in silico Plants最新文献

{"title":"Global sensitivity-based modelling approach to identify suitable Eucalyptus traits for adaptation to climate variability and change","authors":"E. F. Elli, N. Huth, P. Sentelhas, R. Carneiro, C. Alvares","doi":"10.1093/insilicoplants/diaa003","DOIUrl":"https://doi.org/10.1093/insilicoplants/diaa003","url":null,"abstract":"\u0000 Eucalyptus-breeding efforts have been made to identify clones of superior performance for growth and yield and how they will interact with global climate changes. This study performs a global sensitivity analysis for assessing the impact of genetic traits on Eucalyptus yield across contrasting environments in Brazil under present and future climate scenarios. The APSIM Next Generation Eucalyptus model was used to perform the simulations of stemwood biomass (t ha−1) for 7-year rotations across 23 locations in Brazil. Projections for the period from 2020 to 2049 using three global circulation models under intermediate (RCP4.5) and high (RCP8.5) greenhouse gas emission scenarios were performed. The Morris sensitivity method was used to perform a global sensitivity analysis to identify the influence of plant traits on stemwood biomass. Traits for radiation use efficiency, leaf partitioning, canopy light capture and fine root partitioning were the most important, impacting the Eucalyptus yield substantially in all environments under the present climate. Some of the traits targeted now by breeders for current climate will remain important under future climates. However, breeding should place a greater emphasis on photosynthetic temperature response for Eucalyptus in some regions. Global sensitivity analysis was found to be a powerful tool for identifying suitable Eucalyptus traits for adaptation to climate variability and change. This approach can improve breeding strategies by better understanding the gene × environment interactions for forest productivity.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/insilicoplants/diaa003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47467376","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}

{"title":"Within- and cross-species predictions of plant specialized metabolism genes using transfer learning.","authors":"Bethany M Moore,&nbsp;Peipei Wang,&nbsp;Pengxiang Fan,&nbsp;Aaron Lee,&nbsp;Bryan Leong,&nbsp;Yann-Ru Lou,&nbsp;Craig A Schenck,&nbsp;Koichi Sugimoto,&nbsp;Robert Last,&nbsp;Melissa D Lehti-Shiu,&nbsp;Cornelius S Barry,&nbsp;Shin-Han Shiu","doi":"10.1093/insilicoplants/diaa005","DOIUrl":"https://doi.org/10.1093/insilicoplants/diaa005","url":null,"abstract":"<p><p>Plant specialized metabolites mediate interactions between plants and the environment and have significant agronomical/pharmaceutical value. Most genes involved in specialized metabolism (SM) are unknown because of the large number of metabolites and the challenge in differentiating SM genes from general metabolism (GM) genes. Plant models like <i>Arabidopsis thaliana</i> have extensive, experimentally derived annotations, whereas many non-model species do not. Here we employed a machine learning strategy, transfer learning, where knowledge from <i>A. thaliana</i> is transferred to predict gene functions in cultivated tomato with fewer experimentally annotated genes. The first tomato SM/GM prediction model using only tomato data performs well (<i>F</i>-measure = 0.74, compared with 0.5 for random and 1.0 for perfect predictions), but from manually curating 88 SM/GM genes, we found many mis-predicted entries were likely mis-annotated. When the SM/GM prediction models built with <i>A. thaliana</i> data were used to filter out genes where the <i>A. thaliana-</i>based model predictions disagreed with tomato annotations, the new tomato model trained with filtered data improved significantly (<i>F</i>-measure = 0.92). Our study demonstrates that SM/GM genes can be better predicted by leveraging cross-species information. Additionally, our findings provide an example for transfer learning in genomics where knowledge can be transferred from an information-rich species to an information-poor one.</p>","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"2 1","pages":"diaa005"},"PeriodicalIF":3.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/insilicoplants/diaa005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38738446","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}

{"title":"A new tool for discovering transcriptional regulators of co-expressed genes predicts gene regulatory networks that mediate ethylene-controlled root development","authors":"Alexandria F. Harkey, Kira N Sims, G. Muday","doi":"10.1093/insilicoplants/diaa006","DOIUrl":"https://doi.org/10.1093/insilicoplants/diaa006","url":null,"abstract":"\u0000 Gene regulatory networks (GRNs) are defined by a cascade of transcriptional events by which signals, such as hormones or environmental cues, change development. To understand these networks, it is necessary to link specific transcription factors (TFs) to the downstream gene targets whose expression they regulate. Although multiple methods provide information on the targets of a single TF, moving from groups of co-expressed genes to the TF that controls them is more difficult. To facilitate this bottom-up approach, we have developed a web application named TF DEACoN. This application uses a publicly available Arabidopsis thaliana DNA Affinity Purification (DAP-Seq) data set to search for TFs that show enriched binding to groups of co-regulated genes. We used TF DEACoN to examine groups of transcripts regulated by treatment with the ethylene precursor 1-aminocyclopropane-1-carboxylic acid (ACC), using a transcriptional data set performed with high temporal resolution. We demonstrate the utility of this application when co-regulated genes are divided by timing of response or cell-type-specific information, which provides more information on TF/target relationships than when less defined and larger groups of co-regulated genes are used. This approach predicted TFs that may participate in ethylene-modulated root development including the TF NAM (NO APICAL MERISTEM). We used a genetic approach to show that a mutation in NAM reduces the negative regulation of lateral root development by ACC. The combination of filtering and TF DEACoN used here can be applied to any group of co-regulated genes to predict GRNs that control coordinated transcriptional responses.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/insilicoplants/diaa006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49176515","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}

{"title":"Within- and cross-species predictions of plant specialized metabolism genes using transfer learning","authors":"Bethany M. Moore, Peipei Wang, P. Fan, Aaron Lee, Bryan J. Leong, Y. Lou, Craig A. Schenck, K. Sugimoto, R. Last, Melissa D. Lehti-Shiu, Cornelius S. Barry, Shin-Han Shiu","doi":"10.1101/2020.01.13.112102","DOIUrl":"https://doi.org/10.1101/2020.01.13.112102","url":null,"abstract":"Plant specialized metabolites mediate interactions between plants and the environment and have significant agronomical/pharmaceutical value. Most genes involved in specialized metabolism (SM) are unknown because of the large number of metabolites and the challenge in differentiating SM genes from general metabolism (GM) genes. Plant models like Arabidopsis thaliana have extensive, experimentally derived annotations, whereas many non-model species do not. Here we employed a machine learning strategy, transfer learning, where knowledge from A. thaliana is transferred to predict gene functions in cultivated tomato with fewer experimentally annotated genes. The first tomato SM/GM prediction model using only tomato data performs well (F-measure=0.74, compared with 0.5 for random and 1.0 for perfect predictions), but from manually curating 88 SM/GM genes, we found many mis-predicted entries were likely mis-annotated. When the SM/GM prediction models built with A. thaliana data were used to filter out genes where the A. thaliana-based model predictions disagreed with tomato annotations, the new tomato model trained with filtered data improved significantly (F-measure=0.92). Our study demonstrates that SM/GM genes can be better predicted by leveraging cross-species information. Additionally, our findings provide an example for transfer learning in genomics where knowledge can be transferred from an information-rich species to an information-poor one.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48756679","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}

{"title":"Reuse of process-based models: automatic transformation into many programming languages and simulation platforms","authors":"Cyrille Ahmed Midingoyi, C. Pradal, I. Athanasiadis, M. Donatelli, Andreas Enders, D. Fumagalli, Frédérick Garçia, D. Holzworth, G. Hoogenboom, C. Porter, H. Raynal, P. Thorburn, P. Martre","doi":"10.1093/insilicoplants/diaa007","DOIUrl":"https://doi.org/10.1093/insilicoplants/diaa007","url":null,"abstract":"\u0000 The diversity of plant and crop process-based modelling platforms in terms of implementation language, software design and architectural constraints limits the reusability of the model components outside the platform in which they were originally developed, making model reuse a persistent issue. To facilitate the intercomparison and improvement of process-based models and the exchange of model components, several groups in the field joined to create the Agricultural Model Exchange Initiative (AMEI). Agricultural Model Exchange Initiative proposes a centralized framework for exchanging and reusing model components. It provides a modular and declarative approach to describe the specification of unit models and their composition. A model algorithm is associated with each model specification, which implements its mathematical behaviour. This paper focuses on the expression of the model algorithm independently of the platform specificities, and how the model algorithm can be seamlessly integrated into different platforms. We define CyML, a Cython-derived language with minimum specifications to implement model component algorithms. We also propose CyMLT, an extensible source-to-source transformation system that transforms CyML source code into different target languages such as Fortran, C#, C++, Java and Python, and into different programming paradigms. CyMLT is also able to generate model components to target modelling platforms such as DSSAT, BioMA, Record, SIMPLACE and OpenAlea. We demonstrate our reuse approach with a simple unit model and the capacity to extend CyMLT with other languages and platforms. The approach we present here will help to improve the reproducibility, exchange and reuse of process-based models.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/insilicoplants/diaa007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44940072","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}

{"title":"Gillespie-Lindenmayer systems for stochastic simulation of morphogenesis","authors":"M. Cieslak, P. Prusinkiewicz","doi":"10.1093/insilicoplants/diz009","DOIUrl":"https://doi.org/10.1093/insilicoplants/diz009","url":null,"abstract":"Lindenmayer systems (L-systems) provide a useful framework for modelling the development of multicellular structures and organisms. The parametric extension of L-systems allows for incorporating molecular-level processes into the models. Until now, the dynamics of these processes has been expressed using differential equations, implying continuously valued concentrations of the substances involved. This assumption is not satisfied, however, when the numbers of molecules are small. A further extension that accounts for the stochastic effects arising in this case is thus needed. We integrate L-systems and the Gillespie’s Stochastic Simulation Algorithm to simulate stochastic processes in fixed and developing linear structures. We illustrate the resulting formalism with stochastic implementations of diffusion-decay, reaction-diffusion and auxin-transport-driven morphogenetic processes. Our method and software can be used to simulate molecular and higher-level spatially explicit stochastic processes in static and developing structures, and study their behaviour in the presence of stochastic perturbations.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/insilicoplants/diz009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"61382713","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}

{"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":" ","pages":""},"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}

{"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":" ","pages":""},"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}

{"title":"Systems models, phenomics and genomics: three pillars for developing high-yielding photosynthetically efficient crops.","authors":"Tian-Gen Chang,&nbsp;Shuoqi Chang,&nbsp;Qing-Feng Song,&nbsp;Shahnaz Perveen,&nbsp;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":"1 1","pages":""},"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}

{"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":" ","pages":""},"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}