in silico Plants计算植物学
in silico Plants(英文缩写 IN SILICO PLANTS),ISSN 2517-5025,eISSN 2517-5025,中文译名:计算植物学 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
in silico Plants 最新收录文献
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1. Model-based inference of a dual role for HOPS in regulating guard cell vacuole fusion.
PMID:日期:2024-08-30Guard cell movements depend, in part, on the remodelling of vacuoles from a highly fragmented state to a fused morphology during stomata opening. Indeed, full opening of plant stomata requires vacuole fusion to occur. Fusion of vacuole membranes is a highly conserved process in eukaryotes, with key roles played by two multi-subunit complexes: HOPS (homotypic fusion and vacuolar protein sorting) and SNARE (soluble NSF attachment protein receptor). HOPS is a vacuole tethering factor that is thought to chaperone SNAREs from apposing vacuole membranes into a fusion-competent complex capable of rearranging membranes. In plants, recruitment of HOPS subunits to the tonoplast has been shown to require the presence of the phosphoinositide phosphatidylinositol 3-phosphate. However, chemically depleting this lipid induces vacuole fusion. To resolve this counter-intuitive observation regarding the role of HOPS in regulating plant vacuole morphology, we defined a quantitative model of vacuole fusion dynamics and used it to generate testable predictions about HOPS-SNARE interactions. We derived our model by using simulation-based inference to integrate prior knowledge about molecular interactions with limited, qualitative observations of emergent vacuole phenotypes. By constraining the model parameters to yield the emergent outcomes observed for stoma opening-as induced by two distinct chemical treatments-we predicted a dual role for HOPS and identified a stalled form of the SNARE complex that differs from phenomena reported in yeast. We predict that HOPS has contradictory actions at different points in the fusion signalling pathway, promoting the formation of SNARE complexes, but limiting their activity.
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2. {"_":"Graph metric learning quantifies morphological differences between two genotypes of shoot apical meristem cells in .","i":["Arabidopsis"]}
PMID:日期:2023-01-30We present a method for learning 'spectrally descriptive' edge weights for graphs. We generalize a previously known distance measure on graphs (graph diffusion distance [GDD]), thereby allowing it to be tuned to minimize an arbitrary loss function. Because all steps involved in calculating this modified GDD are differentiable, we demonstrate that it is possible for a small neural network model to learn edge weights which minimize loss. We apply this method to discriminate between graphs constructed from shoot apical meristem images of two genotypes of specimens: wild-type and triple mutants with cell division phenotype. Training edge weights and kernel parameters with contrastive loss produce a learned distance metric with large margins between these graph categories. We demonstrate this by showing improved performance of a simple -nearest-neighbour classifier on the learned distance matrix. We also demonstrate a further application of this method to biological image analysis. Once trained, we use our model to compute the distance between the biological graphs and a set of graphs output by a cell division simulator. Comparing simulated cell division graphs to biological ones allows us to identify simulation parameter regimes which characterize mutant versus wild-type cells. We find that mutant cells are characterized by increased randomness of division planes and decreased ability to avoid previous vertices between cell walls.
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3. Modelling of plant circadian clock for characterizing hypocotyl growth under different light quality conditions.
PMID:日期:2022-02-02To 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 . 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.
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4. Within- and cross-species predictions of plant specialized metabolism genes using transfer learning.
PMID:日期:2020-07-30Plant 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 have extensive, experimentally derived annotations, whereas many non-model species do not. Here we employed a machine learning strategy, transfer learning, where knowledge from 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 (-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 data were used to filter out genes where the based model predictions disagreed with tomato annotations, the new tomato model trained with filtered data improved significantly (-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.
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5. {"_":"An explanatory model of temperature influence on flowering through whole-plant accumulation of in .","i":["FLOWERING LOCUS T","Arabidopsis thaliana"]}
PMID:日期:2019-05-15We assessed mechanistic temperature influence on flowering by incorporating temperature-responsive flowering mechanisms across developmental age into an existing model. Temperature influences the leaf production rate as well as expression of (), a photoperiodic flowering regulator that is expressed in leaves. The Framework Model incorporated temperature influence on leaf growth but ignored the consequences of leaf growth on and direct temperature influence of expression. We measured production in differently aged leaves and modified the model, adding mechanistic temperature influence on transcription, and causing whole-plant to accumulate with leaf growth. Our simulations suggest that in long days, the developmental stage (leaf number) at which the reproductive transition occurs is influenced by day length and temperature through , while temperature influences the rate of leaf production and the time (in days) the transition occurs. Further, we demonstrate that is mainly produced in the first 10 leaves in the Columbia (Col-0) accession, and that accumulation alone cannot explain flowering in conditions in which flowering is delayed. Our simulations supported our hypotheses that: (i) temperature regulation of , accumulated with leaf growth, is a component of thermal time, and (ii) incorporating mechanistic temperature regulation of can improve model predictions when temperatures change over time.
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6. Systems models, phenomics and genomics: three pillars for developing high-yielding photosynthetically efficient crops.
PMID:日期:2019-04-25Recent 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.