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Biomolecular machines autonomously convert energy into functions, driving systems away from thermodynamic equilibrium. This energy conversion is achieved by leveraging complex, kinetically asymmetric chemical reaction networks that are challenging to characterize precisely. In contrast, all known synthetic molecular systems in which kinetic asymmetry has been quantified are well described by simple single-cycle networks. Here, we report on a unique light-driven [2]rotaxane that enables the autonomous operation of a synthetic molecular machine with a multi-cycle chemical reaction network. Unlike all prior systems, the present one exploits a photoactive macrocycle, which features a different photoreactivity depending on the binding sites at which it resides. Furthermore, E to Z isomerization reverses the relative affinity of the macrocycle for two binding sites on the axle, resulting in a multi-cycle network. Building on the most recent theoretical advancements, this work quantifies kinetic asymmetry in a multi-cycle network for the first time. Our findings represent the simplest rotaxane capable of autonomous shuttling developed so far and offer a general strategy to generate and quantify kinetic asymmetry beyond single-cycle systems., (© 2024 The Author(s). Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

Can a micron-sized sack of interacting molecules autonomously learn an internal model of a complex and fluctuating environment? We draw insights from control theory, machine learning theory, chemical reaction network theory and statistical physics to develop a general architecture whereby a broad class of chemical systems can autonomously learn complex distributions. Our construction takes the form of a chemical implementation of machine learning's optimization workhorse: gradient descent on the relative entropy cost function, which we demonstrate can be viewed as a form of integral feedback control. We show how this method can be applied to optimize any detailed balanced chemical reaction network and that the construction is capable of using hidden units to learn complex distributions.

Photoreduction of CO2 into hydrocarbons is a potential strategy for reducing atmospheric CO2 and effectively utilizing carbon resources. Cu-deposited TiO2 photocatalysts stand out in this area due to their good photocatalytic activity and potential methanol selectivity. However, the underlying mechanism and factors controlling product selectivity remain less understood. Using first-principles calculations, this study systematically investigates the possible reaction network for CO2 photocatalytic reduction on TiO2 supported Cu-nanorods (nr-Cu/TiO2), driven by the surface-bound *H species generated via a Volmer-like process (H+ + e− + * → *H). Our results reveal that the initial hydrogenation of CO2 on nr-Cu/TiO2 is energetically more favorable via the formate (HCOO) pathway than the carboxyl (COOH) route. Notably, HCOO undergoes further hydrogenation for effective C–O bond cleavage, with H2COOH identified as the key intermediate. Both CO (CO2 → HCOO → H2COOH → H2CO → CO) and CH3OH (CO2 → HCOO → H2COOH → H2CO → CH3OH) production share the H2CO intermediate, with CO formation proceeding via an unexpected "forth-back" mechanism. Energy profiles suggest that CH3OH formation is more favorable than CO formation. Additionally, excess photogenerated electrons were found to enhance CO2 activation and C–O bond cleavage to some extent but have minimal impact on other reaction steps. This study provides atomic-level insights into the CO2 photoreduction mechanism, offering potential guidance for improving product selectivity. [ABSTRACT FROM AUTHOR]

We provide yields from 189 neutrino-driven core-collapse supernova (CCSN) simulations covering zero-age main sequence masses between 11 and |$75\ \mathrm{M}_\odot$| and three different metallicities. Our CCSN simulations have two main advantages compared to previous methods used for applications in Galactic chemical evolution (GCE). First, the mass cut between remnant and ejecta evolves naturally. Secondly, the neutrino luminosities and thus the electron fraction are not modified. Both are key to obtain an accurate nucleosynthesis. We follow the composition with an in situ nuclear reaction network including the 16 most abundant isotopes and use the yields as input in a GCE model of the Milky Way. We adopt a GCE that takes into account infall of gas as well as nucleosynthesis from a large variety of stellar sources. The GCE model is calibrated to reproduce the main features of the solar vicinity. For the CCSN models, we use different calibrations and propagate the uncertainty. We find a big impact of the CCSN yields on our GCE predictions. We compare the abundance ratios of C, O, Ne, Mg, Si, S, Ar, Ca, Ti, and Cr with respect to Fe to an observational data set as homogeneous as possible. From this, we conclude that at least half of the massive stars have to explode to match the observed abundance ratios. If the explosions are too energetic, the high amount of iron will suppress the abundance ratios. With this, we demonstrate how GCE models can be used to constrain the evolution and death of massive stars. [ABSTRACT FROM AUTHOR]

The multi-grid reaction-diffusion master equation (mgRDME) provides a generalization of stochastic compartment-based reaction-diffusion modelling described by the standard reaction-diffusion master equation (RDME). By enabling different resolutions on lattices for biochemical species with different diffusion constants, the mgRDME approach improves both accuracy and efficiency of compartment-based reaction-diffusion simulations. The mgRDME framework is examined through its application to morphogen gradient formation in stochastic reaction-diffusion scenarios, using both an analytically tractable first-order reaction network and a model with a second-order reaction. The results obtained by the mgRDME modelling are compared with the standard RDME model and with the (more detailed) particle-based Brownian dynamics simulations. The dependence of error and numerical cost on the compartment sizes is defined and investigated through a multi-objective optimization problem. [ABSTRACT FROM AUTHOR]

Despite being a powerful tool to model ecological interactions, traditional evolutionary game theory can still be largely improved in the context of population dynamics. One of the current challenges is to devise a cohesive theoretical framework for ecological games with density-dependent (or concentration-dependent) evolution, especially one defined by individual-level events. In this work, I use the notation of reaction networks as a foundation to propose a framework and show that classic two-strategy games are a particular case of the theory. The framework exhibits a strong versatility and provides a standardized language for model design, and I demonstrate its use through a simple example of mating dynamics and parental care. In addition, reaction networks provide a natural connection between stochastic and deterministic dynamics and therefore are suitable to model noise effects on small populations, also allowing the use of stochastic simulation algorithms such as Gillespie's with game models. The methods I present can help to bring evolutionary game theory to new reaches in ecology, facilitate the process of model design, and put different models on a common ground., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Recent technological advances allow us to view chemical mass-action systems as analog computers. In this context, the inputs to a computation are encoded as initial values of certain chemical species while the outputs are the limiting values of other chemical species. In this paper, we design chemical systems that carry out the elementary arithmetic computations of: identification, inversion, m th roots (for m ≥ 2), addition, multiplication, absolute difference, rectified subtraction over non-negative real numbers, and partial real inversion over real numbers. We prove that these "elementary modules" have a speed of computation that is independent of the inputs to the computation. Moreover, we prove that finite sequences of such elementary modules, running in parallel, can carry out composite arithmetic over real numbers, also at a rate that is independent of inputs. Furthermore, we show that the speed of a composite computation is precisely the speed of the slowest elementary step. Specifically, the scale of the composite computation, i.e. the number of elementary steps involved in the composite, does not affect the overall asymptotic speed – a feature of the parallel computing nature of our algorithm. Our proofs require the careful mathematical analysis of certain non-autonomous systems, and we believe this analysis will be useful in different areas of applied mathematics, dynamical systems, and the theory of computation. We close with a discussion on future research directions, including numerous important open theoretical questions pertaining to the field of computation with reaction networks. • Analog chemical computation algorithms for arithmetic at input-independent speeds. • Reaction networks and associated mass-action ODEs for analog computation. • Reaction network modules that efficiently compute in parallel. • Novel reaction network algorithms and comparison with existing algorithms. • Network diagrams for combining molecular computing modules. [ABSTRACT FROM AUTHOR]

[Display omitted] • Ethylbenzene is synthesized by alkylation of benzene using CO 2. • A Single-bed catalyst system for forming ethylbenzene is constructed. • Effect of Brønsted Acid Density and Intragranular Diffusion of HZSM-5 on Product Distribution. • Reaction networks were extensively explored using GC–MS and various model reactions. The selective synthesis of specific value-added aromatic hydrocarbons through CO 2 hydrogenation holds significant strategic importance in mitigating energy and climate issues. However, the process of forming ethylated side chains on benzene rings is challenging, and the methods reported are often limited by multi-bed systems. Here, we show the first CO 2 hydrogenation in tandem coupling with benzene alkylation to synthesize ethylbenzene over a single-bed system containing ZnFeO x and nano ZSM-5 catalysts. The results indicated that the bifunctional catalyst composed of ZnFeO x with a Zn/Fe ratio of 0.5 and nano-HZSM-5 exhibits excellent catalytic performance under optimized conditions. The benzene conversion reaches 28.0 %, ethylbenzene selectivity is 61.3 % and the conversion of CO 2 is remarkably high, achieving 45.0 %. This single-bed system significantly promotes the in-situ utilization of ethylation intermediates. The introduction of zinc promoted the formation of ZnFe 2 O 4 spinel, which increased the number of active sites due to its superior dispersibility and reducibility. Nano-HZSM-5 demonstrated outstanding ethylbenzene selectivity and catalytic stability owing to its unique shape selectivity, moderate acidity, and superior diffusion properties. This study further explores the reaction network and mechanism through GC–MS analysis and various model reactions, elucidating the formation pathways of primary products in detail. This research provides a new strategy for the controllable synthesis of ethylbenzene using CO 2 as a C 1 source in a single-bed system. [ABSTRACT FROM AUTHOR]

• Seven transition metal-incorporated *BEA zeolites were successfully synthesized and exhibited excellent crotonaldehyde selectivity in the self-aldol condensation of acetaldehyde. • The substitution of the heteroatom center effectively modulated the acetaldehyde conversion and the Lewis acid property of the metal-incorporated *BEA zeolite. • The amount of mid-strength Lewis acid was identified as the principal factor responsible for the conversion variations. • Zr-BEA zeolite demonstrated the highest acetaldehyde conversion and optimal crotonaldehyde yield. • A reaction network of acetaldehyde conversion on Zr-BEA zeolite was proposed. Metal-incorporated *BEA zeolites have shown remarkable catalytic performance in the self-aldol condensation of various aldehydes. The substitution of heteroatom sites can modulate the self-aldol condensation performance of aldehydes, while the underlying mechanisms have been insufficiently explored, with even less attention given to acetaldehyde-related processes. Herein, seven transition metal-incorporated *BEA zeolites were post-synthesized and applied in the self-aldol condensation of acetaldehyde. All the M-BEA zeolites exhibited excellent crotonaldehyde selectivity, exceeding 80 % and most surpassing 85 %, with various acetaldehyde conversions. SEM, XRD, N 2 physisorption, FT-IR, UV–Vis, XPS, Py-IR, and NH 3 -TPD were employed to characterize the physicochemical properties. The M-BEA zeolites predominantly exhibit Lewis acidity, and the amount of Lewis acid with medium strength dominated the acetaldehyde conversion variations. The probe-pulse and TPSR experiments were further employed to investigate the reaction pathways to explain the product distribution on the Zr-BEA zeolite, which demonstrated the highest crotonaldehyde yield. The main side products were long-chain unsaturated aldehydes and cyclic compounds from excessive aldol condensation and cyclization. A reaction network for acetaldehyde conversion on Zr-BEA zeolite was ultimately proposed. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • Differences in homo-oligomerization of propylene and 1-butylene were revealed. • Co-oligomerization of olefin mixtures as a promising pathway towards kerosene. • High olefin conversions and kerosene selectivities of up to 85% were reached. • Increased fuel qualities possible by applying two catalyst beds in series. • Fuel characteristics were determined and largely comply with standards. Within this study, the (co–)oligomerization of methanol-based olefins in the C 2-4 range was investigated. The main objective was to increase the yield of oligomers with carbon chain lengths in the range of kerosene (C 9-16). Commercially available mesoporous amorphous mixed silicon-aluminum oxides, optionally modified with nickel species, were applied as catalysts. Initially, single olefin feeds were employed, i.e. homo-oligomerization reactions of pure propylene and pure 1-butylene were studied at 120 °C and 32 bar olefin partial pressure, respectively. The co-oligomerization of olefin mixtures (C 3+4 and C 2+3+4), which can be obtained in Methanol-to-Olefins (MtO) processes, yields product mixtures with reduced selectivities to specific chain lengths. However, selectivities to kerosene-like olefins up to 85 % have been achieved and the main side product is gasoline. Investigations with varying reaction conditions reveal comparable effects as in the case of homo-oligomerization. The use of nickel-free catalysts resulted in the highest selectivities of kerosene-like olefins, but no ethylene was converted. The negative effects of nickel catalysts on fuel quality can be compensated by two consecutive catalyst beds, the first catalyst bed with a nickel-loaded silicon-aluminum oxide for ethylene conversion followed by a catalyst bed of neat silicon-aluminum oxide for the synthesis of highly branched, long chain oligomers. The reaction network for olefin oligomerization reactions is depicted, which can be simplified remarkably in the case of catalysts without nickel. A long-term experiment lasting for more than 200 h was conducted revealing a deactivation of the acid sites of the catalysts, but also the possibility of reactivation. Selectivity to kerosene-like olefins remained above 63 % and fuel characterization showed that the resulting kerosene fraction will be suitable for blending with conventional fuels. [ABSTRACT FROM AUTHOR]

• The adsorption structures of the intermediate species on catalyst are calculated. • The reaction mechanism of CO 2 to methanol is proposed. • A microkinetic model is constructed based on the DFT results. • The impact of different reaction conditions on turnover frequency is analyzed. CO 2 hydrogenation to methanol using supported Cu-based catalysts is a key reaction for promoting carbon neutrality. Nevertheless, the reaction mechanism remains debated, particularly between the HCOO pathway involving a formate intermediate and the COOH route via a carboxyl intermediate. Therefore, density functional theory (DFT) calculations are employed in exploring the mechanism of CO 2 hydrogenation to methanol on Cu/CeO 2 (100) catalyst. The results indicate that the HCOO route is thermodynamically more favorable than the COOH route. The key intermediate in the HCOO pathway is hydroxymethoxy (H 2 COOH*), which dissociates to form formaldehyde (H 2 CO*) as an intermediate, ultimately undergoing stepwise hydrogenation to produce methanol. Additionally, microkinetic analyses reveal the rate-determining step (RDS) in the reaction network and identify the HCOO route as the primary pathway for methanol synthesis on this catalyst. [ABSTRACT FROM AUTHOR]

• Nb@SbN possesses an optimum catalytic activity and selectivity toward nitric oxide degradation. • A universal expression is proposed to correlate the nitric oxide reduction reaction performance, allowing us to easily forecast the ideal catalysts. • The superior activity stems from SbN substrate acting irreplaceable role that can steadfastly maintain the unique spatial structure of local reactive motif. Direct degradation of pollutant nitric oxide (NO) into green fuel ammonia (NH 3) is a major idea to achieve balanced and sustainable development between energy usage and environment purification. The accomplishment of electrochemically driven proton-electron coupling facilitates the birth of high value-added products; nevertheless, this goal is highly impeded by inferior Faraday efficiency (FE) deriving from the contradictive hydrogen evolution reaction (HER) and intricate reaction network channels. In this case, we aim to explore a high performance electro-catalyst toward NH 3 synthesis and break commercial noble metal-based benchmarking. So brand-new single transition metal (TM) atom decorated wavy antimony nitride (SbN) monolayer catalysts (TM@SbN) were discovered by first-principles calculations. By evaluating the experimental feasibility, reactive activity, and FE of 23 single atom catalysts (SACs) of TM@SbN candidates, Nb@SbN was stood out as a promising electro-catalyst for NORR along with a milestone limiting potential record of −0.06 V and an almost 100 % FE toward NH 3 formation. This outstanding electrocatalytic performance is due to the unique SbN substrate, which plays a vital role in tuning the electronic configuration of local TMN 3 motif. Moreover, data-driven formula for depicting the relation between the key endothermic steps of Δ G (*NO→ *NOH) and Δ G (*NO→ *NHO) with catalyst's physical chemistry features were identified using machine learning code. Based on the constructed equation, several promising candidates were exactly predicted with good NORR activity, consistent with our previous DFT-calculated results. Our studies have combined machine learning tool with first-principles calculations to accelerate the selection of target catalysts and offer valuable suggestions for experiments. [ABSTRACT FROM AUTHOR]

The catalytic selective etherification of 5-hydroxymethyl furfural (5-HMF) is considered as a feasible route to prepare the biofuels from biomass feedstocks. In this work, a novel solid acid catalyst derived from the flower-like MoS 2 is synthesized by combination of the facile hydrothermal method and reduction process. Therein, the number of Lewis and Bronsted acidic sites derived from the exposed Mo edges of MoS 2 can be efficiently regulated by changing the hydrogen annealing conditions. The prepared MoS 2 -450-R catalyst exhibited a prominent activity for the conversion of 5-HMF to 5-(methoxymethyl)furanal dimethyl acetal (5-MFDMA) through the tandem acetalization and etherification under N 2 atmosphere, in which a 99.0 % conversion with 83.7 % selectivity of 5-MFDMA is obtained. Further investigations revealed that the abundant acidic sites of MoS 2 -450-R plays a crucial role on the reaction of 5-HMF with methanol. Finally, based on the characterization of catalyst and reaction phenomena, a possible reaction network for the acetalization and etherification of 5-HMF with the methanol has been proposed. [Display omitted] • A new protocol for the selective preparation of furanic ethers is developed. • The tandem acetalization and etherification of 5-HMF is achieved. • The MoS 2 -450-R is a preferable catalyst for the conversion of 5-HMF. • A 99.0 % conversion with 83.7 % product selectivity is obtained at mild conditions. [ABSTRACT FROM AUTHOR]

Two different kinetic models were developed for the kinetic study of Domanic oil shale conversion in the supercritical water. The oil shale reaction order was evaluated with a three-lump reaction scheme taking into account oil shale, gases, and synthetic oil. Contrary to the commonly reported first-order, it was found that a higher order (2.5) is more suitable for the conversion of oil shale at supercritical water conditions. The main reaction mechanism and predictions were obtained using a more detailed reaction network (five-lump model), which precisely estimates the experimental yield of all compounds contemplated. The statistical analysis suggested that the estimated kinetic parameters were suitably optimized, as well as the sensitivity analysis confirmed that these are the optimal values. The conversion of organic matter into gas and coke through free radical reactions exhibits larger rates using supercritical water. Low temperature (380 °C) and short reaction times favor the yield of synthetic oil because when these conditions are exceeded secondary cracking reactions provoke the generation of gases. Gas production is mainly carried out by the conversion of organic matter for brief reaction times and the transformation of carbonates for extended periods. [Display omitted] • Oil shale conversion is properly represented by a reaction order of 2.5. • Organic matter is mainly converted to gas and coke by free radical reactions. • Temperatures near 380 °C favor the production of synthetic oil. • Gas is mainly produced from carbonates at long periods of time. • Kinetic parameters are optimal and properly fit the experimental data. [ABSTRACT FROM AUTHOR]

The partial reduction of nitroarenes with hydroxides in alcoholic media is a venerable yet very direct approach to the synthesis of symmetrical azoxyarenes. Herein, the first application of this method to nitropyridines is disclosed, presenting its adaptation with these challenging but important substrates. [ABSTRACT FROM AUTHOR]

Simulations on an ODE-based model shows that there are many common points between Viedma deracemization and chiral self-assemblies of achiral building blocks towards chiral nanoparticles. Both systems occur in a closed system with energy exchange but no matter exchange with the surroundings and show parallel reversible growth mechanisms which coexist with an irreversible cluster breaking (grinding). The various mechanisms of growth give rise to the formation of polymerization/depolymerization cycles while the consecutive transformation of achiral monomer into chiral cluster results into an indirect enantioselective autocatalysis. Deracemization occurs by the destabilization of the racemic non-equilibrium stationary state likely because of the excess of entropy production generated by the coupling of the reversible cluster growth mechanisms with grinding. Results show that the SMSB bias from the racemic composition occurs already at the oligomeric level of polymerization. Our model goes beyond the scope of the effect of grinding by the stirring of solutions which is thoroughly reported in supramolecular chirality. For instance, some unique characteristics, as those of a SMSB in closed systems, the simultaneous presence of different coupled reversible growth mechanisms, the activation by a depolymerization agent and the reincorporation of oligomers to the polymer growth reactions, could be adapted to replicator selectivity and to the emergence of biological homochirality scenarios. [ABSTRACT FROM AUTHOR]

The reductive amination of fatty acids (FAs) and fatty acid methyl esters (FAMEs) has been identified as a green and effective method to produce N,N-dimethylalkylamines (ADMAs). With current technology, this reaction requires at least two reaction steps. Here, we report a heterogeneous catalytic system for the one-pot synthesis of ADMAs from FA(ME)s, utilizing solely H2 and methylamines (i.e. di- and trimethylamine). The reaction requires two recyclable catalysts: ortho-Nb2O5 for the amidation of FA(ME)s and PtVOx/SiO2 for the hydrogenation of the in situ generated fatty amide to ADMAs. The developed system has a wide range of applicability: it is able to convert all natural FAs to ADMAs (yields up to 90%) and also other tertiary amines were synthesized. Aside from the development of a sustainable and industrially applicable process (e.g. utilizing benign solvents or performing solventless reactions), a kinetic model was developed that describes the reaction rate's relationship with key process parameters such as the H2 pressure and water content. By tuning the reaction conditions, different ratios of primary, secondary and tertiary fatty amines can be obtained. [ABSTRACT FROM AUTHOR]

Inhalable nitric oxide (iNO) is a lifesaving, FDA-approved drug to improve oxygenation in persistent pulmonary hypertension of the newborn. iNO also has many other applications in lung diseases owing to its vasodilatory and antimicrobial effects. However, its wider therapeutic application is often prohibited by the high cost and logistical barriers of traditional NO/N2 gas tanks. Development of low-cost, portable and tankless nitric oxide (NO) generators is a critical need to advance iNO therapy. Here, we describe the feasibility of NO generation by the controlled reduction of nitrite (NO2−) ions. This was accomplished by using ascorbate to reduce NO2− ions mediated by a copper(I/II) redox pair complexed by an azo-crown ether ligand ([Cu(II)L]2+/[Cu(I)L]+) in the solution phase. We found that oxalate, a decomposition product of ascorbate, interferes with the NO generation from the copper-ligand complex. This interference was mitigated, and the reaction was further optimized. NO generation through this method was found to be highly controllable via its proportionality to the flow rate of NO2− injected into a reaction chamber containing the reducing components. Hence, this simple approach adds to the current collection of innovative methods under development to obviate the use of NO tanks for iNO delivery. [ABSTRACT FROM AUTHOR]

Hydrogen-transfer is the primary process responsible for elevating the degree of unsaturation of intermediates in zeolite-catalyzed methanol-to-hydrocarbon reactions, with olefins serving as the typical receptor and alkanes being produced as the by-product. Intriguingly, the introduction of CO was shown to suppress the selectivity of alkanes and enhance the production of aromatics, yet microscopic understanding of this phenomenon remains elusive. Here, based on ab initio molecular dynamics simulations and free energy sampling methods, we discover a non-olefin-induced hydrogen-transfer reaction in the presence of CO, with ketene/acetyl emerging as a more suitable hydrogen-transfer receptor than olefins. This predominant route enhances the degree of unsaturation of olefins without generating additional alkanes, and the produced dienes and acetaldehyde could further contribute to the formation of aromatics. Moreover, we construct a general mechanism applicable to a series of CO-coupled aromatics synthesis reactions, offering distinctive insights and strategies for the optimization of efficiency. The role of CO in zeolite-catalyzed methanol-to-hydrocarbon reactions remains unclear. Here, ab initio molecular dynamics and free energy sampling reveal a non-olefin-induced hydrogen transfer mediated by CO, with ketene/acetyl identified as a superior hydrogen-transfer receptor over olefins. [ABSTRACT FROM AUTHOR]

Exophiala spinifera strain FM, a black yeast and melanized ascomycete, shows potential for oil biodesulfurization by utilizing dibenzothiophene (DBT) as its sole sulfur source. However, the specific pathway and enzymes involved in this process remain unclear due to limited genome sequencing and metabolic understanding of E. spinifera. In this study, we sequenced the complete genome of E. spinifera FM to construct the first genome-scale metabolic model (GSMM) for this organism. Through bioinformatics analysis, we identified genes potentially involved in DBT desulfurization and degradation pathways for hazardous pollutants. We focused on understanding the cost associated with metabolites in sulfur assimilation pathway to assess economic feasibility, optimize resource allocation, and guide metabolic engineering and process design. To overcome knowledge gaps, we developed a genome-scale model for E. spinifera, iEsp1694, enabling a comprehensive investigation into its metabolism. The model was rigorously validated against growth phenotypes and gene essentiality data. Through shadow price analysis, we identified costly metabolites such as 3'-phospho-5'-adenylyl sulfate, 5'-adenylyl sulfate, and choline sulfate when DBT was used as the sulfur source. iEsp1694 encompasses the degradation of aromatic compounds, which serves as a crucial first step in comprehending the pan metabolic capabilities of this strain. [ABSTRACT FROM AUTHOR]

The hemostatic system prevents and stops bleeding, maintaining circulatory integrity after injury. It directly interacts with the complement system, which is key to innate immunity. In coronavirus disease 2019 (COVID-19), dysregulation of the hemostatic and complement systems has been associated with several complications. To understand the essential balance between activation and regulation of these systems, a quantitative systems immunology model can be established. The dynamics of the components are examined under three distinct conditions: the disease state representing symptomatic COVID-19 state, an intervened disease state marked by reduced levels of regulators, and drug interventions including heparin, tranexamic acid, avdoralimab, garadacimab, and tocilizumab. Simulation results highlight key components affected, including thrombin, tissue plasminogen activator, plasmin, fibrin degradation products, interleukin 6 (IL-6), the IL-6 and IL-6R complex, and the terminal complement complex (C5b-9). We explored that the decreased levels of complement factor H and C1-inhibitor significantly elevate these components, whereas tissue factor pathway inhibitor and alpha-2-macroglobulin have more modest effects. Furthermore, our analysis reveals that drug interventions have a restorative impact on these factors. Notably, targeting thrombin and plasmin in the early stages of thrombosis and fibrinolysis can improve the overall system. Additionally, the regulation of C5b-9 could aid in lysing the virus and/or infected cells. In conclusion, this study explains the regulatory mechanisms of the hemostatic and complement systems and illustrates how the biopathway machinery sustains the balance between activation and inhibition. The knowledge that we have acquired could contribute to designing therapies that target the hemostatic and complement systems. [ABSTRACT FROM AUTHOR]

Thawing Arctic permafrost can induce hydrologic change and alter redox conditions, shifting the balance of soil organic matter (SOM) decomposition. There remains uncertainty about how soil saturation and redox transitions impact dissolved and gas phase carbon fluxes, and efforts to link hydrobiogeochemical processes to ecosystem-scale models are limited. This study evaluates SOM decomposition of Arctic tundra soils using column experiments, water chemistry measurements, microbial community analysis, and a PFLOTRAN reactive transport model. Soil columns from a thermokarst channel (TC) and an upland tundra (UC) were exposed to cycles of saturation and drainage, which controlled carbon emissions. During saturation, an outflow of dissolved organic carbon from the UC soil correlated with elevated reduced iron and decreased pH; during drainage, UC carbon dioxide fluxes were 70% higher than TC fluxes. Intermittent methane release was observed for TC, consistent with higher methanogen abundance. Slower drainage in the TC soil correlated with more subtle biogeochemical changes. PFLOTRAN simulations captured experimental trends in soil carbon fluxes, oxygen concentrations, and water contents. The model was then used to evaluate additional soil water drainage rates. This study emphasizes the importance of considering hydrologic change when evaluating and simulating SOM decomposition in dynamic Arctic tundra environments. [ABSTRACT FROM AUTHOR]

Biological systems exhibit complex dynamics that differential equations can often adeptly represent. Ordinary differential equation models are widespread; until recently their construction has required extensive prior knowledge of the system. Machine learning methods offer alternative means of model construction: differential equation models can be learnt from data via model discovery using sparse identification of nonlinear dynamics (SINDy). However, SINDy struggles with realistic levels of biological noise and is limited in its ability to incorporate prior knowledge of the system. We propose a data-driven framework for model discovery and model selection using hybrid dynamical systems: partial models containing missing terms. Neural networks are used to approximate the unknown dynamics of a system, enabling the denoising of the data while simultaneously learning the latent dynamics. Simulations from the fitted neural network are then used to infer models using sparse regression. We show, via model selection, that model discovery using hybrid dynamical systems outperforms alternative approaches. We find it possible to infer models correctly up to high levels of biological noise of different types. We demonstrate the potential to learn models from sparse, noisy data in application to a canonical cell state transition using data derived from single-cell transcriptomics. Overall, this approach provides a practical framework for model discovery in biology in cases where data are noisy and sparse, of particular utility when the underlying biological mechanisms are partially but incompletely known. [ABSTRACT FROM AUTHOR]

Single-cell transcriptomics experiments provide gene expression snapshots of heterogeneous cell populations across cell states. These snapshots have been used to infer trajectories and dynamic information even without intensive, time-series data by ordering cells according to gene expression similarity. However, while single-cell snapshots sometimes offer valuable insights into dynamic processes, current methods for ordering cells are limited by descriptive notions of "pseudotime" that lack intrinsic physical meaning. Instead of pseudotime, we propose inference of "process time" via a principled modeling approach to formulating trajectories and inferring latent variables corresponding to timing of cells subject to a biophysical process. Our implementation of this approach, called Chronocell, provides a biophysical formulation of trajectories built on cell state transitions. The Chronocell model is identifiable, making parameter inference meaningful. Furthermore, Chronocell can interpolate between trajectory inference, when cell states lie on a continuum, and clustering, when cells cluster into discrete states. By using a variety of datasets ranging from cluster-like to continuous, we show that Chronocell enables us to assess the suitability of datasets and reveals distinct cellular distributions along process time that are consistent with biological process times. We also compare our parameter estimates of degradation rates to those derived from metabolic labeling datasets, thereby showcasing the biophysical utility of Chronocell. Nevertheless, based on performance characterization on simulations, we find that process time inference can be challenging, highlighting the importance of dataset quality and careful model assessment. Author summary: Single-cell RNA sequencing can measure the amounts of RNA in individual cells, and although it is a snapshot experiment, cells that are differentiating can be captured in distinct states allowing for inference of "trajectories" or "velocity". Currently, methods that attempt to do so rely heavily on heuristics, with no mechanistic meaning associated with the "pseudotime" they assign to cells. We show that it is possible to infer trajectories under a biophysical model within a principled framework. By developing a trajectory model based on cell state transitions, we demonstrate that it is possible to infer interpretable latent variables, i.e. process time, corresponding to the timing of cells subjected to a biophysical process, as well as transcriptional parameters with biophysical meaning. However, we find this to be a challenging task. By characterizing failure scenarios in simulations and with quantitative assessment on real datasets, we concluded such inference is not always possible, especially when there is insufficient dynamical information embedded in the data. In such cases, our trajectory model allows us to perform model selection to determine if captured cells are better modeled by clusters. Our findings emphasize the importance of thoughtful experimental design and meticulous model assessment for valid trajectory inference. [ABSTRACT FROM AUTHOR]

Thermophysical properties of adsorbates and gas-phase species define the free energy landscape of heterogeneously catalyzed processes and are pivotal for an atomistic understanding of the catalyst performance. These thermophysical properties, such as the free energy or the enthalpy, are typically derived from density functional theory (DFT) calculations. Enthalpies are species-interdependent properties that are only meaningful when referenced to other species. The widespread use of DFT has led to a proliferation of new energetic data in the literature and databases. However, there is a lack of consistency in how DFT data is referenced and how the associated enthalpies or free energies are stored and reported, leading to challenges in reproducing or utilizing the results of prior work. Additionally, DFT suffers from exchange–correlation errors that often require corrections to align the data with other global thermochemical networks, which are not always clearly documented or explained. In this review, we introduce a set of consistent terminology and definitions, review existing approaches, and unify the techniques using the framework of linear algebra. This set of terminology and tools facilitates the correction and alignment of energies between different data formats and sources, promoting the sharing and reuse of ab initio data. Standardization of thermochemistry concepts in computational heterogeneous catalysis reduces computational cost and enhances fundamental understanding of catalytic processes, which will accelerate the computational design of optimally performing catalysts. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) electroreduction has extraordinary significance in curbing CO2 emissions while simultaneously producing value-added chemicals with economic and environmental benefits. In recent years, breakthroughs in designing catalysts, optimizing intrinsic activity, developing reactors, and elucidating reaction mechanisms have continuously driven the advancement of CO2 electroreduction. However, the industrialization of CO2 electroreduction remains a challenging task, with high energy consumption, high costs, limited reaction products, and restricted application scenarios being the issues that urgently need to be addressed. To accelerate the progress of CO2 electroreduction towards practical application, this review shifts the research focus from catalysts to aspects such as reactions and systems, aiming to improve reaction efficiency, reduce technical costs, expand the range of products, and enhance selectivity, offering readers a new perspective. In particular, innovative and specific design strategies such as CO2 reduction coupled with alternative oxidation, co-reduction reaction of CO2 and C/N/O/S-containing species, cascade systems, and integrated CO2 capture and reduction systems are discussed in detail. Additionally, personal views on the opportunities and future challenges of the aforementioned innovative strategies are provided, offering new insights for the future research and development of CO2 electroreduction. [ABSTRACT FROM AUTHOR]

Protein S-palmitoylation is the process by which a palmitoyl fatty acid is attached to a cysteine residue of a protein via a thioester bond. A range of methodologies are available for the detection of protein S-palmitoylation. In this study, two methods for the S-palmitoylation of different proteins were compared after metabolic labeling of cells with 15-hexadecynoic acid (15-YNE) to ascertain their relative usefulness. It was hypothesized that labeling cells with a traceable lipid would affect lipid metabolism and the cellular lipidome. In this study, we developed a method to track 15-YNE incorporation into lipids using liquid chromatography high-resolution mass spectrometry (LC-HRMS) as well as protein palmitoylation in the same sample. We observed a time- and concentration-dependent S-palmitoylation of calnexin and succinate dehydrogenase complex flavoprotein subunit A (SDHA) depending on the cell type. The detection of S-palmitoylation with a clickable fluorophore or biotin azide followed by immunoprecipitation is shown to be equally useful. 15-YNE was observed to be incorporated into a wide array of lipid classes during the process, yet it did not appear to modify the overall lipid composition of the cells. In conclusion, we show that 15-YNE is a useful tracer to detect both protein S-palmitoylation and lipid metabolism in the same sample. [ABSTRACT FROM AUTHOR]

In recent decades, there has been a growing interest in bio-refineries as a crucial element in transitioning to a low-carbon economy. One specific aspect of this interest is the conversion of carbohydrates into separate platform chemicals, such as furfural (FUR), which play a significant functional role in various daily life processes. This research paper focuses on investigating the use of a H-beta catalyst with SiO2/Al2O3 = 28 for producing furfural from xylose in water. Various conditions, such as temperature and initial solution concentration, are studied to determine their effect on FUR yield. The highest FUR yield (40 mol.%) is obtained when FUR is the only product species. We also report that about 90% yield from reaction with fresh catalyst can be achieved after catalyst regeneration. The activation energies for the reaction on the catalyst surface are found to be in the range of 38–75 kJ/mol. A mathematical kinetic model with three irreversible steps is derived to estimate the reaction sequence at 160, 180, and 200 °C. The model takes into account mechanisms such as adsorption, desorption, and transport (internal or external). Our results suggest that the H-beta catalyst shows high activity toward FUR yield and could be a promising alternative for mass-scale production of the latter. [ABSTRACT FROM AUTHOR]

Challenges in drug development for rare diseases such as pulmonary arterial hypertension can be addressed through the use of mathematical modeling. In this study, a quantitative systems pharmacology model of pulmonary arterial hypertension pathophysiology and pharmacology was used to predict changes in pulmonary vascular resistance and six-minute walk distance in the context of oral treprostinil clinical studies. We generated a virtual population that spanned the range of clinical observations and then calibrated virtual patient-specific weights to match clinical trials. We then used this virtual population to predict the results of clinical trials on the basis of disease severity, dosing regimen, time since diagnosis, and co-administered background therapies. The virtual population captured the effect of changes in trial design and patient subpopulation on clinical response. We also demonstrated the virtual trial workflow's potential for enriching populations based on clinical biomarkers to increase likelihood of trial success. [ABSTRACT FROM AUTHOR]

This paper describes and validates an algorithm to solve optimal control problems for agent-based models (ABMs). For a given ABM and a given optimal control problem, the algorithm derives a surrogate model, typically lower-dimensional, in the form of a system of ordinary differential equations (ODEs), solves the control problem for the surrogate model, and then transfers it back to the original ABM. It applies to quite general ABMs and offers several options for the ODE structure, depending on what information about the ABM is to be used. There is a broad range of applications for such an algorithm, since ABMs are used widely in the life sciences, such as ecology, epidemiology, and biomedicine and healthcare, areas where optimal control is an important purpose for modeling, such as for medical digital twin technology. Author summary: The motivation for the work reported in this paper is the development of mathematical tools for medical digital twin development. Based on a computational model of some aspects of human biology, there is a two-way interaction between the physical twin (the patient) and the digital twin (the model). In one direction, the model is periodically calibrated with patient-derived data to evolve together with the patient, making the model into a digital twin, and in the other direction, optimal interventions derived from the digital twin are administered to the patient. In many cases, the underlying computational model does not readily provide optimal control methods to identify interventions. Model types such as agent-based models (ABMs) are often more suitable than models consisting of ordinary differential equations (ODEs). In this paper, we present an algorithm that takes as input a general ABM, together with an optimal control problem and provides as output a solution. This is accomplished by first constructing a surrogate ODE model, solving the optimal control problem, and then lifting it to the ABM. The algorithm provides for several different types of surrogate models, ranging from those that implement mechanistic features of the ABM to purely phenomenological models. The algorithm is validated by applying it to a predator-prey ABM and a metabolic network represented as an ABM. [ABSTRACT FROM AUTHOR]