Thousands Of Reactions Research Articles

Pre-amplification in the context of high-throughput qPCR gene expression experiment.

BackgroundWith the introduction of the first high-throughput qPCR instrument on the market it became possible to perform thousands of reactions in a single run compared to the previous hundreds. In the high-throughput reaction, only limited volumes of highly concentrated cDNA or DNA samples can be added. This necessity can be solved by pre-amplification, which became a part of the high-throughput experimental workflow. Here, we focused our attention on the limits of the specific target pre-amplification reaction and propose the optimal, general setup for gene expression experiment using BioMark instrument (Fluidigm).ResultsFor evaluating different pre-amplification factors following conditions were combined: four human blood samples from healthy donors and five transcripts having high to low expression levels; each cDNA sample was pre-amplified at four cycles (15, 18, 21, and 24) and five concentrations (equivalent to 0.078 ng, 0.32 ng, 1.25 ng, 5 ng, and 20 ng of total RNA). Factors identified as critical for a success of cDNA pre-amplification were cycle of pre-amplification, total RNA concentration, and type of gene. The selected pre-amplification reactions were further tested for optimal Cq distribution in a BioMark Array. The following concentrations combined with pre-amplification cycles were optimal for good quality samples: 20 ng of total RNA with 15 cycles of pre-amplification, 20x and 40x diluted; and 5 ng and 20 ng of total RNA with 18 cycles of pre-amplification, both 20x and 40x diluted.ConclusionsWe set up upper limits for the bulk gene expression experiment using gene expression Dynamic Array and provided an easy-to-obtain tool for measuring of pre-amplification success. We also showed that variability of the pre-amplification, introduced into the experimental workflow of reverse transcription-qPCR, is lower than variability caused by the reverse transcription step.Electronic supplementary materialThe online version of this article (doi:10.1186/s12867-015-0033-9) contains supplementary material, which is available to authorized users.

Mimoza: web-based semantic zooming and navigation in metabolic networks.

BackgroundThe complexity of genome-scale metabolic models makes them quite difficult for human users to read, since they contain thousands of reactions that must be included for accurate computer simulation. Interestingly, hidden similarities between groups of reactions can be discovered, and generalized to reveal higher-level patterns.ResultsThe web-based navigation system Mimoza allows a human expert to explore metabolic network models in a semantically zoomable manner: The most general view represents the compartments of the model; the next view shows the generalized versions of reactions and metabolites in each compartment; and the most detailed view represents the initial network with the generalization-based layout (where similar metabolites and reactions are placed next to each other). It allows a human expert to grasp the general structure of the network and analyze it in a top-down mannerConclusionsMimoza can be installed standalone, or used on-line at http://mimoza.bordeaux.inria.fr/, or installed in a Galaxy server for use in workflows. Mimoza views can be embedded in web pages, or downloaded as COMBINE archives.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0151-5) contains supplementary material, which is available to authorized users.

Catalytic kinetics of single gold nanoparticles observed via optical microwell arrays

Catalytic activities and kinetics are measured at the single-particle level for gold nanoparticles catalyzing a fluorogenic oxidation reaction. This measurement is accomplished by confining the reactions in optically addressable microwell arrays. Citrate-capped gold nanoparticles are isolated in sealed ∼70 fL microwells along with a substrate, and the accumulation of a fluorescent product over time is observed. Thousands of reactions are measured in parallel. Catalytic activities are calculated for each nanoparticle and the activity distribution is analyzed.

A method for quantitative analysis of standard and high-throughput qPCR expression data based on input sample quantity.

Over the past decade rapid advances have occurred in the understanding of RNA expression and its regulation. Quantitative polymerase chain reactions (qPCR) have become the gold standard for quantifying gene expression. Microfluidic next generation, high throughput qPCR now permits the detection of transcript copy number in thousands of reactions simultaneously, dramatically increasing the sensitivity over standard qPCR. Here we present a gene expression analysis method applicable to both standard polymerase chain reactions (qPCR) and high throughput qPCR. This technique is adjusted to the input sample quantity (e.g., the number of cells) and is independent of control gene expression. It is efficiency-corrected and with the use of a universal reference sample (commercial complementary DNA (cDNA)) permits the normalization of results between different batches and between different instruments – regardless of potential differences in transcript amplification efficiency. Modifications of the input quantity method include (1) the achievement of absolute quantification and (2) a non-efficiency corrected analysis. When compared to other commonly used algorithms the input quantity method proved to be valid. This method is of particular value for clinical studies of whole blood and circulating leukocytes where cell counts are readily available.

Numerical modeling of auto-ignition of isolated fuel droplets in microgravity

In this work we present and apply a mathematical model to simulate the auto-ignition of isolated fuel droplets burning in microgravity conditions. The aim is to demonstrate the fundamental role of the low-temperature mechanisms on the auto-ignition process and to show that several experimental observations cannot be explained without considering the formation of cool-flames around the burning droplet.Thus, in order to better clarify the importance of the low-temperature chemistry, a detailed kinetic scheme (with hundreds of species and thousands of reactions) was adopted to model the spontaneous ignition of isolated droplets of n-heptane, n-decane, and n-dodecane in air, in a wide range of operating conditions (with environment temperatures from 600K to 1100K and pressures from 1bar to 20bar).The model was able to correctly identify the typical auto-ignition regimes of n-alkane oxidation. The comparison with the experimental measurements available in the literature was satisfactory: both first-stage and total induction times were reasonably captured by the numerical simulations. The simulations confirmed that the low-temperature chemistry plays a role of paramount importance in the auto-ignition process. In particular, the competition between low- and high-temperature mechanisms was found to explain the different types of auto-ignition which can be experimentally observed.

Knowledge-based generalization of metabolic models.

Genome-scale metabolic model reconstruction is a complicated process beginning with (semi-)automatic inference of the reactions participating in the organism's metabolism, followed by many iterations of network analysis and improvement. Despite advances in automatic model inference and analysis tools, reconstruction may still miss some reactions or add erroneous ones. Consequently, a human expert's analysis of the model will continue to play an important role in all the iterations of the reconstruction process. This analysis is hampered by the size of the genome-scale models (typically thousands of reactions), which makes it hard for a human to understand them. To aid human experts in curating and analyzing metabolic models, we have developed a method for knowledge-based generalization that provides a higher-level view of a metabolic model, masking its inessential details while presenting its essential structure. The method groups biochemical species in the model into semantically equivalent classes based on the ChEBI ontology, identifies reactions that become equivalent with respect to the generalized species, and factors those reactions into generalized reactions. Generalization allows curators to quickly identify divergences from the expected structure of the model, such as alternative paths or missing reactions, that are the priority targets for further curation. We have applied our method to genome-scale yeast metabolic models and shown that it improves understanding by helping to identify both specificities and potential errors.

Knowledge-based generalization of metabolic networks: A practical study

The complex process of genome-scale metabolic network reconstruction involves semi-automatic reaction inference, analysis, and refinement through curation by human experts. Unfortunately, decisions by experts are hampered by the complexity of the network, which can mask errors in the inferred network. In order to aid an expert in making sense out of the thousands of reactions in the organism's metabolism, we developed a method for knowledge-based generalization that provides a higher-level view of the network, highlighting the particularities and essential structure, while hiding the details. In this study, we show the application of this generalization method to 1,286 metabolic networks of organisms in Path2Models that describe fatty acid metabolism. We compare the generalised networks and show that we successfully highlight the aspects that are important for their curation and comparison.

MetDraw: automated visualization of genome-scale metabolic network reconstructions and high-throughput data

Metabolic reaction maps allow visualization of genome-scale models and high-throughput data in a format familiar to many biologists. However, creating a map of a large metabolic model is a difficult and time-consuming process. MetDraw fully automates the map-drawing process for metabolic models containing hundreds to thousands of reactions. MetDraw can also overlay high-throughput 'omics' data directly on the generated maps.

Three milieux for interstellar chemistry: gas, dust, and ice

The interdisciplinary science of astrochemistry is 45 years of age, if we pinpoint its origin to have occurred when the first polyatomic molecules were detected in the interstellar gas. Since that time, the field has grown remarkably from an esoteric area of research to one that unites scientists around the globe. Almost 200 different molecules have been detected in the gas-phase of interstellar clouds, mainly by rotational spectroscopy, while dust particles and their icy mantles in colder regions can be probed by vibrational spectroscopy. Astrochemistry is exciting to scientists in a number of different fields. Astronomers are interested in molecular spectra from the heavens because such spectra are excellent probes of the physical conditions where molecules exist, while chemists are interested in the exotic molecules, their spectra, and the unusual chemical processes that produce and destroy them under conditions often very different from those on our home planet. Chemical simulations involving thousands of reactions are now used to calculate concentrations and spectra of interstellar molecules as functions of time. Even biologists share an interest in the subject, because the interstellar clouds of gas and dust, portions of which collapse to form stars and planetary systems, contain organic molecules that may become part of the initial inventory of new planets and may indeed be the precursors of life. An irresistible subject to its practitioners, astrochemistry is proving to be exciting to a much wider audience. In this perspective article, the field is first introduced, and the emphasis is then placed on the three environments in which chemistry occurs in the interstellar medium: the gas phase, the surfaces of bare dust particles, and the ice mantles that cover bare grains in cold dense interstellar clouds. What we do know and what we do not know is distinguished. The status of chemical simulations for a variety of interstellar sources having to do with stellar and planetary evolution is surveyed. An optimistic view of the future of astrochemistry ends the article.

A run time combustion zoning technique towards the EDC approach in large-scale CFD simulations

Purpose – This paper aims to present a novel run time combustion zoning (RTCZ) technique based on the working principle of eddy dissipation concept (EDC) for combustion modeling. This technique selectively chooses cells in which the full reaction mechanism needs to be solved. The selection criterion is based on the concept of differentiating between combustion and the non-combustion zone. With this approach, considerable reduction in computational load and stability of the solution was observed and even the number of iterations required to achieve a stable solution was significantly reduced. Design/methodology/approach – Computational fluid dynamics (CFD) simulations of real life combustion problems such as industrial scale flares, fuel fired furnaces and IC engines are difficult due to the strong interactions of chemistry with turbulence as well as the wide range distribution of time and length scales. In addition, comprehensive chemical mechanisms for hydrocarbon combustion may include hundreds of species and thousands of reactions that are known in detail for only a limited number of fuels. Even with the most advanced computers, accurate simulation of these problems is not easy. Hence, the modeler needs to have strategies to either simplify the chemistry or to improve the computational efficiency. Findings – The EDC turbulence model has been widely used for treating the interaction between turbulence and the chemistry in combustion problems. In an EDC model, combustion is assumed to occur in a constant pressure reactor, with initial conditions taken as the concentration of the current species and temperature in the cell. With these assumptions, EDC solves the full or simplified reaction mechanism in all the grid cells at all iterations. Originality/value – This paper presents a novel RTCZ technique for improving the computational efficiency, when the EDC model is used in CFD modeling. Considerable reduction in computational time and stability of the solution can be achieved. It was also observed that the number of iterations required to achieve a converged solution was significantly reduced.

Exploration and comparison of inborn capacity of aerobic and anaerobic metabolisms of Saccharomyces cerevisiae for microbial electrical current production

Saccharomyces cerevisiae possesses numerous advantageous biological features, such as being robust, easily handled, mostly non-pathogenic and having high catabolic rates, etc., which can be considered as merits for being used as a promising biocatalyst in microbial fuel cells (MFCs) for electricity generation. Previous studies have developed efficient MFC configurations to convert metabolic electron shuttles, such as cytoplasmic NADH, into usable electric current. However, no studies have elucidated the maximum potential of S. cerevisiae for current output and the underlying metabolic pathways, resulting from the interaction of thousands of reactions inside the cell during MFC operation. To address these two key issues, this study used in silico metabolic engineering techniques, flux balance analysis (FBA), and flux variability analysis with target flux minimization (FATMIN), to model the metabolic perturbation of S. cerevisiae under the MFC-energy extraction. The FBA results showed that, in the cytoplasmic NADH-dependent mediated electron transfer (MET) mode, S. cerevisiae had a potential to produce currents at up to 5.781 A/gDW for the anaerobic and 6.193 A/gDW for the aerobic environments. The FATMIN results showed that the aerobic and anaerobic metabolisms are resilient, relying on six and five contributing reactions respectively for high current production. Two reactions, catalyzed by glutamate dehydrogenase (NAD) (EC 1.4.1.3) and methylene tetrahydrofolate dehydrogenase (NAD) (EC 1.5.1.5), were shared in both current-production modes and contributed to over 80% of the identified maximum current outputs. It is also shown that the NADH regeneration was much less energy costly than biomass production rate. Taken together, our finding suggests that S. cerevisiae should receive more research effort for MFC electricity production.

Inverse Gillespie for inferring stochastic reaction mechanisms from intermittent samples

Gillespie stochastic simulation is used extensively to investigate stochastic phenomena in many fields, ranging from chemistry to biology to ecology. The inverse problem, however, has remained largely unsolved: How to reconstruct the underlying reactions de novo from sparse observations. A key challenge is that often only aggregate concentrations, proportional to the population numbers, are observable intermittently. We discovered that under specific assumptions, the set of relative population updates in phase space forms a convex polytope whose vertices are indicative of the dominant underlying reactions. We demonstrate the validity of this simple principle by reconstructing stochastic models (reaction structure plus propensities) from a variety of simulated and experimental systems, where hundreds and even thousands of reactions may be occurring in between observations. In some cases, the inferred models provide mechanistic insight. This principle can lead to the understanding of a broad range of phenomena, from molecular biology to population ecology.

Enzyme Reaction Annotation Using Cloud Techniques

An understanding of the activities of enzymes could help to elucidate the metabolic pathways of thousands of chemical reactions that are catalyzed by enzymes in living systems. Sophisticated applications such as drug design and metabolic reconstruction could be developed using accurate enzyme reaction annotation. Because accurate enzyme reaction annotation methods create potential for enhanced production capacity in these applications, they have received greater attention in the global market. We propose the enzyme reaction prediction (ERP) method as a novel tool to deduce enzyme reactions from domain architecture. We used several frequency relationships between architectures and reactions to enhance the annotation rates for single and multiple catalyzed reactions. The deluge of information which arose from high-throughput techniques in the postgenomic era has improved our understanding of biological data, although it presents obstacles in the data-processing stage. The high computational capacity provided by cloud computing has resulted in an exponential growth in the volume of incoming data. Cloud services also relieve the requirement for large-scale memory space required by this approach to analyze enzyme kinetic data. Our tool is designed as a single execution file; thus, it could be applied to any cloud platform in which multiple queries are supported.

RCARM: Reaction classification using automated reaction mapping

AbstractDetailed chemical kinetic modeling of gas‐phase reactions can result in automatically generated mechanisms that contain thousands of reactions. In this paper, we describe the development of a rule‐based expert system tool that organizes these reactions into classes such as hydrogen abstraction and beta scission. We have developed 29 simple classification rules, 20 complex (well‐skipping) classification rules, and four second‐stage classification rules. This greatly simplifies the task of the chemical kineticist who wishes to verify, analyze, and gain insights into the reactions comprising the mechanism. This system, which is based on the automated identification of the bonds that break and form in a chemical reaction (the reaction mapping problem), is used to classify reactions in three different mechanisms. © 2012 Wiley Periodicals, Inc. Int J Chem Kinet 45: 125–139, 2013

The role of sensitivity and uncertainty analysis in combustion modelling

Models play an important role in improving our understanding of combustion processes and more and more are able to assist in the design of advanced energy conversion devices. Due to constant improvements in computing power and techniques such as automatic kinetic mechanism generation, we have the ability to represent combustion processes with increasing levels of detail. This is particularly true for kinetic processes where complex mechanisms are being developed which describe the oxidation of both conventional and alternative fuels. These mechanisms may comprise of up to hundreds of species and thousands of reactions with thermo-kinetic data derived from a wide variety of sources including direct measurements, global combustion experiments, and theoretical calculations. However, significant uncertainties in the data used to parametrise combustion models still exist. These input uncertainties propagate through models of combustion devices leading to uncertainties in the prediction of key combustion properties. In order to improve confidence in these models to the extent where they can successfully be used in design, input uncertainties need to be reduced as far as possible. This requires focussing efforts on those parameters which drive predictive uncertainty, which may be identified through sensitivity analysis. The paper will describe the methodologies available for the sensitivity and uncertainty analysis of combustion models with examples focussed on chemical kinetics. It will then discuss how such techniques can be incorporated into strategies for model improvement and will try to provide some future perspectives on how we can proceed in this direction as a research community.

A quantitative explanation for the apparent anomalous temperature dependence of OH + HO2 = H2O + O2 through multi-scale modeling

Kinetic models for complex chemical mechanisms are comprised of tens to thousands of reactions with rate constants informed by data from a wide variety of sources – rate constant measurements, global combustion experiments, and theoretical kinetics calculations. In order to integrate information from distinct data types in a self-consistent manner, a framework for combustion model development is presented that encapsulates behavior across a wide range of chemically relevant scales from fundamental molecular interactions to global combustion phenomena. The resulting kinetic model consists of a set of theoretical kinetics parameters (with constrained uncertainties), which are related through kinetics calculations to temperature/pressure/bath-gas-dependent rate constants (with propagated uncertainties), which in turn are related through physical models to combustion behavior (with propagated uncertainties). Direct incorporation of theory in combustion model development is expected to yield more reliable extrapolation of limited data to conditions outside the validation set, which is particularly useful for extrapolating to engine-relevant conditions where relatively limited data are available. Several key features of the approach are demonstrated for the H2O2 decomposition mechanism, where a number of its constituent reactions continue to have large uncertainties in their temperature and pressure dependence despite their relevance to high-pressure, low-temperature combustion of a variety of fuels. Here, we use the approach to provide a quantitative explanation for the apparent anomalous temperature dependence of OH+HO2=H2O+O2 – in a manner consistent with experimental data from the entire temperature range and ab initio transition-state theory within their associated uncertainties. Interestingly, we do find a rate minimum near 1200K, although the temperature dependence is substantially less pronounced than previously suggested.

Experimental and Skeletal Kinetic Model Study of Compressed Natural Gas Fueled Homogeneous Charge Compression Ignition Engine

Problem statement: In homogeneous charge compression ignition engines fuel oxidation chemistry determines the auto-ignition timing, heat release, reaction intermediates and the ultimate products of combustion. To shorten development time and to understand combustion processes, the use of simulation is increasing. Approach: A model that correctly simulates fuel oxidation at these conditions would be a useful design tool. Detailed models of hydrocarbon fuel oxidation, consisting of hundreds of chemical species and thousands of reactions. A way to lessen the burden was to use a skeletal reaction model, containing only tens of species and reactions. Results: The model was developed from the existing pre-ignition model, which had 10 species, 5 elementary reactions for kinetic and 6 elementary reactions for equilibrium and the standard k-? turbulence model had been used in this investigation. This model combines the chemistry of the low, intermediate and high temperature regions. Conclusion: Simulations are compared with measured and calculated data from the engine operating at the following conditions: speed 1500 RPM, inlet temperature 363-433 K, fuel CNG and ? range 3-5. The simulations are generally in good agreement with the experimental data including temperature, pressure, combustion duration and ignition delay and heat release.

Superessential reactions in metabolic networks

The metabolic genotype of an organism can change through loss and acquisition of enzyme-coding genes, while preserving its ability to survive and synthesize biomass in specific environments. This evolutionary plasticity allows pathogens to evolve resistance to antimetabolic drugs by acquiring new metabolic pathways that bypass an enzyme blocked by a drug. We here study quantitatively the extent to which individual metabolic reactions and enzymes can be bypassed. To this end, we use a recently developed computational approach to create large metabolic network ensembles that can synthesize all biomass components in a given environment but contain an otherwise random set of known biochemical reactions. Using this approach, we identify a small connected core of 124 reactions that are absolutely superessential (that is, required in all metabolic networks). Many of these reactions have been experimentally confirmed as essential in different organisms. We also report a superessentiality index for thousands of reactions. This index indicates how easily a reaction can be bypassed. We find that it correlates with the number of sequenced genomes that encode an enzyme for the reaction. Superessentiality can help choose an enzyme as a potential drug target, especially because the index is not highly sensitive to the chemical environment that a pathogen requires. Our work also shows how analyses of large network ensembles can help understand the evolution of complex and robust metabolic networks.

Modeling the combustion of high molecular weight fuels by a functional group approach

AbstractModeling the combustion behavior of real fuels is a challenging task: Significant analytical efforts are required to characterize the fuel composition, and comprehensive kinetic models are necessary to reproduce the behavior of the different fuel components. Both these aspects become increasingly critical for fuels having a high molecular weight, wherein both the characterization of the single components and the kinetics involved in their oxidation become extremely complex. Indeed, kinetic models for large hydrocarbons can include thousands of species and tens of thousands of reactions. For these reasons, only a limited number of representative components are generally included in the simulations and these large kinetic mechanisms are reduced to simulate the behavior of real fuels in practical conditions. We propose a novel approach to the simulation of the combustion of high molecular weight fuels, wherein the fuel surrogate is defined in terms of pseudospecies including the functional groups contained in the actual fuel. These pseudocomponents, representing linear, branched, aromatic, saturated, and unsaturated structures, can undergo the typical reactions responsible for the low‐temperature ignition of hydrocarbon as well as the interactions occurring in fuel blends. The basics of this concept will be presented, through application to linear and branched alkanes, and the potential of this approach is assessed by means of comparisons with experimental data and detailed kinetic simulations. The potential of this methodology for reducing computational expense in computational fluid dynamics simulations is also highlighted. © 2012 Wiley Periodicals, Inc. Int J Chem Kinet 44: 257–276, 2012

Oxidation and combustion mechanisms of paraffin hydrocarbons: Transfer from C1-C7 to C8H18, C9H20, and C10H22

At the present time, detailed kinetic mechanisms (DKMs) for higher hydrocarbons, which include hundreds of particles and thousands of reactions, are proposed. These DKMs have a number of undoubted advantages because they aspire to description of a wide class of phenomena. However, their application, e.g., for modeling of turbulent combustion, is difficult due to their extreme inconvenience. In addition, they are limited to a certain degree and cannot be considered to be comprehensive. As an alternative to such DKMs, we construct no maximum mechanism in this work, but an optimum mechanism of high- and low-temperature oxidation and combustion of normal paraffin hydrocarbons. This mechanism, in accordance with the previously proposed algorithm, contains only general processes that govern the reaction rate and the formation of basic intermediate and final products. A such mechanism has the status of an nonempirical DKM because all parts, including elementary reactions, have kinetic substantiation. The mechanism itself has two features: (i) Reactions of so-called double addition of oxygen (first, to the alkyl radical, then to the isomerized form of the formed peroxide radical) are lacking because the first addition is considered to be sufficient; (ii) Isomeric compounds and their derivative substances as intermediate particles are not considered, because this means of oxidation is slower than through molecules and radicals of the normal structure. The application of the algorithm results in sufficiently compact mechanisms that are important for modeling chemical processes with the participation of paraffin hydrocarbons C n with large n. Previously, this was done for propane, n-butane, n-pentane, n-hexane, and n-heptane; in the present article, it was done for n-octane, n-nonane, and n-decane. The major feature of all the mechanisms is the appearance of stages, viz., cold and blue flames during low-temperature spontaneous ignition. The direct comparison of the calculation and experiment results is carried out.