Search Results (17)

Data-driven approaches to blood glucose predictive modeling face significant challenges due to the inherent variability in biological systems. While these methods efficiently capture statistical patterns through automated processes, they often lack the biological interpretability necessary to link model behavior with underlying physiological mechanisms. In contrast, physiological models offer accurate mechanistic representations but require complex parameterization and specialized domain expertise. In this work, we present an approach for predicting blood glucose levels (BGLs) leveraging the concept of physiology-informed neural networks (PINNs). This approach addresses the challenge of BGL prediction by incorporating the parameters of insulin and meal dynamics within the architecture of a predictive network. It employs a two-stage learning approach for modeling physiology and predicting BGLs. The neural network is pretrained to approximate the solutions of the physiological dynamics, and the output of this pretrained model, representing the insulin and glucose concentration states, is then fed as input into a predictive model, enabling simultaneous optimization of predictive accuracy and physiological parameter estimation, offering advantages over traditional modeling approaches in terms of personalized prediction and interpretability. The results highlight the model’s ability to estimate physiological parameters while maintaining strong predictive performance that aligns with the underlying physiological principles. This framework offers significant potential for personalized predictive modeling where precise and efficient understanding of individual metabolism is essential. Full article

This study examined how meal frequency under isocaloric conditions affects triglyceride accumulation in adipocytes, focusing on the role of insulin dynamics. Using a mathematical model of carbohydrate–lipid metabolism, we simulated feeding regimens from one to eight meals/day while holding calories and macronutrient ratios constant. A simplified model allowed independent variation in insulin peak amplitude, width, and overlap Results show that, relative to thrice-daily feeding (the reference regimen with stable triglyceride content over one month), infrequent meals (1–2/day) reduce, while frequent meals (5–8/day) increase triglyceride accumulation—most strongly in healthy individuals and attenuated in type 2 diabetes, as parameterized from the literature. Crucially, fat accumulation correlates not with average insulin levels but with its dynamic profile. Metabolic flux analysis revealed that triglyceride accumulation is driven not by changes in synthesis rate but by suppression of lipolysis, which depends on the amplitude, duration, and degree of overlap of insulin peaks. Thus, fat mass is shaped not only by caloric intake but by meal timing, which defines the insulin signal’s temporal structure. These findings highlight that insulin dynamics—not mean concentration—govern lipid metabolism, urging dietary guidelines to account for meal pattern, not just composition or total energy. Full article

Arctic warming is expected to alter permafrost landscapes and shift tundra ecosystems from greenhouse gas sinks to sources. We quantified plant biomass and necromass, carbon stocks, and microbial activity across five Low-Arctic tundra sites in the Yana–Indigirka Lowland (Chokurdakh, NE Siberia) during the 2024 growing season. Above- and below-ground plant biomass was measured by harvest adjacent to 50 × 50 m permanent plots; total C and N were determined by dry combustion on an elemental analyzer. Total organic carbon (TOC) stocks were calculated by horizon from TOC (%), bulk density, and thickness. Microbial basal respiration (BR), substrate-induced respiration (SIR), microbial biomass C (MBC), and the metabolic quotient (qCO₂) were assessed in litter/organic (O), peat (T), and mineral gley horizons. Mean above-ground biomass was 15.8 ± 1.5 t ha⁻¹; total living biomass averaged 43.1 ± 1.6 t ha⁻¹. Below-ground biomass exceeded above-ground by 1.73×. Carbon in above-ground, below-ground, and necromass pools averaged 7.8, 12.2, and 12.5 t C ha⁻¹, respectively. Surface organic horizons dominated ecosystem C storage: litter–peat stocks ranged from 234 to 449 t C ha⁻¹, whereas 0–30 cm mineral layers held 18–50 t C ha⁻¹; total (surface + 0–30 cm) stocks spanned 258–511 t C ha⁻¹ among sites. Key contributors to biomass and C storage were deciduous shrubs (Salix pulchra, Betula nana), bryophytes (notably Aulacomnium palustre), and the graminoids (Eriophorum vaginatum). BR and MBC were highest in O and T horizons (BR up to 21.9 μg C g⁻¹ h⁻¹; MBC up to 70,628 μg C g⁻¹) and declined sharply in mineral soil; qCO₂ decreased from O to mineral horizons, indicating more efficient C use at depth. These in situ data show that Low-Arctic tundra C stocks are concentrated in surface organic layers while microbial communities remain responsive to warming, implying high sensitivity of carbon turnover to thaw and hydrologic change. The dataset supports model parameterization and remote sensing of shrub–tussock tundra carbon dynamics. Full article

Virus-like particles (VLPs) have the potential to become a versatile carrier platform for vaccination against multiple diseases. In the light of short process development timelines and the demand for reliable and robust processes, metabolic modeling of cell culture processes offers great advantages when coupled with a Quality-by-Design (QbD) development approach. A previous work was able to demonstrate the accurate prediction of HEK293F PiggyBac cell concentration as well as VLP titer and metabolite production with a reduced metabolic model. This work presents the reduced metabolic model for a more productive cell line Sleeping Beauty and emphasizes the need for model re-parameterization when the producer cell line changes. The goal of precise prediction for a fed-batch and continuous HEK293 cultivation can, therefore, be achieved. In terms of decision-making for downstream unit operations, a soft sensor for the prediction of main impurities like proteins and DNA was introduced for the first time for the production of lentiviral vectors with several terms describing the release of impurities like DNA and proteins, growth-related protein production, and enzymatic degradation activity associated with cell dissociation in an accurate manner. The additional information can contribute to a more efficient design phase by reducing experimental effort as well as during cultivation with data-based decision-making. With the aid of real-time process data acquisition through process analytical technology (PAT), its predictive power can be enhanced and lead to more reliable processes. Full article

The in vitro biotransformation of phenol at 11 °C was studied using pre-spawn adult rainbow (Oncorhynchus mykiss) (RBT), brook (Salvelinus fontinalis) (BKT), and lake trout (Salvelinus namaycush) (LKT) hepatic microsomal preparations. The incubations were optimized for time, cofactor concentration, pH, and microsomal protein concentration. Formation of Phase I ring-hydroxylation and Phase II glucuronidation metabolites was quantified using HPLC with dual-channel electrochemical and UV detection. The biotransformation of phenol over a range of substrate concentrations (1 to 180 mM) was quantified, and the Michaelis–Menten kinetics constants, Km and Vmax, for the formation of hydroquinone (HQ), catechol (CAT), and phenylglucuronide (PG) were calculated. Species differences were noted in the Km values for Phase I enzyme production of HQ and CAT, with the following rank order of apparent enzyme affinity for substrate: RBT > BKT = LKT. However, no apparent differences in the Km for Phase II metabolism of phenol to PG were detected. Conversely, while there were no apparent differences in Vmax between species for HQ or CAT formation, the apparent maximum capacity for PG formation was significantly less in LKT than that observed for RBT and BKT. These experiments provide a means to quantify metabolic activation and deactivation of xenobiotics in fish, to compare activation and deactivation reactions across species, and to act as a guide for future predictions of new chemical biotransformation pathways and rates in fish. These experiments provided the necessary rate and capacity (Km and Vmax) inputs that are required to parameterize a fish physiologically based toxicokinetic (PB-TK) model for a reactive chemical that is readily biotransformed, such as phenol. In the future, an extensive database of these rate and capacity parameters on important fish species for selected chemical structures will be needed to allow the effective use of predictive models for reactive, biotransformation chemicals in aquatic toxicology and environmental risk assessment. Full article

The aim of the study is to develop a population pharmacokinetic (PopPK) model and to investigate the influence of CYP3A5/CYP3A4 and ABCB1 single nucleotide polymorphisms (SNPs) on the Tacrolimus PK parameters after LCP-Tac formulation in stable adult renal transplant patients. The model was developed, using NONMEM v7.5, from full PK profiles from a clinical study (n = 30) and trough concentrations (C₀) from patient follow-up (n = 68). The PK profile of the LCP-Tac formulation was best described by a two-compartment model with linear elimination, parameterized in elimination (CL/F) and distributional (CL_D/F) clearances and central compartment (Vc/F) and peripheral compartment (Vp/F) distribution volumes. A time-lagged first-order absorption process was characterized using transit compartment models. According to the structural part of the base model, the LCP-Tac showed an absorption profile characterized by two transit compartments and a mean transit time of 3.02 h. Inter-individual variability was associated with CL/F, Vc/F, and Vp/F. Adding inter-occasion variability (IOV) on CL/F caused a statistically significant reduction in the model minimum objective function MOFV (p < 0.001). Genetic polymorphism of CYP3A5 and a cluster of CYP3A4/A5 SNPs statistically significantly influenced Tac CL/F. In conclusion, a PopPK model was successfully developed for LCP-Tac formulation in stable renal transplant patients. CYP3A4/A5 SNPs as a combined cluster including three different phenotypes (high, intermediate, and poor metabolizers) was the most powerful covariate to describe part of the inter-individual variability associated with apparent elimination clearance. Considering this covariate in the initial dose estimation and during the therapeutic drug monitoring (TDM) would probably optimize Tac exposure attainments. Full article

Physiologically based pharmacokinetic (PBPK) modeling has a number of applications, including assessing drug–drug interactions (DDIs) in polymorphic populations, and should be iteratively refined as science progresses. The Simcyp Simulator is annually updated and version 21 included updates to hepatic and intestinal CYP2C19 enzyme abundance, including addition of intermediate and rapid metabolizer phenotypes and changes to the ultra-rapid metabolizer enzyme abundance, with implications for population clearance and DDI predictions. This work details verification of the updates with sensitive CYP2C19 substrates, omeprazole and lansoprazole, using available clinical data from literature. Multiple assessments were performed, including recovery of areas under the concentration-time curve (AUC) and C_max from compiled datasets for each drug, recovery of victim DDI ratios with CYP2C19 and/or CYP3A4 inhibition and recovery of relative exposure between phenotypes. Simulated data were within respective acceptance criteria for >80% of omeprazole AUC values, >70% of lansoprazole AUC and C_max, >60% of AUC and C_max DDI ratios and >80% of exposure ratios between different phenotypes. Recovery of omeprazole C_max was lower (>50–70% within 2-fold) and possibly attributed to the variety of formulations used in the clinical dataset. Overall, the results demonstrated that the updated data used to parameterize CYP2C19 phenotypes reasonably described the pharmacokinetics of omeprazole and lansoprazole in genotyped or phenotyped individuals. Full article

Can a replicase be found in the vast sequence space by random drift? We partially answer this question through a proof-of-concept study of the times of occurrence (hitting times) of some critical events in the origins of life for low-dimensional RNA sequences using a mathematical model and stochastic simulation studies from Python software. We parameterize fitness and similarity landscapes for polymerases and study a replicating population of sequences (randomly) participating in template-directed polymerization. Under the ansatz of localization where sequence proximity correlates with spatial proximity of sequences, we find that, for a replicating population of sequences, the hitting and establishment of a high-fidelity replicator depends critically on the polymerase fitness and sequence (spatial) similarity landscapes and on sequence dimension. Probability of hitting is dominated by landscape curvature, whereas hitting time is dominated by sequence dimension. Surface chemistries, compartmentalization, and decay increase hitting times. Compartmentalization by vesicles reveals a trade-off between vesicle formation rate and replicative mass, suggesting that compartmentalization is necessary to ensure sufficient concentration of precursors. Metabolism is thought to be necessary to replication by supplying precursors of nucleobase synthesis. We suggest that the dynamics of the search for a high-fidelity replicase evolved mostly during the final period and, upon hitting, would have been followed by genomic adaptation of genes and to compartmentalization and metabolism, effecting degree-of-freedom gains of replication channel control over domain and state to ensure the fidelity and safe operations of the primordial genetic communication system of life. Full article

Pelagic calcification shapes the carbon budget of lakes and the sensitivity of dissolved inorganic carbon (DIC) responses to lake metabolism. This process, being tightly linked to primary production, needs to be understood within the context of summer eutrophication which is increasing due to human stressors and global change. Most lake carbon budget models do not account for calcification because the conditions necessary for its occurrence are not well constrained. This study aims at identifying ratios between calcification and primary production and the drivers that control these ratios in freshwater. Using in situ incubations in several European freshwater lakes, we identify a strong relationship between calcite saturation and the ratio between calcification and net ecosystem production (NEP) (p-value < 0.001, R² = 0.95). NEP-induced calcification is a short-term process that is potentiated by the increase in calcite saturation occurring at longer time scales, usually reaching the highest levels in summer. The resulting summer calcification event has effects on the DIC equilibria, causing deviations from the metabolic 1:1 stoichiometry between DIC and dissolved oxygen (DO). The strong dependency of the ratio between NEP and calcification on calcite saturation can be used to develop a suitable parameterization to account for calcification in lake carbon budgets. Full article

Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed. Full article

When Saccharomyces cerevisiae undergoes heat stress it stimulates several changes that are necessary for its survival, notably in carbon metabolism. Notable changes include increase in trehalose production and glycolytic flux. The increase in glycolytic flux has been postulated to be due to the regulatory effects in upper glycolysis, but this has not been confirmed. Additionally, trehalose is a useful industrial compound for its protective properties. A model of trehalose metabolism in S. cerevisiae was constructed using Convenient Modeller, a software that uses a combination of convenience kinetics and a genetic algorithm. The model was parameterized with quantitative omics under standard conditions and validated using data collected under heat stress conditions. The completed model was used to show that feedforward activation of pyruvate kinase by fructose 1,6-bisphosphate during heat stress contributes to the increase in metabolic flux. We were also able to demonstrate in silico that overexpression of enzymes involved in production and degradation of trehalose can lead to higher trehalose yield in the cell. By integrating quantitative proteomics with metabolic modelling, we were able to confirm that the flux increase in trehalose metabolic pathways during heat stress is due to regulatory effects and not purely changes in enzyme expression. The overexpression of enzymes involved in trehalose metabolism is a potential approach to be exploited for trehalose production without need for increasing temperature. Full article

Human hepatocellular carcinoma (HCC) is the most common type of primary liver cancer in adults and the most common cause of death in people with cirrhosis. While previous metabolic studies of HCC have mainly focused on the glucose metabolism (Warburg effect), less attention has been paid to tumor-specific features of the lipid metabolism. Here, we applied a computational approach to analyze major pathways of fatty acid utilization in individual HCC. To this end, we used protein intensity profiles of eleven human HCCs to parameterize tumor-specific kinetic models of cellular lipid metabolism including formation, enlargement, and degradation of lipid droplets (LDs). Our analysis reveals significant inter-tumor differences in the lipid metabolism. The majority of HCCs show a reduced uptake of fatty acids and decreased rate of β-oxidation, however, some HCCs display a completely different metabolic phenotype characterized by high rates of β-oxidation. Despite reduced fatty acid uptake in the majority of HCCs, the content of triacylglycerol is significantly enlarged compared to the tumor-adjacent tissue. This is due to tumor-specific expression profiles of regulatory proteins decorating the surface of LDs and controlling their turnover. Our simulations suggest that HCCs characterized by a very high content of triglycerides comprise regulatory peculiarities that render them susceptible to selective drug targeting without affecting healthy tissue. Full article

Global Sensitivity Analysis (GSA) is a technique that numerically evaluates the significance of model parameters with the aim of reducing the number of parameters that need to be estimated accurately from experimental data. In the work presented herein, we explore different methods and criteria in the sensitivity analysis of a recently developed mathematical model to describe Chinese hamster ovary (CHO) cell metabolism in order to establish a strategic, transferable framework for parameterizing mechanistic cell culture models. For that reason, several types of GSA employing different sampling methods (Sobol’, Pseudo-random and Scrambled-Sobol’), parameter deviations (10%, 30% and 50%) and sensitivity index significance thresholds (0.05, 0.1 and 0.2) were examined. The results were evaluated according to the goodness of fit between the simulation results and experimental data from fed-batch CHO cell cultures. Then, the predictive capability of the model was tested against four different feeding experiments. Parameter value deviation levels proved not to have a significant effect on the results of the sensitivity analysis, while the Sobol’ and Scrambled-Sobol’ sampling methods and a 0.1 significance threshold were found to be the optimum settings. The resulting framework was finally used to calibrate the model for another CHO cell line, resulting in a good overall fit. The results of this work set the basis for the use of a single mechanistic metabolic model that can be easily adapted through the proposed sensitivity analysis method to the behavior of different cell lines and therefore minimize the experimental cost of model development. Full article

The metabolic activity of water-limited ecosystems is strongly linked to the timing and magnitude of precipitation pulses that can trigger disproportionately high (i.e., hot-moments) ecosystem CO₂ fluxes. We analyzed over 2-years of continuous measurements of soil CO₂ efflux (Fs) under vegetation (Fs_veg) and at bare soil (Fs_bare) in a water-limited grassland. The continuous wavelet transform was used to: (a) describe the temporal variability of Fs; (b) test the performance of empirical models ranging in complexity; and (c) identify hot-moments of Fs. We used partial wavelet coherence (PWC) analysis to test the temporal correlation between Fs with temperature and soil moisture. The PWC analysis provided evidence that soil moisture overshadows the influence of soil temperature for Fs in this water limited ecosystem. Precipitation pulses triggered hot-moments that increased Fs_veg (up to 9000%) and Fs_bare (up to 17,000%) with respect to pre-pulse rates. Highly parameterized empirical models (using support vector machine (SVM) or an 8-day moving window) are good approaches for representing the daily temporal variability of Fs, but SVM is a promising approach to represent high temporal variability of Fs (i.e., hourly estimates). Our results have implications for the representation of hot-moments of ecosystem CO₂ fluxes in these globally distributed ecosystems. Full article

Environmental conditions and silviculture fundamentally alter the metabolism of individual trees and, therefore, need to be studied at that scale. However, changes in forest biomass density (Mg C ha⁻¹) may be decoupled from changes in growth (kg C year⁻¹) when the latter also accelerates the life cycle of trees and strains access to light, nutrients, and water. In this study, we refer to an individual-based model of forest biomass dynamics to constrain the magnitude of system feedbacks associated with ontogeny and competition and estimate the scaling relationship between changes in tree growth and forest biomass density. The model was driven by fitted equations of annual aboveground biomass growth (G_ag), probability of recruitment (P_r), and probability of mortality (P_m) parameterized against field observations of black spruce (Picea mariana (Mill.) BSP), interior Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco), and western hemlock (Tsuga heterophylla (Raf.) Sarg.). A hypothetical positive step-change in mean tree growth was imposed half way through the simulations and landscape-scale responses were then evaluated by comparing pre- and post-stimulus periods. Imposing a 100% increase in tree growth above calibrated predictions (i.e., contemporary rates) only translated into 36% to 41% increases in forest biomass density. This corresponded with a tree-growth elasticity of forest biomass (ε_G,SB) ranging from 0.33 to 0.55. The inelastic nature of stand biomass density was attributed to the dependence of mortality on intensity of competition and tree size, which decreased stand density by 353 to 495 trees ha⁻¹, and decreased biomass residence time by 10 to 23 years. Values of ε_G,SB depended on the magnitude of the stimulus. For example, a retrospective scenario in which tree growth increased from 50% below contemporary rates up to contemporary rates indicated values of ε_G,SB ranging from 0.66 to 0.75. We conclude that: (1) effects of warming and increasing atmospheric concentrations of carbon dioxide and reactive nitrogen on biomass production are greatly diminished, but not entirely precluded, scaling up from individual trees to forest landscapes; (2) the magnitude of decoupling is greater for a contemporary baseline than it is for a pre-industrial baseline; and (3) differences in the magnitude of decoupling among species were relatively small. To advance beyond these estimates, studies must test the unverified assumptions that effects of tree size and stand competition on rates of recruitment, mortality, and growth are independent of climate change and atmospheric concentrations of carbon dioxide and nitrogen. Full article