Thousands Of Reactions Research Articles

Research advances of high-throughput cell-based drug screening systems based on microfluidic technique

药物筛选是新药研发的关键步骤,创新药物的发现需要采用适当的药物作用靶点对大量化合物样品进行筛选。高通量筛选系统能够实现数千个反应同时测试和分析,大大提高了药物筛选的实验规模和效率。其中基于细胞水平的高通量药物筛选系统因为更加接近人体生理条件,成为主要的筛选模式。而目前发展成熟的高通量细胞筛选系统主要基于多孔板,存在细胞培养条件单一、耗时费力、试剂消耗量大等问题,且较难实现复杂的组合药物筛选。微流控技术作为一种在微米尺度通道中操纵和控制微流体的技术,具有微量、高效、高通量和自动化的优点,能较好地克服多孔板筛选系统的不足,为构建细胞高通量药物筛选系统提供了一种高效、可靠的技术手段。微流控系统在细胞培养材料、芯片结构设计和流体控制方面均可灵活变化,能更好地实现对细胞生长微环境的调控和模拟。文章综述了基于微流控技术的细胞水平高通量药物筛选系统的研究进展,按照不同的微流体操控模式,对基于灌注流、液滴和微阵列的3种类型的微流控细胞筛选系统进行了分类介绍,并分别总结了它们的优缺点,最后展望了微流控细胞水平高通量药物筛选系统的发展前景,提出了该领域目前存在的问题以及解决问题的方向。

Causal interactions from proteomic profiles: Molecular data meet pathway knowledge

SummaryWe present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's traditional approach of explaining changes in data using prior knowledge but does this at the scale of hundreds of thousands of reactions. This is a specific example of how to automate scientific reasoning processes and illustrates the power of mapping from experimental data to prior knowledge via logic programming. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at http://causalpath.org.

Structure reveals why radical enzyme isn’t radical

The family of enzymes known as the radical S -adenosyl- l -methionine (SAM) enzymes catalyzes thousands of reactions across biology. Most radical SAM enzymes work via a mechanism in which cleavage of SAM forms a radical. But tryptophan 2C methyltransferase (TsrM), which catalyzes the addition of a methyl group to the indole of l -tryptophan in the synthesis of the antibiotic thiostrepton, isn’t a typical radical SAM enzyme. It uses SAM as a substrate but doesn’t form a radical. Squire J. Booker and Hayley L. Knox of Pennsylvania State University, Catherine L. Drennan and Percival Yang-Ting Chen of the Massachusetts Institute of Technology, and coworkers report structures of TsrM from the bacterium Kitasatospora setae that may help explain the enzyme’s anomalous behavior ( Nat. Chem. Biol. 2021, DOI: 10.1038/s41589-020-00717-y ). The team proposes that SAM plays a dual role in the reaction. It donates a methyl group to the enzyme’s

Structure reveals why radical enzyme isn’t radical

The family of enzymes known as the radical S -adenosyl- l -methionine (SAM) enzymes catalyzes thousands of reactions across biology. Most radical SAM enzymes work via a mechanism in which cleavage ...

Opportunities at the Interface of Network Science and Metabolic Modeling.

Metabolism plays a central role in cell physiology because it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimization principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology.

Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation

Combustion is a complex chemical system which involves thousands of chemical reactions and generates hundreds of molecular species and radicals during the process. In this work, a neural network-based molecular dynamics (MD) simulation is carried out to simulate the benchmark combustion of methane. During MD simulation, detailed reaction processes leading to the creation of specific molecular species including various intermediate radicals and the products are intimately revealed and characterized. Overall, a total of 798 different chemical reactions were recorded and some new chemical reaction pathways were discovered. We believe that the present work heralds the dawn of a new era in which neural network-based reactive MD simulation can be practically applied to simulating important complex reaction systems at ab initio level, which provides atomic-level understanding of chemical reaction processes as well as discovery of new reaction pathways at an unprecedented level of detail beyond what laboratory experiments could accomplish.

Cloud based tool for analysis of chemical kinetic mechanisms

The size and complexity of chemical kinetic models used to simulate combustion of hydrocarbon fuels continue to increase with our improved understanding of the underlying physical mechanisms, along with the improved ability to utilize such models as the result of increased computational power and efficiency. As mechanisms grow beyond thousands of species and tens of thousands of reactions, it becomes increasingly difficult to manually check for errors and physical inconsistencies. We present several automated methods to check for such issues in the specification of chemical reaction models. First, we demonstrate how discontinuities in thermodynamic data can cause simulation difficulties manifested as long wall-clock times and convergence failures. To correct this type of problem, we describe an automated method for refitting thermodynamic parameters. We also outline several methods to check the timescales of reaction rate coefficients to ensure physical consistency. All the methods are made available through a web-based tool (https://combustiontools.llnl.gov) intended to aid mechanism developers and users to improve the accuracy and performance of fuel models used by the combustion community.

Fluxer: a web application to compute, analyze and visualize genome-scale metabolic flux networks.

Next-generation sequencing has paved the way for the reconstruction of genome-scale metabolic networks as a powerful tool for understanding metabolic circuits in any organism. However, the visualization and extraction of knowledge from these large networks comprising thousands of reactions and metabolites is a current challenge in need of user-friendly tools. Here we present Fluxer (https://fluxer.umbc.edu), a free and open-access novel web application for the computation and visualization of genome-scale metabolic flux networks. Any genome-scale model based on the Systems Biology Markup Language can be uploaded to the tool, which automatically performs Flux Balance Analysis and computes different flux graphs for visualization and analysis. The major metabolic pathways for biomass growth or for biosynthesis of any metabolite can be interactively knocked-out, analyzed and visualized as a spanning tree, dendrogram or complete graph using different layouts. In addition, Fluxer can compute and visualize the k-shortest metabolic paths between any two metabolites or reactions to identify the main metabolic routes between two compounds of interest. The web application includes >80 whole-genome metabolic reconstructions of diverse organisms from bacteria to human, readily available for exploration. Fluxer enables the efficient analysis and visualization of genome-scale metabolic models toward the discovery of key metabolic pathways.

Axisymmetric mixed convective propulsion of a non-Newtonian fluid through a ciliated tubule

Thousands of chemical reactions occur in the human body when certain biological fluids, such as blood, semen, mucus, and synovial joint materials, move in various organs. These reactions play a vital role in regulating the life sustaining metabolic processes in the body. Analysis of thermal effects on these chemical reactions is relatively a new area in modern clinical medications. The present study investigates a simulation of the combined response due to heat and mass transport mechanisms taking place in the human body during the flow of physiological fluids. In particular, we focus our attention on the human male reproductive system, wherein the semen transports through the ductus efferentes due to metachronal waves of cilia. The constitutive relations of the robust Jeffrey viscoelastic fluid are used to model the human semen. The mathematical model of the present problem constitutes the axisymmetric flow of a Jeffrey fluid inside a vertical tubule under the influence of mixed convective heat and mass transfers. The inner side of the tubule is covered with ciliated structures. The influence of thermal behaviors of various metabolic processes in the human body due to an external heat source or sink is also taken into account. The mathematical formulation consists of using the approach of lubrication theory approximation; the nonlinear momentum, energy, and concentration equations are simplified to get analytical solutions. Explicit expressions for temperature, concentration, velocity, pressure gradient, and volume flow rate of the proposed bodily fluid (i.e., human semen) are formulated. The expression for the volume flow rate is used to estimate the volume flux of the semen under the influence of various parameters. A comparison between the theoretical and experimentally obtained values of the flow rate of the human semen is also made. It is noted that our calculated values are very close to the estimated values. Industrial applications of the present results are obvious in the fabrication of artificial cilia pumping systems for microfluidic flow systems.

Structural assessment of lumped reaction networks with correlating parameters

In this paper, we consider a lumped reaction network developed for studying the pyrolysis of real plastic waste carried out in a laboratory-scale two-stage batch reactor, with the primary intention of recovering valuable fuel components. Generally, the kinetic parameters of the reaction network, whether it is a lumped or detailed one, might correlate to some extent directly through the amounts of products to be formed. If the formation of two or more products has a strong correlation, we might even combine the corresponding reactions to reduce the size of the reaction network. This modeling step instinctively occurs when we have thousands of reactions, but it is usually not carried out in the case of a lumped model because of its elementary nature. Yet, in this work, we would like to show that taking the correlations between the formations of the pseudocomponents into consideration very much has its advantages. We show that these correlations can be utilized to increase the number of lumps included in the model, increasing its accuracy by applying some not so complicated structural modifications to the original model. This way, we can optimize the structure of the lumped reaction network so that we can capture the characteristics of the measurement rather efficiently without using an overcomplicated reaction model full of uncertain parameters.

Viscoadaptation Controls Diffusion and Intracellular Reaction Rates in Response to Heat and Energy Availability

Cells must precisely orchestrate thousands of reactions in both time and space. Yet reaction kinetics are highly dependent on uncontrollable environmental conditions such as temperature and energy availability. Here, we uncover a mechanism by which budding yeast counteract these environmental fluctuations by modulating viscosity to control the rate of cellular processes. This “viscoadaptation” is achieved via two carbohydrates, trehalose and glycogen, whose regulated production varies the viscosity of the intracellular environment, slowing the motion of individual proteins while maintaining their solubility. Viscoadaptation functions as both an acute stress response and a homeostatic mechanism, allowing cells growing from 22°C to 40°C to maintain constant rates of intracellular diffusion and diffusion-controlled chemical reactions. Multiple conditions that lower ATP trigger viscoadaptation, linking energy availability to reaction rate control. Viscoadaptation reveals viscosity to be a tunable property by which cells regulate diffusion-controlled processes dynamically in response to a changing environment.

Causal Interactions from Proteomic Profiles: Molecular Data Meets Pathway Knowledge

We present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's approach of explaining changes in data using prior knowledge in their memory and in the literature, but at the scale of hundreds of thousands of reactions. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. We first demonstrate the validity of the method on cell line perturbation experiments. In discovery mode we then identify 1) differential signaling within breast cancer and ovarian cancer, 2) subtype-specific signaling in these cancers, and 3) recurrent signaling relations across 32 cancer types. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at causalpath.org.

Degree centrality of combustion reaction networks for analysing and modelling combustion processes

Combustion research still needs more advanced fundamental understanding of combustion chemistry and dynamics from molecule scale to particle. The latter is also needed for soot and nanoparticles formation and combustion system control such as homogeneous charge compression ignition engine, and flame regimes and instability. The complex interactions between hundreds of species linked within thousands of reactions continue to be a challenge to analyse and model. The focus on this paper is to develop a method to facilitate the modelling and analysing of the detailed kinetics chemistry of fuels combustion. Through the use of combustion reaction networks (CRNs) analysis of degree centrality, principal species are identified during a combustion process by exploiting the introduced definition of principal species. A principal, central or the more active species of a combustion process, at a specific time step or cell of area/volume mesh, is the more tied up to other species in the CRN and so have the largest value of degree centrality. The accuracy of the dynamic identification of principal species, locally adapted to the thermochemical conditions at each time step/cell of the simulated combustion process, used by the employed directed relation graphs method of mechanisms reduction, is proved. The simulations were carried out using an adjusted dynamic adaptive chemistry approach of detailed chemistry implementing. It is demonstrated that an ‘active’ species in a combustion system would not necessary be considered as a part of important species set needed for its predictive simulations.

Filtering maxRatio results with machine learning models increases quantitative PCR accuracy over the fit point method

With qPCR reaching thousands of reactions per run, assay validation needs automation. We applied support vector machine to qPCR analysis and we could identify reactions with 100% accuracy, dispensing them from further validation. We achieved a greatly reduced workload that could improve high-throughput qPCR analysis.

Network reduction methods for genome-scale metabolic models.

Genome-scale metabolic models (GSMs) provide a comprehensive representation of cellular metabolism. GSMs provide a mechanistic link between cellular genotypes and metabolic phenotypes, and are thus widely used to analyze metabolism at the systems level. GSMs consist of hundreds or thousands of reactions. They have thus largely been analyzed with computationally efficient constraint-based methods such as flux-balance analysis, limiting their scope and phenotype prediction accuracy. Computationally more demanding but potentially more informative methods, such as kinetic and dynamic modeling, are currently limited to small or medium-sized models. Thus, it is desirable to achieve unbiased stoichiometric reductions of large-scale metabolic models to small, coarse-grained model representations that capture significant metabolic modules. Here, we review published automated and semiautomated methods used for large-scale metabolic model reduction. The top-down methods discussed provide minimal networks that retain a set of user-protected phenotypes, but may reduce the model's metabolic and phenotypic versatility. In contrast, the two bottom-up approaches reviewed retain a more unbiased set of phenotypes; at the same time, these methods require the partitioning of the GSM into metabolic subsystems by the user, and make strong assumptions on the subsystems' connections and their states, respectively.

Viscoadaptation Controls Diffusion and Reaction Rates in Response to Heat and Energy Availability

Cells must precisely orchestrate thousands of reactions in both time and space. Yet reaction kinetics are highly dependent on uncontrollable environmental conditions such as temperature and energy availability. Here, we uncover a mechanism by which budding yeast counteract these environmental fluctuations by modulating viscosity to control the rate of cellular processes. This “viscoadaptation” is achieved via two carbohydrates, trehalose and glycogen, whose regulated production varies the viscosity of the intracellular environment, slowing the motion of individual proteins while maintaining their solubility. Viscoadaptation functions as both an acute stress response and a homeostatic mechanism, allowing cells growing from 22℃ to 40℃ to maintain constant rates of intracellular diffusion and diffusion-controlled chemical reactions. Multiple conditions that lower ATP trigger viscoadaptation, linking energy availability to reaction rate control. Viscoadaptation reveals viscosity to be a tunable property by which cells regulate diffusion-controlled processes dynamically in response to a changing environment.

Interplay of Head, Tail, and Mid-Chain Radicals in Bulk Free-Radical and Reversible Degenerative Addition Fragmentation Chain-Transfer Polymerizations of Vinyl Acetate

A detailed kinetic model for isothermal bulk free-radical and degenerative reversible addition–fragmentation chain-transfer (RAFT) polymerizations of vinyl acetate is presented up to monomer conversions of ca. 0.95, considering a distinction between head and tail end-chain radicals and primary and tertiary mid-chain radicals (MCRs). Diffusional limitations are taken into account for conventional initiation and termination, with a reduction of the gel-effect as chain transfer to the monomer lowers the apparent termination reactivities. The tail radicals are essential for the accurate description of backbiting and RAFT deactivation and the primary MCRs for chain transfer to the polymer. The model is based on the application of the method of moments with over 100 macrospecies types and thousands of reactions to obtain the temporal evolution of the monomer conversion, the number and mass average chain length, the number fraction of short- and long-chain branches, the number fraction of unsaturated chains, and the number fraction of chains with head-to-head defects. After a successful model validation of the experimental literature data, the model is applied to understand the complex interplay of the radical types involved and to highlight the control of the branching and unsaturation amounts under RAFT polymerization conditions. Head-to-head defects can however not be avoided for realistic average chain lengths.

Escherichia coli-Based Cell-Free Protein Synthesis: Protocols for a robust, flexible, and accessible platform technology.

Over the last 50 years, Cell-Free Protein Synthesis (CFPS) has emerged as a powerful technology to harness the transcriptional and translational capacity of cells within a test tube. By obviating the need to maintain the viability of the cell, and by eliminating the cellular barrier, CFPS has been foundational to emerging applications in biomanufacturing of traditionally challenging proteins, as well as applications in rapid prototyping for metabolic engineering, and functional genomics. Our methods for implementing an E. coli-based CFPS platform allow new users to access many of these applications. Here, we describe methods to prepare extract through the use of enriched media, baffled flasks, and a reproducible method of tunable sonication-based cell lysis. This extract can then be used for protein expression capable of producing 900 µg/mL or more of super folder green fluorescent protein (sfGFP) in just 5 h from experimental setup to data analysis, given that appropriate reagent stocks have been prepared beforehand. The estimated startup cost of obtaining reagents is $4,500 which will sustain thousands of reactions at an estimated cost of $0.021 per µg of protein produced or $0.019 per µL of reaction. Additionally, the protein expression methods mirror the ease of the reaction setup seen in commercially available systems due to optimization of reagent pre-mixes, at a fraction of the cost. In order to enable the user to leverage the flexible nature of the CFPS platform for broad applications, we have identified a variety of aspects of the platform that can be tuned and optimized depending on the resources available and the protein expression outcomes desired.

Gsmodutils: a python based framework for test-driven genome scale metabolic model development.

MotivationGenome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management. For example, when genome annotations are updated or new understanding regarding behaviour is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build, test and learn cycle.ResultsAs part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimizing error between model versions.Availability and implementationThe software framework described within this paper is open source and freely available from http://github.com/SBRCNottingham/gsmodutils.Supplementary information Supplementary data are available at Bioinformatics online.

Transferable Kinetic Monte Carlo Models with Thousands of Reactions Learned from Molecular Dynamics Simulations.

Molecular dynamics (MD) simulation of complex chemistry typically involves thousands of atoms propagating over millions of time steps, generating a wealth of data. Traditionally these data are used to calculate some aggregate properties of the system and then discarded, but we propose that these data can be reused to study related chemical systems. Using approximate chemical kinetic models and methods from statistical learning, we study hydrocarbon chemistries under extreme thermodynamic conditions. We discover that a single MD simulation can contain sufficient information about reactions and rates to predict the dynamics of related yet different chemical systems using kinetic Monte Carlo (KMC) simulation. Our learned KMC models identify thousands of reactions and run 4 orders of magnitude faster than MD. The transferability of these models suggests that we can viably reuse data from existing MD simulations to accelerate future simulation studies and reduce the number of new MD simulations required.