in silico Plants

Carbon allocation governs how plants partition assimilates among leaves, roots, stems, and reproductive organs, directly shaping growth and yield. Most crop models represent this partitioning using fixed or empirically derived coefficients, which limits their ability to predict how allocation responds to genotype, environment, or their interaction. Eco-evolutionary optimality theory offers an alternative where the allocation strategy is allowed to emerge from the marginal costs and benefits of investing carbon in each organ. While this approach has been applied successfully to trees, it has not previously been developed for herbaceous plants. Here, we present DAESIM2-Plant, a mechanistic plant growth model that couples physiological processes including photosynthesis, stomatal conductance, plant hydraulics, and canopy radiative transfer, with an eco-evolutionary optimal allocation scheme for leaves and roots. This is applied to wheat by coupling it to a source-sink grain production module. Using a series of idealized sensitivity experiments, we show that the model reproduces well-documented patterns of plasticity in carbon allocation: diminishing returns on leaf investment as canopy closure and water limitation are approached, a shift in allocation toward roots under drier conditions, and a root:shoot response that depends jointly on soil moisture and the plant's existing root:leaf balance. Simulating a full growing season across a gradient of soil moisture levels reveals a threshold-like response in canopy development, biomass accumulation, and grain yield, with yield constrained by the same assimilate supply that governs vegetative growth during the critical period and grain filling. This provides a promising basis for generalizing model behaviour across a wide range of environmental conditions. Evaluating these results against experimental and field trial data remains an important next step.

Background and aims: Perennial root systems develop over decades and exhibit continuous turnover of structural and absorptive components, yet most root architecture models treat woody roots as permanent and neglect root age structure. We developed an empirically constrained modelling framework that explicitly represents age-structured demography, growth regulation and dormancy in perennial root systems. Methods: We extended the functional-structural plant model CPlantBox to simulate perennial root development over decades by integrating age-dependent survival, secondary radial growth and climate-driven dormancy. The model was parameterised using unique three-dimensional root system data from young and mature grapevines, together with dendrochronological root age measurements, and coupled to root hydraulic properties. Key results: Empirical analyses revealed multi-cohort age distributions and substantial turnover of lignified structural roots, contradicting the assumption of a static woody scaffold. The model reproduced age distributions, order-specific survival probabilities and fine-to-structural root length ratios, and generated realistic, genotype-specific three-dimensional architectures. Simulated uptake profiles and hydraulic scenarios showed strong age- and architecture-driven effects. Conclusions: Explicitly representing woody root age distributions and age-dependent processes may fundamentally change predictions of perennial root system architecture and function. The framework advances root modelling beyond annual paradigms and provides a transferable tool for analysing long-term architectural and hydraulic consequences of genotype x climate interactions in perennial crops.

Efficient yield prediction under field conditions remains a key challenge for crop breeders and agronomists. YieldLearn is an interpretable machine-learning pipeline developed to estimate yield-related traits in durum wheat (Triticum durum) using physiological indicators measured by the Hansatech Instruments Pocket PEA. The workflow integrates fluorescence parameters (Area, Fo, Fm, Fv/Fm, PI Inst.) with field-measured agronomic traits (Booting, Height, Maturity, Lodging, Yield kg/plot, and Adjusted Yield kg/ha). Five supervised learning algorithms multinomial logistic regression, random forest, support vector machine (radial kernel), k-nearest neighbors, and decision tree were evaluated using five-fold cross-validation. Models achieved average classification accuracy around 0.57 with AUC values exceeding 0.58. SHAP (SHapley Additive exPlanations) visualizations revealed that high Fv/Fm and PI Inst. values were positively associated with yield potential, whereas elevated Fo indicated stress-induced yield reduction. An interactive Shiny App enables real-time prediction and feature-importance exploration. Given the small sample size (165 plots) and absence of environmental covariates such as temperature or soil moisture, YieldLearn should be regarded as a reproducible exploratory framework rather than a production-ready predictor. Nonetheless, it offers a valuable foundation for guiding experimental design, validating physiological indicators, and supporting the next generation of scalable, interpretable crop-yield models.

Genotype-by-environment interactions can complicate plant breeding because genetic effects may vary across weather regimes, locations, and years. Although genomic prediction models can use dense marker data to predict complex crop traits, they often provide limited information about how marker relevance changes across environmental conditions. This study introduces a graph-based framework for estimating environment-associated marker rankings from multi-environment crop trial data. We introduce an attention-based graph neural network that jointly represents SNP markers and weather-based environment vectors as interacting nodes and is designed to learn environment-associated marker rankings from supervised yield data. The model is trained using yield prediction as the supervised objective, and the learned marker-level attention signals are aggregated to estimate marker rankings overall and within groups of similar environments. We evaluate the framework on two genotype-by-environment datasets. In both datasets, we examine whether learned marker rankings are stable across independent training runs and determine the overlap between high-ranked markers and external GWAS references. In a maize dataset with data from 212 different site-years, we rank markers separately within each weather-defined environment cluster and define a global marker set as the markers shared across all clusters. This provides a structured way to examine how marker rankings vary across weather-defined groups and which markers remain important across environments. We further encode each weather variable with a separate LSTM combined by attention, which makes the contribution of individual weather variables readable, and compare the resulting variable ranking against an independent SHAP-based attribution. Genes located near the top-ranked markers are also examined for functional enrichment. Overall, the results suggest that graph-based, environment-conditioned marker attribution may complement genomic prediction as an exploratory tool for studying context-dependent genomic signals in multi-environment trials.

Drought stress significantly reduces tomato (Solanum lycopersicum L.) productivity, and early detection is critical to minimize yield losses through timely interventions. In this study, we developed Drought-Spec-Net, a hybrid 1D convolutional neural network that integrates local and global spectral feature extraction to detect early drought stress from visible and near infrared (Vis NIR) spectra data of tomato seedlings. The model was trained on 378 samples using an 80:20 train test split, with 20% of the training set reserved for validation. DroughtSpecNet outperformed the evaluated baseline and state of the art models, achieving 97% accuracy, 95% precision, 98% recall, and an F1 score of 97%. To improve the agronomic interpretation of the model outputs, predicted drought probabilities were converted into a literature-informed potential yield impact indicator using a maximum impact level of 60%. On the test set (76 samples), mapped potential yield-impact values ranged from 0% to 60%, with an average reduction of 12.97%. We also conducted an initial experiment using our greenhouse RGB dataset, collected daily from drought treated and well-watered tomato plants at West Virginia State University (WVSU). From this dataset, 44 images were selected for ilastik-based canopy segmentation, producing plant-level drought severity indices (DSI) with a mean of 0.28, median of 0.14, and range of 0.01 to 0.91. Additionally, we trained and fine-tuned a large language model (LLM) based on PLLaMA7BInstruct, called AgriLLaMA, for automated agronomic report generation from Drought-Spec-Net outputs. The generated reports summarize predicted stress levels, mapped potential yield impacts, and preliminary management considerations. This integrated approach not only improves early drought stress detection but also delivers quantitative and interpretable estimates of potential productivity losses, providing a complete framework connecting physiological stress detection to actionable agricultural outcomes.

Breeding programmes choose genomic prediction models by accuracy rankings, and the metric behind a ranking decides which model wins. Selecting the best genotypes within a trial depends only on the order of the predictions, so a metric that guides the choice must be unchanged by any order-preserving change of the predictions and respond when their order is reversed. Of the 22 official metrics of the Genomes to Fields maize competition, one meets both conditions, the within-environment rank correlation; the within-environment Pearson correlation is protected against rescaling only; and twenty fail, including the root mean squared error that decided the 2022 ranking. Switching between the competitions two official metrics reverses 27 % of team pairs, and an exact decomposition shows why pooled metrics mislead: in three constructed panels, 94-95 % of the attainable square of pooled correlation is carried by the environment means. The accuracy difference a trial can resolve falls as about 3/{surd}N with its N genotype-environment cells: at least 0.03 at the competitions design and 0.04-0.12 once calibrated on real panels, against 0.003 between the first two teams; tests that two correlations differ declare far smaller differences, which do not reliably change selection. In soybean, where the two most accurate methods stand clear of most others by more than the trial resolves, ranking by either within-environment correlation selects better material, 98-99 % of the attainable selection gain in held-out environments against 13-34 % for error-magnitude rankings; in common bean and a Chinese maize population, where the leading methods lie within the resolvable gap, all rules pick material of similar value. GPverdict runs these checks on any programmes cross-validated predictions.

Breedbase ( is a comprehensive, web-based, open-source breeding management and analysis software for plant breeding that was introduced to address gaps with existing software for plant breeding data. Breedbase is continually improved and developed, with new features being added as required by the breeding community. Since its inception, the capabilities of Breedbase were improved to better serve the diverse data modeling needs of additional crops beyond our original ensemble of cassava, banana, yam, sweet potato, tomato, rice, and wheat. We describe here major updates that have been made in the past 4 years, including (1) improved handling of repetitive measurements; (2) a redesigned experimental treatments interface; (3) a more fine-grained user permission system; (4) improved trial quality control workflows; (5) intercropping support; (6) the addition of a decision meeting feature that helps decision makers make the best selection with the available data; (7) transgenics and tracking tools and finally, (8) an improved field layout tool which consolidates several trials in one field. These new features integrate seamlessly with the previous feature set of Breedbase - making it one of the most feature rich breeding management softwares available.

Gene regulatory networks (GRNs) establish the spatio-temporal gene expression patterns necessary for reproducible development of organisms. MicroRNAs have emerged as important regulators of gene expression controlling either target gene levels, spatial patterns or timing. These molecular functions may vary depending on how they are integrated into GRNs. Here we used the Arabidopsis leaf as a model to study the genetic and molecular interactions between the CUP-SHAPED COTYLEDON 2 (CUC2) transcription factor and its negative regulator, miR164A. Quantitative morphometric analyses showed that miR164A acts from early stages during leaf development regulating CUC2 expression dynamic rather than the spatial distribution of CUC2. At the molecular level, CUC2 directly activates MIR164A expression, forming a single negative feedback loop. Further, using a ratiometric approach, we identified a cis-regulatory element (CRE) sufficient to drive CUC2-dependent MIR164A expression at the leaf sinus. Ordinary differential equation (ODE) modelling suggests that CUC2-CRE may control the timely accumulation of CUC2. Accordingly, quantification of CUC2 protein accumulation dynamics using a spatio-temporal mean cartography strategy in CRISPR cis-regulatory variants at the MIR164A locus revealed that CUC2-dependent MIRA164A expression is required to maintain both the precise timing of CUC2 expression and CUC2 levels. Altogether, our work provides experimental evidence for a specific role of the CUC2-MIR164A module in shaping leaf development, highlighting the contribution of network topology to GRN functions.

Dysfunctions in transcription cause severe human diseases. In eukaryotes, transcription initiation by RNA polymerase (Pol) II requires the formation of the pre-initiation complex (PIC) composed of general transcription factors (GTFs), Pol II, and an essential multiprotein coactivator, Mediator. In vitro experiments with GTFs and Pol II have depicted a linear sequence of PIC assembly. However, PIC assembly paths in vivo at the genomic scale and Mediator's role in these paths remain poorly understood. One strategy characterizes ChIP-seq occupancy alterations in Mediator mutants, but analysis of these data still lacks a formalized interpretation framework. In this study, we address this need by developing a quantitative framework that scores candidate assembly paths according to their ability to explain these alterations. This framework combines a model of PIC assembly based on a system of differential equations with parameter optimization based on a penalized criterion. We applied this new framework to datasets from two yeast Mediator mutants, in the Med17 and Med10 subunits. Our results show that the best-supported PIC assembly paths differ from those inferred in vitro. In particular, we show that it is important to distinguish the core TFIIH module from the TFIIK kinase module, and that similar paths starting with Mediator or TBP receive different levels of support. Furthermore, although multiple paths fit the data equally well, they differ markedly for specific gene groups. Taken together, the results show that the path can depend on the gene, and are also compatible with more than one path being operational in vivo for a single gene. This work improves our understanding of PIC assembly paths and transcription initiation, and opens perspectives for future modeling and experimental studies.

Chlorophyll fluorescence imaging is a powerful tool used to characterize abiotic and biotic stress response of photosynthesis in plants. However, extracting metrics such as Fv/Fm (maximum quantum yield of photosystem II) from chlorophyll fluorescence images remains a substantial bottleneck in large measurement campaigns, often requiring significant manual effort. To resolve this issue, we developed fvfmPy, a user-friendly program which enables rapid analysis of chlorophyll fluorescence images of arrays of leaf discs. fvfmPy uses algorithmic image segmentation to identify regions of interest and extract fluorescence metrics from tens or hundreds of leaf samples per image with little to no manual input. fvfmPy is developed in Python and includes a graphical user interface enabling intuitive parameter adjustment and real-time quality control checking. We additionally developed fvfmR, an R wrapper package for fvfmPy. Fv/Fm values extracted with fvfmPy were indistinguishable from those obtained using manual methods (r2 > 0.99) but required 94% less time to complete (13 s vs. 200 s per 100 measurements). fvfmPy and fvfmR were built to directly integrate with existing fluorescence software packages and can greatly increase throughput of fluorescence measurements to help address key challenges in agriculture and ecology.

Globally, contaminated soils pose serious risks to agriculture, biodiversity of ecosystems, and human health. Although a variety of monitoring and remediation methods have been developed over the years, there remains a lack of theoretical modelling to better understand the impact of contamination on soil carbon in a microbially-mediated system. In this study, we develop a model which incorporates contaminated soil organic carbon, uncontaminated soil organic carbon, dissolved organic carbon, microbes, extracellular enzymes, and CO2, to explore how different amounts of contamination and modes of contamination (pulse and press) affect microbially-mediated carbon flux within both vegetated and barren environments. The model represents contamination as a pool of contaminated soil organic carbon that is more resistant to microbial degradation than uncontaminated soil organic carbon. The results show that, in barren environments, the amount and type of contamination has no notable impact on the soil system. In contrast to this, in vegetated environments, plants mitigate the impact of large contamination inputs with differences in outcome depending on the amount and type of contamination.

In common wheat (Triticum aestivum L.), variation at the Vrn1 and Ppd1 loci changes plant phenology, resulting in differential adaptation useful to breeders. Because the effects of phenology on grain yield are conditional on environmental factors, the incorporation of markers for Vrn1 and Ppd1 alleles into breeding approaches has proven difficult. Historic phenotypic, genotypic, and environmental data can provide insight into the relationship between major phenology alleles and grain yield as mediated by environmental variables. Analyses of eight years of breeding trials (1,038 lines at 219 site-years) determined that weak vrn1 alleles and Ppd1 insensitivity alleles have variable effects. These effects change depending on an environment's winter temperature and latitude, respectively, but the environmentally-driven changes in effect of vrn1 alleles are much more variable than effects of Ppd1 alleles. Mediation analyses showed phenologically-dependent environmental variables are one mechanism through which phenological changes created by vrn1 and Ppd1 variants alter yield across multiple developmental stages. Environmentally-driven relationships between flowering time and yield were modeled to estimate yield allele effects as a function of effects on phenology and environmental variables. Weak winter alleles at vrn1 may play a stabilizing role on yield, due to the correlation of winter conditions that drive larger effects with later-season warm temperatures that penalize later maturity. A better mechanistic understanding of how these loci drive environment-specific yield effects may facilitate utilization of markers for these alleles and improve predictive modeling of yield.

Genotype imputation offers a biological application for optimization methods that may eventually run on larger quantum computers. We developed QuImputer to encode allele-frequency priors and signed linkage disequilibrium (LD) in a reduced Ising/QUBO objective, with only missing genotypes represented as optimization variables. The same objective can be passed to exhaustive classical search, quantum approximate optimization algorithm simulation, or quantum hardware. We evaluated the formulation on 100 rice accessions across 12 chromosomes, estimating LD and allele frequency from the same post-masking observed matrix supplied to Beagle 5.4. Evaluation was restricted to known truth at sites with exact dosage coding and an alternate allele that was not the major allele, with MAF below 0.50, yielding 1.88 billion scored genotypes. Beagle achieved higher rare-ALT sensitivity (2.52% versus 0.54%), F1 (0.047 versus 0.011), Matthews correlation coefficient (0.088 versus 0.044), and pooled hard-call dosage R-squared (0.231 versus 0.014). QuImputer produced fewer false ALT calls, with Type I error of 0.038% versus 0.223%. Analyses of a chromosome-1 subset showed limited local LD support for many masked rare-ALT carriers. In five small IBM Fez validation problems, the selected minimum-energy outputs matched unique all-reference exact optima; the pooled frequency of those optima was 1.21-2.63%. Further IBM Fez executions covered eleven sparse instances with 12-97 variables and a separate 120-variable instance, retaining all measurements. Selected outputs recovered the exact optima in eight instances through 49 variables. Larger instances remained below the all-reference baseline; noiseless comparisons distinguished finite-shot sampling from deviations in hardware output. QuImputer provides an LD-informed formulation and a route to quantum-hardware execution, together with an empirical assessment of the limitations of the evaluated configuration. These results establish a basis for studying richer information models and alternative classical or quantum solvers.

Non-destructive methods for real-time phenotyping of root growth and development are essential to accelerate the breeding of climate-resilient crops. We present DIRTNet (Digital Investigations of Root Traits Net), a hybrid deep learning model that interprets in-soil signals collected by Fiber Bragg Grating (FBG) sensors. We first evaluated DIRTNet in a controlled simulation study in which metal rods of varying diameters were inserted into soil to emulate root growth. Using augmented training data with an 80:20 train-test split, DIRTNet surpassed baseline models, achieving 0.97 accuracy for root depth prediction and 0.94 for root diameter prediction. We then applied the best-performing models to maize (Zea mays) roots monitored by six FBG sensors placed at different soil location and depths. A control trial and a drought-stress trial yielded 192 samples, split 80:20 into training and holdout sets; the hold-out portion was divided equally into validation and test sets. Augmentation was applied only to the training data. On real maize root data, DIRTNet achieved 0.92 accuracy for both root depth and root width prediction across variable growth periods, covering root mass lengths up to 20 cm and diameters up to 10 cm. The prediction accuracy is independent of the soil moisture and drought condition. In a fixed-interval analysis, accuracy reached 0.97 for days 25-42 after planting and 0.93 for days 25-48, demonstrating robust performance as root complexity increased.

We have developed a method to integrate transcriptomic coexpression networks across diverse experimental conditions within a single species. Our framework expands previous pannetwork approaches into a hypergraph based pannetwork. It first identifies coexpressed gene clusters within each individual dataset and extracts frequently co-expressed gene sets across multiple datasets using a frequent itemset mining algorithm. This process yields a hypergraph where each hyperedge is assigned a frequency (universality, U). We then extracted a subnetwork comprising high U hyperedges as the core network and formed modules within it. We applied our method to 106 Escherichia coli transcriptome datasets from the GEO database. The modularity of the core network peaked at a universality cutoff of 15, which was subsequently used to define it. Approximately 70% of the resulting core modules correspond to operons, and conversely, approximately 70% of all operons are covered by these core modules. We visualized the core network via an inter-modular network and analyzed core module dataset relationships using a modularity profile matrix. Based on these analyses, we successfully visualized the dynamic reorganization of the coexpression network in response to environmental changes in bacteria.

- C2 photosynthesis reduces photorespiratory carbon loss by shuttling glycine from mesophyll (M) to bundle sheath (BS) tissues, where glycine decarboxylase (GDC) releases CO2 that accumulates around rubisco. C2 photosynthesis has been considered a constitutive character, but as a metabolic trait, it may acclimate to the rate of photorespiratory glycine production. - We grew eleven species from two eudicot and two grass lineages spanning C3 to C2 phenotypes at 32 {degrees}C days and 200 or 800 {micro}mol mol-1 CO2. Acclimation was assessed using leaf gas exchange, GDC P-protein immunolocalization, M and BS ultrastructure, photosynthetic enzyme activity, and photosynthesis modelling. - Low relative to high growth CO2 enhanced C2-related phenotypes in sub-C2 and C2 species but not C3 or proto-Kranz species. Sub-C2 species positioned chloroplasts closer to mitochondria and had lower modelled BS conductance. C2 species increased rubisco capacity and GDC allocation to the BS. Aspartate aminotransferase activity increased in both sub-C2 and C2 species. - Acclimation to greater photorespiratory conditions strengthens C2 photosynthesis, but only in species whose BS has crossed a threshold of organelle and GDC investment. We hypothesize that acclimation could expose C2-related traits to selection and facilitate the origin of C4 photosynthesis.

In this study, we develop a mathematical model of the hypothalamic pituitary adrenal (HPA) axis and systematically investigate the model complexity required to reproduce observed hormone dynamics. The model is fitted to ACTH and cortisol measurements from five healthy volunteers reported in the literature. We show that simultaneous reproduction of both hormone profiles requires a delay differential equation model with at least five state variables among the model architectures considered. Following fitting the volunteer data, we investigate the response of the model to sustained stress using bifurcation analysis. The coupled system exhibits bifurcations over a range of stress intensities. This analysis reveals bifurcations whose critical stress thresholds depend strongly on the modulatory state of the system. These results provide a mathematical framework for investigating transitions between physiological and dysregulated HPA axis dynamics, which may be related to states of health and disease.

Climate change is reshaping weed population dynamics, creating non-stationary management challenges for which static decision rules are insufficient. This chapter presents and evaluates an open surveillance toolkit of artificial intelligence tools for recognizing and mapping Ambrosia artemisiifolia (common ragweed) at complementary spatial scales, tested across two phenological phases (seedling, September 2024; adult, December 2024) in a lentil paddock in central Chile. At the field scale, a YOLOv11 detection model coupled with Slicing Aided Hyper Inference achieved mAP50 of 0.886. Cross-domain experiments revealed that single-site models collapse catastrophically when deployed in new geographies (mAP50 dropping to 0.108), but multi-domain training recovers performance to 0.874. At the satellite scale, PRESTO foundation model embeddings correlated strongly with ground-truth weed density (r = 0.739); geographically weighted regression explained up to 91% of local density variance. All methods are released as the ragweed-ai-toolkit (https://github.com/agroia-lab/ragweed-ai-toolkit), a modular open-source Python package enabling adaptation to new species and geographies.

Rubber vines (Cryptostegia madagascariensis and C. grandiflora) are highly invasive weeds in the semi-arid landscapes of many tropical regions. The rust fungus Maravalia cryptostegiae has provided a spetatular and sustainable control of invasive C. grandiflora populations in Australia, after its scientific and quarantine-authorized release in the context of a classical biological control program. The Caatinga biome of Northeastern Brazil is under a devastating invasion by C. madagascariensis. This species is also susceptible to M. cryptostegia but its epidemic potential towards C. madagascariensis in Brazil remains unknown. We developed a weather-driven epidemiological model integrating experimentally derived relationships for infection as a function of temperature and wetness duration and for temperature-dependent latent development. Three infection-response models were fitted to 20 experimental temperature x wetness combinations from the literature and compared using information criteria, grouped cross-validation and a parametric bootstrap. The selected model, in which thermal breadth increases with wetness duration, was best supported (R2 = 0.931; {Delta}AICc [≥] 3.3 relative to the alternatives), and prolonged wetness significantly broadened the thermal range favorable for infection ({gamma} = 0.30 oC h-1; bootstrap P = 0.002). The infection and latent-development models were coupled with 25 years (2001-2025) of hourly NASA POWER data for 65 grid cells representing rubber-vine-infested locations in Northeastern Brazil, with wetness defined as hours with relative humidity equal or greater than 90%. Infection potential was concentrated in March-June (73% of the annual total, peaking in April), coinciding with the regional rainy season, and was highest in the interior of northern and central Ceara. Predicted potential varied more than a hundredfold among grid cells and was lowest along parts of the Ceara coast and the lower Jaguaribe valley, which include some of the largest infestations; the magnitude of these contrasts depended on the humidity threshold used to define wetness. Applied with identical rules to 13 historical release sites in Queensland, Australia, the model reproduced the documented wet-season timing of rust activity, and predicted potential was on average higher at sites with reported high rust impact, although the difference was not statistically significant. These results indicate strong seasonal and geographic structuring of weather-driven epidemic potential and provide testable hypotheses for selecting release periods and locations for M. cryptostegiae in Brazil.

SUMOylation is an essential and evolutionarily conserved post-translational modification that enables rapid cellular adaptation to environmental stress. Despite its importance, how the SUMOylation machinery controls its dynamics remains poorly understood. Here, we integrated a mathematical model of the plant SUMO cycle with quantitative imaging and biochemical data to decode the regulatory logic of SUMOylation dynamics. The model reveals a fundamental principle: steady-state SUMOylation levels are determined not by the full complement of SUMO-cycle components, but by the abundance of SUMO modifier proteins and E1 enzymes, together with SUMO protease abundance and activity. Strikingly, when we integrated fluorescence measurements from reporters for SUMO-cycle components in Arabidopsis roots after three hours of abiotic stress, the model predicted decreased SUMOylation levels across several tissues, contrary to prevailing evidence. Motivated by this unexpected prediction, we performed time-resolved imaging to better characterize the dynamics of key SUMO-cycle components after salt and osmotic stress. These data revealed highly dynamic component levels within the first three hours of stress that were not captured in the previous measurements. Incorporating this temporal data into the model uncovered a twin-peaked SUMOylation response to salt stress: an early surge underpinned by protease dynamics, followed by a decline and a second peak driven by elevated SUMO modifier and E2 levels. Western blot analysis revealed a corresponding twin-peaked pattern of SUMOylation. Together, our findings demonstrate that the plant SUMO cycle is a highly dynamic system that achieves tissue- and stress-specific control of SUMOylation through the coordinated interplay of multiple SUMO-cycle components.