental design, data analysis and information aggregation in an effort to gain a deeper understanding of various aspects of a biological process of interest.
{"title":"Dynamic modelling of the iron deficiency modulated transcriptome response in Arabidopsis thaliana roots","authors":"Alexandr Koryachko, Anna Matthiadis, Samiul Haque, D. Muhammad, J. Ducoste, James M. Tuck, Terri A. Long, Cranos M. Williams","doi":"10.1093/INSILICOPLANTS/DIZ005","DOIUrl":"https://doi.org/10.1093/INSILICOPLANTS/DIZ005","url":null,"abstract":"The iron deficiency response in plants is a complex biological process with a host of influencing factors. The ability to precisely modulate this process at the transcriptome level would enable genetic manipulations allowing plants to survive in nutritionally poor soils and accumulate increased iron content in edible tissues. Despite the collected experimental data describing different aspects of the iron deficiency response in plants, no attempts have been made towards aggregating this information into a descriptive and predictive model of gene expression changes over time. We formulated and trained a dynamic model of the iron deficiency induced transcriptional response in Arabidopsis thaliana. Gene activity dynamics were modelled with a set of ordinary differential equations that contain biologically tractable parameters. The trained model was able to capture and account for a significant difference in mRNA decay rates under iron sufficient and iron deficient conditions, approximate the expression behaviour of currently unknown gene regulators, unveil potential synergistic effects between the modulating transcription factors and predict the effect of double regulator mutants. The presented modelling approach illustrates a framework for experimental design, data analysis and information aggregation in an effort to gain a deeper understanding of various aspects of a biological process of interest.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/INSILICOPLANTS/DIZ005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"61382473","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 : 2019-01-01DOI: 10.1093/INSILICOPLANTS/DIZ004
B. Holland, N. Monk, R. Clayton, C. Osborne
Improving crop yield is essential to meet increasing global food demands. Boosting crop yield requires the coordination of carbon acquisition by leaves and carbon utilization by roots and seeds. Simple modelling approaches may be used to explain how this coordination is achieved within plant growth. Here, the limits to allocation strategies and the influence of maintenance costs are explored by analysing the sensitivity of a simple root–shoot carbon allocation model for vegetative and reproductive growth. The model is formulated based on fundamental constraints on plant growth and therefore can be applied to all plants. This general but quantitative approach shows that the relative costs of root and leaf respiration alter the relationship between carbon allocation and final plant size, enabling a range of allocation strategies to produce a similar total amount of plant material during vegetative growth. This plasticity is enhanced by increasing assimilation rate within the model. Results show that high leaf allocation during vegetative growth promotes early reproduction with respect to yield. Having higher respiration in leaves than roots delays the optimal age to reproduce for plants with high leaf allocation during vegetative growth and increases the restrictions on flowering time for plants with high root allocation during vegetative growth. It is shown that, when leaf respiration is higher than root respiration, reallocating carbon towards the roots can increase the total amount of plant material. This analysis indicates that crop improvement strategies should consider the effects of maintenance costs on growth, a previously under-appreciated mechanism for yield enhancement.
{"title":"A theoretical analysis of how plant growth is limited by carbon allocation strategies and respiration","authors":"B. Holland, N. Monk, R. Clayton, C. Osborne","doi":"10.1093/INSILICOPLANTS/DIZ004","DOIUrl":"https://doi.org/10.1093/INSILICOPLANTS/DIZ004","url":null,"abstract":"Improving crop yield is essential to meet increasing global food demands. Boosting crop yield requires the coordination of carbon acquisition by leaves and carbon utilization by roots and seeds. Simple modelling approaches may be used to explain how this coordination is achieved within plant growth. Here, the limits to allocation strategies and the influence of maintenance costs are explored by analysing the sensitivity of a simple root–shoot carbon allocation model for vegetative and reproductive growth. The model is formulated based on fundamental constraints on plant growth and therefore can be applied to all plants. This general but quantitative approach shows that the relative costs of root and leaf respiration alter the relationship between carbon allocation and final plant size, enabling a range of allocation strategies to produce a similar total amount of plant material during vegetative growth. This plasticity is enhanced by increasing assimilation rate within the model. Results show that high leaf allocation during vegetative growth promotes early reproduction with respect to yield. Having higher respiration in leaves than roots delays the optimal age to reproduce for plants with high leaf allocation during vegetative growth and increases the restrictions on flowering time for plants with high root allocation during vegetative growth. It is shown that, when leaf respiration is higher than root respiration, reallocating carbon towards the roots can increase the total amount of plant material. This analysis indicates that crop improvement strategies should consider the effects of maintenance costs on growth, a previously under-appreciated mechanism for yield enhancement.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/INSILICOPLANTS/DIZ004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46699584","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 : 2019-01-01DOI: 10.1093/INSILICOPLANTS/DIY004
H. Brown, N. Huth, D. Holzworth, E. Teixeira, E. Wang, R. Zyskowski, B. Zheng
{"title":"A generic approach to modelling, allocation and redistribution of biomass to and from plant organs","authors":"H. Brown, N. Huth, D. Holzworth, E. Teixeira, E. Wang, R. Zyskowski, B. Zheng","doi":"10.1093/INSILICOPLANTS/DIY004","DOIUrl":"https://doi.org/10.1093/INSILICOPLANTS/DIY004","url":null,"abstract":"","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/INSILICOPLANTS/DIY004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"61382456","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 : 2019-01-01DOI: 10.1093/insilicoplants/diz010
G. Hammer, C. Messina, A. Wu, M. Cooper
The potential to add significant value to the rapid advances in plant breeding technologies associated with statistical whole-genome prediction methods is a new frontier for crop physiology and modelling. Yield advance by genetic improvement continues to require prediction of phenotype based on genotype, and this remains challenging for complex traits despite recent advances in genotyping and phenotyping. Crop models that capture physiological knowledge and can robustly predict phenotypic consequences of genotype-by-environment-by-management (G×E×M) interactions have demonstrated potential as an integrating tool. But does this biological reality come with a degree of complexity that restricts applicability in crop improvement? Simple, high-speed, parsimonious models are required for dealing with the thousands of genotypes and environment combinations in modern breeding programs utilizing genomic prediction technologies. In contrast, it is often considered that greater model complexity is needed to evaluate potential of putative variation in specific traits in target environments as knowledge on their underpinning biology advances. Is this a contradiction leading to divergent futures? Here it is argued that biological reality and parsimony do not need to be independent and perhaps should not be. Models structured to readily allow variation in the biological level of process algorithms, while using coding and computational advances to facilitate high-speed simulation, could well provide the structure needed for the next generation of crop models needed to support and enhance advances in crop improvement technologies. Beyond that, the trans-scale and transdisciplinary dialogue among scientists that will be required to construct such models effectively is considered to be at least as important as the models.
{"title":"Biological reality and parsimony in crop models—why we need both in crop improvement!","authors":"G. Hammer, C. Messina, A. Wu, M. Cooper","doi":"10.1093/insilicoplants/diz010","DOIUrl":"https://doi.org/10.1093/insilicoplants/diz010","url":null,"abstract":"The potential to add significant value to the rapid advances in plant breeding technologies associated with statistical whole-genome prediction methods is a new frontier for crop physiology and modelling. Yield advance by genetic improvement continues to require prediction of phenotype based on genotype, and this remains challenging for complex traits despite recent advances in genotyping and phenotyping. Crop models that capture physiological knowledge and can robustly predict phenotypic consequences of genotype-by-environment-by-management (G×E×M) interactions have demonstrated potential as an integrating tool. But does this biological reality come with a degree of complexity that restricts applicability in crop improvement? Simple, high-speed, parsimonious models are required for dealing with the thousands of genotypes and environment combinations in modern breeding programs utilizing genomic prediction technologies. In contrast, it is often considered that greater model complexity is needed to evaluate potential of putative variation in specific traits in target environments as knowledge on their underpinning biology advances. Is this a contradiction leading to divergent futures? Here it is argued that biological reality and parsimony do not need to be independent and perhaps should not be. Models structured to readily allow variation in the biological level of process algorithms, while using coding and computational advances to facilitate high-speed simulation, could well provide the structure needed for the next generation of crop models needed to support and enhance advances in crop improvement technologies. Beyond that, the trans-scale and transdisciplinary dialogue among scientists that will be required to construct such models effectively is considered to be at least as important as the models.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/insilicoplants/diz010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"61382773","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 : 2019-01-01DOI: 10.1093/insilicoplants/diz011
Dominik Schmidt, K. Kahlen
Leaf shape plays a key role in the interaction of a plant with its environment, best-known in the plant’s light harvest. Effects of the environment on the interplay of canopy architecture and physiological functioning can be estimated using functional-structural plant models (FSPMs). In order to reduce the complexity of canopy simulations, leaf shape models used in FSPMs are often simple prototypes scaled to match current leaf area. L-Cucumber is such an FSPM, whose leaf prototype mimics average real leaf shape of unstressed cucumber plants well. However, adaptation processes or stress responses may lead to non-proportional changes in leaf geometries, which, for example, could affect length to width ratios or curvatures. The current leaf shape model in L-Cucumber is static and hence does not incorporate changes in leaf shape within or between plants. Thus, the aim of this study was to estimate leaf shape variation and exemplarily study its effects on FSPM simulations. Three-dimensional leaf coordinate data from a salt stress study were analysed with a robust Bayesian mixed-effects model for estimating leaf shape depending on rank, size and salinity. Results showed that positional and size variation rather than salinity levels dominated 3D leaf shape patterns of cucumber. Considering variable leaf shapes in relation to this main sources of variation in L-Cucumber simulations, only minor effects compared to a realistic, yet static average shape were found. However, with similar computational demands variation in shapes other studies highly sensitive to shape dynamics, for example, pesticide spraying might be affected more strongly.
{"title":"Positional variation rather than salt stress dominates changes in three-dimensional leaf shape patterns in cucumber canopies","authors":"Dominik Schmidt, K. Kahlen","doi":"10.1093/insilicoplants/diz011","DOIUrl":"https://doi.org/10.1093/insilicoplants/diz011","url":null,"abstract":"Leaf shape plays a key role in the interaction of a plant with its environment, best-known in the plant’s light harvest. Effects of the environment on the interplay of canopy architecture and physiological functioning can be estimated using functional-structural plant models (FSPMs). In order to reduce the complexity of canopy simulations, leaf shape models used in FSPMs are often simple prototypes scaled to match current leaf area. L-Cucumber is such an FSPM, whose leaf prototype mimics average real leaf shape of unstressed cucumber plants well. However, adaptation processes or stress responses may lead to non-proportional changes in leaf geometries, which, for example, could affect length to width ratios or curvatures. The current leaf shape model in L-Cucumber is static and hence does not incorporate changes in leaf shape within or between plants. Thus, the aim of this study was to estimate leaf shape variation and exemplarily study its effects on FSPM simulations. Three-dimensional leaf coordinate data from a salt stress study were analysed with a robust Bayesian mixed-effects model for estimating leaf shape depending on rank, size and salinity. Results showed that positional and size variation rather than salinity levels dominated 3D leaf shape patterns of cucumber. Considering variable leaf shapes in relation to this main sources of variation in L-Cucumber simulations, only minor effects compared to a realistic, yet static average shape were found. However, with similar computational demands variation in shapes other studies highly sensitive to shape dynamics, for example, pesticide spraying might be affected more strongly.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/insilicoplants/diz011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"61382335","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 : 2019-01-01DOI: 10.1093/INSILICOPLANTS/DIZ002
Xinguang Zhu
The rate of progress in biological science today can be compared with that of physical science at the beginning of the 20th century. With so many new discoveries emerging on a daily basis, are these discoveries simply the application or manifestation of basic scientific principles such as the central dogma of molecular biology, the fundamental theorems of genetics, or the basic genetic principles of epigenetics, or will they have a larger impact? Are there grand challenges to be found in biological science research beyond the current stage of fact observation and the elucidation of the underlying mechanistic basis of individual cases using the basic principles discovered long ago? The answer is ‘yes’. In the field of plant science, though there is ever-increased resolution of the mechanistic details of the plant growth and development, we are far from being able to predict the growth and development of plants of particular genotypes under different environments, let alone predict changes in plant growth and development for an altered genotype and under a changed environment. This is one major reason why we label biology as an ‘experimental’ science, which is essentially another way of saying that models for whole plants are far from being mechanistic, sufficiently robust and satisfactorily predictive. Is there any hope of developing such models given the complexity of biological systems? There are good reasons to be optimistic. Pioneering researchers have developed many models that enable the accurate quantitative prediction of biological performances, such as the prediction of photosynthetic CO2 uptake rates under either steadystate (Farquhar et al. 1980) or dynamic conditions (Zhu et al. 2013). Models simulating many other physiological plant processes have also been developed (see reviews in Zhu et al. 2015; Chang and Zhu 2017). It is foreseeable that, as modules for individual processes become available, robust and complete models for plant growth and development will be within reach. This will not be a simple task. Even for the model organism Arabidopsis thaliana, there are about 30 000 genes, whose action and interaction among them and with their micro-environments, underlie growth and develop. Creating highly robust models to predict the behaviour of such a complex system with detailed descriptions of the mechanisms of all underlying genetic, biochemical, biophysical, and associated physical and chemical processes represent a huge challenge ahead. If such models are developed, they can be used to support plant science research, such as to study the mechanistic basis of natural variations of plant structure and function, to study the responses, acclimation, adaption and evolutionary trajectories of plants under environmental changes, in addition to the currently widely appreciated roles of plant models in guiding crop engineering, breeding or cultivation (Zhu et al. 2015; Marshall-Colon et al. 2017; Xiao et al. 2017). There are a few resear
今天生物科学的发展速度可以与20世纪初的物理科学相比。每天都有如此多的新发现出现，这些发现仅仅是基本科学原理的应用或表现，比如分子生物学的中心法则、遗传学的基本定理或表观遗传学的基本遗传原理，还是它们会产生更大的影响?在生物科学研究中，超越目前阶段的事实观察和使用很久以前发现的基本原理来阐明个体病例的潜在机制基础，是否会发现巨大的挑战?答案是肯定的。在植物科学领域，虽然对植物生长发育的机理细节的分辨率不断提高，但我们还远远不能预测特定基因型植物在不同环境下的生长发育，更不能预测基因型改变和环境变化下植物生长发育的变化。这就是为什么我们把生物学称为“实验”科学的一个主要原因，这实际上是另一种说法，即整个植物的模型远非机械的、足够健壮的和令人满意的预测。考虑到生物系统的复杂性，开发这样的模型有希望吗?我们有充分的理由保持乐观。开创性的研究人员开发了许多模型，能够准确定量预测生物性能，例如预测稳态(Farquhar et al. 1980)或动态条件下的光合CO2吸收率(Zhu et al. 2013)。模拟许多其他植物生理过程的模型也被开发出来(见Zhu et al. 2015;Chang and Zhu 2017)。可以预见，随着单个过程的模块变得可用，用于植物生长和发育的健壮和完整的模型将触手可及。这不是一项简单的任务。即使是模式生物拟南芥(Arabidopsis thaliana)，也有大约3万个基因，它们之间以及与微环境的作用和相互作用是生长和发育的基础。创建高度稳健的模型来预测这样一个复杂系统的行为，并详细描述所有潜在的遗传、生化、生物物理和相关的物理和化学过程的机制，这是一个巨大的挑战。如果这些模型被开发出来，它们可以用于支持植物科学研究，例如研究植物结构和功能自然变化的机制基础，研究植物在环境变化下的响应、驯化、适应和进化轨迹，以及目前广泛认可的植物模型在指导作物工程、育种或栽培方面的作用(Zhu et al. 2015;Marshall-Colon et al. 2017;Xiao et al. 2017)。有几个研究领域对解决这一重大挑战至关重要。首先，确定控制植物生长、发育和植物与环境相互作用的不同方面的新的生物实体或新的调节机制，将继续成为植物科学中产生重大发现的领域。在这里，我强调，除了单个基因及其产物的标准定性表征，如mRNA和蛋白质的结构和调控，它们的动态或动力学性质，如它们的合成速率、降解速率和Michaelis-Menten常数，应该是未来研究的另一个主要重点。这些定量数据是建立描述这些生物实体动态变化的速率方程所必需的。二是发展准率
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