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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. [ABSTRACT FROM AUTHOR]

Background: Celiac disease (CeD) is an autoimmune condition characterized by an aberrant immune response triggered by the ingestion of gluten, which damages epithelial cells lining the small intestine. Small intestinal epithelial cells (sIECs) play key roles in the enzymatic digestion and absorption of nutrients, maintaining gut barrier integrity, and regulating immune response. Chronic inflammation and tissue damage associated with CeD disrupt the intricate network of metabolic processes in sIECs that support these functions, impairing their ability to perform their essential roles. However, the specific disrupted metabolic processes underlying sIECs dysfunction in CeD remain largely undefined. Methods: To address this knowledge gap, personalized, sex-specific genome-scale models of sIECs metabolism were constructed using transcriptional data from intestinal biopsies of 42 subjects with active CeD, CeD in remission (on a gluten-free diet), and non-CeD controls. These models were computationally simulated under relevant dietary conditions for each group of subjects to assess the activity of several metabolic tasks essential for sIECs function and to profile metabolite secretion into the bloodstream and intestinal lumen. Results: Significant alterations in the activity of 28 essential metabolic tasks were observed in active CeD and remission CeD, impacting critical processes integral to sIECs function such as oxidative stress regulation, nucleotide synthesis and DNA repair, energy production, and polyamine and amino acid metabolism. Additionally, altered secretion profiles of several metabolites, encompassing amino acids, vitamins, polyamines, lipids, and fatty acids, into the bloodstream were detected in active CeD and remission CeD patients. These findings were partially supported by comparisons with independent external datasets and further corroborated through extensive review of existing literature. Furthermore, a drug target analysis was performed, identifying 22 FDA-approved drugs that target genes encoding impaired sIECs metabolic functions in CeD, potentially helping to restore their normal activity. Conclusions: This study unveils new insights into the metabolic reprogramming of sIECs in CeD, highlighting specific dysregulated metabolic processes that compromise cellular functions essential for gut health. These findings offer a foundation for developing therapeutic interventions targeting impaired metabolic processes in CeD. [ABSTRACT FROM AUTHOR]

AbstractUnderstanding 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Osteoporosis is most prevalent in postmenopausal women and is a widespread condition that impacts millions of individuals worldwide. A decrease in bone mineral density and elevated susceptibility to fractures as a result of bone architecture deterioration are the primary attributes of osteoporosis. Due to the challenging nature of treating osteoporosis as a chronic condition, further research into innovative and efficacious therapeutic and animal models that aim to test these drug approaches is necessary. Numerous preclinical and clinical studies have focused on identifying the most efficacious therapeutic approaches for this condition. Various types of treatment are available for osteoporosis, which vary based on particular characteristics of the patient's condition. These comprise a combination of recently developed and established medications. This review focuses on novel therapeutic approaches for osteoporosis and their corresponding mechanisms of action, pathophysiology, the animal model, and the parameters that are assessed. [ABSTRACT FROM AUTHOR]

Reaction system (RS) belongs to a type of qualitative computing model inspired by biochemical reactions taking place inside biological cells. It concerns more the interactions and causality among reactions rather than concrete concentrations of chemical entities. Many biochemical processes and models can be represented in the form of reaction systems so that complex relations and ultimate products of a variety of reactions can be revealed qualitatively. The reaction system works in parallel mode. Software simulation of this kind of model may suffer from the penalty of inefficient parallelism for the limited performance of CPU/GPU, especially for the simulation of large-scale models. Considering potential applications of reaction systems in disease diagnoses and in drug developments, hardware implementation of reaction systems provides a better way to accelerate computations involved. In this paper, an FPGA implementation method of a reaction system called RSFIM is proposed. Two small-scale models, i.e., the reaction system of intermediate filaments self-assembly and heat shock response, are implemented on FPGA, achieving a computing speed of 2 × 10 8 steps per second. For large-scale models, the ErbB reaction system is implemented, obtaining a speedup of 7.649 × 10 4 compared with its highest performance GPU simulation so far. The reaction system binary counter, which is a quantitative model, is also implemented by the Boolean explanation of the qualitative character of the reaction system. FPGA implementation of reaction systems opens a novel research line to speed up the simulations of reaction systems and other biological models in the perspective of parallel digital circuits. [ABSTRACT FROM AUTHOR]

Biological signalling systems are complex, and efforts to build mechanistic models must confront a huge parameter space, indirect and sparse data, and frequently encounter multiscale and multiphysics phenomena. We present HOSS, a framework for Hierarchical Optimization of Systems Simulations, to address such problems. HOSS operates by breaking down extensive systems models into individual pathway blocks organized in a nested hierarchy. At the first level, dependencies are solely on signalling inputs, and subsequent levels rely only on the preceding ones. We demonstrate that each independent pathway in every level can be efficiently optimized. Once optimized, its parameters are held constant while the pathway serves as input for succeeding levels. We develop an algorithmic approach to identify the necessary nested hierarchies for the application of HOSS in any given biochemical network. Furthermore, we devise two parallelizable variants that generate numerous model instances using stochastic scrambling of parameters during initial and intermediate stages of optimization. Our results indicate that these variants produce superior models and offer an estimate of solution degeneracy. Additionally, we showcase the effectiveness of the optimization methods for both abstracted, event-based simulations and ODE-based models. Author summary: Biochemical pathway models integrate quantitative and qualitative data to understand cell functioning, disease effects, and to test treatments in silico. Constructing and optimizing these models is challenging due to the complexity and multitude of variables and parameters involved. Although hundreds of biochemical models have been developed and are available in repositories, they are rarely reused. To enhance the utilization of these models in biomedicine, we propose HOSS, an innovative hierarchical model optimization method. HOSS takes advantage of the modular structure of pathway models by breaking down large mechanistic computational models into smaller modules. These modules are then optimized progressively, starting with input modules and following causality paths. This method significantly reduces the computational burden as each step involves solving a simpler problem. By making the optimization process more manageable, HOSS accelerates the lifecycle of biochemical models and promotes their broader use in biomedical research and applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: Extracellular auxin maxima and minima are important to control plant developmental programs. Auxin gradients are provided by the concerted action of proteins from the three major plasma membrane (PM) auxin transporter classes AUX1/LAX, PIN and ATP‐BINDING CASSETTE subfamily B (ABCB) transporters. But neither genetic nor biochemical nor modeling approaches have been able to reliably assign the individual roles and interplay of these transporter types.Based on the thermodynamic properties of the transporters, we show here by mathematical modeling and computational simulations that the concerted action of different auxin transporter types allows the adjustment of specific transmembrane auxin gradients. The dynamic flexibility of the 'auxin homeostat' comes at the cost of an energy‐consuming 'auxin cycling' across the membrane.An unexpected finding was that potential functional ABCB‐PIN synchronization appears to allow an optimization of the trade‐off between the speed of PM auxin gradient adjustment on the one hand and ATP consumption and disturbance of general anion homeostasis on the other.In conclusion, our analyses provide fundamental insights into the thermodynamic constraints and flexibility of transmembrane auxin transport in plants. [ABSTRACT FROM AUTHOR]

Biofilm growth in porous media changes not only the hydrodynamic properties of the medium (reduction in porosity and permeability, and increase in dispersivity), but also the transport itself (breakthrough curves display increasingly fast first arrivals and long tails). These features are well reproduced by multicontinuum models (Multi‐Rate Mass Transfer, MRMT) which can be used to describe reactive transport in heterogeneous porous media and facilitate the simulation of reactions that are localized within biofilms. Here, we present a conceptual and numerical model of biochemical reactive transport with dynamic biofilm growth based on MRMT formulations. Mass exchange between mobile water and immobile biofilm aggregates is represented by a memory function, which simplifies definition of MRMT parameters. We successfully tested this model on two sets of laboratory data and found that (a) a basic model based on the growth of uniformly sized biofilm aggregates fails to reproduce laboratory tracer tests and rate of biofilm growth, while a fractal growth model, which we obtain by integrating the memory functions of biofilm aggregates with a power law distribution, does; (b) the biofilm memory function evolves as the biofilm grows; and (c) the early time portion of eluted volume tracer breakthrough curves are independent of flow rate, whereas the tail becomes heavier when the flow rate is decreased, which implies that both advection controlled and diffusion controlled mass exchange coexist in biofilms. These findings imply that porous media biofilms are essentially different from those developing in human tissues or open spaces. Key Points: Biofilm aggregates with fractal size distributions are required for simulating growth and transport dynamicsThe evolution of anomalous transport during biofilm growth is well described by the fractal MRMT modelSolute exchange between biofilm and mobile water is both advective and diffusive [ABSTRACT FROM AUTHOR]

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 ( V c m a x , 0 25 ), 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 V c m a x , 0 25 and GPP estimation. CNN methods achieved the best V c m a x , 0 25 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 V c m a x , 0 25 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. [ABSTRACT FROM AUTHOR]

Objectives: Protein malnutrition (PMN) is a significant public health concern that can aggravate pathological states. The impact of early malnutrition on metabolism needs extensive evaluation. Current models employ short-term diet restriction and are neither ethically correct nor clinically relevant. This study aimed to develop a PMN rat model to evaluate the effects of a low-protein diet (LPD) on physiological, hematological, biochemical, and histological changes affected by malnourishment from postweaning to the 40th week. Materials and Methods: The PMN model was developed in Wistar rats (post-weaning) by assigning animals to patented LPD (10% protein) and a control group to a normal diet (18% protein). Developed model was confirmed by biometric, biochemcial parameters and Gomez classification of malnutrition. Results: LPD-induced PMN showed stunted growth, altered biochemical (albumin range, 1.9 - 2.4 g/dL, total protein range, 5.1 - 6.4 g/dL), and hematological markers mean corpuscular volume (52.03 ± 1.34, 47.45 ± 0.44, p≤0.01), mean corpuscular hemoglobin (17.67 ± 0.47, 15.37 ± 0.18, p≤0.001) and mean corpuscular haemoglobin concentration (33.87 ± 0.22, 32.37 ± 0.24, p≤0.001) and significantly affected hepatic histology. A long-term study was conducted to analyze the pattern of developmental PMN and its stabilization over time. Conclusion: The developed PMN rat model imitates clinical conditions and is confirmed as a stable, reproducible, and reliable model for short- and long-term studies. The clinical relevance of this approach opens new avenues for research in treatment, drug development, molecular interactions, and disease model development. [ABSTRACT FROM AUTHOR]

An analysis of observability and detectability for continuous stirred tank reactor model of selected biochemical processes has been addressed in this paper. In particular, properties of observability or detectability of the considered system model have been proved under uncertain system dynamics in view of various sets of system measured outputs. It is related to considering system dynamics depending on initial conditions and the impact of inputs taking into account a given measured output. The method of indistinguishable state trajectories (indistinguishable dynamics) and tools based on the Lyapunov second method were used to investigate the observability and detectability properties. The analysis was performed for eight cases of different sets of measured outputs with association to the realistic features of measuring devices. The obtained research results are essential for system state estimation that involves the synthesis of state observers. The proposed approach may be successfully applied to the complex biochemical non-linear uncertain systems modelled as continuous stirred tank reactors. [ABSTRACT FROM AUTHOR]

Photorespiration is an essential process related to photosynthesis that is initiated following the oxygenation reaction catalyzed by rubisco, the initial enzyme of the Calvin–Benson–Bassham cycle. This reaction produces an inhibitory intermediate that is recycled back into the Calvin–Benson–Bassham cycle by photorespiration which requires the use of energy and the release of a portion of the carbon as CO2. The energy use and CO2 release of canonical photorespiration form a foundation for biochemical models used to describe and predict leaf carbon exchange and energy use (ATP and NAPDH). The ATP and NADPH demand of canonical photorespiration is thought to be different than that of the Calvin–Benson–Bassham cycle, requiring increased flexibility in the ratio of ATP and NADPH from the light reactions. Photorespiration requires many reactions across the chloroplasts, mitochondria and peroxisomes and involves many intermediates. Growing evidence indicates that these intermediates do not all stay in photorespiration as typically assumed and instead feed into other aspects of metabolism and leave as glycine, serine, and methylene‐THF. Here we discuss how alternative flux through and from canonical photorespiration alters the ATP and NADPH requirements of metabolism following rubisco oxygenation using additional derivations of biochemical models of leaf photosynthesis and energetics. Using these new derivations, we determine that the ATP and NADPH demands of photorespiration are highly sensitive to alternative flux in ways that fundamentally changes how photorespiration contributes to the ratio of total ATP and NADPH demand. Specifically, alternative flows of carbon through photorespiration could reduce ATP and NADPH demand ratio to values below what is produced from linear electron transport. Summary statement: All carbon produced following rubisco‐catalyzed reaction with oxygen may not flow through textbook photorespiration. Here we review the consequences of this "noncanonical" carbon flow to the ATP and NADPH demands of this vital process. [ABSTRACT FROM AUTHOR]

A biochemical pathway consists of a series of interconnected biochemical reactions to accomplish specific life activities. The participating reactants and resultant products of a pathway, including gene fragments, proteins, and small molecules, coalesce to form a complex reaction network. Biochemical pathways play a critical role in the biochemical domain as they can reveal the flow of biochemical reactions in living organisms, making them essential for understanding life processes. Existing studies of biochemical pathway networks are mainly based on experimentation and pathway database analysis methods, which are plagued by substantial cost constraints. Inspired by the success of representation learning approaches in biomedicine, we develop the biochemical pathway prediction (BPP) platform, which is an automatic BPP platform to identify potential links or attributes within biochemical pathway networks. Our BPP platform incorporates a variety of representation learning models, including the latest hypergraph neural networks technology to model biochemical reactions in pathways. In particular, BPP contains the latest biochemical pathway-based datasets and enables the prediction of potential participants or products of biochemical reactions in biochemical pathways. Additionally, BPP is equipped with an SHAP explainer to explain the predicted results and to calculate the contributions of each participating element. We conduct extensive experiments on our collected biochemical pathway dataset to benchmark the effectiveness of all models available on BPP. Furthermore, our detailed case studies based on the chronological pattern of our dataset demonstrate the effectiveness of our platform. Our BPP web portal, source code and datasets are freely accessible at https://github.com/Glasgow-AI4BioMed/BPP. [ABSTRACT FROM AUTHOR]

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Background and Purpose: Parkinson's disease (PD) is a prevalent neurodegenerative movement disorder characterized by motor dysfunction. Environmental factors, especially manganese (Mn), contribute significantly to PD. Existing therapies are focused on motor coordination, whereas nonmotor features such as neuropsychiatric symptoms are often neglected. Daidzein (DZ), a phytoestrogen, has piqued interest due to its antioxidant, anti‐inflammatory, and anxiolytic properties. Therefore, we anticipate that DZ might be an effective drug to alleviate the nonmotor symptoms of Mn‐induced Parkinsonism. Experimental Approach: Naïve zebrafish were exposed to 2 mM of Mn for 21 days and intervened with DZ. Nonmotor symptoms such as anxiety, social behaviour, and olfactory function were assessed. Acetylcholinesterase (AChE) activity and antioxidant enzyme status were measured from brain tissue through biochemical assays. Dopamine levels and histology were performed to elucidate neuroprotective mechanism of DZ. Key Results: DZ exhibited anxiolytic effects in a novel environment and also improved intra and inter fish social behaviour. DZ improved the olfactory function and response to amino acid stimuli in Mn‐induced Parkinsonism. DZ reduced brain oxidative stress and AChE activity and prevented neuronal damage. DZ increased DA level in the brain, collectively contributing to neuroprotection. Conclusion and Implications: DZ demonstrated a promising effect on alleviating nonmotor symptoms such as anxiety and olfactory dysfunction, through the mitigation of cellular damage. These findings underscore the therapeutic potential of DZ in addressing nonmotor neurotoxicity induced by heavy metals, particularly in the context of Mn‐induced Parkinsonism. [ABSTRACT FROM AUTHOR]

Rubisco activity is highly regulated and frequently limits carbon assimilation in crop plants. In the chloroplast, various metabolites can inhibit or modulate Rubisco activity by binding to its catalytic or allosteric sites, but this regulation is complex and still poorly understood. Using rice Rubisco, we characterised the impact of various chloroplast metabolites which could interact with Rubisco and modulate its activity, including photo-respiratory intermediates, carbohydrates, amino acids; as well as specific sugar-phosphates known to inhibit Rubisco activity - CABP (2-carboxy-D-arabinitol 1,5-bisphosphate) and CA1P (2-carboxy-D-arabinitol 1-phosphate) through in vitro enzymatic assays and molecular docking analysis. Most metabolites did not directly affect Rubisco in vitro activity under both saturating and limiting concentrations of Rubisco substrates, CO2 and RuBP (ribulose-1,5-bisphosphate). As expected, Rubisco activity was strongly inhibited in the presence of CABP and CA1P. High physiologically relevant concentrations of the carboxylation product 3-PGA (3-phosphoglyceric acid) decreased Rubisco activity by up to 30%. High concentrations of the photosynthetically derived hexose phosphates fructose 6-phosphate (F6P) and glucose 6-phosphate (G6P) slightly reduced Rubisco activity under limiting CO2 and RuBP concentrations. Biochemical measurements of the apparent Vmax and Km for CO2 and RuBP (at atmospheric O2 concentration) and docking interactions analysis suggest that CABP/CA1P and 3-PGA inhibit Rubisco activity by binding tightly and loosely, respectively, to its catalytic sites (i.e. competing with the substrate RuBP). These findings will aid the design and biochemical modelling of new strategies to improve the regulation of Rubisco activity and enhance the efficiency and sustainability of carbon assimilation in rice. [ABSTRACT FROM AUTHOR]

Purpose: Biochemical recurrence (BCR) following radical prostatectomy (RP) is a significant concern for patients with prostate cancer. Reliable prediction models are needed to identify patients at risk for BCR and facilitate appropriate management. This study aimed to develop and validate a clinical-radiomics model based on preoperative [18 F]PSMA-1007 PET for predicting BCR-free survival (BRFS) in patients who underwent RP for prostate cancer. Materials and methods: A total of 236 patients with histologically confirmed prostate cancer who underwent RP were retrospectively analyzed. All patients had a preoperative [18 F]PSMA-1007 PET/CT scan. Radiomics features were extracted from the primary tumor region on PET images. A radiomics signature was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The performance of the radiomics signature in predicting BRFS was assessed using Harrell's concordance index (C-index). The clinical-radiomics nomogram was constructed using the radiomics signature and clinical features. The model was externally validated in an independent cohort of 98 patients. Results: The radiomics signature comprised three features and demonstrated a C-index of 0.76 (95% CI: 0.60–0.91) in the training cohort and 0.71 (95% CI: 0.63–0.79) in the validation cohort. The radiomics signature remained an independent predictor of BRFS in multivariable analysis (HR: 2.48, 95% CI: 1.47–4.17, p < 0.001). The clinical-radiomics nomogram significantly improved the prediction performance (C-index: 0.81, 95% CI: 0.66–0.95, p = 0.007) in the training cohort and (C-index: 0.78 95% CI: 0.63–0.89, p < 0.001) in the validation cohort. Conclusion: We developed and validated a novel [18 F]PSMA-1007 PET-based clinical-radiomics model that can predict BRFS following RP in prostate cancer patients. This model may be useful in identifying patients with a higher risk of BCR, thus enabling personalized risk stratification and tailored management strategies. [ABSTRACT FROM AUTHOR]

Mathematical models of biochemical reaction networks are an important and emerging tool for the study of cell signaling networks involved in disease processes. One promising potential application of such mathematical models is the study of how disease-causing mutations promote the signaling phenotype that contributes to the disease. It is commonly assumed that one must have a thorough characterization of the network readily available for mathematical modeling to be useful, but we hypothesized that mathematical modeling could be useful when there is incomplete knowledge and that it could be a tool for discovery that opens new areas for further exploration. In the present study, we first develop a mechanistic mathematical model of a G-protein coupled receptor signaling network that is mutated in almost all cases of uveal melanoma and use model-driven explorations to uncover and explore multiple new areas for investigating this disease. Modeling the two major, mutually-exclusive, oncogenic mutations (Gαq/11 and CysLT2R) revealed the potential for previously unknown qualitative differences between seemingly interchangeable disease-promoting mutations, and our experiments confirmed oncogenic CysLT2R was impaired at activating the FAK/YAP/TAZ pathway relative to Gαq/11. This led us to hypothesize that CYSLTR2 mutations in UM must co-occur with other mutations to activate FAK/YAP/TAZ signaling, and our bioinformatic analysis uncovers a role for co-occurring mutations involving the plexin/semaphorin pathway, which has been shown capable of activating this pathway. Overall, this work highlights the power of mechanism-based computational systems biology as a discovery tool that can leverage available information to open new research areas. [ABSTRACT FROM AUTHOR]

Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways. [ABSTRACT FROM AUTHOR]

Drosophila melanogaster has been at the forefront of genetic studies and biochemical modeling for over a century. Yet, the functions of many genes are still unknown, mainly because no phenotypic data are available. Herein, we present the first evidence data regarding the particular molecular and other quantifiable phenotypes, such as viability and anatomical anomalies, induced by a novel P{lacW} insertional mutant allele of the CG18135 gene. So far, the CG18135 functions have only been theorized based on electronic annotation and presumptive associations inferred upon high-throughput proteomics or RNA sequencing experiments. The descendants of individuals harboring the CG18135P{lacW}CG18135 allele were scored in order to assess mutant embryonic, larval, and pupal viability versus Canton Special (CantonS). Our results revealed that the homozygous CG18135P{lacW}CG18135/CG18135P{lacW}CG18135 genotype determines significant lethality both at the inception of the larval stage and during pupal development. The very few imago escapers that either breach or fully exit the puparium exhibit specific eye depigmentation, wing abnormal unfolding, strong locomotor impairment with apparent spasmodic leg movements, and their maximum lifespan is shorter than 2 days. Using the quantitative real-time PCR (qRT-PCR) method, we found that CG18135 is upregulated in male flies, but an unexpected gene upregulation was also detected in heterozygous mutants compared to wild-type flies, probably because of regulatory perturbations induced by the P{lacW} transposon. Our work provides the first phenotypic evidence for the essential role of CG18135, a scenario in accordance with the putative role of this gene in carbohydratebinding processes. [ABSTRACT FROM AUTHOR]

This paper studies mathematical properties of reaction systems, which is a formal model introduced by Ehrenfeucht and Rozenberg and inspired by biochemical reactions that occur in living cells. Numerous studies have focused on reaction system ranks of functions specified by minimal reaction systems, where the rank refers to the smallest size among the specifying reaction systems. We particularly study the reaction system ranks for a class of union-additive functions specified by minimal reaction systems introduced by Salomaa, which is closed under taking composition. More precisely, when the signature size is two, we obtain a general formula for the reaction system ranks that shows the reaction system rank of each function from this subclass depends on the characteristic of the function. Then, we study the reaction system ranks of such functions with signature size three, as well as establishing a general upper bound for reaction system ranks of such functions with one-to-one signatures for any background set. [ABSTRACT FROM AUTHOR]

Modeling terrestrial gross primary productivity (GPP) is central to predicting the global carbon cycle. Much interest has been focused on the environmentally induced dynamics of photosystem energy partitioning and how improvements in the description of such dynamics assist the prediction of light reactions of photosynthesis and therefore GPP. The maximum quantum yield of photosystem II (ΦPSIImax) is a key parameter of the light reactions that influence the electron transport rate needed for supporting the biochemical reactions of photosynthesis. ΦPSIImax is generally treated as a constant in biochemical photosynthetic models even though a constant ΦPSIImax is expected only for non-stressed plants. We synthesized reported ΦPSIImax values from pulse-amplitude-modulated fluorometry measurements in response to variable temperatures across the globe. We found that ΦPSIImax is strongly affected by prevailing temperature regimes with declined values in both hot and cold conditions. To understand the spatiotemporal variability in ΦPSIImax , we analyzed the temperature effect on ΦPSIImax across plant functional type (PFT) and habitat climatology. The analysis showed that temperature's impact on ΦPSIImax is shaped more by climate than by PFT for plants with broad latitudinal distributions or in regions with extreme temperature variability. There is a trade-off between the temperature range within which ΦPSIImax remains maximal and the overall rate of decline of ΦPSIImax outside the temperature range such that species cannot be simultaneously tolerant and resilient to extreme temperatures. Our study points to a quantitative approach for improving electron transport and photosynthetic productivity modeling under changing climates at regional and global scales. [ABSTRACT FROM AUTHOR]

For eukaryotic cells to heal wounds, respond to immune signals, or metastasize, they must migrate, often by adhering to extracellular matrix (ECM). Cells may also deposit ECM components, leaving behind a footprint that influences their crawling. Recent experiments showed that some epithelial cell lines on micropatterned adhesive stripes move persistently in regions they have previously crawled over, where footprints have been formed, but barely advance into unexplored regions, creating an oscillatory migration of increasing amplitude. Here, we explore through mathematical modeling how footprint deposition and cell responses to footprint combine to allow cells to develop oscillation and other complex migratory motions. We simulate cell crawling with a phase field model coupled to a biochemical model of cell polarity, assuming local contact with the deposited footprint activates Rac1, a protein that establishes the cell's front. Depending on footprint deposition rate and response to the footprint, cells on micropatterned lines can display many types of motility, including confined, oscillatory, and persistent motion. On two-dimensional (2D) substrates, we predict a transition between cells undergoing circular motion and cells developing an exploratory phenotype. Small quantitative changes in a cell's interaction with its footprint can completely alter exploration, allowing cells to tightly regulate their motion, leading to different motility phenotypes (confined vs. exploratory) in different cells when deposition or sensing is variable from cell to cell. Consistent with our computational predictions, we find in earlier experimental data evidence of cells undergoing both circular and exploratory motion. [ABSTRACT FROM AUTHOR]

Deep learning models adapted from natural language processing offer new opportunities for the prediction of active compounds via machine translation of sequential molecular data representations. For example, chemical language models are often derived for compound string transformation. Moreover, given the principal versatility of language models for translating different types of textual representations, off-the-beaten-path design tasks might be explored. In this work, we have investigated generative design of active compounds with desired potency from target sequence embeddings, representing a rather provoking prediction task. Therefore, a dual-component conditional language model was designed for learning from multimodal data. It comprised a protein language model component for generating target sequence embeddings and a conditional transformer for predicting new active compounds with desired potency. To this end, the designated "biochemical" language model was trained to learn mappings of combined protein sequence and compound potency value embeddings to corresponding compounds, fine-tuned on individual activity classes not encountered during model derivation, and evaluated on compound test sets that were structurally distinct from training sets. The biochemical language model correctly reproduced known compounds with different potency for all activity classes, providing proof-of-concept for the approach. Furthermore, the conditional model consistently reproduced larger numbers of known compounds as well as more potent compounds than an unconditional model, revealing a substantial effect of potency conditioning. The biochemical language model also generated structurally diverse candidate compounds departing from both fine-tuning and test compounds. Overall, generative compound design based on potency value-conditioned target sequence embeddings yielded promising results, rendering the approach attractive for further exploration and practical applications. Scientific contribution: The approach introduced herein combines protein language model and chemical language model components, representing an advanced architecture, and is the first methodology for predicting compounds with desired potency from conditioned protein sequence data. [ABSTRACT FROM AUTHOR]

Aiming at the spatial pattern phenomenon in biochemical reactions, an enzyme-reaction Sporns-Seelig model with cross-diffusion is chosen as study object. Applying the central manifold theory, normal form method, local Hopf bifurcation theorem and perturbation theory, we study Turing instability of the spatially homogeneous Hopf bifurcation periodic solutions. At last, the theoretical results are verified by numerical simulations. [ABSTRACT FROM AUTHOR]

Neural Cellular Automata (NCA) are a powerful combination of machine learning and mechanistic modelling. We train NCA to learn complex dynamics from time series of images and PDE trajectories. Our method is designed to identify underlying local rules that govern large scale dynamic emergent behaviours. Previous work on NCA focuses on learning rules that give stationary emergent structures. We extend NCA to capture both transient and stable structures within the same system, as well as learning rules that capture the dynamics of Turing pattern formation in nonlinear Partial Differential Equations (PDEs). We demonstrate that NCA can generalise very well beyond their PDE training data, we show how to constrain NCA to respect given symmetries, and we explore the effects of associated hyperparameters on model performance and stability. Being able to learn arbitrary dynamics gives NCA great potential as a data driven modelling framework, especially for modelling biological pattern formation. Author summary: Pattern formation is ubiquitous in biological systems, across many length and time scales—from vegetation stripes in deserts, to the spots of a leopard's skin, and the fine detail of stem cell differentiation in an embryo. While many simple rules that create complex patterns are known, reverse engineering the mechanisms responsible from an observed pattern has generally remained a difficult problem. In this work we build on the connections between machine learning, cellular automata, and partial differential equations, to create models that can learn the underlying mechanisms that yield any desired emergent pattern. To do this we consider Neural Cellular Automata (a mix of neural networks and cellular automata). We describe how they are built and trained, and we show that they are capable of reproducing a wide range of complex emergent behaviours. Previous work focuses on learning stationary patterns, but we focus on patterns that evolve in time. We show these models can learn classic Turing patterns (biochemical models of spot and stripe formation), as well as learning to morph through an arbitrary sequence of images. We believe this hybrid between machine learning and mechanistic modelling has great potential for modelling biological growth, regeneration, and pattern formation. [ABSTRACT FROM AUTHOR]

Massive storage of dihydrogen (H 2) in underground geological storage (UGSs) will be necessary to meet future H 2 production ambitions. Such storage in deep aquifers was simulated in a high-pressure reactor, and the evolution of the 3 phases (natural gas/H 2 , formation water and rock) with autochthonous microorganisms was monitored over several weeks. These results show that methanogens do not necessarily dominate the community, but that sulfate-reducing activity and formate bioproduction are systematically present. The experimental data were fed into a biochemical model in PHREEQC to better understand the interplay between the phenomena observed. In particular, it was shown that the microbial activities associated with H 2 consumption led to alkalinisation, which could explain, at least in part, the slower rate at which H 2 disappeared, even if sufficient CO 2 and sulfate remained in the system. Combined with a supposed local nutrient depletion, these results are encouraging for H 2 storage in deep aquifers. • Injection of H 2 in three deep aquifers was studied in a high-pressure reactor. • Multidisciplinary approach including experiment analyses and biochemical modeling. • Low sulfate content sites should be favored for H 2 storage in deep aquifers. • Storage behavior needs to be studied site-specifically. [ABSTRACT FROM AUTHOR]

Proteasome inhibitors are used in the therapy of several cancers, and clinical trials are underway for their use in the treatment of glioblastoma (GBM). However, GBM becomes resistant to chemotherapy relatively rapidly. Recently, the overexpression of ribonucleotide reductase (RNR) genes was found to mediate therapy resistance in GBM. The use of combinations of chemotherapeutic agents is considered a promising direction in cancer therapy. The present work aimed to evaluate the efficacy of the combination of proteasome and RNR inhibitors in yeast and GBM cell models. We have shown that impaired proteasome function results in increased levels of RNR subunits and increased enzyme activity in yeast. Co-administration of the proteasome inhibitor bortezomib and the RNR inhibitor hydroxyurea was found to significantly reduce the growth rate of S. cerevisiae yeast. Accordingly, the combination of bortezomib and another RNR inhibitor gemcitabine reduced the survival of DBTRG-05MG compared to the HEK293 cell line. Thus, yeast can be used as a simple model to evaluate the efficacy of combinations of proteasome and RNR inhibitors. [ABSTRACT FROM AUTHOR]

The main goal of this study was to investigate the novel combined Ultrasonication and Free Nitrous Acid (FNA) pretreatment on biodegradability and kinetics of thickened waste-activated sludge (TWAS). Partial factorial design with four levels of (0, 600, 1500, and 3000 KJ/Kg) for ultrasonication and 0, 0.7, 1.4, and 2.8 mg HNO2-N/L for FNA dose were examined creating 16 different combinations. Results revealed that combined pretreatment could significantly improve solubilization and solid destruction compared to solo pretreatments. The highest organic matter solubilization of 25.6% and volatile suspended solids destruction of 21.7% were observed when 2.8 mg HNO2-N/L and 1500 KJ/Kg were combined. Moreover, combining the pretreatments further enhanced biodegradability up to the highest percentage of 50.3% when pretreatment of 3000 KJ/Kg and 2.8 mg HNO2-N/L was applied. Also, the experimental data from a biochemical methane potential test was fitted well into First Order Kinetic and Modified Gompertz models, given that the coefficients of determination, R2, for models at all treatment levels were above 99%. [ABSTRACT FROM AUTHOR]

Objectives: To develop radiomics models based on automatic segmentation of the pretreatment apparent diffusion coefficient (ADC) maps for predicting the biochemical recurrence (BCR) of advanced prostate cancer (PCa). Methods: A total of 100 cases with pathologically confirmed PCa were retrospectively included in this study. These cases were randomly divided into training (n = 70) and test (n = 30) datasets. Two predictive models were constructed based on the combination of age, prostate specific antigen (PSA) level, Gleason score, and clinical staging before therapy and the prostate area (Model_1) or PCa area (Model_2). Another two predictive models were constructed based on only prostate area (Model_3) or PCa area (Model_4). The area under the receiver operating characteristic curve (ROC AUC) and precision‐recall (PR) curve analysis were used to analyze the models' performance. Results: Sixty‐five patients without BCR (BCR−) and 35 patients with BCR (BCR+) were confirmed. The age, PSA, volume, diameter and ADC value of the prostate and PCa were not significantly different between the BCR− and BCR+ groups or between the training and test datasets (all p > 0.05). The AUCs were 0.637 (95% CI: 0.434–0.838), 0.841 (95% CI: 0.695–0.940), 0.840 (95% CI: 0.698–0.983), and 0.808 (95% CI: 0.627–0.988) for Model_1 to Model_4 in the test dataset without significant difference. The 95% bootstrap confidence intervals for the areas under the PR curve of the four models were not statistically different. Conclusion: The radiomics models based on automatically segmented prostate and PCa areas on the pretreatment ADC maps developed in our study can be promising in predicting BCR of advanced PCa. [ABSTRACT FROM AUTHOR]

Summary Catalytic reaction networks serve as fundamental models for understanding biochemical systems. CatReNet is a novel software designed to facilitate interactive analysis of such networks. It offers fast and exact algorithms for computing various types of self-sustaining autocatalytic subnetworks, including so-called CAFs (constructively autocatalytic food-generated networks), RAFs (reflexively autocatalytic food-generated networks), and pseudo-RAFs. It provides dynamic visualizations to aid exploration and understanding. Availability and implementation This open-source Java application runs on Linux, MacOS, and Windows. It is available at https://github.com/husonlab/catrenet under a GPL3 license. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Development and validation of [18 F]-PSMA-1007 PET-based radiomics model to predict biochemical recurrence-free survival following radical prostatectomy" published in the European Journal of Nuclear Medicine & Molecular Imaging. The correction addresses an error in Figure 7 of the original article. The incorrect version of the figure is shown, followed by the correct version. The correction has been made by the authors. The publisher, Springer Nature, remains neutral in terms of jurisdictional claims and institutional affiliations. The authors of the article are Tiancheng Li, Mimi Xu, Shuye Yang, Guolin Wang, Yinuo Liu, Kaifeng Liu, Kui Zhao, and Xinhui Su. [Extracted from the article]

Poly(ethylene terephthalate) (PET) is a major plastic polymer utilized in the single-use and textile industries. The discovery of PET-degrading enzymes (PETases) has led to an increased interest in the biological recycling of PET in addition to mechanical recycling. IsPETase from Ideonella sakaiensis is a candidate catalyst, but little is understood about its structurefunction relationships with regards to PET degradation. To understand the effects of mutations on IsPETase productivity, we develop a directed evolution assay to identify mutations beneficial to PET film degradation at 30 °C. IsPETase also displays enzyme concentration-dependent inhibition effects, and surface crowding has been proposed as a causal phenomenon. Based on total internal reflectance fluorescence microscopy and adsorption experiments, IsPETase is likely experiencing crowded conditions on PET films. Molecular dynamics simulations of IsPETase variants reveal a decrease in active site flexibility in free enzymes and reduced probability of productive active site formation in substrate-bound enzymes under crowding. Hence, we develop a surface crowding model to analyze the biochemical effects of three hit mutations (T116P, S238N, S290P) that enhanced ambient temperature activity and/or thermostability. We find that T116P decreases susceptibility to crowding, resulting in higher PET degradation product accumulation despite no change in intrinsic catalytic rate. In conclusion, we show that a macromolecular crowdingbased biochemical model can be used to analyze the effects of mutations on properties of PETases and that crowding behavior is a major property to be targeted for enzyme engineering for improved PET degradation. [ABSTRACT FROM AUTHOR]

We evaluate whether cumulative photosynthesis (An,C) could predict the time of harvest in strawberry better than light (IC) or temperature (Ta,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 An,C calculated by totalling the cumulative rate from flowering to harvest in each fruit. IC and Ta,C were calculated by totalling daily cumulative light intensity and average temperature. A new index (D) was defined as the normalised variation of An,C, IC, and Ta,C, and a statistical analysis was conducted on the means for each index. The results showed that An,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 IC or Ta,C, these results can be used to improve the forecasting and optimisation of yield. However, further research is required to substantiate these claims. [ABSTRACT FROM AUTHOR]

BACKGROUND: This study aimed to establish chemometric models using Raman spectroscopy data for biochemical monitoring of rabies Virus‐Like Particles (VLP) production based on baculovirus/insect cell system. The models were developed using fresh and stored samples from the initial development stages (Schott culture flasks). The following modeling techniques were assessed: partial least squares (PLS) and artificial neural networks (ANN). The effects of spectral filtering approaches, spectral ranges (400–1850 cm−1; 100–3425 cm−1), and sample cryopreservation were also considered. The applicability of the models was evaluated using experimental data from assays carried out in a benchtop bioreactor. RESULTS: The results showed that the prediction capacity of the chemometric models was negatively impacted when samples from rabies VLP production were cryopreserved. Further studies are needed to confirm the maximum storage time for samples (< 4 months) without a significant difference in model predictions compared to those from an in line database. The dilution of the sample should be kept constant throughout the rabies VLP development stages. A nonlinear correlation was observed between dilution and the predicted values of biochemical parameters from Raman spectral data. The choice of spectral filtering has a major impact on the prediction accuracy of chemometric models. CONCLUSION: The optimal filtering approach should be individually optimized for each biochemical parameter. The ANN models were significantly more suitable for biochemical monitoring than the PLS approach. The 400–1850 cm−1 Raman shift range is recommended for biochemical monitoring of rabies VLP using a baculovirus/insect cell platform when samples are cell‐free. © 2023 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligence (AI) has shown promising applications in various scientific domains, including biochemical analysis research. However, the effectiveness of AI in modeling small-scale, imbalanced datasets remains an open question in such fields. This study explores the capabilities of eight basic AI algorithms, including ridge regression, logistic regression, random forest regression, and others, in modeling a small, imbalanced clinical dataset (total n = 387, class 0 = 27, class 1 = 360) related to the records of the biochemical blood tests from the patients with multiple wasp stings (MWS). Through rigorous evaluation using k-fold cross-validation and comprehensive scoring, we found that none of the models could effectively model the data. Even after fine-tuning the hyperparameters of the best-performing models, the results remained below acceptable thresholds. The study highlights the challenges of applying AI to small-scale datasets with imbalanced groups in biochemical or clinical research and emphasizes the need for novel algorithms tailored to small-scale data. The findings also call for further exploration into techniques such as transfer learning and data augmentation, and they underline the importance of understanding the minimum dataset scale required for effective AI modeling in biochemical contexts. [ABSTRACT FROM AUTHOR]

Metabolism results from enzymatic- and non-enzymatic interactions of several molecules, is easily parameterized with the dissociation constant and occurs via biochemical networks. The dissociation constant is an empirically determined parameter and cannot be used directly to investigate in silico models of biochemical networks. Here, we develop and present an algorithm to define, compute and assess the relevance of the probable dissociation constant for every reaction of a biochemical network. The reactants and reactions of this network are modelled by a stoichiometry number matrix. The algorithm computes the null space and then serially generates subspaces by combinatorially summing the spanning vectors that are non-trivial and unique. This is done until the terms of each row either monotonically diverge or form an alternating sequence whose terms can be partitioned into subsets with almost the same number of oppositely signed terms. For a selected null space-generated subspace the algorithm utilizes several statistical and mathematical descriptors to select and bin terms from each row into distinct outcome-specific subsets. The terms of each subset are summed, mapped to the real-valued open interval 0 , ∞ and used to populate a reaction-specific outcome vector. The p1-norm for this vector is then the probable dissociation constant for this reaction. These steps are continued until every reaction of a modelled network is unambiguously annotated. The assertions presented are complemented by computational studies of a biochemical network for aerobic glycolysis. The fundamental premise of this work is that every row of a null space-generated subspace is a valid reaction and can therefore, be modelled as a reaction-specific sequence vector with a dimension that corresponds to the cardinality of the subspace after excluding all trivial- and redundant-vectors. A major finding of this study is that the row-wise sum or the sum of the terms contained in each reaction-specific sequence vector is mapped unambiguously to a positive real number. This means that the probable dissociation constants, for all reactions, can be directly computed from the stoichiometry number matrix and are suitable indicators of outcome for every reaction of the modelled biochemical network. Additionally, we find that the unambiguous annotation for a biochemical network will require a minimum number of iterations and will determine computational complexity. [ABSTRACT FROM AUTHOR]

Systems of differential equations with polynomial right-hand sides are very common in applications. In particular, when restricted to the positive orthant, they appear naturally (according to the law of mass-action kinetics) in ecology, population dynamics, as models of biochemical interaction networks, and models of the spread of infectious diseases. Their mathematical analysis is very challenging in general; in particular, it is very difficult to answer questions about the long-term dynamics of the variables (species) in the model, such as questions about persistence and extinction. Even if we restrict our attention to mass-action systems, these questions still remain challenging. On the other hand, if a polynomial dynamical system has a weakly reversible single linkage class ( W R 1 ) realization, then its long-term dynamics is known to be remarkably robust: all the variables are persistent (i.e., no species goes extinct), irrespective of the values of the parameters in the model. Here we describe an algorithm for finding W R 1 realizations of polynomial dynamical systems, whenever such realizations exist. [ABSTRACT FROM AUTHOR]

Existing literature offers conflicting conclusions about whether early acute cellular rejection influences long‐term outcomes in liver transplantation. We retrospectively collected donor and recipient data on all adult, first‐time liver transplants performed at a single center between 2008 and 2020. We divided this population into two cohorts based on the presence of early biopsy‐proven acute cellular rejection (EBPR) within the first 90 days post‐transplant and compared outcomes between the groups. There were 896 liver transplants that met inclusion criteria with 112 cases (12.5%) of EBPR. Recipients who developed EBPR had higher biochemical Model for End‐Stage Liver Disease scores (28 vs. 24, p <.01), but other donor and recipient characteristics were similar. Recipients with EBPR had similar overall survival compared to patients without EBPR (p =.09) but had decreased graft survival (p <.05). EBPR was also associated with decreased time to first episode of late (> 90 days post‐transplant) rejection (p <.0001) and increased vulnerability to bacterial and viral infection (p <.05). In subgroup analysis of recipients with autoimmune indications for liver transplantation, EBPR had a more pronounced association with patient death (hazard ratio [HR] 3.9, p <.05) and graft loss (HR 4.0, p <.01). EBPR after liver transplant is associated with inferior graft survival, increased susceptibility to late rejections, and increased vulnerability to infection. [ABSTRACT FROM AUTHOR]

The problem of estimation of unmeasured state variables and unknown reaction kinetic functions for selected biochemical processes modelled as a continuous stirred tank reactor is addressed in this paper. In particular, a new hierarchical (sequential) state observer is derived to generate stable and robust estimates of the state variables and kinetic functions. The developed hierarchical observer uses an adjusted asymptotic observer and an adopted super-twisting sliding mode observer. The stability of the proposed hierarchical observer is investigated under uncertainty in the system dynamics. The stability analysis of the estimation error dynamics is carried out based on the methodology associated with linear parametervarying systems and sliding mode regimes. The developed hierarchical observer is implemented in the Matlab/Simulink environment and its performance is validated via simulation. The obtained satisfactory estimation results demonstrate high effectiveness of the devised hierarchical observer. [ABSTRACT FROM AUTHOR]

In this work extensive misconceptions of university students' —from nutrition area— about the metabolism of carbohydrates (CHM) in the human organism have been documented. The results lead to consider their difficulties concerning the learning of a complex set of imbricated biochemical models involved. Pursuant to these considerations, three physiological models are proposed as conceptual axes around which CHM in the human organism could be taught, in order to avoid fragmentation in students' knowledge and to give simple physiological contexts where to integrate those biochemical models. These contexts are: (a) a physiological model of the carbon cycle, (b) a physiological model of blood glucose uptake and homeostasis, and (c) a physiological model of the availability of small metabolites. [ABSTRACT FROM AUTHOR]

Motivation Developing biochemical models in systems biology is a complex, knowledge-intensive activity. Some modelers (especially novices) benefit from model development tools with a graphical user interface. However, as with the development of complex software, text-based representations of models provide many benefits for advanced model development. At present, the tools for text-based model development are limited, typically just a textual editor that provides features such as copy , paste , find , and replace. Since these tools are not "model aware," they do not provide features for: (i) model b uilding such as autocompletion of species names; (ii) model a nalysis such as hover messages that provide information about chemical species; and (iii) model t ranslation to convert between model representations. We refer to these as BAT features. Results We present VSCode-Antimony, a tool for building, analyzing, and translating models written in the Antimony modeling language, a human readable representation of Systems Biology Markup Language (SBML) models. VSCode-Antimony is a source editor, a tool with language-aware features. For example, there is autocompletion of variable names to assist with model building, hover messages that aid in model analysis, and translation between XML and Antimony representations of SBML models. These features result from making VSCode-Antimony model-aware by incorporating several sophisticated capabilities: analysis of the Antimony grammar (e.g. to identify model symbols and their types); a query system for accessing knowledge sources for chemical species and reactions; and automatic conversion between different model representations (e.g. between Antimony and SBML). Availability and implementation VSCode-Antimony is available as an open source extension in the VSCode Marketplace https://marketplace.visualstudio.com/items?itemName=stevem.vscode-antimony. Source code can be found at https://github.com/sys-bio/vscode-antimony. [ABSTRACT FROM AUTHOR]

The cannabinoid receptor 1 (CB1R) plays a pivotal role in regulating various physiopathological processes, thus positioning itself as a promising and sought-after therapeutic target. However, the search for specific and effective CB1R ligands has been challenging, prompting the exploration of drug repurposing (DR) strategies. In this study, we present an innovative DR approach that combines computational screening and experimental validation to identify potential Food and Drug Administration (FDA)-approved compounds that can interact with the CB1R. Initially, a large-scale virtual screening was conducted using molecular docking simulations, where a library of FDA-approved drugs was screened against the CB1R's three-dimensional structures. This in silico analysis allowed us to prioritize compounds based on their binding affinity through two different filters. Subsequently, the shortlisted compounds were subjected to in vitro assays using cellular and biochemical models to validate their interaction with the CB1R and determine their functional impact. Our results reveal FDA-approved compounds that exhibit promising interactions with the CB1R. These findings open up exciting opportunities for DR in various disorders where CB1R signaling is implicated. In conclusion, our integrated computational and experimental approach demonstrates the feasibility of DR for discovering CB1R modulators from existing FDA-approved compounds. By leveraging the wealth of existing pharmacological data, this strategy accelerates the identification of potential therapeutics while reducing development costs and timelines. The findings from this study hold the potential to advance novel treatments for a range of CB1R -associated diseases, presenting a significant step forward in drug discovery research. [ABSTRACT FROM AUTHOR]

Background: A method combining offline techniques and the just-in-time learning strategy (JITL) is proposed, because the biochemical reaction process often encounters changing features and parameters over time. Methods: Firstly, multiple sub-databases in the fermentation process are constructed offline by an improved fuzzy C-means algorithm and the sample data are adaptively pruned by a similarity query threshold. Secondly, an improved eXtreme Gradient Boosting (XGBoost) method is used on the online modeling stage to build soft sensor models, and the multi-similarity-driven just-in-time learning strategy is used to increase the diversity of the model. Finally, to improve the generalization of the whole algorithm, the output of the base learner is fused by an improved Stacking integration model and then the predictive output is performed. Results: Applying the constructed soft sensor model to the problem of predicting cell concentration and product concentration in Pichia pastoris fermentation process. The experimental results show that the root mean square error of the cell concentration is 0.0260, the coefficient of determination is 0.9945, the root mean square error of the product concentration is 2.6688, and the coefficient of determination is 0.9970. It shows that the proposed method has the advantages of timely prediction and high prediction accuracy, which validates the effectiveness and practicality of the method. Conclusion: The JS-ISSA-XGBoost is an extensive and excellent soft measurement model that meets the practical needs for real-time monitoring of parameters and prediction of control in biochemical reactions. [ABSTRACT FROM AUTHOR]

Astrocytes control the formation of specific synaptic circuits via cell adhesion and secreted molecules. Astrocyte synaptogenic functions are dependent on the establishment of their complex morphology. However, it is unknown if distinct neuronal cues differentially regulate astrocyte morphogenesis. δ-Catenin was previously thought to be a neuron-specific protein that regulates dendrite morphology. We found δ-catenin is also highly expressed by astrocytes and required both in astrocytes and neurons for astrocyte morphogenesis. δ-Catenin is hypothesized to mediate transcellular interactions through the cadherin family of cell adhesion proteins. We used structural modeling and biochemical analyses to reveal that δ-catenin interacts with the N-cadherin juxtamembrane domain to promote N-cadherin surface expression. An autism-linked δ-catenin point mutation impaired N-cadherin cell surface expression and reduced astrocyte complexity. In the developing mouse cortex, only lower-layer cortical neurons express N-cadherin. Remarkably, when we silenced astrocytic N-cadherin throughout the cortex, only lower-layer astrocyte morphology was disrupted. These findings show that δ-catenin controls astrocyte–neuron cadherin interactions that regulate layer-specific astrocyte morphogenesis. [ABSTRACT FROM AUTHOR]

Interstitial cystitis/painful bladder syndrome (IC/PBS) is a chronic disease for which no effective treatment is available. Transforming growth factor-β (TGF-β) is thought to be involved in the pathogenesis of IC/PBS, and previous studies have suggested that administrations of a TGF-β inhibitor significantly ameliorated IC/PBS in a mouse model. However, the molecular mechanisms underlying the therapeutic effect of a TGF-b inhibitor on IC/PBS has not been comprehensively analyzed. TGF-β has a variety of actions, such as regulation of immune cells and fibrosis. In our study, we induced IC/PBS-like disease in mice by an intravesical administration of hydrogen peroxide (H₂O₂) and examined the effects of three TGF-β inhibitors, Repsox, SB431542, and SB505124, on the urinary functions as well as histological and gene expression profiles in the bladder. TGF-β inhibitor treatment improved urinary function and histological changes in the IC/PBS mouse model, and SB431542 was most effective among the TGF-β inhibitors. In our present study, TGF-β inhibitor treatment improved abnormal enhancement of nociceptive mechanisms, immunity and inflammation, fibrosis, and dysfunction of bladder urothelium. These results show that multiple mechanisms are involved in the improvement of urinary function by TGF-β inhibitor. [ABSTRACT FROM AUTHOR]