The challenges posed by climate change have had a crucial impact on global food security, with crop yields negatively affected by abiotic and biotic stresses. Consequently, the identification of abiotic stress-responsive genes (SRGs) in crops is essential for augmenting their resilience. This study presents a computational model utilizing machine learning techniques to predict genes in Chinese cabbage that respond to four abiotic stresses: cold, heat, drought, and salt. To construct this model, data from relevant studies regarding responses to these abiotic stresses were compiled, and the protein sequences encoded by abiotic SRGs were converted into numerical representations for subsequent analysis. For the selected feature set, six distinct machine learning binary classification algorithms were employed. The results demonstrate that the constructed models can effectively predict SRGs associated with the four types of abiotic stresses, with the area under the receiver operating characteristic curve (auROC) for the models being 81.42%, 87.92%, 80.85%, and 88.87%, respectively. For each type of stress, a distinct number of stress-resistant genes was predicted, and the ten genes with the highest scores were selected for further analysis. To facilitate the implementation of the proposed strategy by users, an online prediction server, has been developed. This study provides new insights into computational approaches to the identification of abiotic SRGs in Chinese cabbage as well as in other plants.

The circadian clock orchestrates photosynthetic and metabolic processes in plants, but the molecular mechanisms underlying the photoperiodic regulation of photosynthetic yield remain poorly understood. Here, we integrated computational modeling and experimental validation to investigate how the skeletal photoperiod modulates photosynthetic efficiency in celery (Apium graveolens L.). Our model revealed that endogenous circadian rhythms dynamically regulate photosynthetic gene expression (e.g., Lhcb1, psbA, RbcS1, and atpA) and photosynthetic parameters (net photosynthetic rate and stomatal conductance) through interactions between clock components (CCA1/LHY and PRR9/PRR7) and light signaling. In particular, the 3L:3D skeleton photoperiod induced the highest 24 h photosynthetic accumulation (a 32% and 22% increase in chlorophyll and nitrogen content, respectively, vs. 12L:12D), outperforming continuous light (LL) and longer photoperiods. Rhythmic peaks of photosynthetic genes aligned with circadian-driven oscillations in the photosynthetic parameters, while a strong negative correlation between the net photosynthetic rate (Pn) and intercellular CO₂ concentration (Ci) emerged under 3L:3D cycles. Model simulations demonstrated robustness in capturing phase-specific gene expression and parameter dynamics across photoperiods, highlighting the role of the circadian clock in optimizing energy use. These results demonstrate that abnormal L/D cycles, particularly 3L:3D, increase photosynthetic yield by enhancing circadian-regulated metabolic coordination, providing a low-energy, high-efficiency strategy for agricultural productivity. This work advances our understanding of photoperiodic manipulation in crop systems and provides a predictive framework for circadian-informed crop management.

Light is a key environmental factor regulating plant photosynthesis, yet the coordinated effects of photoperiod and light intensity on photosynthetic dynamics in non-heading Chinese cabbage remain unclear. Using experimental data, we developed a circadian clock-controlled photosynthesis model integrating photoperiod and light-intensity inputs. The model was calibrated and evaluated against experimental observations and was then used to analyze circadian gene expression, diurnal net photosynthetic rate ($Pn$), light-period net CO₂ assimilation, and the marginal carbon benefit of increasing daily light integral (DLI). Under a constant DLI of 9.216 mol·m⁻²·d⁻¹, extending the photoperiod from 8 to 20 h while reducing light intensity from 320 to 128 µmol·m⁻²·s⁻¹ lowered and delayed the instantaneous $Pn$ peak but progressively increased light-period net CO₂ assimilation. Among the treatments examined, 20L:4D produced the highest net CO₂ assimilation. Moreover, across the simulated DLI range of 5–19 mol·m⁻²·d⁻¹, the 20-h photoperiod consistently produced the highest net assimilation, whereas the marginal carbon benefit of additional light declined by approximately 62%. The results suggest that distributing a fixed daily light input over a longer photoperiod at lower instantaneous light intensity can enhance daily carbon gain within the tested range, whereas increasing DLI produces progressively diminishing photosynthetic returns. This study provides a modeling framework for evaluating photoperiod–light intensity coordination and provides a quantitative basis for optimizing light–environment management in controlled-environment agriculture.

Plant physiology and development, ranging from leaf growth and flowering time in Arabidopsis thaliana to tuberization in potato, are profoundly influenced by ambient light con-ditions. It has been acknowledged that light significantly inhibits hypocotyl elongation. However, how light patterns affect early seedling development remains unclear. To ad-dress this, we developed a logistic model for Arabidopsis thaliana hypocotyl elongation. The model incorporated the circadian clock and the PHYTOCHROME-INTERACTING FAC-TOR (PIF)-mediated signaling pathways, capturing the biological characteristics of limited hypocotyl growth. Through numerical simulations, we identified several artificial photoperiods that can accelerate hypocotyl elongation, and thereby promote rapid growth. Compared with the conventional 12L:12D (12 h light/12 h dark) photoperiod, inserting a 4-h light pulse into the dark phase of an 8L:16D cycle advanced the elongation-rate peak by 5.5 h and shortened the growth cycle by 32%. Furthermore, under a 9L:9D photoperi-odic stress, the growth peak occurs 6 h earlier, and the growth cycle is shortened by 25%. These findings highlight the potential role of artificial photoperiods in promoting hypo-cotyl elongation, providing a potential strategy for light control of plant photomorpho-genesis and development.

Light–dark cycles regulate hypocotyl elongation in Arabidopsis thaliana through the circadian clock and PHYTOCHROME-INTERACTING FACTORs (PIFs), but the quantitative relationship between photoperiodic patterns, PIF4/PIF5 dynamics, and growth remains unclear. Here, we modified the circadian-clock-regulated hypocotyl elongation model by integrating a previously established Arabidopsis clock framework with PIF4/PIF5 regulatory interactions and a saturation term in the growth equation. Mathematical properties (existence, uniqueness, non-negativity and boundedness) of the model were analyzed. The model was parameterized using published PIF4 expression and hypocotyl growth datasets and evaluated against photoperiod-dependent PIF4 expression, hypocotyl length responses, and growth dynamics in wild-type and clock- or PIF4-related genetic backgrounds. The modified growth equation captured the saturating nature of hypocotyl elongation under constant darkness (DD), while simulations showed that shorter photoperiods enhance nighttime PIF4/PIF5 protein accumulation and increase hypocotyl growth rates. Bifurcation analysis indicated that sufficient expression of PSEUDO-RESPONSE REGULATOR 5 (PRR5)/TIMING OF CAB EXPRESSION 1 (TOC1) is required to induce PIF4/PIF5 transcript oscillations under the DD condition. The model further predicted that, compared with the 12 h light/12 h dark (12L:12D) condition, the 5L:5D:7L:7D and 6L:5D:6L:7D photoperiod regimes reduced the growth cycle by 23.74% and 22.18%, respectively. This framework serves as a quantitative tool for dissecting photoperiod-dependent hypocotyl elongation and screening optimized artificial light strategies.