Biochemical Model Research Articles

Erythrocytic indices of clinical blood analysis and reference intervals among men and women aged 18–45 years

Relevance. The determination of reference intervals (RI) in clinical blood analysis for erythrocytes and their specific parameters: mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and red cell distribution width (RDW), allows us to use these parameters for differential diagnostics of various pathological conditions from variants of norm.Objective. Calculate the RI for erythrocyte parameters of a complete blood count for patients in a certain age group (18– 45 years) with normal indicators of iron homeostasis. The reference ranges may vary depending on the analytical systems and diagnostic reagents used.Material and methods. The study included blood samples from 158 healthy volunteers aged 18–45 years, of whom 127 (80.4 %) were women and 31 (19.6 %) were men. The data were obtained from the «KDL-TEST» company database for the period from 01.01.2023 to 01.01.2024. The criteria for inclusion in the study were: age from 18 to 45 years, clinical blood test results, and iron homeostasis within the RI of the «KDL-TEST» laboratory, absence of signs of an inflammatory process based on normal levels of C-reactive protein (CRP). Analyses were performed using a hematological analyzer Mindray BC- 6800 (manufactured by Mindray, China) and an automatic biochemical analyzer model AU-5800 (Beckman Coulter, USA) using IRON reagents (Beckman Coulter, USA) for the photometric colorimetric method and CRP-latex (Beckman Coulter, USA) immunoturbidimetric method.Results. The studies revealed a decrease in the upper limit of the RI for red blood cells (RBC) and their indices (RBC, HGB, HCT, MCV, MCH, MCHC, RDW-CV) in women compared with the RI of the Russian National Standard (2009), which amounted to 4 % for the number of red blood cells, 5 % for hemoglobin, 2 % for hematocrit, 3.8 % for MCV and 3.5 % for MCH, as well as 4.2 % for MCHC; and a decrease in the upper limit of the RI of erythrocytes and erythrocyte indices (RBC, HGB, HCT, MCV, MCH, MCHC, RDW-CV) in relation to the RI of the Russian National Standard (2009) in men for erythrocytes by 3.9 %, hemoglobin by 4, 6 %, MCV — by 1.9 %, MCH — by 4 %, MCHC — by 5.8 %. No significant differences were found in the values of erythrocytes and their specific parameters, hemoglobin, and hematocrit, between the data of the hematology analyzers Mindray BC-6800 and Sysmex XE series (p >0.05).Conclusions. A decrease in the RI of clinical blood analysis in women and men for some hemogram parameters (RBC, HGB, HCT, MCV, MCH, MCHC, RDW-CV) in comparison with the RI of clinical blood analysis of the Russian National Standard (2009), which are within the generally accepted statistically acceptable deviations, was found. The use of automated hematology analyzers did not significantly affect the RI of erythrocytes or their specific parameters.

A hybrid deep learning model based on signal decomposition and dynamic feature selection for forecasting the influent parameters of wastewater treatment plants.

Accurate prediction of influent parameters such as chemical oxygen demand (COD) and biochemical oxygen demand over five days (BOD5) is crucial for optimizing wastewater treatment processes, enhancing efficiency, and reducing costs. Traditional prediction methods struggle to capture the dynamic variations of influent parameters. Mechanistic biochemical models are unable to predict these parameters, and conventional machine learning methods show limited accuracy in forecasting key water quality indicators such as COD and BOD5. This study proposes a hybrid model that combines signal decomposition and deep learning to improve the accuracy of COD and BOD5 predictions. Additionally, a new dynamic feature selection (DFS) mechanism is introduced to optimize feature selection in real-time, reducing model redundancy and enhancing prediction stability. The model achieved R2 values of 0.88 and 0.96 for COD, and 0.75 and 0.93 for BOD5 across two wastewater treatment plants. RMSE and MAE values were significantly reduced, with decreases of 14.93% and 12.55% for COD at WWTP No. 5, and 20.89% and 20.40% for COD at WWTP No. 7. For BOD5, RMSE and MAE decreased by 3.56% and 5.28% at WWTP No. 5, and by 10.06% and 10.20% at WWTP No. 7. These results highlight the effectiveness of the proposed model and DFS mechanism in improving prediction accuracy and model performance. This approach provides valuable insights for wastewater treatment optimization and broader time series forecasting applications.

Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments

Understanding the oscillating behaviors that govern organisms’ internal biological processes requires interdisciplinary efforts combining both biological and computer experiments, as the latter can complement the former by simulating perturbed conditions with higher resolution. Harmonizing the two types of experiment, however, poses significant statistical challenges due to identifiability issues, numerical instability, and ill behavior in high dimension. This article devises a new Bayesian calibration framework for oscillating biochemical models. The proposed Bayesian model is estimated relying on an advanced Markov chain Monte Carlo (MCMC) technique which can efficiently infer the parameter values that match the simulated and observed oscillatory processes. Also proposed is an approach to sensitivity analysis based on the intervention posterior. This approach measures the influence of individual parameters on the target process by using the obtained MCMC samples as a computational tool. The proposed framework is illustrated with circadian oscillations observed in a filamentous fungus, Neurospora crassa.

A Systematic Investigation of the Fractional Biochemical Reaction Model via a Statistical Approach

Analyzing the Michaelis-Menten kinetics biochemical reaction model is essential in understanding enzyme-catalyzed reactions. Traditional models frequently fail to incorporate the complexities of fractional derivatives. This study employs the homotopy perturbation method (HPM) and the homotopy analysis method (HAM) to derive analytical expressions for enzyme, substrate, inhibitor, product, and other intermediate species concentrations. The methodologies include the application of fractional derivatives to improve the accuracy and adaptability of the model.The use of HPM and HAM in this context allows for a more comprehensive understanding of the biochemical reaction kinetics. The comparison of these two methodologies using numerical illustrations provides insights into their respective accuracies and applicability in biochemical modeling. Statistical analysis further strengthens the validation of these methods. Numerical examples demonstrate the effectiveness of HPM and HAM in dealing with the complexities associated with the Michaelis-Menten kinetics biochemical reaction model. The results highlight the improved precision in depicting the concentrations of various species within the reaction model. This study provides a comparison of the HAM findings with those produced by the HPM, demonstrating that the HAM approximate solutions are only valid for a short period, but the HPM results are very accurate and valid for a long time. This emphasizes the fact that the HPM applies to a wide range of nonlinear models and is reliable and promising when compared to other approaches. These approaches enhance the comprehension and application of biochemical reaction models, demonstrating efficacy through practical numerical results.

Pattern formation for a reversible biochemical reaction model with cross-diffusion and Michalis saturation

Pattern formation for a reversible biochemical reaction model with cross-diffusion and Michalis saturation

Investigation of a Biochemical Model with Recycling in Case of Negative Cooperativity

The objective of this paper is to find new dynamic perspectives in a well-known two dimensional nonlinear system which is a modification of the phosphofructo kinase model by incorporating recycling of the product, p, into the substrate, s. Specifically, we investigate the affect of the negative cooperativity on the number of equilibria and their stability. Moreover, in the parameter space, we analyze analytically and numerically the number of periodic oscillations (solutions) and their stability using Lyapunov coefficients (in other words, quantities and focus values). Thus, we obtain that three different dynamical conditions (regimes) take place: (1) structurally unstable, (2) the existence of an unstable limit cycle with an external stable limit cycle, and (3) the existence of a stable limit cycle with an external unstable limit cycle. Moreover, for a zero rate of product synthesis (due to e.g., defective enzyme), we obtain that the modified system has a first integral.

CLIPA protein pairs function as cofactors for prophenoloxidase activation in Anopheles gambiae.

Insect prophenoloxidases (proPO) are activated during immune responses by a proPO activating protease (PAP) in the presence of a high molecular weight cofactor assembled from serine protease homologs (SPH) that lack proteolytic activity. PAPs and the SPHs have a similar architecture, with an amino-terminal clip domain and a carboxyl-terminal protease domain. The SPHs belong to CLIPA subfamily of SP-related proteins. In Manduca sexta, a well characterized biochemical model system for insect immunity, the functional SPH cofactor contains one molecule each from two SPH subfamilies, SPH-I and SPH-II. In Anopheles gambiae, three SPHI-SPHII pairs (CLIPs A4-A6, A4-A7Δ, and A4-A12) were previously reported as cofactors for CLIPB9-mediated activation of proPO2 and proPO7. In this study, we produced recombinant proteins for two splicing variants of CLIPA7, proCLIPA7s (s for short), proCLIPA7f (f for full-length) and proCLIPA14. We cleaved each along with proCLIPA4 using M. sexta PAP3 and found that the CLIPA pairs A4-A7s and A4-A14 are better than A4-A7f in generating highly active PO2 or PO7. CLIPA7f and CLIPA7s, products of alternative splicing, have different strengths as cofactors in combination with CLIPA4. Because mRNA for CLIPA7f is expressed at a significantly higher level than CLIPA7s, cofactors with the weaker combination A4-A7f may predominate in hemolymph, resulting in a potential dampening effect on proPO activation as a regulatory mechanism for altering the strength of the melanization response. A. gambiae CLIPB10xa is involved in proPO activation but its role as a PAP was not established using mosquito proPOs. Here we showed that factor Xa-treated proCLIPB10Xa activated proCLIPs A7s, A7f, A14, A4 (poorly), and proPO2. At higher concentrations, CLIPB10xa efficiently activated proPO2 in the absence of a cofactor, but at low concentrations it required a CLIPA cofactor, suggesting that highly active PO2 can be generated at low concentration of CLIPB10 in cooperation with an SPH cofactor in vivo. Using cofactors generated by PAP3, we demonstrated the order of efficacy for proPO2 activation by B10Xa is A4-A6 > A4-A14 or A4-A7s > A4-A7f > A4-A12. This agrees with their relative strengths as cofactors for proPO2 and proPO7 activation by M. sexta PAP3. In summary, we further developed an in vitro assay system to elucidate biochemical details of the complex process of proPO activation in A. gambiae.

Efficient Finite-Difference Estimation of Second-Order Parametric Sensitivities for Stochastic Discrete Biochemical Systems

Biochemical reaction systems in a cell exhibit stochastic behaviour, owing to the unpredictable nature of the molecular interactions. The fluctuations at the molecular level may lead to a different behaviour than that predicted by the deterministic model of the reaction rate equations, when some reacting species have low population numbers. As a result, stochastic models are vital to accurately describe system dynamics. Sensitivity analysis is an important method for studying the influence of the variations in various parameters on the output of a biochemical model. We propose a finite-difference strategy for approximating second-order parametric sensitivities for stochastic discrete models of biochemically reacting systems. This strategy utilizes adaptive tau-leaping schemes and coupling of the perturbed and nominal processes for an efficient sensitivity estimation. The advantages of the new technique are demonstrated through its application to several biochemical system models with practical significance.

Stern-Brocot arithmetic in dynamics of a biochemical reaction model.

A simple almost fifty year old four-variable model of the peroxidase-oxidase reaction has been studied using 2D isospike stability diagrams, 2D maximum Lyapunov exponent diagrams, and other nonlinear numerical methods. The model contains two positive feedback loops. For slightly different sets of parameters, compared to the original parameters, the model reveals a wealth of dynamic behaviors, not previously reported for this model. For example, contrary to expectations, the model is capable of reproducing all early observations of mixed-mode and bursting oscillations and chaos. Furthermore, for some parameters, the mixed-mode oscillations are organized according to Stern-Brocot arithmetic. The regions of mixed-mode oscillations are separated by narrow regions of chaotic dynamics.

The effect of nitrogen input on methane uptake in a wet and a dry year from a temperate desert steppe

Methane is the second most important greenhouse gas, and soils in arid region can oxidise large amounts of atmospheric methane, thereby contributing to mitigating climate warming. Elevating input of atmospheric nitrogen (N) and precipitation change significantly affect the strength of methane sink (uptake from the atmosphere), but this is still unclear in the desert steppe. Therefore, a field simulation N input (Nip) control experiment with a wet year (2019) and a dry year (2021) was done to elucidate the impact of Nip on methane sink in a typical desert steppe of Eurasia. The result showed that this desert steppe was a net sink of atmospheric methane with annaul uptake rate of 3.88 kg CH4 ha−1. And found that methane uptake was much lower in a wet year (33.9 ± 1.6 μg C m−2 h−1, 2019) than that in a dry year (46.9 ± 3.1 μg C m−2 h−1, 2021), which was mainly mediated by soil water-filled pore space. The effect of Nip on methane uptake was varied, both promoting (0.4 % – 1317%) and inhibiting (0.5% – 270.5%). And the inconsistent response of methane uptake was observed to Nip in a wet and a dry year: the methane uptake was decreased significantly with the increase of Nip rate in a wet year (p < 0.05); however, Nip did not significantly affect methane uptake overall in a dry year (p > 0.05). This may attribute to the inhibitory effect of Nip on methane uptake depended on soil moisture (p < 0.01). The abundance ratio of pmoA to mcrA gene was identified as the most significant influencing factors of methane uptake rather than soil inorganic N (NH4+-N or NO3−-N) content. Furthermore, soil moisture had an important indirect effect on methane uptake, mainly through influncing the abundance ratio of pmoA to mcrA gene. Overall, we suggest that the role of soil water-filled pore space and the abundance ratio of pmoA to mcrA gene should be considered when developing biochemical models of methane uptake in arid areas.

Monitoring the Degree of Gansu Zokor Damage in Chinese Pine by Hyperspectral Remote Sensing

Chinese pine has been extensively planted in the Loess Plateau, but it faces significant threats from Gansu zokor. Traditional methods for monitoring rodent damage rely on manual surveys to assess damage rates but are time-consuming and often underestimate the actual degree of damage, particularly in mildly affected pines. This study proposes a remote sensing monitoring method that integrates hyperspectral analysis with physiological and biochemical parameter models to enhance the accuracy of rodent damage detection. Using ASD Field Spec 4, we analyzed spectral data from 125 Chinese pine needles, measuring chlorophyll (CHC), carotenoid (CAC), and water content (WAC). Through correlation analysis, we identified sensitive vegetation indices (VIs) and red-edge parameters (REPs) linked to different levels of damage. We report several key results. The 680 nm spectral band is instrumental in monitoring damage, with significant decreases in CHC, CAC, and WAC corresponding to increased damage severity. We identified six VIs and five REPs, which were later predicted using stepwise regression (SR), support vector machine (SVM), and random forest (RF) models. Among all models, the vegetation index-based RF model exhibited the best predictive performance, achieving coefficient of determination (R2) values of 0.988, 0.949, and 0.999 for CHC, CAC, and WAC, with root mean square errors (RMSEs) of 0.115 mg/g, 0.042 mg/g, and 0.007 mg/g, and mean relative errors (MREs) of 8.413%, 9.169%, and 1.678%. This study demonstrates the potential of hyperspectral remote sensing technology for monitoring rodent infestations in Chinese pines, providing a reliable basis for large-scale assessments and effective management strategies for pest control.

In vitro analysis of a competitive inhibition model for T7 RNA polymerase biosensors

T7 RNA polymerase (T7 RNAP) biosensors, in which T7 RNAP transcribes some reporter gene or signal in response to external stimuli, have wide applications in synthetic biology and metabolic engineering. We adapted a biochemical reaction network model and used an in vitro transcription assay to determine network parameters for different T7 RNAP constructs. Under conditions where template DNA is limiting, the EC50 values of native and engineered T7 RNAPs ranged from 33 nM (29–37 95 % c.i.) to 570 nM (258–714 95 % c.i.) (wild-type T7 RNAP). The measured EC50 values were largely insensitive to free magnesium, pH, or other buffer conditions. Many biosensor configurations use a split RNAP construct, where the C-terminal (CT7) and N-terminal T7 (NT7) are fused to proximity induced dimerization modules. We used proteolysis and ion exchange chromatography to prepare a CT7 (80 kDa) product. The impact of free CT7 on T7 RNAP transcriptional activity was well described by a competitive inhibition model, with an inhibitory constant KI = 23 nM (18–28 95 % c.i.) of the sensor. These model parameters will be useful for forward modeling and design of T7 RNAP-based genetic circuits.

Complex p53 dynamics regulated by miR-125b in cellular responses to reactive oxidative stress and DNA damage.

In response to distinct cellular stresses, the p53 exhibits distinct dynamics. These p53 dynamics subsequently control cell fate. However, different stresses can generate the same p53 dynamics with different cell fate outcomes, suggesting that the integration of dynamic information from other pathways is important for cell fate regulation. The interactions between miRNA-125b, p53, and reactive oxygen species (ROS) are significant in the context of cellular stress responses and apoptosis. However, the regulating mechanism of miR-125b with p53 is not fully studied. The dynamics of p53 and its response to the miR-125b regulation are still open questions. In the present study, we try to answer some of these fundamental questions based on basic model built from available experimental reports. The miR-125b-p53 regulatory network is modeled using a set of 11 molecular species variables. The biochemical network of miR-125b-p53, described by 22 reaction channels, is represented by coupled ordinary differential equations (ODEs) using the mass action law of chemical kinetics. These ODEs are solved numerically using the standard fourth-order Runge-Kutta method to analyze the dynamical behavior of the system. The biochemical network model we designed is based on both experimental and theoretical reported data. The p53 dynamics driven by miR-125b exhibit five distinct dynamical states: first and second stable states, first and second dynamical states, and a sustained oscillation state. These different p53 dynamical states may correspond to various cellular conditions. If the stress induced by miR-125b is weak, the system will be weakly activated, favoring a return to normal functioning. However, if the stress is significantly strong, the system will move to an active state. To sustain this active state, which is far from equilibrium with little scope for returning to normal conditions, the system may transition to an apoptotic state by crossing through other intermediate states, as it is unlikely to regain normal functioning. The p53 dynamical states show a multifractal nature, contributed by both short- and long-range correlations. The networks illustrated from these dynamical states follow hierarchical scale-free features, exhibiting an assortative nature with an absence of the centrality-lethality rule. Furthermore, the active dynamical state is generally closer to hierarchical characteristics and is self-organized. Our research study reveals that significant activity of miR-125b on the p53 regulatory network and its dynamics can only be observed when the system is slightly activated by ROS. However, this process does not necessarily require the direct study of ROS activity. These findings elucidate the mechanisms by which cells integrate signaling pathways with distinct temporal activity patterns to encode stress specificity and direct diverse cell fate decisions.

Language model-guided anticipation and discovery of unknown metabolites.

Despite decades of study, large parts of the mammalian metabolome remain unexplored. Mass spectrometry-based metabolomics routinely detects thousands of small molecule-associated peaks within human tissues and biofluids, but typically only a small fraction of these can be identified, and structure elucidation of novel metabolites remains a low-throughput endeavor. Biochemical large language models have transformed the interpretation of DNA, RNA, and protein sequences, but have not yet had a comparable impact on understanding small molecule metabolism. Here, we present an approach that leverages chemical language models to discover previously uncharacterized metabolites. We introduce DeepMet, a chemical language model that learns the latent biosynthetic logic embedded within the structures of known metabolites and exploits this understanding to anticipate the existence of as-of-yet undiscovered metabolites. Prospective chemical synthesis of metabolites predicted to exist by DeepMet directs their targeted discovery. Integrating DeepMet with tandem mass spectrometry (MS/MS) data enables automated metabolite discovery within complex tissues. We harness DeepMet to discover several dozen structurally diverse mammalian metabolites. Our work demonstrates the potential for language models to accelerate the mapping of the metabolome.

Evidence for widespread thermal acclimation of canopy photosynthesis

Plants acclimate to temperature by adjusting their photosynthetic capacity over weeks to months. However, most evidence for photosynthetic acclimation derives from leaf-scale experiments. Here we address the scarcity of evidence for canopy-scale photosynthetic acclimation by examining the correlation between maximum photosynthetic rates (Amax,2,000) and growth temperature (Tair¯) across a range of concurrent temperatures and canopy foliage quantity, using data from >200 eddy covariance sites. We detect widespread thermal acclimation of canopy-scale photosynthesis, demonstrated by enhanced Amax,2,000 under higher Tair¯, across flux sites with adequate water availability. A 14-day period is identified as the most relevant timescale for acclimation across all sites, with a range of 12–25 days for different plant functional types. The mean apparent thermal acclimation rate across all ecosystems is 0.41 (−0.38–1.04 for 5th–95th percentile range) µmol m−2 s−1 °C−1, with croplands showing the largest acclimation rates and grasslands the lowest. Incorporating an optimality-based prediction of leaf photosynthetic capacities into a biochemical photosynthesis model is shown to improve the representation of thermal acclimation. Our results underscore the critical need for enhanced understanding and modelling of canopy-scale photosynthetic capacity to accurately predict plant responses to warmer growing seasons.

Tooth-Binding Graphene Quantum Dots Silver Nanocomposites for Prevention of Dental Caries.

The objectives of this study were to develop a tooth-binding graphene quantum dots silver nanocomposites (ALN-GQDs-Ag) and evaluate their antibacterial, mineralising, and discolouring properties for the prevention of dental caries. In this study, ALN-GQDs-Ag were developed by synthesising nano silver (Ag) with graphene quantum dots (GQDs) and functionalised GQDs with alendronate (ALN). ALN-GQDs-Ag were characterised by transmission electron microscopy (TEM), zeta potential analysis, X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy. The cytotoxicity of ALN-GQDs-Ag against human gingival fibroblasts (HGF-1) and stem cells from human exfoliated deciduous teeth (SHED) was examined using a colorimetric assay with reference to silver nitrate solution. The affinity of ALN-GQDs-Ag for hydroxyapatite particles was investigated using inductively coupled plasma spectroscopy (ICP). The antibacterial properties of ALN-GQDs-Ag against Streptococcus mutans were evaluated by scanning electron microscopy (SEM), confocal laser scanning microscopy (CLSM), and colony-forming units counting (CFUs). The mineralisation properties of ALN-GQDs-Ag on human dentine were assessed using micro-computed tomography (micro-CT), scanning electron microscopy (SEM), and Fourier transform infrared in a biochemical cycling model. The discolouring properties of ALN-GQDs-Ag on artificial dentine caries were determined using spectrophotometry. TEM, Zeta potential, XPS, FTIR, and Raman spectroscopy confirmed the synthesis of stable spherical ALN-GQD-Ag nanocomposites with a 10.3 ± 5.5 nm diameter. The colorimetric assay demonstrated that ALN-GQDs-Ag were less cytotoxic than silver nitrate to HGF-1 and SHED (p<0.001). ICP showed that ALN-GQDs-Ag were bound to hydroxyapatite. SEM, CLSM, and CFUs showed that ALN-GQDs-Ag was bactericidal and inhibited biofilm growth of Streptococcus mutans. Micro-CT, SEM, and FTIR showed that ALN-GQDs-Ag repressed dentine demineralisation under a cariogenic challenge. Spectrophotometry revealed no significant discolouration of dentine caries in the ALN-GQDs-Ag. This study developed a biocompatible and tooth-binding ALN-GQDs-Ag with promising antibacterial, mineralising, and non-discolouring properties. ALN-GQDs-Ag could be a novel anti-caries agent for preventing dentine caries if translated for clinical use.

Dissipation at limited resolutions: power law and detection of hidden dissipative scales

Abstract Non-equilibrium systems, in particular, living organisms, are maintained by irreversible transformations of energy that drive diverse functions. Quantifying their irreversibility, as measured by energy dissipation, is essential for understanding the underlying mechanisms. However, existing techniques usually overlook experimental limitations, either by assuming full information or by employing a coarse-graining method that requires knowledge of the structure behind hidden degrees of freedom. Here, we study the inference of dissipation from finite-resolution measurements by employing a recently developed model-free estimator that considers both the sequence of coarse-grained transitions and the waiting time distributions: σ 2 = σ 2 ℓ + σ 2 t . The dominant term σ 2 ℓ originates from the sequence of observed transitions. We find that it scales with resolution following a power law. Comparing the scaling exponent with a previous estimator highlights the importance of accounting for flux correlations at lower resolutions. σ 2 t comes from asymmetries in waiting time distributions. It is non-monotonic in resolution, with its peak position revealing characteristic scales of the underlying dissipative process, consistent with observations in the actomyosin cortex of starfish oocytes. Alternatively, the characteristic scale can be detected in a crossover of the scaling of σ 2 ℓ . This provides a novel perspective for extracting otherwise hidden characteristic dissipative scales directly from dissipation measurements. We illustrate these results in biochemical models as well as complex networks. Overall, this study highlights the significance of resolution considerations in non-equilibrium systems, providing insight into the interplay between experimental resolution, entropy production and underlying complexity.

Fast dehydration reduces bundle sheath conductance in C4 maize and sorghum.

In the face of anthropogenic warming, drought poses an escalating threat to food production. C4 plants offer promise in addressing this threat. C4 leaves operate a biochemical CO2 concentrating mechanism that exchanges metabolites between two partially isolated compartments (mesophyll and bundle sheath), which confers high-productivity potential in hot climates boosting water use efficiency. However, when C4 leaves experience dehydration, photosynthesis plummets. This paper explores the physiological mechanisms behind this decline. In a fast dehydration experiment, we measured the fluxes and isotopic composition of water and CO2 in the gas exchanged by leaves, and we interpreted results using a novel biochemical model and analysis of elasticity. Our findings show that, while CO2 supply to the mesophyll and to the bundle sheath persisted during dehydration, there was a decrease in CO2 conductance at the bundle sheath-mesophyll interface. We interpret this as causing a slowdown of intercellular metabolite exchange - an essential feature of C4 photosynthesis. This would impede the supply of reducing power to the bundle sheath, leading to phosphoglycerate accumulation and feedback inhibition of Rubisco carboxylation. The interplay between this rapid sensitivity and the effectiveness of coping strategies that C4 plants deploy may be an overlooked driver of their competitive performance.

A modified variational approach to noisy cell signaling.

Signaling in cells is full of noise and, hence, described with stochastic biochemical models. Thus, an efficient computation algorithm for these fluctuating reactions is much needed. Apart from the very popular Monte Carlo simulation, methods based on probability distributions are frequently desired due to their analytical tractability and possible numerical advantages in diverse circumstances, among which the variational approach is the most notable. In this paper, new basis functions are proposed to better depict possibly complex distribution profiles, and an extra regularization scheme is supplied to the variational equation to remove occasional degeneracy-induced singularities during the evolution. The new extension is applied to four typical biochemical reaction models and restores the Gillespie results accurately but with greatly reduced simulation time. This modified variational approach is expected to work in a wide range of cell signaling networks.

Accurate Estimation of Gross Primary Production of Paddy Rice Cropland with UAV Imagery-Driven Leaf Biochemical Model

Accurate estimation of gross primary production (GPP) of paddy rice fields is essential for understanding cropland carbon cycles, yet remains challenging due to spatial heterogeneity. In this study, we integrated high-resolution unmanned aerial vehicle (UAV) imagery into a leaf biochemical properties-based model for improving GPP estimation. The key parameter, maximum carboxylation rate at the top of the canopy (Vcmax,025), was quantified using various spatial information representation methods, including mean (μref) and standard deviation (σref) of reflectance, gray-level co-occurrence matrix (GLCM)-based features, local binary pattern histogram (LBPH), and convolutional neural networks (CNNs). Our models were evaluated using a two-year eddy covariance (EC) system and UAV measurements. The result shows that incorporating spatial information can vastly improve the accuracy of Vcmax,025 and GPP estimation. CNN methods achieved the best Vcmax,025 estimation, with an R of 0.94, an RMSE of 19.44 μmol m−2 s−1, and an MdAPE of 11%, and further produced highly accurate GPP estimates, with an R of 0.92, an RMSE of 6.5 μmol m−2 s−1, and an MdAPE of 23%. The μref-GLCM texture features and μref-LBPH joint-driven models also gave promising results. However, σref contributed less to Vcmax,025 estimation. The Shapley value analysis revealed that the contribution of input features varied considerably across different models. The CNN model focused on nir and red-edge bands and paid much attention to the subregion with high spatial heterogeneity. The μref-LBPH joint-driven model mainly prioritized reflectance information. The μref-GLCM-based features joint-driven model emphasized the role of GLCM texture indices. As the first study to leverage the spatial information from high-resolution UAV imagery for GPP estimation, our work underscores the critical role of spatial information and provides new insight into monitoring the carbon cycle.