Biochemical Model Research Articles

The effect of flow rate on the ammonia oxidation rate in the riverway biofilm in-situ remediation and modeling

The biofilm in-situ remediation is one of the most important and practical techniques for river remediation. The biochemical function of biofilms and the hydraulics of the river are both significant aspects of river remediation study, however, they belong to distinctive research points. To date, little research has combined the biochemical process of biofilm and the hydraulics of the river. In this study, ammonia oxidation rate (AOR) was used to characterize the activity of biofilms and measured at different flow rates. It was increased with the flow rate increasing before the peak (26.13 g-N/m3/h) at 0.3 m/s and then decreased to 0 at 0.414 m/s directly. Meanwhile, the skewness equation of flow rate (Fr)−AOR was established based on previous hydraulics and biology research. The kinetic parameters of the equations were determined by fitting the measured values of AOR (R2 > 0.95), which could be used to simulate the AOR of the river simulators and open channels. Besides, the distribution characteristics of the AOR at various flow rates in different sections were obtained. Overall, this paper provides a novel perspective by studying the internal biochemical model of biofilm and the hydraulic model with multiple influencing factors further to improve the biofilm-hydraulics coupling model.

Molecular basis of TASL recruitment by the peptide/histidine transporter 1, PHT1

PHT1 is a histidine /oligopeptide transporter with an essential role in Toll-like receptor innate immune responses. It can act as a receptor by recruiting the adaptor protein TASL which leads to type I interferon production via IRF5. Persistent stimulation of this signalling pathway is known to be involved in the pathogenesis of systemic lupus erythematosus (SLE). Understanding how PHT1 recruits TASL at the molecular level, is therefore clinically important for the development of therapeutics against SLE and other autoimmune diseases. Here we present the Cryo-EM structure of PHT1 stabilized in the outward-open conformation. By combining biochemical and structural modeling techniques we propose a model of the PHT1-TASL complex, in which the first 16 N-terminal TASL residues fold into a helical structure that bind in the central cavity of the inward-open conformation of PHT1. This work provides critical insights into the molecular basis of PHT1/TASL mediated type I interferon production.

Effect of microclimate and photosynthesis on strawberry reproductive growth in a greenhouse: using cumulative leaf photosynthesis as an index to predict the time of harvest

ABSTRACT We evaluate whether cumulative photosynthesis (A n,C) could predict the time of harvest in strawberry better than light (I C) or temperature (T a,C) as forecast indices. We monitored the daily dynamics of flowering, fruit set, and harvest in over 2,700 fruits across the growing season in a greenhouse in Japan. Photosynthesis was calculated using a biochemical model, and A n,C calculated by totalling the cumulative rate from flowering to harvest in each fruit. I C and T a,C were calculated by totalling daily cumulative light intensity and average temperature. A new index (D) was defined as the normalised variation of A n,C, I C, and T a,C, and a statistical analysis was conducted on the means for each index. The results showed that A n,C had the lowest D (0.114), suggesting that it was a better index for predicting the time of harvest. This study suggests the importance of considering the accumulation of photosynthates in individual fruit, which cannot be achieved by I C or T a,C, these results can be used to improve the forecasting and optimisation of yield. However, further research is required to substantiate these claims.

Biochemical models of SIR and SIRS: Effects of bilinear incidence rate on infection-free and endemic states.

Understanding and forecasting the progression of disease epidemics is possible through the study of nonlinear epidemic biochemical models that describe the relationship among susceptible, infected, and immune individuals in a population. In this paper, by determining the algebraic invariant planes and studying the Hopf bifurcation on these invariant planes, we study the stability of the Hopf bifurcation in the infection-free and endemic states of the SIR and SIRS epidemic models with bilinear incidence rate. We analyze the stability of the limit cycles of the bilinear incidence SIR and SIRS models at the steady state point where infection vanishes and at the endemic steady state point where the system behaves in an oscillatory manner. We demonstrate the algebraic results by numerical simulations for parameter values that satisfy the conditions for both free and endemic states.

Predictive Biochemical Model of Frailty and Mortality After Acute Myocardial Infarction

Frailty, characterized by reduced resistance to stressors, is associated with adverse outcomes in patients with myocardial infarction. The Fried score is commonly used to assess frailty but has several limitations. This study aimed to evaluate the relation between frailty and blood biomarkers and their predictive value for long-term mortality using a biochemical model. A total of 2 cohorts of elderly patients (>65 years old) hospitalized for acute coronary syndrome were included. Geriatric assessments and several blood biomarkers were measured. The predictive models for frailty were developed using logistic regression. The survival models were also developed using Cox regression. Among 466 patients, 9 biomarkers were significantly associated with frailty. Between these biomarkers, white blood cells count, hemoglobin, and fibrinogen showed the highest predictive power. Model 1, without growth differentiation factor 15 (GDF-15), showed a better accuracy in predicting the mortality than the Fried score. Model 2, with GDF-15, had a stronger correlation with frailty but had a lower predictive power for survival. Frailty is associated with dysregulation in the physiological systems and several biomarkers were linked to this fact in our study. However, the inclusion of GDF-15 did not significantly improve the model's predictive ability. Frailty assessment using blood biomarkers can provide valuable prognostic information in elderly patients with acute coronary syndrome.

A semiempirical method optimized for modeling proteins

ContextIn recent years, semiempirical methods such as PM6, PM6-D3H4, and PM7 have been increasingly used for modeling proteins, in particular enzymes. These methods were designed for more general use, and consequently were not optimized for studying proteins. Because of this, various specific errors have been found that could potentially cast doubt on the validity of these methods for modeling phenomena of biochemical interest such as enzyme catalytic mechanisms and protein-ligand interactions. To correct these and other errors, a new method specifically designed for use in organic and biochemical modeling has been developed.MethodsTwo alterations were made to the procedures used in developing the earlier PMx methods. A minor change was made to the theoretical framework, which affected only the non-quantum theory interatomic interaction function, while the major change involved changing the training set for optimizing parameters, moving the focus to systems of biochemical significance. This involved both the selection of reference data and the weighting factors, i.e., the relative importance that the various data were given. As a result of this change of focus, the accuracy in prediction of heats of formation, hydrogen bonding, and geometric quantities relating to non-covalent interactions in proteins was improved significantly.

Advances and Developments in Monitoring and Inversion of the Biochemical Information of Crop Nutrients Based on Hyperspectral Technology

Crop nutrient biochemical information (mainly including chlorophyll class and nutrient elements mainly nitrogen, phosphorus and potassium) is an important basis for revealing crop growth and development patterns and their relationship with the environment. Hyperspectral technology has been rapidly developed and applied in crop nutrient biochemical information monitoring research. This paper firstly describes the theoretical basis of hyperspectral technology for monitoring crop nutrients and biochemical information. Then, the research progress of hyperspectral technology in monitoring nutrient and biochemical information of crops in different growth periods or different growth environments is outlined. Meanwhile, the shortcomings of the current technology in these research directions and the future research trends are discussed. Finally, the modeling methods for building crop nutrient biochemical information monitoring models by applying hyperspectral data are systematically outlined. And the effects of different spectral pre-processing methods, spectral effective information extraction methods and modeling algorithms on the accuracy of monitoring models are analyzed. On this basis, the challenges and prospects of hyperspectral technology in monitoring crop nutrient biochemical information are presented, aiming to provide relevant theoretical basis and technical reference for the research related to monitoring and inversion of crop physiological parameters based on hyperspectral technology.

Thermodynamic Bound on the Asymmetry of Cross-Correlations.

The principle of microscopic reversibility says that, in equilibrium, two-time cross-correlations are symmetric under the exchange of observables. Thus, the asymmetry of cross-correlations is a fundamental, measurable, and often-used statistical signature of deviation from equilibrium. Here we find a simple and universal inequality that bounds the magnitude of asymmetry by the cycle affinity, i.e., the strength of thermodynamic driving. Our result applies to a large class of systems and all state observables, and it suggests a fundamental thermodynamic cost for various nonequilibrium functions quantified by the asymmetry. It also provides a powerful tool to infer affinity from measured cross-correlations, in a different and complementary way to the thermodynamic uncertainty relations. As an application, we prove a thermodynamic bound on the coherence of noisy oscillations, which was previously conjectured by Barato and Seifert [Phys. Rev. E 95, 062409 (2017)PRESCM2470-004510.1103/PhysRevE.95.062409]. We also derive a thermodynamic bound on directed information flow in a biochemical signal transduction model.

Improving the predictions of leaf photosynthesis during and after short-term heat stress with current rice models.

In response to increasing global warming, extreme heat stress significantly alters photosynthetic production. While numerous studies have investigated the temperature effects on photosynthesis, factors like vapour pressure deficit (VPD), leaf nitrogen, and feedback of sink limitation during and after extreme heat stress remain underexplored. This study assessed photosynthesis calculations in seven rice growth models using observed maximum photosynthetic rate (Pmax ) during and after short-term extreme heat stress in multi-year environment-controlled experiments. Biochemical models (FvCB-type) outperformed light response curve-based models (LRC-type) when incorporating observed leaf nitrogen, photosynthetically active radiation, temperatures, and intercellular CO2 concentration (Ci ) as inputs. Prediction uncertainty during heat stress treatment primarily resulted from variation in temperatures and Ci . Improving FVPD (the slope for the linear effect of VPD on Ci /Ca ) to be temperature-dependent, rather than constant as in original models, significantly improved Ci prediction accuracy under heat stress. Leaf nitrogen response functions led to model variation in leaf photosynthesis predictions after heat stress, which was mitigated by calibrated nitrogen response functions based on active photosynthetic nitrogen. Additionally, accounting for observed differences in carbohydrate accumulation between panicles and stems during grain filling improved the feedback of sink limitation, reducing Ci overestimation under heat stress treatments.

Rethinking the potential productivity of crassulacean acid metabolism by integrating metabolic dynamics with shoot architecture, using the example of Agave tequilana.

Terrestrial CAM plants typically occur in hot semiarid regions, yet can show high crop productivity under favorable conditions. To achieve a more mechanistic understanding of CAM plant productivity, a biochemical model of diel metabolism was developed and integrated with 3-D shoot morphology to predict the energetics of light interception and photosynthetic carbon assimilation. Using Agave tequilana as an example, this biochemical model faithfully simulated the four diel phases of CO2 and metabolite dynamics during the CAM rhythm. After capturing the 3-D form over an 8-yr production cycle, a ray-tracing method allowed the prediction of the light microclimate across all photosynthetic surfaces. Integration with the biochemical model thereby enabled the simulation of plant and stand carbon uptake over daily and annual courses. The theoretical maximum energy conversion efficiency of Agave spp. is calculated at 0.045-0.049, up to 7% higher than for C3 photosynthesis. Actual light interception, and biochemical and anatomical limitations, reduced this to 0.0069, or 15.6 Mg ha-1 yr-1 dry mass annualized over an 8-yr cropping cycle, consistent with observation. This is comparable to the productivity of many C3 crops, demonstrating the potential of CAM plants in climates where little else may be grown while indicating strategies that could raise their productivity.

Preterm Preeclampsia Risk Modelling: Examining Hemodynamic, Biochemical, and Biophysical Markers Prior to Pregnancy.

Preeclampsia (PE) is a leading cause of maternal and perinatal death globally and can lead to unplanned preterm birth. Predicting risk for preterm or early-onset PE, has been investigated primarily after conception, and particularly in the early and mid-gestational periods. However, there is a distinct clinical advantage in identifying individuals at risk for PE prior to conception, when a wider array of preventive interventions are available. In this work, we leverage machine learning techniques to identify potential pre-pregnancy biomarkers of PE in a sample of 80 women, 10 of whom were diagnosed with preterm preeclampsia during their subsequent pregnancy. We explore prospective biomarkers derived from hemodynamic, biophysical, and biochemical measurements and several modeling approaches. A support vector machine (SVM) optimized with stochastic gradient descent yields the highest overall performance with ROC AUC and detection rates up to .88 and .70, respectively on subject-wise cross validation. The best performing models leverage biophysical and hemodynamic biomarkers. While preliminary, these results indicate the promise of a machine learning based approach for detecting individuals who are at risk for developing preterm PE before they become pregnant. These efforts may inform gestational planning and care, reducing risk for adverse PE-related outcomes.Clinical Relevance- This work considers the development and optimization of pre-pregnancy biomarkers for improving the identification of preterm (early-onset) preeclampsia risk prior to conception.

Predictive power of non-identifiable models

Resolving practical non-identifiability of computational models typically requires either additional data or non-algorithmic model reduction, which frequently results in models containing parameters lacking direct interpretation. Here, instead of reducing models, we explore an alternative, Bayesian approach, and quantify the predictive power of non-identifiable models. We considered an example biochemical signalling cascade model as well as its mechanical analogue. For these models, we demonstrated that by measuring a single variable in response to a properly chosen stimulation protocol, the dimensionality of the parameter space is reduced, which allows for predicting the measured variable’s trajectory in response to different stimulation protocols even if all model parameters remain unidentified. Moreover, one can predict how such a trajectory will transform in the case of a multiplicative change of an arbitrary model parameter. Successive measurements of remaining variables further reduce the dimensionality of the parameter space and enable new predictions. We analysed potential pitfalls of the proposed approach that can arise when the investigated model is oversimplified, incorrect, or when the training protocol is inadequate. The main advantage of the suggested iterative approach is that the predictive power of the model can be assessed and practically utilised at each step.

Qualitative Analysis of the Minimal Higgins Model of Glycolysis

Glycolysis, one of the leading metabolic pathways, involves many different periodic oscillations emerging at positive steady states of the biochemical models describing this essential process. One of the models employing the molecular diffusion of intermediates is the Higgins biochemical model to explain sustained oscillations. In this paper, we investigate the center-focus problem for the minimal Higgins model for general values of the model parameters with the help of computational algebra. We demonstrate that the model always has a stable focus point by finding a general form of the first Lyapunov number. Then, varying two of the model parameters, we obtain the first three coefficients of the period function for the stable focus point of the model and prove that the singular point is actually a bi-weak monodromic equilibrium point of type [1, 2]. Additionally, we prove that there are two (small) intervals for a chosen parameter a > 0 for which one critical period bifurcates from this singular point after small perturbations

Integrating multiple regulations on enzyme activity: the case of phosphoenolpyruvate carboxykinases.

Data on protein post-translational modifications (PTMs) increased exponentially in the last years due to the refinement of mass spectrometry techniques and the development of databases to store and share datasets. Nevertheless, these data per se do not create comprehensive biochemical knowledge. Complementary studies on protein biochemistry are necessary to fully understand the function of these PTMs at the molecular level and beyond, for example, designing rational metabolic engineering strategies to improve crops. Phosphoenolpyruvate carboxykinases (PEPCKs) are critical enzymes for plant metabolism with diverse roles in plant development and growth. Multiple lines of evidence showed the complex regulation of PEPCKs, including PTMs. Herein, we present PEPCKs as an example of the integration of combined mechanisms modulating enzyme activity and metabolic pathways. PEPCK studies strongly advanced after the production of the recombinant enzyme and the establishment of standardized biochemical assays. Finally, we discuss emerging open questions for future research and the challenges in integrating all available data into functional biochemical models.

Characterization of a Glycoside Hydrolase Family 157 Endo-β-1,3-Glucanase That Displays Antifungal Activity against Phytopathogens.

β-1,3-Glucan-degrading enzymes are widely used in fields such as food processing, plant protection, and breweries. In this work, we identified a glycoside hydrolase (GH) family 157 endo-β-1,3-glucanase (BsGlc157A) from Bacteroides sp. M27 and characterized its biochemical properties, structural model, and antifungal activity. Enzymological characterization indicated that BsGlc157A performs its optimal catalytic activity at pH 6.0 and 40 °C. BsGlc157A adopted the classic (β/α)8 TIM-barrel structure. Two catalytic residues, the nucleophile (Glu215) and the proton donor (Glu123), were confirmed via structural modeling and site-directed mutagenesis. Moreover, BsGlc157A hydrolyzed curdlan into a series of oligosaccharides with polymerization degrees 2-5 and exhibited inhibitory effects on the hyphal growth of typical fruit pathogenic fungi (Monilinia fructicola, Alternaria alternata, and Colletotrichum gloeosporioides), thereby illustrating effective biocontrol activity. These results revealed the catalytic properties and the application potential of GH family 157 β-1,3-glucanase, thus providing valuable biochemistry information about the group of carbohydrate-active enzymes.

Discovering selective antiferroptotic inhibitors of the 15LOX/PEBP1 complex noninterfering with biosynthesis of lipid mediators

Programmed ferroptotic death eliminates cells in all major organs and tissues with imbalanced redox metabolism due to overwhelming iron-catalyzed lipid peroxidation under insufficient control by thiols (Glutathione (GSH)). Ferroptosis has been associated with the pathogenesis of major chronic degenerative diseases and acute injuries of the brain, cardiovascular system, liver, kidneys, and other organs, and its manipulation offers a promising new strategy for anticancer therapy. This explains the high interest in designing new small-molecule-specific inhibitors against ferroptosis. Given the role of 15-lipoxygenase (15LOX) association with phosphatidylethanolamine (PE)-binding protein 1 (PEBP1) in initiating ferroptosis-specific peroxidation of polyunsaturated PE, we propose a strategy of discovering antiferroptotic agents as inhibitors of the 15LOX/PEBP1 catalytic complex rather than 15LOX alone. Here we designed, synthesized, and tested a customized library of 26 compounds using biochemical, molecular, and cell biology models along with redox lipidomic and computational analyses. We selected two lead compounds, FerroLOXIN-1 and 2, which effectively suppressed ferroptosis invitro and invivo without affecting the biosynthesis of pro-/anti-inflammatory lipid mediators invivo. The effectiveness of these lead compounds is not due to radical scavenging or iron-chelation but results from their specific mechanisms of interaction with the 15LOX-2/PEBP1 complex, which either alters the binding pose of the substrate [eicosatetraenoyl-PE (ETE-PE)] in a nonproductive way or blocks the predominant oxygen channel thus preventing the catalysis of ETE-PE peroxidation. Our successful strategy may be adapted to the design of additional chemical libraries to reveal new ferroptosis-targeting therapeutic modalities.

A subcellular biochemical model for T6SS dynamics reveals winning competitive strategies.

The type VI secretion system (T6SS) is a broadly distributed interbacterial weapon that can be used to eliminate competing bacterial populations. Although unarmed target populations are typically used to study T6SS function in vitro, bacteria most likely encounter other T6SS-armed competitors in nature. However, the connection between subcellular details of the T6SS and the outcomes of such mutually lethal battles is not well understood. Here, we incorporate biological data derived from natural competitors of Vibrio fischeri light organ symbionts to build a biochemical model for T6SS at the single-cell level, which we then integrate into an agent-based model (ABM). Using the ABM, we isolate and experiment with strain-specific physiological differences between competitors in ways not possible with biological samples to identify winning strategies for T6SS-armed populations. Through in vitro experiments, we discover that strain-specific differences exist in T6SS activation speed. ABM simulations corroborate that faster activation is dominant in determining survival during competition. Once competitors are fully activated, the energy required for T6SS creates a tipping point where increased weapon building and firing becomes too costly to be advantageous. Through ABM simulations, we identify the threshold where this transition occurs in the T6SS parameter space. We also find that competitive outcomes depend on the geometry of the battlefield: unarmed target cells survive at the edges of a range expansion where unlimited territory can be claimed. Alternatively, competitions within a confined space, much like the light organ crypts where natural V. fischeri compete, result in the rapid elimination of the unarmed population.

The influence of biochemical parameters on primary production in the Gulf of Gdańsk region: A model study

Understanding the changing levels of biochemical parameters and the factors that influence them throughout the seasons is crucial for comprehending the dynamics of marine ecosystems. It also helps us identify potential threats that could harm their condition, aiding decision-making processes related to their protection. This study focuses on examining the variations in nutrients (such as nitrates, phosphates, and silicates), dissolved oxygen, and phytoplankton within the Gulf of Gdańsk. Additionally, we analyze the primary production process at three representative locations. To achieve this, we used data from the EcoFish biochemical numerical model. To ensure the model's accuracy, we compared its results with in situ data from the ICES database. The comparison revealed high correlations and minimal errors. Furthermore, we investigated how limiting factors impact primary phytoplankton production and demonstrated how the intensity of spring diatom blooms influences the nature of cyanobacterial blooms in the summer.

Effect of sludge retention time (SRT) on micro-ecological niche competition kinetics between ammonia-oxidizing organisms (AOO) and nitrite-oxidizing organisms (NOO) in a single system

As a method for controlling the nitrification process, sludge retention time (SRT) control is based on the micro-ecological niche competition between ammonia-oxidizing organisms (AOO) and nitrite-oxidizing organisms (NOO). In this study, AOO and NOO were domesticated and cultured under different SRT conditions (40 d, 20 d, 10 d, and 5 d) in four continuous stirred-tank reactors (CSTRs) to investigate the relationship between their ecological niche shift and the typical environmental conditions. As the SRT decreased, the proportion of AOO colonies gradually increased and achieved spatial ecological niche competition superiority, which may be related to their maximum growth rate and ecological niche width. Moreover, the ecological niche width of the typical environmental impact parameters of AOO and NOO changed significantly with the SRT, which may be related to the changes in their internal flora. In addition, a micro-ecological niche competition model and kinetic systems were established and simulated on the basis of existing microbial biochemical models. The established model could be used to simulate and predict the ecological niche shift process of AOO and NOO under different SRT conditions in the CSTRs. In summary, this study explains the mechanism and basic kinetic parameters of SRT control in two-step nitrification based on micro-ecological niche competition theory. And a niche competition model has been established, providing a new method for controlling the process.

Contribution of Proteins and Peptides to the Impact of a Soy Protein Isolate on Oxidative Stress and Inflammation-Associated Biomarkers in an Innate Immune Cell Model.

The innate and adaptative immune systems are involved in the regulation of inflammatory and oxidative processes and mediators such as reactive oxygen species (ROS) and nitric oxide (NO). The exacerbated action of these players results in an oxidative stress status and chronic inflammation, which is responsible for the development of non-communicable diseases (NCDs). By modulating these mediators, bioactive compounds in food can exert a key role in the prevention of several NCDs. Among these compounds, soybean proteins and peptides such as lunasin have been considered to be among the most promising. The aim of this study was to obtain and characterize a soluble protein-enriched extract from a commercial soybean protein isolate and fractionate it into different fractions through ultrafiltration. Their antioxidant and immunomodulatory properties were then evaluated using biochemical and cell models. A total of 535 proteins (from 282 protein groups) were identified in the extract, in which the presence of the peptide lunasin was confirmed. The enrichment of this peptide was achieved in the 3-10 kDa fraction. The protective effects against the oxidative stress induced by LPS in the macrophage model could have been mediated by the radical scavenging capacity of the peptides present in the soybean samples. Under basal conditions, the extract and its ultrafiltered fractions activated macrophages and induced the release of NO. However, under challenged conditions, the whole extract potentiated the NO-stimulating effects of LPS, whereas the fraction containing 3-10 kDa peptides, including lunasin, counteracted the LPS-induced NO increase. Our findings suggest a promising role of soybean protein as an ingredient for functional foods and nutraceuticals aimed at promoting health and preventing oxidative stress and/or immune-alteration-associated diseases.