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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]

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]

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]

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]

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]

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]

Motivation Annotations of biochemical models provide details of chemical species, documentation of chemical reactions, and other essential information. Unfortunately, the vast majority of biochemical models have few, if any, annotations, or the annotations provide insufficient detail to understand the limitations of the model. The quality and quantity of annotations can be improved by developing tools that recommend annotations. For example, recommender tools have been developed for annotations of genes. Although annotating genes is conceptually similar to annotating biochemical models, there are important technical differences that make it difficult to directly apply this prior work. Results We present AMAS , a system that predicts annotations for elements of models represented in the Systems Biology Markup Language (SBML) community standard. We provide a general framework for predicting model annotations for a query element based on a database of annotated reference elements and a match score function that calculates the similarity between the query element and reference elements. The framework is instantiated to specific element types (e.g. species, reactions) by specifying the reference database (e.g. ChEBI for species) and the match score function (e.g. string similarity). We analyze the computational efficiency and prediction quality of AMAS for species and reactions in BiGG and BioModels and find that it has subsecond response times and accuracy between 80% and 95% depending on specifics of what is predicted. We have incorporated AMAS into an open-source, pip-installable Python package that can run as a command-line tool that predicts and adds annotations to species and reactions to an SBML model. Availability and implementation Our project is hosted at https://github.com/sys-bio/AMAS , where we provide examples, documentation, and source code files. Our source code is licensed under the MIT open-source license. [ABSTRACT FROM AUTHOR]

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

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

Chrysin is a flavonoid with various biological activities. However, its low water solubility and strong metabolism render its oral bioavailability rather poor. This study aimed to develop a stable solid dispersion formulation of chrysin to improve the dissolution of chrysin, so as to increase its oral bioavailability and improve its antihyperlipidemic activities. A solid dispersion of chrysin was prepared using a solvent evaporation method, with Plasdone® S630 as the hydrophilic carrier. The formulations were characterized via X-ray diffraction, in vitro dissolution studies, and stability studies. An in-situ perfusion model was used to evaluate the absorption rates. Plasma pharmacokinetics and antihyperlipidemic performance after the oral administration of the chrysin formulations were investigated in rats. It was found that the solid dispersion of chrysin prepared using the drug–polymer mass ratio of 1:6 can form the optimized formulation. X-ray diffraction results showed that the chrysin was in an amorphous state in this optimized formulation. The cumulative release percentage of the optimized solid dispersion of chrysin at pH 1.2 and pH 6.8 was elevated to above 90% within 24 h, indicating that the formulation could enhance the dissolution rates of chrysin. Stability studies showed that the optimized formulation presented acceptable long-term storage stability, but it was susceptible to high temperature and humidity. The solid dispersion of chrysin showed higher absorption rates in the in-situ perfusion model. Pharmacokinetic studies revealed that Cmax and AUC after the intragastric administration of solid dispersion of chrysin were appreciably higher than those resulting from chrysin suspension. The oral bioavailability of the solid dispersion of chrysin was 41 times higher than that of chrysin suspension. Pharmacological studies suggested that the solid dispersion of chrysin was more powerful than chrysin raw material in improving biochemical indicators in the hyperlipidemic model in rats. This study reveals the potential use of a novel oral formulation of chrysin to reduce the currently required high dose. [ABSTRACT FROM AUTHOR]

Background: Cancer-associated fibroblasts (CAFs) are an essential component of the tumor immune microenvironment that are involved in extracellular matrix (ECM) remodeling. We aim to investigate the characteristics of CAFs in prostate cancer and develop a biochemical recurrence (BCR)-related CAF signature for predicting the prognosis of PCa patients. Methods: The bulk RNA-seq and relevant clinical information were obtained from the TCGA and GEO databases, respectively. The infiltration scores of CAFs in prostate cancer patients were calculated using the MCP counter and EPIC algorithms. The single-cell RNA sequencing (scRNA-seq) was downloaded from the GEO database. Subsequently, univariate Cox regression analysis was employed to identify prognostic genes associated with CAFs. We identified two subtypes (C1 and C2) of prostate cancer that were associated with CAFs via non-negative matrix factorization (NMF) clustering. In addition, the BCR-related CAF signatures were constructed using Lasso regression analysis. Finally, a nomogram model was established based on the risk score and clinical characteristics of the patients. Results: Initially, we found that patients with high CAF infiltration scores had shorter biochemical recurrence-free survival (BCRFS) times. Subsequently, CAFs in four pairs of tumors and paracancerous tissues were identified. We discovered 253 significantly differentially expressed genes, of which 13 had prognostic significance. Using NMF clustering, we divided PCa patients into C1 and C2 subgroups, with the C1 subgroup having a worse prognosis and substantially enriched cell cycle, homologous recombination, and mismatch repair pathways. Furthermore, a BCR-related CAFs signature was established. Multivariate COX regression analysis confirmed that the BCR-related CAFs signature was an independent prognostic factor for BCR in PCa. In addition, the nomogram was based on the clinical characteristics and risk scores of the patient and demonstrated high accuracy and reliability for predicting BCR. Lastly, our findings indicate that the risk score may be a useful tool for predicting PCa patients' sensitivity to immunotherapy and drug treatment. Conclusion: NMF clustering based on CAF-related genes revealed distinct TME immune characteristics between groups. The BCR-related CAF signature accurately predicted prognosis and immunotherapy response in prostate cancer patients, offering a promising new approach to cancer treatment. [ABSTRACT FROM AUTHOR]

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

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

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

Two well-established numerical representations of the coagulation cascade either initiated by the intrinsic system (Chatterjee et al., PLOS Computational Biology 2010) or the extrinsic system (Butenas et al., Journal of Biological Chemistry, 2004) were compared with thrombin generation assays under realistic pathological conditions. Biochemical modifications such as the omission of reactions not relevant to the case studied, the modification of reactions related to factor XI activation and auto-activation, the adaptation of initial conditions to the thrombin assay system, and the adjustment of some of the model parameters were necessary to align in vitro and in silico data. The modified models are able to reproduce thrombin generation for a range of factor XII, XI, and VIII deficiencies, with the coagulation cascade initiated either extrinsically or intrinsically. The results emphasize that when existing models are extrapolated to experimental parameters for which they have not been calibrated, careful adjustments are required. [ABSTRACT FROM AUTHOR]

ATP-Binding Cassette (ABC) transporters are a broad family of biological machines, found in most prokaryotic and eukaryotic cells, performing the crucial import or export of substrates through both plasma and organellar membranes, and maintaining a steady concentration gradient driven by ATP hydrolysis. Building upon the present biophysical and biochemical characterization of ABC transporters, we propose here a model whose solution reveals that these machines are an exact molecular realization of the autonomous Maxwell Demon, a century-old abstract device that uses an energy source to drive systems away from thermodynamic equilibrium. In particular, the Maxwell Demon does not perform any direct mechanical work on the system, but simply selects which spontaneous processes to allow and which ones to forbid based on information that it collects and processes. In its autonomous version, the measurement device is embedded in the system itself. In the molecular model introduced here, the different operations that characterize Maxwell Demons (measurement, feedback, resetting) are features that emerge from the biochemical and structural properties of ABC transporters, revealing the crucial role of allostery to process information. Our framework allows us to develop an explicit bridge between the molecular-level description and the higher-level language of information theory for ABC transporters. Transport processes across membranes are instrumental for establishing correct cellular functions and are performed by molecular motors such as ABC transporters. A biochemical model for these machines reveals that they are molecular realizations of autonomous Maxwell Demons, abstract devices using information to drive systems out of equilibrium. [ABSTRACT FROM AUTHOR]

Stochastic modeling of biochemical processes at the cellular level has been the subject of intense research in recent years. The Chemical Master Equation is a broadly utilized stochastic discrete model of such processes. Numerous important biochemical systems consist of many species subject to many reactions. As a result, their mathematical models depend on many parameters. In applications, some of the model parameters may be unknown, so their values need to be estimated from the experimental data. However, the problem of parameter value inference can be quite challenging, especially in the stochastic setting. To estimate accurately the values of a subset of parameters, the system should be sensitive with respect to variations in each of these parameters and they should not be correlated. In this paper, we propose a technique for detecting collinearity among models' parameters and we apply this method for selecting subsets of parameters that can be estimated from the available data. The analysis relies on finite-difference sensitivity estimations and the singular value decomposition of the sensitivity matrix. We illustrated the advantages of the proposed method by successfully testing it on several models of biochemical systems of practical interest. [ABSTRACT FROM AUTHOR]

The efficiency of food irradiation depends on the accuracy of the irradiation dose range that is sufficient for inhibiting microbiological growth without causing an irreversible change to the physical and chemical properties of foods. This study suggests that the concentration of hemoglobin derivatives can be used as a criterion for establishing the limit for chilled beef irradiation at which irradiation-induced oxidation becomes irreversible. The express spectrophotometry method for estimating the hemoglobin derivative concentration shows a nonlinear increase in methemoglobin concentration from 15% to 50% in beef irradiated by accelerated electrons with the doses ranging from 250 Gy to 10,000 Gy. The monitoring of the hemoglobin derivative concentration for three days after irradiation shows nonmonotonous dependencies of methemoglobin concentration in beef in the storage time since the oxidation of hemoglobin occur as a result of irradiation and biochemical processes in beef during storage. The proposed method based on the quantitative analysis of the hemoglobin derivative concentration can be used to estimate the oxidation level for irradiation of foods containing red blood cells. The study proposes a model that describes the change in hemoglobin derivative concentration in beef after irradiation considering that oxidation of hemoglobin can be triggered by the direct ionization caused by accelerated electrons, biochemical processes as a result of bacterial activity, and reactive oxygen species appearing during irradiation and storage. This research throws light on the mechanisms behind food irradiation during storage that should be taken into account for selecting the optimal parameters of irradiation. [ABSTRACT FROM AUTHOR]

CSTR operations entailing high nonlinearity and complexity such as multiple input and output steady-states present a real challenge to chemical engineers and process designers. The input multiplicity in chemical reactions leads to control probems such as process instability and low efficiency. Therefore, it is of critical importance to predict and avoid the multiplicity regions during reactor operation. Since the bifurcation analysis of biochemical processes with nonideal mixing has been carried out by the authors in the previous publication (Yang, C. Y., D. C. Tsai, and Y. S. Chien. 2021. "The Strategy Developed for High Conversion and the Multiplicity Problems of Biochemical Reaction in a Real CSTR with Cholette's Model." International Journal of Chemical Reactor Engineering 19: 1245–70), the goal of the present work is to use Sturm's method, Routh stability criteria and the discriminator roots method with the tangent analysis method to derive the input multiplicity conditions in substrate inhibition in a real CSTR based on Chollete's model. Four kinetic schemes are used in the analysis as examples to show that all three methods can precisely obtain the bifurcation starting point for the input multiplicity. In addition to the multiple input steady-states, the start-up diagram obtained by the discriminator root method is of critical importance to avoid operating in the input multiplicity regions. [ABSTRACT FROM AUTHOR]

Kinesins are microtubule-dependent motor proteins, some of which moonlight as microtubule polymerases, such as the yeast protein Kip2. Here, we show that the CLIP-170 ortholog Bik1 stabilizes Kip2 at microtubule ends where the motor domain of Kip2 promotes microtubule polymerization. Live-cell imaging and mathematical estimation of Kip2 dynamics reveal that disrupting the Kip2-Bik1 interaction aborts Kip2 dwelling at microtubule ends and abrogates its microtubule polymerization activity. Structural modeling and biochemical experiments identify a patch of positively charged residues that enables the motor domain to bind free tubulin dimers alternatively to the microtubule shaft. Neutralizing this patch abolished the ability of Kip2 to promote microtubule growth both in vivo and in vitro without affecting its ability to walk along microtubules. Our studies suggest that Kip2 utilizes Bik1 as a cofactor to track microtubule tips, where its motor domain then recruits free tubulin and catalyzes microtubule assembly. [ABSTRACT FROM AUTHOR]

As a post-pandemic scenario, communal health issues are studied by many experts throughout the world. Most significant elements of life are air and water. However, as per Indian environmental laws, industries throughout the country are trying to maintain the water contamination level within the acceptable limits. But, in the case of pharmaceutical industries, many times it became difficult to remove the drug byproducts from the water reservoir constructed for water filtrations and after that wastewater is forwarded to nearby rivers if wastewater quality index is within the limit. This wastewater handling cost is more and hence to provide an optimum cost solution this paper presents a new biochemical Struts model which further segregates the different precipitations which can carry heavy metals and can be used for other industrial purposes by recycling water internally instead of draining into rivers/sea, etc. [ABSTRACT FROM AUTHOR]

Cellular and biochemical studies of nonhomologous DNA end joining (NHEJ) have long established that nuclease and polymerase action are necessary for the repair of a very large fraction of naturally-arising double-strand breaks (DSBs). This conclusion is derived from NHEJ studies ranging from yeast to humans and all genetically-tractable model organisms. Biochemical models derived from recent real-time and structural studies have yet to incorporate physical space or timing for DNA end processing. In real-time single molecule FRET (smFRET) studies, we analyzed NHEJ synapsis of DNA ends in a defined biochemical system. We described a Flexible Synapsis (FS) state in which the DNA ends were in proximity via only Ku and XRCC4:DNA ligase 4 (X4L4), and in an orientation that would not yet permit ligation until base pairing between one or more nucleotides of microhomology (MH) occurred, thereby allowing an in-line Close Synapsis (CS) state. If no MH was achievable, then XLF was critical for ligation. Neither FS or CS required DNA-PKcs, unless Artemis activation was necessary to permit local resection and subsequent base pairing between the two DNA ends being joined. Here we conjecture on possible 3D configurations for this FS state, which would spatially accommodate the nuclease and polymerase processing steps in an iterative manner. The FS model permits repeated attempts at ligation of at least one strand at the DSB after each round of nuclease or polymerase action. In addition to activation of Artemis, other possible roles for DNA-PKcs are discussed. • Hydrogen bonding (via base pairing) and base stacking aid DSB junction stabilization and are important prior to ligation. • Spatial and temporal considerations suggest that the initial synapsis must be flexible to permit end modification. • Flexible synapsis permits multiple iterations of end modification, based on cellular NHEJ and biochemical reconstitutions. • Differences between crude extracts and purified systems may relate to the need to remove histones from the DSB repair zone. [ABSTRACT FROM AUTHOR]

Accurate phenotype prediction based on genetic information has numerous societal applications, such as crop design or cellular factories. Epistasis, when biological components interact, complicates modelling phenotypes from genotypes. Here we show an approach to mitigate this complication for polarity establishment in budding yeast, where mechanistic information is abundant. We coarse-grain molecular interactions into a so-called mesotype, which we combine with gene expression noise into a physical cell cycle model. First, we show with computer simulations that the mesotype allows validation of the most current biochemical polarity models by quantitatively matching doubling times. Second, the mesotype elucidates epistasis emergence as exemplified by evaluating the predicted mutational effect of key polarity protein Bem1p when combined with known interactors or under different growth conditions. This example also illustrates how unlikely evolutionary trajectories can become more accessible. The tractability of our biophysically justifiable approach inspires a road-map towards bottom-up modelling complementary to statistical inferences. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'. [ABSTRACT FROM AUTHOR]

In drug design, the prediction of new active compounds from protein sequence data has only been attempted in a few studies thus far. This prediction task is principally challenging because global protein sequence similarity has strong evolutional and structural implications, but is often only vaguely related to ligand binding. Deep language models adapted from natural language processing offer new opportunities to attempt such predictions via machine translation by directly relating amino acid sequences and chemical structures to each based on textual molecular representations. Herein, we introduce a biochemical language model with transformer architecture for the prediction of new active compounds from sequence motifs of ligand binding sites. In a proof-of-concept application on inhibitors of more than 200 human kinases, the Motif2Mol model revealed promising learning characteristics and an unprecedented ability to consistently reproduce known inhibitors of different kinases. [ABSTRACT FROM AUTHOR]

Subclinical leaflet thrombosis (SLT) is a potentially serious complication of aortic valve replacement with a bioprosthetic valve in which blood clots form on the replacement valve. SLT is associated with increased risk of transient ischemic attacks and strokes and can progress to clinical leaflet thrombosis. SLT following aortic valve replacement also may be related to subsequent structural valve deterioration, which can impair the durability of the valve replacement. Because of the difficulty in clinical imaging of SLT, models are needed to determine the mechanisms of SLT and could eventually predict which patients will develop SLT. To this end, we develop methods to simulate leaflet thrombosis that combine fluid–structure interaction and a simplified thrombosis model that allows for deposition along the moving leaflets. Additionally, this model can be adapted to model deposition or absorption along other moving boundaries. We present convergence results and quantify the model's ability to realize changes in valve opening and pressures. These new approaches are an important advancement in our tools for modeling thrombosis because they incorporate both adhesion to the surface of the moving leaflets and feedback to the fluid–structure interaction. [ABSTRACT FROM AUTHOR]

Organisms often use cyclic changes in the concentrations of chemical species to precisely time biological functions. Underlying these biochemical clocks are chemical reactions and transport processes, which are inherently stochastic. Understanding the physical basis for robust biochemical oscillations in the presence of fluctuations has thus emerged as an important problem. In a previous paper [C. del Junco and S. Vaikuntanathan, Phys. Rev. E 101, 012410 (2020)], we explored this question using the non-equilibrium statistical mechanics of single-ring Markov state models of biochemical networks that support oscillations. Our finding was that they can exploit non-equilibrium driving to robustly maintain the period and coherence of oscillations in the presence of randomness in the rates. Here, we extend our work to Markov state models consisting of a large cycle decorated with multiple small cycles. These additional cycles are intended to represent alternate pathways that the oscillator may take as it fluctuates about its average path. Combining a mapping to single-cycle networks based on first passage time distributions with our previously developed theory, we are able to make analytical predictions for the period and coherence of oscillations in these networks. One implication of our predictions is that a high energy budget can make different network topologies and arrangements of rates degenerate as far as the period and coherence of oscillations are concerned. Excellent agreement between analytical and numerical results confirms that this is the case. Our results suggest that biochemical oscillators can be more robust to fluctuations in the path of the oscillator when they have a high energy budget. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is a serious public health problem characterized by progressive kidney function loss leading to end-stage renal disease (ESRD) that demands dialysis or kidney transplantation. Early detection can prevent or delay progression to ESRD. The study aimed to gain new insights into the perturbed biochemical reactions and to identify novel distinct biomarkers between ESRD and CKD. Serum samples of 32 patients with ESRD (n = 13) and CKD (n = 19) were analyzed using chemical isotope labeling liquid chromatography-mass spectrometry metabolomics approach. A total of 193 metabolites were significantly altered in ESRD compared to CKD and were mainly involved in aminoacyl-tRNA biosynthesis, branched-chain amino acid (BCAA) biosynthesis, taurine metabolism, and tryptophan metabolism. Three kynurenine derivatives, namely, 2-aminobenzoic acid, xanthurenic acid, and hydroxypicolinic acid were upregulated in ESRD compared to CKD due to the significant decrease in glomerular filtration rate with the progression of CKD to ESRD. N-Hydroxy-isoleucine, 2-aminobenzoic acid, and picolinic acid yielded AUC > 0.99 when analyzed using Receiver Operating Characteristic (ROC) analysis. Our findings suggest that inhibiting the kynurenine pathway might be a promising target to delay CKD progression and that metabolites with high discriminative ability might serve as potential prognostic biomarkers to monitor the progression of CKD to ESRD or used in combination with current markers to indicate the status of kidney damage better. [ABSTRACT FROM AUTHOR]

The long-term behaviors of biochemical systems are often described by their steady states. Deriving these states directly for complex networks arising from real-world applications, however, is often challenging. Recent work has consequently focused on network-based approaches. Specifically, biochemical reaction networks are transformed into weakly reversible and deficiency zero generalized networks, which allows the derivation of their analytic steady states. Identifying this transformation, however, can be challenging for large and complex networks. In this paper, we address this difficulty by breaking the complex network into smaller independent subnetworks and then transforming the subnetworks to derive the analytic steady states of each subnetwork. We show that stitching these solutions together leads to the the analytic steady states of the original network. To facilitate this process, we develop a user-friendly and publicly available package, COMPILES (COMPutIng anaLytic stEady States). With COMPILES, we can easily test the presence of bistability of a CRISPRi toggle switch model, which was previously investigated via tremendous number of numerical simulations and within a limited range of parameters. Furthermore, COMPILES can be used to identify absolute concentration robustness (ACR), the property of a system that maintains the concentration of particular species at a steady state regardless of any initial concentrations. Specifically, our approach completely identifies all the species with and without ACR in a complex insulin model. Our method provides an effective approach to analyzing and understanding complex biochemical systems. Author summary: Steady states often describe the long-term behaviors of biochemical systems, which are typically based on ordinary differential equations. To derive a steady state analytically, significant attention has been given in recent years to network-based approaches. While this approach allows a steady state to be derived as long as a network has a special structure, complex and large networks rarely have this structural property. We address this difficulty by breaking the network into smaller and more manageable independent subnetworks, and then use the network-based approach to derive the analytic steady state of each subnetwork. Stitching these solutions together allows us to derive the analytic steady state of the original network. To facilitate this process, we develop a user-friendly and publicly available package, COMPILES. COMPILES identifies critical biochemical properties such as the presence of bistability in a genetic toggle switch model and absolute concentration robustness in a complex insulin signaling pathway model. [ABSTRACT FROM AUTHOR]

The concept of chelation therapy as a valuable therapeutic approach in neurological disorders led us to develop multi-target, non-toxic, lipophilic, brain-permeable compounds with iron chelation and anti-apoptotic properties for neurodegenerative diseases, such as Parkinson's disease (PD), Alzheimer's disease (AD), age-related dementia and amyotrophic lateral sclerosis (ALS). Herein, we reviewed our two most effective such compounds, M30 and HLA20, based on a multimodal drug design paradigm. The compounds have been tested for their mechanisms of action using animal and cellular models such as APP/PS1 AD transgenic (Tg) mice, G93A-SOD1 mutant ALS Tg mice, C57BL/6 mice, Neuroblastoma × Spinal Cord-34 (NSC-34) hybrid cells, a battery of behavior tests, and various immunohistochemical and biochemical techniques. These novel iron chelators exhibit neuroprotective activities by attenuating relevant neurodegenerative pathology, promoting positive behavior changes, and up-regulating neuroprotective signaling pathways. Taken together, these results suggest that our multifunctional iron-chelating compounds can upregulate several neuroprotective-adaptive mechanisms and pro-survival signaling pathways in the brain and might function as ideal drugs for neurodegenerative disorders, such as PD, AD, ALS, and aging-related cognitive decline, in which oxidative stress and iron-mediated toxicity and dysregulation of iron homeostasis have been implicated. [ABSTRACT FROM AUTHOR]

Living systems use enzymatic reaction networks to process biochemical information and make decisions in response to external or internal stimuli. Herein, we present a modular and reusable platform for molecular information processing using enzymes immobilised in hydrogel beads and compartmentalised in a continuous stirred tank reactor. We demonstrate how this setup allows us to perform simple arithmetic operations, such as addition, subtraction and multiplication, using various concentrations of substrates or inhibitors as inputs and the production of a fluorescent molecule as the readout. [ABSTRACT FROM AUTHOR]

Living systems use enzymatic reaction networks to process biochemical information and make decisions in response to external or internal stimuli. Herein, we present a modular and reusable platform for molecular information processing using enzymes immobilised in hydrogel beads and compartmentalised in a continuous stirred tank reactor. We demonstrate how this setup allows us to perform simple arithmetic operations, such as addition, subtraction and multiplication, using various concentrations of substrates or inhibitors as inputs and the production of a fluorescent molecule as the readout. [ABSTRACT FROM AUTHOR]

We propose a two-dimensional model of biochemical activation process, whereby self-propelling particles of finite correlation times are injected at the center of a circular cavity with constant rate equal to the inverse of their lifetime; activation is triggered when one such particle hits a receptor on the cavity boundary, modeled as a narrow pore. We numerically investigated this process by computing the particle mean-first exit times through the cavity pore as a function of the correlation and injection time constants. Due to the breach of the circular symmetry associated with the positioning of the receptor, the exit times may depend on the orientation of the self-propelling velocity at injection. Stochastic resetting appears to favor activation for large particle correlation times, where most of the underlying diffusion process occurs at the cavity boundary. [ABSTRACT FROM AUTHOR]

We present a transient large eddy simulation (LES) modeling approach for simulating the interlinked physics describing free surface hydrodynamics, multiphase mixing, reaction kinetics, and mass transport in bioreactor systems. Presented case‐studies include non‐reacting and reacting bioreactor systems, modeled through the inclusion of uniform reaction rates and more complex biochemical reactions described using Contois type kinetics. It is shown that the presence of reactions can result in a non‐uniform spatially varying species concentration field, the magnitude and extent of which is directly related to the reaction rates and the underlying variations in the local volumetric mass transfer coefficient. [ABSTRACT FROM AUTHOR]

The bio-cell cycle is controlled by a complex biochemical network of signaling pathways. Modeling such challenging networks accurately is imperative for the understanding of their detailed dynamical behavior. In this paper, we construct, analyze, and verify a hybrid Petri net (HPN) model of a complex biochemical network that captures the role of an important protein (namely p53) in deciding the fate of the cell. We model the behavior of the cell nucleus and cytoplasm as two stochastic and continuous Petri nets, respectively, combined together into a single HPN. We use simulative model checking to verify three different properties that capture the dynamical behavior of p53 protein with respect to the intensity of the ionizing radiation (IR) to which the cell is exposed. For each IR dose, 1000 simulation runs are carried out to verify each property. Our verification results showed that the fluctuations in p53, which relies on IR intensity, are compatible with the findings of the preceding simulation studies that have previously examined the role of p53 in cell fate decision. [ABSTRACT FROM AUTHOR]

To investigate if a magnetic resonance imaging (MRI)-based model reduced postoperative biochemical failure (BF) incidence in patients with prostate cancer (PCa). From June 2018 to January 2020, we retrospectively analyzed 967 patients who underwent prostate bi-parametric MRI and radical prostatectomy (RP). After inclusion criteria were applied, 446 patients were randomized into research (n = 335) and validation cohorts (n = 111) at a 3:1 ratio. In addition to clinical variables, MRI models also included MRI parameters. The area under the curve (AUC) of receiver operating characteristic and decision curves were analyzed. The risk of postoperative BF, defined as persistently high or re-elevated prostate serum antigen (PSA) levels in patients with PCa with no clinical recurrence. In the research (age 69 [63–74] years) and validation cohorts (age 69 [64–74] years), the postoperative BF incidence was 22.39% and 27.02%, respectively. In the research cohort, the AUC of baseline and MRI models was 0.780 and 0.857, respectively, with a significant difference (P < 0.05). Validation cohort results were consistent (0.753 vs. 0.865, P < 0.05). At a 20% risk threshold, the false positive rate in the MRI model was lower when compared with the baseline model (31% [95% confidence interval (CI): 9–39%] vs. 44% [95% CI: 15–64%]), with the true positive rate only decreasing by a little (83% [95% CI: 63–94%] vs. 87% [95% CI: 75–100%]). 32 of 100 RPs can been performed, with no raise in quantity of patients with missed BF. We developed and verified a MRI-based model to predict BF incidence in patients after RP using preoperative clinical and MRI-related variables. This model could be used in clinical settings. [ABSTRACT FROM AUTHOR]

Dynamical models in the form of systems of ordinary differential equations have become a standard tool in systems biology. Many parameters of such models are usually unknown and have to be inferred from experimental data. Gradient-based optimization has proven to be effective for parameter estimation. However, computing gradients becomes increasingly costly for larger models, which are required for capturing the complex interactions of multiple biochemical pathways. Adjoint sensitivity analysis has been pivotal for working with such large models, but methods tailored for steady-state data are currently not available. We propose a new adjoint method for computing gradients, which is applicable if the experimental data include steady-state measurements. The method is based on a reformulation of the backward integration problem to a system of linear algebraic equations. The evaluation of the proposed method using real-world problems shows a speedup of total simulation time by a factor of up to 4.4. Our results demonstrate that the proposed approach can achieve a substantial improvement in computation time, in particular for large-scale models, where computational efficiency is critical. Author summary: Large-scale dynamical models are nowadays widely used for the analysis of complex processes and the integration of large-scale data sets. However, computational cost is often a bottleneck. Here, we propose a new gradient computation method that facilitates the parameterization of large-scale models based on steady-state measurements. The method can be combined with existing gradient computation methods for time-course measurements. Accordingly, it is an essential contribution to the environment of computationally efficient approaches for the study of large-scale screening and omics data, but not tailored to biological applications, and, therefore, also useful beyond the field of computational biology. [ABSTRACT FROM AUTHOR]

We present a data-driven point of view for rare events, which represent conformational transitions in biochemical reactions modeled by overdamped Langevin dynamics on manifolds in high dimensions. We first reinterpret the transition state theory and the transition path theory from the optimal control viewpoint. Given a point cloud probing the manifold, we construct a discrete Markov chain with a Q-matrix computed from an approximated Voronoi tesselation via the point cloud. We use this Q-matrix to compute a discrete committor function whose level set automatically orders the point cloud. Then based on the committor function, an optimally controlled random walk on point clouds is constructed and utilized to efficiently sample transition paths, which become an almost sure event in O(1) time instead of a rare event in the original reaction dynamics. To compute the mean transition path efficiently, a local averaging algorithm based on the optimally controlled random walk is developed, which adapts the finite temperature string method to the controlled Monte Carlo samples. Numerical examples on sphere/torus including a conformational transition for the alanine dipeptide in vacuum are conducted to illustrate the data-driven solver for the transition path theory on point clouds. The mean transition path obtained via the controlled Monte Carlo simulations highly coincides with the computed dominant transition path in the transition path theory. [ABSTRACT FROM AUTHOR]

Green plant regeneration efficiency (GPRE) via in vitro anther culture results from biochemical pathways and cycle dysfunctions that may affect DNA and histone methylation, with gene expression influencing whole cell functioning. The reprogramming from gametophytic to sporophytic fate is part of the phenomenon. While DNA methylation and sequence changes related to the GPRE have been described, little attention was paid to the biochemical aspects of the phenomenon. Furthermore, only a few theoretical models that describe the complex relationships between biochemical aspects of GPRE and the role of Cu(II) ions in the induction medium and as cofactors of enzymatic reactions have been developed. Still, none of these models are devoted directly to the biochemical level. Fourier transform infrared (FTIR) spectroscopy was used in the current study to analyze triticale regenerants derived under various in vitro tissue culture conditions, including different Cu(II) and Ag(I) ion concentrations in the induction medium and anther culture times. The FTIR spectra of S-adenosyl-L-methionine (SAM), glutathione, and pectins in parallel with the Cu(II) ions, as well as the evaluated GPRE values, were put into the structural equation model (SEM). The data demonstrate the relationships between SAM, glutathione, pectins, and Cu(II) in the induction medium and how they affect GPRE. The SEM reflects the cell functioning under in vitro conditions and varying Cu(II) concentrations. In the presented model, the players are the Krebs and Yang cycles, the transsulfuration pathway controlled by Cu(II) ions acting as cofactors of enzymatic reactions, and the pectins of the primary cell wall. [ABSTRACT FROM AUTHOR]

The National Renewable Energy Laboratory (NREL) published a model in 2017 that enables the minimum selling price of lignocellulosic sugar to be calculated. The model can be modified to suit any biomass feedstock and operational design. In the present case, the model is used to understand the economics of a process configuration that incorporates liquefaction as a preprocessing step in corn‐stover‐fed biorefinery. This study demonstrates a quantitative approach utilizing an existing biorefinery setup to simulate the biomass liquefaction technique while estimating the price of the resulting sugar product. The objective is to understand whether the addition of liquefaction methodology – and the substitution of acid pretreatment – can reduce the cost of lignocellulosic sugars. The reason for setting up a liquefaction unit at the start is to reduce the yield stress of biomass slurry so that the flow remains unrestricted downstream. The liquefaction process is also unique because it uses no chemicals and saves the cost of pretreatment. The pretreatment step is bypassed because the output of liquefaction can be fed into enzyme hydrolysis just after simple cooking. The liquefaction process can be performed in three modes: enzyme, enzyme mimetic, and a combination of enzyme and enzyme mimetic. The results from the BC1707 model indicate the minimum cost for the enzyme liquefaction route. © 2022 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

The problem of proving observability/detectability properties for selected non-linear uncertain model of biochemical processes has been addressed in this paper. In particular, the analysis of observability/detectability in the face of parametric and unstructured uncertainty in system dynamics transformed into unknown inputs, and unknown initial conditions has been performed. Various sets of system measured outputs were taken into account during the research. The considered biochemical processes were modelled as a continuous stirred tank reactor with the microbial growth reaction and microbial mortality with the aggregated substrate and biomass concentrations in aerobic phase. Classical tools based on differential geometry and the method of indistinguishable state trajectories (indistinguishable dynamics) were used to verify the properties of the system. The observability/detectability analysis was performed for nine cases covering a wide range of possible combinations of system measured outputs and unknown inputs. The obtained results of are crucial meaning for system state reconstruction (estimation), which involves the synthesis of state observers. [ABSTRACT FROM AUTHOR]

Cilia are ubiquitous eukaryotic organelles impotant for cellular motility, signaling, and sensory reception. Cilium formation requires intraflagellar transport of structural and signaling components and involves 22 different proteins organized into intraflagellar transport (IFT) complexes IFT‐A and IFT‐B that are transported by molecular motors. The IFT‐B complex constitutes the backbone of polymeric IFT trains carrying cargo between the cilium and the cell body. Currently, high‐resolution structures are only available for smaller IFT‐B subcomplexes leaving > 50% structurally uncharacterized. Here, we used Alphafold to structurally model the 15‐subunit IFT‐B complex. The model was validated using cross‐linking/mass‐spectrometry data on reconstituted IFT‐B complexes, X‐ray scattering in solution, diffraction from crystals as well as site‐directed mutagenesis and protein‐binding assays. The IFT‐B structure reveals an elongated and highly flexible complex consistent with cryo‐electron tomographic reconstructions of IFT trains. The IFT‐B complex organizes into IFT‐B1 and IFT‐B2 parts with binding sites for ciliary cargo and the inactive IFT dynein motor, respectively. Interestingly, our results are consistent with two different binding sites for IFT81/74 on IFT88/70/52/46 suggesting the possibility of different structural architectures for the IFT‐B1 complex. Our data present a structural framework to understand IFT‐B complex assembly, function, and ciliopathy variants. Synopsis: Construction and maintenance of cilia is mediated by the the bidirectional trafficking of 22 different proteins organized into IFT‐A and IFT‐B complexes, with only structures of smaller IFT‐B assemblies solved. Here, a combination of Alphafold structural modeling and biochemical analysis offers a structural model of the 15‐subunit IFT‐B complex. Validated structural model for 15‐subunit IFT‐B reveals an elongated and highly flexible complex consistent with cryo‐electron tomographic reconstructions of IFT trains.The IFT‐B complex divides into IFT‐B1 and IFT‐B2 parts with binding sites for ciliary cargo and the inactive IFT dynein motor, respectively.Structural modeling and biochemical data are consistent with two different binding sites for IFT81/74 on IFT88/70/52/46 suggesting the possibility of two different conformations of the IFT‐B1 complex. [ABSTRACT FROM AUTHOR]