Thousands Of Reactions Research Articles

Liver gene transfer for metabolite detoxification in inherited metabolic diseases.

Inherited metabolic disorders (IMDs) are a growing group of genetic diseases caused by defects in enzymes that mediate cellular metabolism, often resulting in the accumulation of toxic substrates. The liver is a highly metabolically active organ that hosts several thousands of chemical reactions. As such, it is an organ frequently affected in IMDs. In this article, we review current approaches for liver-directed gene-based therapy aimed at metabolite detoxification in a variety of IMDs. Moreover, we discuss current unresolved challenges in gene-based therapies for IMDs.

Species selection for automatic chemical kinetic mechanism generation

AbstractMany important chemical kinetic systems require detailed chemical kinetic models to resolve. These detailed kinetic models can involve thousands of species and hundreds of thousands of chemical reactions, making them difficult to construct by hand. Modern automatic mechanism generation algorithms can mostly be divided into two classes: rule and rate based. Rule‐based generators choose species based on user defined constraints on species and reaction classes. Rate‐based generators generate a much larger set of potentially important species and reactions and then choose which ones to add based on running simulations of species and reactions deemed important and calculating the flux to potentially important species. In principle, the latter is preferable, as it requires the user to make far fewer assumptions about what is important in the system. However, while the effectiveness of the rate‐based approach has been demonstrated in a wide variety of systems, it has also been demonstrated to have difficulty picking up important low‐flux chemistries. Here we present a discussion of the challenges associated with rate‐based mechanism generation and new algorithms that are able to efficiently mitigate these challenges improving species selection during mechanism generation in a set of case studies.

Integrated Study on Methane Activation: Exploring Main Group Frustrated Lewis Pairs through Density Functional Theory, Machine Learning, and Machine-Learned Force Fields.

Frustrated Lewis Pairs (FLP) are an important advance in metal-free catalysis due to their ability to activate a variety of small molecules. Many studies have focused on a very limited sample of Lewis acids and bases. Herein, we disclose an automated exploration algorithm using density functional methods, artificial neural networks (ANNs), and a molecule builder that incentivizes the exploration of favorable FLP space for the activation of methane via two mechanisms: deprotonation and hydride abstraction. The exploration algorithm creates FLPs with different Lewis acids (LA), Lewis bases (LB), and their substituents (LA/LB), which proved successful in quickly converging in the favorable chemical space, suggesting chemically sound structures, and generating thousands of potential candidates for methane activating FLPs. By modeling thousands of reactions, an FLP database of methane activation was created, allowing one to data mine properties, e.g., adduct bond length, highest occupied molecular orbital-lowest-unoccupied molecular orbital (HOMO-LUMO) gap, global electrophilicity index, favored Lewis acids/bases/substituents, and substituent steric volume. These properties not only successfully narrow the FLP chemical space but also provide meaningful insight into the chemical nature of competent methane activators. The machine learning discovery strategy disclosed here is general enough to be applicable to many chemical optimization tasks. This study also investigates the efficacy of a Machine-Learned Force Field (MLFF) in predicting the formation energies of Frustrated Lewis Pairs (FLPs). Our model, exhibiting a test error of ±10 kcal/mol, highlighted impressive computational efficiency by enabling the calculation of all possible FLP permutations within our chemical space. The MLFF demonstrated proficiency in predicting energies, providing a significant acceleration compared to quantum mechanics methods. However, challenges emerged in accurately capturing forces, necessitating recourse to classical force fields for reliable structure relaxation. The present study sheds light on the MLFF's potential as a tool for rapid energy predictions, emphasizing the need for further refinement to enhance its accuracy, particularly in force predictions, to expand its utility in chemical simulations.

Forward and reverse uncertainty analyses for RRKM/master equation based kinetic predictions: A case study of ethyl with oxygen

AbstractIn the realm of combustion kinetic modeling, the norm involves employing thousands of reactions to delineate the chemical conversion of hundreds of species. Notably, theoretically predicted rate coefficients and branching ratios, derived through the RRKM/master equation (ME) model, play an increasing role in kinetic modeling. Thus minimizing the uncertainty of theoretical prediction across wide working conditions is crucial to refine a kinetic model. The present study takes ethyl (C2H5) + oxygen (O2) reaction system to show that combined forward and reverse uncertainty analysis can be used to further constrain calculated rate coefficients and branching ratios, which were already calculated by high‐level quantum chemistry methods. Forward global uncertainty analysis with the artificial neural network‐high dimensional model representation (ANN‐HDMR) method is employed to select key parameters affecting total rate coefficients of C2H5 + O2 and branching ratios of C2H5 + O2 = C2H4 + HO2 (C1). Reverse uncertainty analysis with Bayesian method was then applied to refine the key input parameters based on experimental data at working conditions selected by sensitivity entropy. Although the target RRKM/ME model system was built on high level theoretical calculations, the combined forward and reverse uncertainty analyses are still able to reduce uncertainties of predicted total rate coefficients of C2H5 + O2 and branching ratios for C1 across a wide range of working conditions. Specifically, the uncertainties of total rate coefficient and C1 branching ratio have been reduced from 1.46 and 1.52 to 1.30 and 1.36 at 298 K and 1 Torr. The analysis process proposed in the present work effectively extrapolates the constraint ability of accurate measured data at one condition to wide working conditions based on the RRKM/ME model.

Reproduction propaganda: The state hails, citizens responds—A case study of Vietnamese governmental Facebook

Since the turn of the 21st century, aging populations and low fertility rates have alarmed governments across Europe and East Asia. The Communist state of Vietnam is now experiencing the same situation. The government has recently started a propaganda campaign to encourage its citizens to not postpone marriage and having children. This article explores one recent attempt—a viral post published on the government's Facebook page in November 2023 urging young citizens to marry before 30, attracting hundreds of thousands of reactions and comments. The article examines this heteronormative message and netizens’ comments, which show humor, resistance, and critiques of the government's welfare system. Using the concepts of (counter-) interpellation, it reveals the nuances of the power relations between the state and its people in this case study where biopolitics and personal choice intersect, illuminating how social networks provide a discursive space for mundane political engagement.

Magnetic Silica-Coated Fluorescent Microspheres (MagSiGlow) for Simultaneous Detection of Tumor-Associated Proteins.

Multiplexed bead assays for solution-phase biosensing often encounter cross-over reactions during signal amplification steps, leading to unwanted false positive and high background signals. Current solutions involve complex custom-designed and costly equipment, limiting their application in simple laboratory setup. In this study, we introduce a straightforward protocol to adapt a multiplexed single-bead assay to standard fluorescence imaging plates, enabling the simultaneous analysis of thousands of reactions per plate. This approach focuses on the design and synthesis of bright fluorescent and magnetic microspheres (MagSiGlow) with multiple fluorescent wavelengths serving as unique detection markers. The imaging-based, single-bead assay, combined with a scripted algorithm, allows the detection, segmentation, and co-localization on average of 7500 microspheres per field of view across five imaging channels in less than one second. We demonstrate the effectiveness of this method with remarkable sensitivity at low protein detection limits (100 pg/mL). This technique showed over 85 % reduction in signal cross-over to the solution-based method after the concurrent detection of tumor-associated protein biomarkers. This approach holds the promise of substantially enhancing high throughput biosensing for multiple targets, seamlessly integrating with rapid image analysis algorithms.

Magnetic Silica‐Coated Fluorescent Microspheres (MagSiGlow) for Simultaneous Detection of Tumor‐Associated Proteins

AbstractMultiplexed bead assays for solution‐phase biosensing often encounter cross‐over reactions during signal amplification steps, leading to unwanted false positive and high background signals. Current solutions involve complex custom‐designed and costly equipment, limiting their application in simple laboratory setup. In this study, we introduce a straightforward protocol to adapt a multiplexed single‐bead assay to standard fluorescence imaging plates, enabling the simultaneous analysis of thousands of reactions per plate. This approach focuses on the design and synthesis of bright fluorescent and magnetic microspheres (MagSiGlow) with multiple fluorescent wavelengths serving as unique detection markers. The imaging‐based, single‐bead assay, combined with a scripted algorithm, allows the detection, segmentation, and co‐localization on average of 7500 microspheres per field of view across five imaging channels in less than one second. We demonstrate the effectiveness of this method with remarkable sensitivity at low protein detection limits (100 pg/mL). This technique showed over 85 % reduction in signal cross‐over to the solution‐based method after the concurrent detection of tumor‐associated protein biomarkers. This approach holds the promise of substantially enhancing high throughput biosensing for multiple targets, seamlessly integrating with rapid image analysis algorithms.

Reaction path identification and validation from molecular dynamics simulations of hydrocarbon pyrolysis

AbstractCreation of complex chemical mechanisms for hydrocarbon pyrolysis and combustion is challenging due to the large number of species and reactions involved. Reactive molecular dynamics (RMD) enables the simulation of thousands of reactions and the discovery of previously unknown components of the reaction network. However, due to the inherent imprecision of reactive force fields, it is necessary to verify RMD‐obtained reaction paths using more accurate methods such as Density Functional Theory (DFT). We demonstrate a method for identification and confirmation of reaction pathways from RMD that supplement an established mechanism, using the example of benzene formation from n‐heptane and iso‐octane pyrolysis. We establish a validation workflow to extract reaction geometries from RMD and optimize transition states using the Nudged‐Elastic‐Band method on semi‐empirical and quantum mechanical levels of theory. Our findings demonstrate that the widely recognized ReaxFF parameterization, CHO2016, can identify known pathways from a established soot formation mechanism while also indicating new ones. We also show that CHO2016 underestimates hydrogen migration barriers by up to as compared to DFT and can lower activation barriers significantly for spin‐forbidden reactions. This highlights the necessity for validation or potentially even reparametrization of CHO2016.

A Data-Based Hybrid Chemistry Acceleration Framework for the Low-Temperature Oxidation of Complex Fuels

The oxidation of complex hydrocarbons is a computationally expensive process involving detailed mechanisms with hundreds of chemical species and thousands of reactions. For low-temperature oxidation, an accurate account of the fuel-specific species is required to correctly describe the pyrolysis stage of oxidation. In this study, we develop a hybrid chemistry framework to model and accelerate the low-temperature oxidation of complex hydrocarbon fuels. The framework is based on a selection of representative species that capture the different stages of ignition, heat release, and final products. These species are selected using a two-step principal component analysis of the reaction rates of simulation data. Artificial neural networks (ANNs) are used to model the source terms of the representative species during the pyrolysis stage up to the transition time. This ANN-based model is coupled with C0–C4 foundational chemistry, which is used to model the remaining species up to the transition time and all species beyond the transition time. Coupled with the USC II mechanism as foundational chemistry, this framework is demonstrated using simple reactor homogeneous chemistry and perfectly stirred reactor (PSR) calculations for n-heptane oxidation over a range of composition and thermodynamic conditions. The hybrid chemistry framework accurately captures correct physical behavior and reproduces the results obtained using detailed chemistry at a fraction of the computational cost.

Low-dimensional representations of genome-scale metabolism

Cellular metabolism is a highly interconnected network with thousands of reactions that convert nutrients into the molecular building blocks of life. Metabolic connectivity varies greatly with cellular context and environmental conditions, and it remains a challenge to compare genome-scale metabolism across cell types because of the high dimensionality of the reaction flux space. Here, we employ self-supervised learning and genome-scale metabolic models to compress the flux space into low-dimensional representations that preserve structure across cell types. We trained variational autoencoders (VAEs) on large fluxomic data (N = 800, 000) sampled from patient-derived models for various cancer cell types. The VAE embeddings have an improved ability to distinguish cell types than the uncompressed fluxomic data, and sufficient predictive power to classify cell types with high accuracy. We tested the ability of these classifiers to assign cell type identities to unlabelled patient-derived metabolic models not employed during VAE training. We further employed the pre-trained VAE to embed another 38 cell types and trained multilabel classifiers that display promising generalization performance. Our approach distils the metabolic space into a semantically rich vector that can be used as a foundation for predictive modelling, clustering or comparing metabolic capabilities across organisms.

Sensitivity analysis of aromatic chemistry to gas-phase kinetics in a dark molecular cloud model.

The increasingly large number of complex organic molecules detected in the interstellar medium necessitates robust kinetic models that can be relied upon for investigating the involved chemical processes. Such models require rate coefficients for each of the thousands of reactions; the values of these are often estimated or extrapolated, leading to large uncertainties that are rarely quantified. We have performed a global Monte Carlo and a more local one-at-a-time sensitivity analysis on the gas-phase rate coefficients in a 3-phase dark cloud model. Time-dependent sensitivities have been calculated using four metrics to determine key reactions for the overall network as well as for the cyanonaphthalene molecule in particular, an important interstellar species that is severely under-produced by current models. All four metrics find that reactions involving small, reactive species that initiate hydrocarbon growth have large effects on the overall network. Cyanonaphthalene is most sensitive to a number of these reactions as well as ring-formation of the phenyl cation (C6H5+) and aromatic growth from benzene to naphthalene. Future efforts should prioritize constraining rate coefficients of key reactions and expanding the network surrounding these processes. These results highlight the strength of sensitivity analysis techniques to identify critical processes in complex chemical networks, such as those often used in astrochemical modeling.

Patterns of change in regulatory modules of chemical reaction systems induced by network modification.

Cellular functions are realized through the dynamics of chemical reaction networks formed by thousands of chemical reactions. Numerical studies have empirically demonstrated that small differences in network structures among species or tissues can cause substantial changes in dynamics. However, a general principle for behavior changes in response to network structure modifications is not known. The chemical reaction system possesses substructures called buffering structures, which are characterized by a certain topological index being zero. It was proven that the steady-state response to modulation of reaction parameters inside a buffering structure is localized in the buffering structure. In this study, we developed a method to systematically identify the loss or creation of buffering structures induced by the addition of a single degradation reaction from network structure alone. This makes it possible to predict the qualitative and macroscopic changes in regulation that will be caused by the network modification. This method was applied to two reaction systems: the central metabolic system and the mitogen-activated protein kinases signal transduction system. Our method enables identification of reactions that are important for biological functions in living systems.

Data-driven prediction of the output composition of an atmospheric pressure plasma jet

Cold atmospheric plasma (CAP) in open air hosts numerous chemical species engaged in thousands of chemical reactions. Comprehensive diagnosis of its chemical composition is important across various fields from medicine, where reactive oxygen and nitrogen play key roles, to surface modification. In applications, a centimeter-scale helium–air jet operates for minutes, featuring micrometer-sized streamers and an atmospheric pressure-induced collision frequency in the hundreds of GHz range. To address this intricate multi-scale issue, we introduce a machine learning approach: using a physics-informed neural network (PINN) to tackle the multi-scale complexities inherent in predicting the complete list of species concentrations, gas temperature, and electron temperature of a CAP jet supplied with a mixture of helium and air. Experimental measurements of O3, N2O, and NO2 concentrations downstream of the plasma jet, combined with fundamental physics laws, the conservation of mass and charge, constrain the PINN, enabling it to predict the concentrations of all species that are not available from the experiment, along with gas and electron temperatures. The results, therefore, obey all the physical laws we provided and can have a chemical balance with the measured concentrations. This methodology holds promise for describing and potentially regulating complex systems with limited experimental datasets.

Machine Learning and Informatics Based Elucidation of Reaction Pathways for Upcycling Model Polyolefin to Aromatics.

Catalytic upcycling of plastics results in a complex network of potentially thousands of reactions and intermediates. Manual analysis of such a network using ab initio methods to identify plausible reaction pathways and rate-controlling steps is intractable. Here, we combine informatics-based reaction network generation and machine learning based thermochemistry calculation to identify plausible (nonelementary step) pathways involved in dehydroaromatization of a model polyolefin, n-decane, to form aromatic products. All 78 aromatic molecules found involve a sequence comprising dehydrogenation, β-scission, and cyclization steps (in slightly different order). The plausible flux-carrying pathway depends on the family of reactions that is rate-controlling while the thermodynamic bottleneck is the first dehydrogenation step of n-decane. The adopted workflow is system agnostic and can be applied to understand the overall thermochemistry of other upcycling systems.

Organizing Enzymes on Self‐Assembled Protein Cages for Cascade Reactions

AbstractCells use self‐assembled biomaterials such as lipid membranes or proteinaceous shells to coordinate thousands of reactions that simultaneously take place within crowded spaces. However, mimicking such spatial organization for synthetic applications in engineered systems remains a challenge, resulting in inferior catalytic efficiency. In this work, we show that protein cages as an ideal scaffold to organize enzymes to enhance cascade reactions both in vitro and in living cells. We demonstrate that not only enzyme‐enzyme distance but also the improved Km value contribute to the enhanced reaction rate of cascade reactions. Three sequential enzymes for lycopene biosynthesis have been co‐localized on the exterior of the engineered protein cages in Escherichia coli, leading to an 8.5‐fold increase of lycopene production by streamlining metabolic flux towards its biosynthesis. This versatile system offers a powerful tool to achieve enzyme spatial organization for broad applications in biocatalysis.

Organizing Enzymes on Self-Assembled Protein Cages for Cascade Reactions.

Cells use self-assembled biomaterials such as lipid membranes or proteinaceous shells to coordinate thousands of reactions that simultaneously take place within crowded spaces. However, mimicking such spatial organization for synthetic applications in engineered systems remains a challenge, resulting in inferior catalytic efficiency. In this work, we show that protein cages as an ideal scaffold to organize enzymes to enhance cascade reactions both in vitro and in living cells. We demonstrate that not only enzyme-enzyme distance but also the improved Km value contribute to the enhanced reaction rate of cascade reactions. Three sequential enzymes for lycopene biosynthesis have been co-localized on the exterior of the engineered protein cages in Escherichia coli, leading to an 8.5-fold increase of lycopene production by streamlining metabolic flux towards its biosynthesis. This versatile system offers a powerful tool to achieve enzyme spatial organization for broad applications in biocatalysis.

Computing optimal factories in metabolic networks with negative regulation.

MotivationA factory in a metabolic network specifies how to produce target molecules from source compounds through biochemical reactions, properly accounting for reaction stoichiometry to conserve or not deplete intermediate metabolites. While finding factories is a fundamental problem in systems biology, available methods do not consider the number of reactions used, nor address negative regulation.MethodsWe introduce the new problem of finding optimal factories that use the fewest reactions, for the first time incorporating both first- and second-order negative regulation. We model this problem with directed hypergraphs, prove it is NP-complete, solve it via mixed-integer linear programming, and accommodate second-order negative regulation by an iterative approach that generates next-best factories.ResultsThis optimization-based approach is remarkably fast in practice, typically finding optimal factories in a few seconds, even for metabolic networks involving tens of thousands of reactions and metabolites, as demonstrated through comprehensive experiments across all instances from standard reaction databases.Availability and implementationSource code for an implementation of our new method for optimal factories with negative regulation in a new tool called Odinn, together with all datasets, is available free for non-commercial use at http://odinn.cs.arizona.edu.

Box Model Intercomparison of Cloud Chemistry

AbstractChemical processes in clouds and fogs can substantially alter atmospheric oxidant budgets and lead to aerosol mass formation. However, many regional and global models do not include detailed aqueous‐phase chemical mechanisms due to the (a) lack of complete understanding of the chemical processes and (b) computational burden of adding constituents. Current gas‐aqueous chemistry 0‐dimensional models were evaluated in a cloud‐chemistry box model intercomparison based on a mid‐September 2016 cloud chemistry event at Whiteface Mountain, New York. Multiphase mechanisms in the five participating models ranged from those appropriate for 3‐d models to highly complex with thousands of reactions. This study focused on oxidant levels in both phases and aqueous‐phase sulfate and organic acid formation. Comparison of gas‐phase‐only chemistry gives very similar oxidant predictions at night but shows significant differences during daytime with the hydroxyl radical (OH) variability of about an order of magnitude. The variability in the model results increases substantially with aqueous chemistry due to different Henry's Law constants, aqueous‐phase reaction rate constants, and chemical mechanisms. Using a prescribed liquid water content and pH value of 4.5, modeled aqueous OH, aldehyde, and organic acid concentrations differ by over an order of magnitude in daytime. Simulations were also conducted at a pH = 5.1, predicted variable pH, and with added transition metal ion chemistry. While we compare predicted and measured inorganic anions and water‐soluble organic carbon, we cannot do so for aqueous‐phase oxidant concentrations due to the lack of measurements. We highlight a need for recommended equilibrium and aqueous‐phase rate constants.

FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks.

Mathematical models of biochemical networks can largely facilitate the comprehension of the mechanisms at the basis of cellular processes, as well as the formulation of hypotheses that can be tested by means of targeted laboratory experiments. However, two issues might hamper the achievement of fruitful outcomes. On the one hand, detailed mechanistic models can involve hundreds or thousands of molecular species and their intermediate complexes, as well as hundreds or thousands of chemical reactions, a situation generally occurring in rule-based modeling. On the other hand, the computational analysis of a model typically requires the execution of a large number of simulations for its calibration, or to test the effect of perturbations. As a consequence, the computational capabilities of modern Central Processing Units can be easily overtaken, possibly making the modeling of biochemical networks a worthless or ineffective effort. To the aim of overcoming the limitations of the current state-of-the-art simulation approaches, we present in this paper FiCoS, a novel "black-box" deterministic simulator that effectively realizes both a fine-grained and a coarse-grained parallelization on Graphics Processing Units. In particular, FiCoS exploits two different integration methods, namely, the Dormand-Prince and the Radau IIA, to efficiently solve both non-stiff and stiff systems of coupled Ordinary Differential Equations. We tested the performance of FiCoS against different deterministic simulators, by considering models of increasing size and by running analyses with increasing computational demands. FiCoS was able to dramatically speedup the computations up to 855×, showing to be a promising solution for the simulation and analysis of large-scale models of complex biological processes.

Two-temperature balance equations implementation, numerical validation and application to H2O–He microwave induced plasmas

Global Models are widely used to study reaction kinetics in low-temperature plasma discharges. The governing conservative equations are simplified into a system of ordinary differential equations in order to provide computationally feasible conditions to study complex chemistries with hundreds of species and thousands of reactions. This paper presents a detailed two-temperature global model for a H2O–He mixture. The model developed in this work uses a statistical thermodynamics approach to solve the heavy particles energy equation self-consistently together with the electron energy and particles balance equations in order to improve the description of reactive plasma environments. Three analytical test cases are presented to validate and demonstrate the capability of this newly developed functionality embedded in PLASIMO software’s [] global model module. The developed H2O–He models are compared with the reported results for a radio-frequency plasma [] and then with experimental measured electron densities and gas temperature for a microwave induced plasma. In addition, conversion and energy efficiencies of hydrogen and hydrogen peroxide productions are compared with experimental values (only for hydrogen) for a pure H2O microwave induced plasma and with available literature results. This comparison underlines the challenges toward finding an optimal plasma configuration and conditions for production of hydrogen from water. The three analytical test cases for validation of the gas-temperature balance implementation in the PLASIMO global model and the detailed developed H2O–He model can be used as benchmarks for other global plasma models. The PLASIMO input files for the presented H2O–He model are available as supplementary materials (https://stacks.iop.org/PSST/30/075007/mmedia); for any future update, please consult the PLASIMO website, https://plasimo.phys.tue.nl.